research topic quantitative for stem

Best 151+ Quantitative Research Topics for STEM Students

In today’s rapidly evolving world, STEM (Science, Technology, Engineering, and Mathematics) fields have gained immense significance. For STEM students, engaging in quantitative research is a pivotal aspect of their academic journey. Quantitative research involves the systematic collection and interpretation of numerical data to address research questions or test hypotheses. Choosing the right research topic is essential to ensure a successful and meaningful research endeavor.

In this blog, we will explore 151+ quantitative research topics for STEM students. Whether you are an aspiring scientist, engineer, or mathematician, this comprehensive list will inspire your research journey. But we understand that the journey through STEM education and research can be challenging at times. That’s why we’re here to support you every step of the way with our Engineering Assignment Help service.

What is Quantitative Research in STEM?

Table of Contents

Quantitative research is a scientific approach that relies on numerical data and statistical analysis to draw conclusions and make predictions. In STEM fields, quantitative research encompasses a wide range of methodologies, including experiments, surveys, and data analysis. The key characteristics of quantitative research in STEM include:

Data Collection: Systematic gathering of numerical data through experiments, observations, or surveys.
Statistical Analysis: Application of statistical techniques to analyze data and draw meaningful conclusions.
Hypothesis Testing: Testing hypotheses and theories using quantitative data.
Replicability: The ability to replicate experiments and obtain consistent results.
Generalizability: Drawing conclusions that can be applied to larger populations or phenomena.

Importance of Quantitative Research Topics for STEM Students

Quantitative research plays a pivotal role in STEM education and research for several reasons:

1. Empirical Evidence

It provides empirical evidence to support or refute scientific theories and hypotheses.

2. Data-Driven Decision-Making

STEM professionals use quantitative research to make informed decisions, from designing experiments to developing new technologies.

3. Innovation

It fuels innovation by providing data-driven insights that lead to the creation of new products, processes, and technologies.

4. Problem Solving

STEM students learn critical problem-solving skills through quantitative research, which are invaluable in their future careers.

5. Interdisciplinary Applications

Quantitative research transcends STEM disciplines, facilitating collaboration and the tackling of complex, real-world problems.

Also Read: Google Scholar Research Topics

Quantitative Research Topics for STEM Students

Now, let’s explore important quantitative research topics for STEM students:

Biology and Life Sciences

Here are some quantitative research topics in biology and life science:

1. The impact of climate change on biodiversity.

2. Analyzing the genetic basis of disease susceptibility.

3. Studying the effectiveness of vaccines in preventing infectious diseases.

4. Investigating the ecological consequences of invasive species.

5. Examining the role of genetics in aging.

6. Analyzing the effects of pollution on aquatic ecosystems.

7. Studying the evolution of antibiotic resistance.

8. Investigating the relationship between diet and lifespan.

9. Analyzing the impact of deforestation on wildlife.

10. Studying the genetics of cancer development.

11. Investigating the effectiveness of various plant fertilizers.

12. Analyzing the impact of microplastics on marine life.

13. Studying the genetics of human behavior.

14. Investigating the effects of pollution on plant growth.

15. Analyzing the microbiome’s role in human health.

16. Studying the impact of climate change on crop yields.

17. Investigating the genetics of rare diseases.

Let’s get started with some quantitative research topics for stem students in chemistry:

1. Studying the properties of superconductors at different temperatures.

2. Analyzing the efficiency of various catalysts in chemical reactions.

3. Investigating the synthesis of novel polymers with unique properties.

4. Studying the kinetics of chemical reactions.

5. Analyzing the environmental impact of chemical waste disposal.

6. Investigating the properties of nanomaterials for drug delivery.

7. Studying the behavior of nanoparticles in different solvents.

8. Analyzing the use of renewable energy sources in chemical processes.

9. Investigating the chemistry of atmospheric pollutants.

10. Studying the properties of graphene for electronic applications.

11. Analyzing the use of enzymes in industrial processes.

12. Investigating the chemistry of alternative fuels.

13. Studying the synthesis of pharmaceutical compounds.

14. Analyzing the properties of materials for battery technology.

15. Investigating the chemistry of natural products for drug discovery.

16. Analyzing the effects of chemical additives on food preservation.

17. Investigating the chemistry of carbon capture and utilization technologies.

Here are some quantitative research topics in physics for stem students:

1. Investigating the behavior of subatomic particles in high-energy collisions.

2. Analyzing the properties of dark matter and dark energy.

3. Studying the quantum properties of entangled particles.

4. Investigating the dynamics of black holes and their gravitational effects.

5. Analyzing the behavior of light in different mediums.

6. Studying the properties of superfluids at low temperatures.

7. Investigating the physics of renewable energy sources like solar cells.

8. Analyzing the properties of materials at extreme temperatures and pressures.

9. Studying the behavior of electromagnetic waves in various applications.

10. Investigating the physics of quantum computing.

11. Analyzing the properties of magnetic materials for data storage.

12. Studying the behavior of particles in plasma for fusion energy research.

13. Investigating the physics of nanoscale materials and devices.

14. Analyzing the properties of materials for use in semiconductors.

15. Studying the principles of thermodynamics in energy efficiency.

16. Investigating the physics of gravitational waves.

17. Analyzing the properties of materials for use in quantum technologies.

Engineering

Let’s explore some quantitative research topics for stem students in engineering:

1. Investigating the efficiency of renewable energy systems in urban environments.

2. Analyzing the impact of 3D printing on manufacturing processes.

3. Studying the structural integrity of materials in aerospace engineering.

4. Investigating the use of artificial intelligence in autonomous vehicles.

5. Analyzing the efficiency of water treatment processes in civil engineering.

6. Studying the impact of robotics in healthcare.

7. Investigating the optimization of supply chain logistics using quantitative methods.

8. Analyzing the energy efficiency of smart buildings.

9. Studying the effects of vibration on structural engineering.

10. Investigating the use of drones in agricultural practices.

11. Analyzing the impact of machine learning in predictive maintenance.

12. Studying the optimization of transportation networks.

13. Investigating the use of nanomaterials in electronic devices.

14. Analyzing the efficiency of renewable energy storage systems.

15. Studying the impact of AI-driven design in architecture.

16. Investigating the optimization of manufacturing processes using Industry 4.0 technologies.

17. Analyzing the use of robotics in underwater exploration.

Environmental Science

Here are some top quantitative research topics in environmental science for students:

1. Investigating the effects of air pollution on respiratory health.

2. Analyzing the impact of deforestation on climate change.

3. Studying the biodiversity of coral reefs and their conservation.

4. Investigating the use of remote sensing in monitoring deforestation.

5. Analyzing the effects of plastic pollution on marine ecosystems.

6. Studying the impact of climate change on glacier retreat.

7. Investigating the use of wetlands for water quality improvement.

8. Analyzing the effects of urbanization on local microclimates.

9. Studying the impact of oil spills on aquatic ecosystems.

10. Investigating the use of renewable energy in mitigating greenhouse gas emissions.

11. Analyzing the effects of soil erosion on agricultural productivity.

12. Studying the impact of invasive species on native ecosystems.

13. Investigating the use of bioremediation for soil cleanup.

14. Analyzing the effects of climate change on migratory bird patterns.

15. Studying the impact of land use changes on water resources.

16. Investigating the use of green infrastructure for urban stormwater management.

17. Analyzing the effects of noise pollution on wildlife behavior.

Computer Science

Let’s get started with some simple quantitative research topics for stem students:

1. Investigating the efficiency of machine learning algorithms for image recognition.

2. Analyzing the security of blockchain technology in financial transactions.

3. Studying the impact of quantum computing on cryptography.

4. Investigating the use of natural language processing in chatbots and virtual assistants.

5. Analyzing the effectiveness of cybersecurity measures in protecting sensitive data.

6. Studying the impact of algorithmic trading in financial markets.

7. Investigating the use of deep learning in autonomous robotics.

8. Analyzing the efficiency of data compression algorithms for large datasets.

9. Studying the impact of virtual reality in medical simulations.

10. Investigating the use of artificial intelligence in personalized medicine.

11. Analyzing the effectiveness of recommendation systems in e-commerce.

12. Studying the impact of cloud computing on data storage and processing.

13. Investigating the use of neural networks in predicting disease outbreaks.

14. Analyzing the efficiency of data mining techniques in customer behavior analysis.

15. Studying the impact of social media algorithms on user behavior.

16. Investigating the use of machine learning in natural language translation.

17. Analyzing the effectiveness of sentiment analysis in social media monitoring.

Mathematics

Let’s explore the quantitative research topics in mathematics for students:

1. Investigating the properties of prime numbers and their distribution.

2. Analyzing the behavior of chaotic systems using differential equations.

3. Studying the optimization of algorithms for solving complex mathematical problems.

4. Investigating the use of graph theory in network analysis.

5. Analyzing the properties of fractals in natural phenomena.

6. Studying the application of probability theory in risk assessment.

7. Investigating the use of numerical methods in solving partial differential equations.

8. Analyzing the properties of mathematical models for population dynamics.

9. Studying the optimization of algorithms for data compression.

10. Investigating the use of topology in data analysis.

11. Analyzing the behavior of mathematical models in financial markets.

12. Studying the application of game theory in strategic decision-making.

13. Investigating the use of mathematical modeling in epidemiology.

14. Analyzing the properties of algebraic structures in coding theory.

15. Studying the optimization of algorithms for image processing.

16. Investigating the use of number theory in cryptography.

17. Analyzing the behavior of mathematical models in climate prediction.

Earth Sciences

Here are some quantitative research topics for stem students in earth science:

1. Investigating the impact of volcanic eruptions on climate patterns.

2. Analyzing the behavior of earthquakes along tectonic plate boundaries.

3. Studying the geomorphology of river systems and erosion.

4. Investigating the use of remote sensing in monitoring wildfires.

5. Analyzing the effects of glacier melt on sea-level rise.

6. Studying the impact of ocean currents on weather patterns.

7. Investigating the use of geothermal energy in renewable power generation.

8. Analyzing the behavior of tsunamis and their destructive potential.

9. Studying the impact of soil erosion on agricultural productivity.

10. Investigating the use of geological data in mineral resource exploration.

11. Analyzing the effects of climate change on coastal erosion.

12. Studying the geomagnetic field and its role in navigation.

13. Investigating the use of radar technology in weather forecasting.

14. Analyzing the behavior of landslides and their triggers.

15. Studying the impact of groundwater depletion on aquifer systems.

16. Investigating the use of GIS (Geographic Information Systems) in land-use planning.

17. Analyzing the effects of urbanization on heat island formation.

Health Sciences and Medicine

Here are some quantitative research topics for stem students in health science and medicine:

1. Investigating the effectiveness of telemedicine in improving healthcare access.

2. Analyzing the impact of personalized medicine in cancer treatment.

3. Studying the epidemiology of infectious diseases and their spread.

4. Investigating the use of wearable devices in monitoring patient health.

5. Analyzing the effects of nutrition and exercise on metabolic health.

6. Studying the impact of genetics in predicting disease susceptibility.

7. Investigating the use of artificial intelligence in medical diagnosis.

8. Analyzing the behavior of pharmaceutical drugs in clinical trials.

9. Studying the effectiveness of mental health interventions in schools.

10. Investigating the use of gene editing technologies in treating genetic disorders.

11. Analyzing the properties of medical imaging techniques for early disease detection.

12. Studying the impact of vaccination campaigns on public health.

13. Investigating the use of regenerative medicine in tissue repair.

14. Analyzing the behavior of pathogens in antimicrobial resistance.

15. Studying the epidemiology of chronic diseases like diabetes and heart disease.

16. Investigating the use of bioinformatics in genomics research.

17. Analyzing the effects of environmental factors on health outcomes.

Quantitative research is the backbone of STEM fields, providing the tools and methodologies needed to explore, understand, and innovate in the world of science and technology . As STEM students, embracing quantitative research not only enhances your analytical skills but also equips you to address complex real-world challenges. With the extensive list of 155+ quantitative research topics for stem students provided in this blog, you have a starting point for your own STEM research journey. Whether you’re interested in biology, chemistry, physics, engineering, or any other STEM discipline, there’s a wealth of quantitative research topics waiting to be explored. So, roll up your sleeves, grab your lab coat or laptop, and embark on your quest for knowledge and discovery in the exciting world of STEM.

I hope you enjoyed this blog post about quantitative research topics for stem students.

8 easiest programming language to learn for beginners.

There are so many programming languages you can learn. But if you’re looking to start with something easier. We bring to you a list of…

10 Online Tutoring Help Benefits

Do you need a computer science assignment help? Get the best quality assignment help from computer science tutors at affordable prices. They always presented to help…

189+ Good Quantitative Research Topics For STEM Students

Quantitative research is an essential part of STEM (Science, Technology, Engineering, and Mathematics) fields. It involves collecting and analyzing numerical data to answer research questions and test hypotheses.

In 2023, STEM students have a wealth of exciting research opportunities in various disciplines. Whether you’re an undergraduate or graduate student, here are quantitative research topics to consider for your next project.

If you are looking for the best list of quantitative research topics for stem students, then you can check the given list in each field. It offers STEM students numerous opportunities to explore and contribute to their respective fields in 2023 and beyond.

Whether you’re interested in astrophysics, biology, engineering, mathematics, or any other STEM field.

Also Read: Most Exciting Qualitative Research Topics For Students

What Is Quantitative Research

Table of Contents

Quantitative research is a type of research that focuses on the organized collection, analysis, and evaluation of numerical data to answer research questions, test theories, and find trends or connections between factors. It is an organized, objective way to do study that uses measurable data and scientific methods to come to results.

Quantitative research is often used in many areas, such as the natural sciences, social sciences, economics, psychology, education, and market research. It gives useful information about patterns, trends, cause-and-effect relationships, and how often things happen. Quantitative tools are used by researchers to answer questions like “How many?” and “How often?” “Is there a significant difference?” or “What is the relationship between the variables?”

In comparison to quantitative research, qualitative research uses non-numerical data like conversations, notes, and open-ended surveys to understand and explore the ideas, experiences, and points of view of people or groups. Researchers often choose between quantitative and qualitative methods based on their research goals, questions, and the type of thing they are studying.

How To Choose Quantitative Research Topics For STEM

Here’s a step-by-step guide on how to choose quantitative research topics for STEM:

Step 1:- Identify Your Interests and Passions

Start by reflecting on your personal interests within STEM. What areas or subjects in STEM excite you the most? Choosing a topic you’re passionate about will keep you motivated throughout the research process.

Step 2:- Review Coursework and Textbooks

Look through your coursework, textbooks, and class notes. Identify concepts, theories, or areas that you found particularly intriguing or challenging. These can be a source of potential research topics.

Step 3:- Consult with Professors and Advisors

Discuss your research interests with professors, academic advisors, or mentors. They can provide valuable insights, suggest relevant topics, and guide you toward areas with research opportunities.

Step 4:- Read Recent Literature

Explore recent research articles, journals, and publications in STEM fields. This will help you identify current trends, gaps in knowledge, and areas where further research is needed.

Step 5:- Narrow Down Your Focus

Once you have a broad area of interest, narrow it down to a specific research focus. Consider questions like:

What specific problem or phenomenon do you want to investigate?
Are there unanswered questions or controversies in this area?
What impact could your research have on the field or society?

Step 6:- Consider Resources and Access

Assess the resources available to you, including access to laboratories, equipment, databases, and funding. Ensure that your chosen topic aligns with the resources you have or can access.

Step 7:- Think About Practicality

Consider the feasibility of conducting research on your chosen topic. Are the data readily available, or will you need to collect data yourself? Can you complete the research within your available time frame?

Step 8:- Define Your Research Question

Formulate a clear and specific research question or hypothesis. Your research question should guide your entire study and provide a focus for your data collection and analysis.

Step 9:- Conduct a Literature Review

Dive deeper into the existing literature related to your chosen topic. This will help you understand the current state of research, identify gaps, and refine your research question.

Step 10:- Consider the Impact

Think about the potential impact of your research. How does your topic contribute to the advancement of knowledge in your field? Does it have practical applications or implications for society?

Step 11:- Brainstorm Research Methods

Determine the quantitative research methods and data collection techniques you plan to use. Consider whether you’ll conduct experiments, surveys, data analysis, simulations, or use existing datasets.

Step 12:- Seek Feedback

Share your research topic and ideas with peers, advisors, or mentors. They can provide valuable feedback and help you refine your research focus.

Step 13:- Assess Ethical Considerations

Consider ethical implications related to your research, especially if it involves human subjects, sensitive data, or potential environmental impacts. Ensure that your research adheres to ethical guidelines.

Step 14:- Finalize Your Research Topic

Once you’ve gone through these steps, finalize your research topic. Write a clear and concise research proposal that outlines your research question, objectives, methods, and expected outcomes.

Step 15:- Stay Open to Adjustments

Be open to adjusting your research topic as you progress. Sometimes, new insights or challenges may lead you to refine or adapt your research focus.

Following are the most interesting quantitative research topics for stem students. These are given below.

Quantitative Research Topics In Physics and Astronomy

Quantum Computing Algorithms : Investigate new algorithms for quantum computers and their potential applications.
Dark Matter Detection Methods : Explore innovative approaches to detect dark matter particles.
Quantum Teleportation : Study the principles and applications of quantum teleportation.
Exoplanet Characterization : Analyze data from telescopes to characterize exoplanets.
Nuclear Fusion Modeling : Create mathematical models for nuclear fusion reactions.
Superconductivity at High Temperatures : Research the properties and applications of high-temperature superconductors.
Gravitational Wave Analysis : Analyze gravitational wave data to study astrophysical phenomena.
Black Hole Thermodynamics : Investigate the thermodynamics of black holes and their entropy.

Quantitative Research Topics In Biology and Life Sciences

Genome-Wide Association Studies (GWAS) : Conduct GWAS to identify genetic factors associated with diseases.
Pharmacokinetics and Pharmacodynamics : Study drug interactions in the human body.
Ecological Modeling : Model ecosystems to understand population dynamics.
Protein Folding : Research the kinetics and thermodynamics of protein folding.
Cancer Epidemiology : Analyze cancer incidence and risk factors in specific populations.
Neuroimaging Analysis : Develop algorithms for analyzing brain imaging data.
Evolutionary Genetics : Investigate evolutionary patterns using genetic data.
Stem Cell Differentiation : Study the factors influencing stem cell differentiation.

Engineering and Technology Quantitative Research Topics

Renewable Energy Efficiency : Optimize the efficiency of solar panels or wind turbines.
Aerodynamics of Drones : Analyze the aerodynamics of drone designs.
Autonomous Vehicle Safety : Evaluate safety measures for autonomous vehicles.
Machine Learning in Robotics : Implement machine learning algorithms for robot control.
Blockchain Scalability : Research methods to scale blockchain technology.
Quantum Computing Hardware : Design and test quantum computing hardware components.
IoT Security : Develop security protocols for the Internet of Things (IoT).
3D Printing Materials Analysis : Study the mechanical properties of 3D-printed materials.

Quantitative Research Topics In Mathematics and Statistics

Following are the best Quantitative Research Topics For STEM Students in mathematics and statistics.

Prime Number Distribution : Investigate the distribution of prime numbers.
Graph Theory Algorithms : Develop algorithms for solving graph theory problems.
Statistical Analysis of Financial Markets : Analyze financial data and market trends.
Number Theory Research : Explore unsolved problems in number theory.
Bayesian Machine Learning : Apply Bayesian methods to machine learning models.
Random Matrix Theory : Study the properties of random matrices in mathematics and physics.
Topological Data Analysis : Use topology to analyze complex data sets.
Quantum Algorithms for Optimization : Research quantum algorithms for optimization problems.

Experimental Quantitative Research Topics In Science and Earth Sciences

Climate Change Modeling : Develop climate models to predict future trends.
Biodiversity Conservation Analysis : Analyze data to support biodiversity conservation efforts.
Geographic Information Systems (GIS) : Apply GIS techniques to solve environmental problems.
Oceanography and Remote Sensing : Use satellite data for oceanographic research.
Air Quality Monitoring : Develop sensors and models for air quality assessment.
Hydrological Modeling : Study the movement and distribution of water resources.
Volcanic Activity Prediction : Predict volcanic eruptions using quantitative methods.
Seismology Data Analysis : Analyze seismic data to understand earthquake patterns.

Chemistry and Materials Science Quantitative Research Topics

Nanomaterial Synthesis and Characterization : Research the synthesis and properties of nanomaterials.
Chemoinformatics : Analyze chemical data for drug discovery and materials science.
Quantum Chemistry Simulations : Perform quantum simulations of chemical reactions.
Materials for Renewable Energy : Investigate materials for energy storage and conversion.
Catalysis Kinetics : Study the kinetics of chemical reactions catalyzed by materials.
Polymer Chemistry : Research the properties and applications of polymers.
Analytical Chemistry Techniques : Develop new analytical techniques for chemical analysis.
Sustainable Chemistry : Explore green chemistry approaches for sustainable materials.

Computer Science and Information Technology Topics

Natural Language Processing (NLP) : Work on NLP algorithms for language understanding.
Cybersecurity Analytics : Analyze cybersecurity threats and vulnerabilities.
Big Data Analytics : Apply quantitative methods to analyze large data sets.
Machine Learning Fairness : Investigate bias and fairness issues in machine learning models.
Human-Computer Interaction (HCI) : Study user behavior and interaction patterns.
Software Performance Optimization : Optimize software applications for performance.
Distributed Systems Analysis : Analyze the performance of distributed computing systems.
Bioinformatics Data Mining : Develop algorithms for mining biological data.

Good Quantitative Research Topics Students In Medicine and Healthcare

Clinical Trial Data Analysis : Analyze clinical trial data to evaluate treatment effectiveness.
Epidemiological Modeling : Model disease spread and intervention strategies.
Healthcare Data Analytics : Analyze healthcare data for patient outcomes and cost reduction.
Medical Imaging Algorithms : Develop algorithms for medical image analysis.
Genomic Medicine : Apply genomics to personalized medicine approaches.
Telemedicine Effectiveness : Study the effectiveness of telemedicine in healthcare delivery.
Health Informatics : Analyze electronic health records for insights into patient care.

Agriculture and Food Sciences Topics

Precision Agriculture : Use quantitative methods for optimizing crop production.
Food Safety Analysis : Analyze food safety data and quality control.
Aquaculture Sustainability : Research sustainable practices in aquaculture.
Crop Disease Modeling : Model the spread of diseases in agricultural crops.
Climate-Resilient Agriculture : Develop strategies for agriculture in changing climates.
Food Supply Chain Optimization : Optimize food supply chain logistics.
Soil Health Assessment : Analyze soil data for sustainable land management.

Social Sciences with Quantitative Approaches

Educational Data Mining : Analyze educational data for improving learning outcomes.
Sociodemographic Surveys : Study social trends and demographics using surveys.
Psychometrics : Develop and validate psychological measurement instruments.
Political Polling Analysis : Analyze political polling data and election trends.
Economic Modeling : Develop economic models for policy analysis.
Urban Planning Analytics : Analyze data for urban planning and infrastructure.
Climate Policy Evaluation : Evaluate the impact of climate policies on society.

Environmental Engineering Quantitative Research Topics

Water Quality Assessment : Analyze water quality data for environmental monitoring.
Waste Management Optimization : Optimize waste collection and recycling programs.
Environmental Impact Assessments : Evaluate the environmental impact of projects.
Air Pollution Modeling : Model the dispersion of air pollutants in urban areas.
Sustainable Building Design : Apply quantitative methods to sustainable architecture.

Quantitative Research Topics Robotics and Automation

Robotic Swarm Behavior : Study the behavior of robot swarms in different tasks.
Autonomous Drone Navigation : Develop algorithms for autonomous drone navigation.
Humanoid Robot Control : Implement control algorithms for humanoid robots.
Robotic Grasping and Manipulation : Study robotic manipulation techniques.
Reinforcement Learning for Robotics : Apply reinforcement learning to robotic control.

Quantitative Research Topics Materials Engineering

Additive Manufacturing Process Optimization : Optimize 3D printing processes.
Smart Materials for Aerospace : Research smart materials for aerospace applications.
Nanostructured Materials for Energy Storage : Investigate energy storage materials.
Corrosion Prevention : Develop corrosion-resistant materials and coatings.

Nuclear Engineering Quantitative Research Topics

Nuclear Reactor Safety Analysis : Study safety aspects of nuclear reactor designs.
Nuclear Fuel Cycle Analysis : Analyze the nuclear fuel cycle for efficiency.
Radiation Shielding Materials : Research materials for radiation protection.

Quantitative Research Topics In Biomedical Engineering

Medical Device Design and Testing : Develop and test medical devices.
Biomechanics Analysis : Analyze biomechanics in sports or rehabilitation.
Biomaterials for Medical Implants : Investigate materials for medical implants.

Good Quantitative Research Topics Chemical Engineering

Chemical Process Optimization : Optimize chemical manufacturing processes.
Industrial Pollution Control : Develop strategies for pollution control in industries.
Chemical Reaction Kinetics : Study the kinetics of chemical reactions in industries.

Best Quantitative Research Topics In Renewable Energy

Energy Storage Systems : Research and optimize energy storage solutions.
Solar Cell Efficiency : Improve the efficiency of photovoltaic cells.
Wind Turbine Performance Analysis : Analyze and optimize wind turbine designs.

Brilliant Quantitative Research Topics In Astronomy and Space Sciences

Astrophysical Simulations : Simulate astrophysical phenomena using numerical methods.
Spacecraft Trajectory Optimization : Optimize spacecraft trajectories for missions.
Exoplanet Detection Algorithms : Develop algorithms for exoplanet detection.

Quantitative Research Topics In Psychology and Cognitive Science

Cognitive Psychology Experiments : Conduct quantitative experiments in cognitive psychology.
Emotion Recognition Algorithms : Develop algorithms for emotion recognition in AI.
Neuropsychological Assessments : Create quantitative assessments for brain function.

Geology and Geological Engineering Quantitative Research Topics

Geological Data Analysis : Analyze geological data for mineral exploration.
Geological Hazard Prediction : Predict geological hazards using quantitative models.

Great Quantitative Research Topics In Cybersecurity

Network Intrusion Detection : Develop quantitative methods for intrusion detection.
Cryptocurrency Analysis : Analyze blockchain data and cryptocurrency trends.

Mathematical Biology Quantitative Research Topics

Epidemiological Modeling : Model disease spread and control in populations.
Population Genetics : Analyze genetic data to understand population dynamics.

Quantitative Research Topics In Chemical Analysis

Analytical Chemistry Methods : Develop quantitative methods for chemical analysis.
Spectroscopy Analysis : Analyze spectroscopic data for chemical identification.

Mathematics Education Quantitative Research Topics

Mathematics Curriculum Analysis : Analyze curriculum effectiveness in mathematics education.
Mathematics Assessment Development : Develop quantitative assessments for mathematics skills.

Quantitative Research Topics In Social Research

Social Network Analysis : Analyze social network structures and dynamics.
Survey Research : Conduct quantitative surveys on social issues and trends.

Quantitative Research Topics In Computational Neuroscience

Neural Network Modeling : Model neural networks and brain functions computationally.
Brain Connectivity Analysis : Analyze functional and structural brain connectivity.

Best Topics In Transportation Engineering

Traffic Flow Modeling : Model and optimize traffic flow in urban areas.
Public Transportation Efficiency : Analyze the efficiency of public transportation systems.

Good Quantitative Research Topics In Energy Economics

Energy Policy Analysis : Evaluate the economic impact of energy policies.
Renewable Energy Cost-Benefit Analysis : Assess the economic viability of renewable energy projects.

Quantum Information Science

Quantum Cryptography Protocols : Develop and analyze quantum cryptography protocols.
Quantum Key Distribution : Study the security of quantum key distribution systems.

Human Genetics

Genome Editing Ethics : Investigate ethical issues in genome editing technologies.
Population Genomics : Analyze genomic data for population genetics research.

Marine Biology

Coral Reef Health Assessment : Quantitatively assess the health of coral reefs.
Marine Ecosystem Modeling : Model marine ecosystems and biodiversity.

Data Science and Machine Learning

Machine Learning Explainability : Develop methods for explaining machine learning models.
Data Privacy in Machine Learning : Study privacy issues in machine learning applications.
Deep Learning for Image Analysis : Develop deep learning models for image recognition.

Environmental Engineering

Robotics and automation, materials engineering, nuclear engineering, biomedical engineering, chemical engineering, renewable energy, astronomy and space sciences, psychology and cognitive science, geology and geological engineering, forensic science, cybersecurity, mathematical biology, chemical analysis, mathematics education, quantitative social research, computational neuroscience, quantitative research topics in transportation engineering, quantitative research topics in energy economics, topics in quantum information science, amazing quantitative research topics in human genetics, quantitative research topics in marine biology, what is a common goal of qualitative and quantitative research.

A common goal of both qualitative and quantitative research is to generate knowledge and gain a deeper understanding of a particular phenomenon or topic. However, they approach this goal in different ways:

1. Understanding a Phenomenon

Both types of research aim to understand and explain a specific phenomenon, whether it’s a social issue, a natural process, a human behavior, or a complex event.

2. Testing Hypotheses

Both qualitative and quantitative research can involve hypothesis testing. While qualitative research may not use statistical hypothesis tests in the same way as quantitative research, it often tests hypotheses or research questions by examining patterns and themes in the data.

3. Contributing to Knowledge

Researchers in both approaches seek to contribute to the body of knowledge in their respective fields. They aim to answer important questions, address gaps in existing knowledge, and provide insights that can inform theory, practice, or policy.

4. Informing Decision-Making

Research findings from both qualitative and quantitative studies can be used to inform decision-making in various domains, whether it’s in academia, government, industry, healthcare, or social services.

5. Enhancing Understanding

Both approaches strive to enhance our understanding of complex phenomena by systematically collecting and analyzing data. They aim to provide evidence-based explanations and insights.

6. Application

Research findings from both qualitative and quantitative studies can be applied to practical situations. For example, the results of a quantitative study on the effectiveness of a new drug can inform medical treatment decisions, while qualitative research on customer preferences can guide marketing strategies.

7. Contributing to Theory

In academia, both types of research contribute to the development and refinement of theories in various disciplines. Quantitative research may provide empirical evidence to support or challenge existing theories, while qualitative research may generate new theoretical frameworks or perspectives.

Conclusion – Quantitative Research Topics For STEM Students

So, selecting a quantitative research topic for STEM students is a pivotal decision that can shape the trajectory of your academic and professional journey. The process involves a thoughtful exploration of your interests, a thorough review of the existing literature, consideration of available resources, and the formulation of a clear and specific research question.

Your chosen topic should resonate with your passions, align with your academic or career goals, and offer the potential to contribute to the body of knowledge in your STEM field. Whether you’re delving into physics, biology, engineering, mathematics, or any other STEM discipline, the right research topic can spark curiosity, drive innovation, and lead to valuable insights.

Moreover, quantitative research in STEM not only expands the boundaries of human knowledge but also has the power to address real-world challenges, improve technology, and enhance our understanding of the natural world. It is a journey that demands dedication, intellectual rigor, and an unwavering commitment to scientific inquiry.

What is quantitative research in STEM?

Quantitative research in this context is designed to improve our understanding of the science system’s workings, structural dependencies and dynamics.

What are good examples of quantitative research?

Surveys and questionnaires serve as common examples of quantitative research. They involve collecting data from many respondents and analyzing the results to identify trends, patterns

What are the 4 C’s in STEM?

They became known as the “Four Cs” — critical thinking, communication, collaboration, and creativity.

13 Best Tips To Write An Assignment

Whenever the new semester starts, you will get a lot of assignment writing tasks. Now you enter the new academic…

How To Do Homework Fast – 11 Tips To Do Homework Fast

Homework is one of the most important parts that have to be done by students. It has been around for…

110+ Best Quantitative Research Topics for STEM Students

Explore engaging quantitative research topics for STEM students. This guide covers the basics, popular areas, and tips for success to help you make an impact.

Quantitative research uses data and numbers to uncover insights. Whether you’re into computer science, engineering, or natural sciences, it’s a powerful tool for discovery.

Ready to get started? Let’s dive in!

Table of Contents

Quantitative Research Topics for STEM Students PDF

Understanding quantitative research.

Quantitative research uses numerical data and statistical methods to find patterns and draw conclusions.

Key Characteristics

Objectivity: Minimizes personal bias.
Numerical Data: Focuses on measurable data.
Generalizability: Makes broad conclusions from samples.
Structured Design: Follows a set research plan.
Statistical Analysis: Uses statistics to analyze data.

Quantitative vs. Qualitative Research

Quantitative: Deals with numbers and statistical analysis.
Qualitative: Explores non-numerical data like text and images.

The Research Process

Identify the Problem: Define the research question.
Formulate Hypotheses: Create testable statements.
Collect Data: Use surveys, experiments, or observations.
Analyze Data: Apply statistical methods.
Interpret Findings: Draw conclusions based on results.

These basics help in designing and conducting effective quantitative research.

Popular Quantitative Research Methods

Check out popular quantitative research methods:-

Description: Collect data via questionnaires or interviews.
Use: Measure attitudes, opinions, or behaviors.
Example: Assessing student satisfaction with online learning.

Experiments

Description: Manipulate variables to see effects.
Use: Determine cause-and-effect relationships.
Example: Testing a new drug’s effectiveness.

Correlational Studies

Description: Examine relationships between variables.
Use: Identify patterns and trends.
Example: Linking air pollution to respiratory issues.

Causal-Comparative Research

Description: Compare groups without random assignment.
Use: Explore cause-and-effect when experiments aren’t possible.
Example: Comparing student performance across socioeconomic backgrounds.

Observational Studies

Description: Observe and record behavior in natural settings.
Use: Study behaviors not suitable for experiments.
Example: Observing animal behavior in the wild.

Content Analysis

Description: Analyze text or visual content for data.
Use: Study media or document content.
Example: Analyzing trends in scientific papers.

Longitudinal Studies

Description: Collect data from the same group over time.
Use: Track changes and developments.
Example: Monitoring plant growth under various conditions.

These methods help researchers choose the best approach for their questions.

Quantitative Research Topics for STEM Students

Check out quantitative research topics for STEM students:-

Friction : Compare friction on different surfaces.
Light Diffraction : Measure light patterns through slits.
Heat Engines : Test efficiency with different fluids.
Magnetism : Study magnetic field strength in wires.
Quantum : Analyze electron patterns in a slit experiment.
Sound Absorption : Test materials for sound absorption.
Gravity : Study forces in planetary motion.
Fluid Flow : Measure flow rates in different conditions.
Radioactivity : Compare decay rates of isotopes.
Metal Expansion : Measure how metals expand when heated.
Reaction Rates : Study catalysts’ effect on reaction speed.
Gas Solubility : Test gas dissolving in liquids at different temps.
Battery Efficiency : Compare power in different battery types.
Reaction Yield : Measure product yield in reactions.
Buffer Solutions : Test buffers’ ability to resist pH changes.
Organic Reactions : Study reaction speed in organic compounds.
Equilibrium : Analyze shifts in chemical equilibrium.
Adsorption : Test adsorption on solid surfaces.
Heat Changes : Measure energy in chemical reactions.
Polymer Size : Compare sizes of different polymers.
Gene Linkage : Study gene inheritance patterns.
Antibiotics : Test bacteria growth with antibiotics.
Invasive Species : Measure impact on native species.
BMI vs Heart Rate : Compare BMI with heart rates.
Blood Glucose : Measure blood sugar before/after meals.
Photosynthesis : Test plant growth under various light.
Reaction Times : Compare responses to visual and sound stimuli.
Cell Growth : Measure cell growth under different nutrients.
Vaccine Response : Test antibody production after vaccines.
Animal Behavior : Study stress effects on animal behavior.

Environmental Science

Soil Pollution : Measure heavy metals in soil.
Glacier Melt : Track glacier melting rates.
Energy Use : Compare renewable energy in homes.
Composting : Test compost methods for waste reduction.
Water Oxygen : Measure oxygen in water bodies.
Air Pollution : Compare urban and rural air quality.
Species Richness : Measure species diversity in forests.
Carbon Storage : Compare carbon storage in trees.
Soil Erosion : Measure soil loss in farms.
Solar Panels : Test solar efficiency in different weather.

Engineering

Material Strength : Test building materials’ strength.
Power Loss : Measure power loss in transmission lines.
Gear Efficiency : Compare efficiency of gear types.
Road Surfaces : Study effects of road materials on fuel use.
Software Bugs : Count bugs in different coding languages.
Chemical Reactors : Test reactor yields at various temps.
Airfoil Lift : Measure lift in different wing designs.
Prosthetics : Compare materials used in prosthetics.
Water Treatment : Test effectiveness of water treatment.
Robot Accuracy : Measure precision in robotic arms.

Mathematics

Probability : Analyze outcome probabilities in experiments.
Cooling Rates : Measure cooling rates using calculus.
Cryptography : Study algebra in encryption methods.
Shape Geometry : Calculate area and perimeter of shapes.
Population Models : Model population growth rates.
Prime Numbers : Analyze prime number distribution.
Graphics : Test matrix operations in computer graphics.
Combinations : Study combinations in optimization problems.
Game Strategy : Analyze game strategies mathematically.
Resource Allocation : Optimize resources in production.

Computer Science

Data Patterns : Analyze data clusters in large datasets.
AI Accuracy : Test machine learning models’ precision.
Cyber-Attacks : Measure attack frequency on networks.
Algorithm Performance : Compare sorting algorithm speeds.
User Interface : Test user satisfaction in different designs.
Object Detection : Measure accuracy in computer vision.
Sentiment Analysis : Test algorithms in sentiment detection.
Blockchain Speed : Measure transaction speeds in blockchain.
Encryption : Test security of different encryption methods.
Big Data : Analyze performance in big data systems.

Medicine and Health

Disease Spread : Study disease spread in dense populations.
Drug Dosage : Measure drug effectiveness at different doses.
Vaccine Impact : Test vaccine success rates.
Diet Impact : Measure diet effects on cholesterol.
Imaging Accuracy : Compare diagnostic imaging methods.
Heart Rate : Study heart rate variability in stress.
Cancer Treatment : Compare effectiveness of cancer treatments.
Surgery Recovery : Measure recovery time in joint surgeries.
Mental Health : Study anxiety and depression rates.
Gene Expression : Analyze gene activity in disorders.

Astronomy and Space Science

Star Brightness : Measure star brightness and distance.
Impact Craters : Study craters and asteroid sizes.
Universe Expansion : Analyze cosmic background radiation.
Space Propulsion : Test deep space propulsion systems.
Binary Stars : Study orbits in binary star systems.
Exoplanet Detection : Measure planet detection accuracy.
Dark Matter : Analyze dark matter in galaxies.
Solar Radiation : Track solar radiation changes.
Solar Flares : Study effects of solar flares on satellites.
Space Chemistry : Measure chemicals in space clouds.

These topics are now more concise while still providing a clear focus for quantitative research.

Tips for Choosing a Research Topic

After brainstorming research topics, refine your ideas with these steps:

Narrow Your Topic

Define specific research questions.
Determine the scope and depth of your study.
Identify key variables to measure.

Literature Review

Explore existing research to find gaps.
Review how previous studies were done.
Identify relevant theories to support your work.

Feasibility Assessment

Check if you have access to necessary data.
Evaluate time and resource requirements.
Secure any needed approvals or permissions.

Following these steps will help turn a broad idea into a focused research project.

Conducting Quantitative Research

Check out the best tips for coducting quantitative research:-

Data Collection Methods

Surveys: use questionnaires or interviews..

Pros: Efficient for large data.
Cons: Risk of bias, less detail.

Experiments: Change variables to see effects.

Pros: Shows cause-and-effect.
Cons: Time-consuming, costly, ethical issues.

Observations: Record behavior systematically.

Pros: Natural data, captures unexpected behavior.
Cons: Observer bias, time-consuming.

Data Analysis Techniques

Use: Stats analysis, hypothesis testing.
Use: Data manipulation, visualization, machine learning.

Research Ethics and Data Privacy

Informed Consent: Ensure participants agree voluntarily.
Data Privacy: Protect confidentiality.
Data Integrity: Maintain accuracy and avoid misconduct.

Writing a Research Paper

Clear Writing: Use concise academic language.
Structure: Follow standard format (intro, methods, results, discussion).
Data Visualization: Use graphs and charts.
Citation Style: Follow APA or MLA.
Proofreading: Check for clarity and grammar.

These steps help ensure rigorous, ethical research and clear communication.

Ethical Considerations in Quantitative Research

Ethical conduct is essential in research for protecting participants, ensuring integrity, and building trust.

Importance of Ethical Research

Protects Participants: Avoids harm and privacy issues.
Ensures Integrity: Keeps findings reliable.
Builds Trust: Gains public confidence.

Informed Consent

Clear Info: Explain the study clearly.
Voluntary: Participation should be free of pressure.
Right to Withdraw: Participants can leave anytime.

Data Privacy

Confidentiality: Keep identities and data secure.
Anonymity: Use data without personal identifiers when possible.
Security: Protect data from unauthorized access.

Research Integrity

Honesty: Report findings accurately.
Avoid Plagiarism: Credit sources properly.
Manage Data: Keep records organized and complete.

Adhering to these principles ensures ethical and trustworthy research.

Challenges and Opportunities in Quantitative Research

Quantitative research has its challenges but can be highly effective with the right approach.

Data Quality: Ensure accuracy and handle errors.
Sample Size: Find the right balance—avoid too small or too large.
Causality: Correlation doesn’t equal causation.
Generalizability: Ensure findings apply broadly.

Big Data and Advanced Analytics

Vast Datasets: Discover new patterns.
Advanced Analytics: Use AI and machine learning for insights.
Predictive Modeling: Forecast trends and guide decisions.

Interdisciplinary Collaboration

Diverse Perspectives: Gain fresh insights.
Complementary Expertise: Combine strengths from different fields.
Real-World Impact: Increase practical applications.

By tackling these challenges and leveraging new tools, researchers can achieve meaningful results.

Overcoming Challenges in Quantitative Research

Quantitative research can face challenges, but these strategies can help:

Data Quality

Clean Data: Fix errors and inconsistencies.
Handle Missing Data: Use statistical methods for imputation.
Validate Data: Cross-check with other sources.

Sample Size

Power Analysis: Determine the right sample size.
Sampling Techniques: Use probability methods.
Combine Data: Aggregate data from various sources.
Randomization: Randomly assign participants.
Control Factors: Manage confounding variables.
Longitudinal Studies: Track changes over time.

Generalizability

Representative Sample: Reflect the target population.
Replicate Studies: Test across different settings.
Strong Framework: Base findings on solid theory.

Big Data and Analytics

Manage Data: Efficiently store and access data.
Mine Data: Extract valuable insights.
Apply Machine Learning: Discover patterns and make predictions.

Using these strategies can help address challenges and improve research outcomes.

Real-world Examples of Successful Quantitative Research Projects

Quantitative research drives progress in many fields. Here are some examples:

Medicine and Healthcare

Clinical Trials: Test new treatments.
Epidemiological Studies: Find disease risk factors.
Health Economics: Assess healthcare costs and benefits.

Business and Economics

Market Research: Study consumer behavior.
Financial Modeling: Forecast market trends.
Operations Research: Improve supply chains.

Social Sciences

Education Research: Evaluate teaching methods .
Political Science: Analyze voting and public opinion.
Sociology: Examine social trends.

Natural Sciences

Physics: Test scientific theories.
Chemistry: Study chemical reactions.
Biology: Research genetic patterns.
Product Testing: Check product performance.
Structural Analysis: Assess building strength.
Process Optimization: Enhance manufacturing efficiency.

These examples highlight the diverse applications and impact of quantitative research.

Collaborate with Other Researchers

Collaboration is crucial in research. Here’s how to do it effectively:

Finding Collaborators

Shared Interests: Look for those with similar research topics.
Different Skills: Seek out varied expertise.
Institutional Links: Partner within or outside your institution.
Online Networks: Use research sites and social media.

Building Collaborations

Communicate Clearly: Keep discussions open and honest.
Set Goals: Define objectives and expectations.
Define Roles: Outline each person’s responsibilities.
Handle Conflicts: Plan for resolving disagreements.
Build Trust: Foster respectful relationships.

Challenges to Address

Manage Time: Balance joint and solo work.
Clarify Ownership: Agree on who owns the research.
Respect Differences: Manage cultural and background differences.
Authorship Rules: Decide on publication credit.

Tools to Use

Collaboration Software: Use Google Drive, Slack , or Teams.
Project Management: Organize with Trello or Asana.
Video Calls: Meet via Zoom or Skype.

Effective collaboration leads to productive research.

Quantitative Research Topics for STEM Students in the Philippines

Check out quantitative research topics for STEM students in the Philippines

Agriculture and Food Science

Climate Impact on Rice : Study how climate change affects rice yields.
Organic vs. Soil Health : Compare soil health in organic and conventional farming.
Extension Programs : Evaluate agricultural extension program effectiveness.
Aquaculture Benefits : Assess economic benefits of aquaculture.
Sustainable Farming : Develop sustainable crop management methods.
Organic Pest Control : Test organic pest control methods.
Water Efficiency : Study water usage in farming.
Fertilizer Effects : Compare soil health with different fertilizers.
Food Security : Improve food security strategies.
Agri-Tech : Explore technology in farming.

Information and Communications Technology (ICT)

Digital Skills and Jobs : Study how digital skills affect jobs.
Internet and Education : Analyze internet access and education.
E-Learning Impact : Evaluate e-learning platforms.
Digital Divide : Examine the digital divide’s effect on rural areas.
Cybersecurity Education : Increase cybersecurity awareness.
Social Media and Studies : Study social media’s impact on learning.
Tech Access and Jobs : Compare tech access and job prospects.
Learning Apps : Assess mobile learning apps.
E-Governance : Investigate benefits of e-governance.
Digital Training : Evaluate digital skills training.
Deforestation and Wildlife : Study deforestation’s effect on wildlife.
Pollution and Health : Analyze air pollution and health issues.
Renewable Energy : Evaluate renewable energy’s effect on emissions.
Climate and Erosion : Study climate change and coastal erosion.
Biodiversity : Develop strategies to conserve biodiversity.
Water Pollution : Investigate water pollution sources.
Soil Erosion : Study land use and soil erosion.
Plastic Waste : Analyze plastic waste impact on marine life.
Renewable Adoption : Assess renewable energy adoption.
Climate Adaptation : Explore climate adaptation strategies.
Local Materials : Test local materials in earthquakes.
Housing Efficiency : Evaluate energy efficiency in housing.
Infrastructure Impact : Assess infrastructure’s effect on poverty.
Energy Costs : Analyze costs of renewable energy projects.
Building Materials : Research sustainable materials.
Water Tech : Develop water conservation technologies.
Smart Grids : Investigate smart grid benefits.
Transportation Solutions : Explore urban transportation improvements.
Disaster-Resistant Structures : Design structures for disasters.
Green Certifications : Study green building certifications.

Medical and Health Sciences

Disease Prevalence : Study non-communicable disease rates.
Maternal Health : Evaluate programs reducing maternal deaths.
Malnutrition Impact : Investigate malnutrition’s effect on growth.
Healthcare Access : Analyze access based on socioeconomic status.
Vaccination Impact : Assess vaccination’s role in disease prevention.
Mental Health : Improve mental health awareness.
Chronic Disease : Study chronic disease management.
Health Tech : Explore healthcare technology.
Nutrition Programs : Evaluate nutritional intervention effects.
Health Education : Study health education program effectiveness.

Quantitative research is crucial in STEM fields, offering a structured way to study complex phenomena. By choosing a focused topic, using rigorous methods, and analyzing data effectively, students can make impactful contributions.

Success in quantitative research comes from curiosity, perseverance, and a drive to discover new knowledge. Embrace challenges as chances for growth and innovation.

Combining theory with practical application, your research can push the boundaries of knowledge and benefit society.

270+ Unique Cyber Security Research Topics For Students

250+ Best Cloud Computing Research Topics for students 2024

100+ Quantitative Research Topics For Students

Quantitative research is a research strategy focusing on quantified data collection and analysis processes. This research strategy emphasizes testing theories on various subjects. It also includes collecting and analyzing non-numerical data.

Quantitative research is a common approach in the natural and social sciences , like marketing, business, sociology, chemistry, biology, economics, and psychology. So, if you are fond of statistics and figures, a quantitative research title would be an excellent option for your research proposal or project.

How to Get a Title of Quantitative Research

How to make quantitative research title, what is the best title for quantitative research, amazing quantitative research topics for students, creative quantitative research topics, perfect quantitative research title examples, unique quantitative research titles, outstanding quantitative research title examples for students, creative example title of quantitative research samples, outstanding quantitative research problems examples, fantastic quantitative research topic examples, the best quantitative research topics, grade 12 quantitative research title for students, list of quantitative research titles for high school, easy quantitative research topics for students, trending topics for quantitative research, quantitative research proposal topics, samples of quantitative research titles, research title about business quantitative.

Finding a great title is the key to writing a great quantitative research proposal or paper. A title for quantitative research prepares you for success, failure, or mediocre grades. This post features examples of quantitative research titles for all students.

Putting together a research title and quantitative research design is not as easy as some students assume. So, an example topic of quantitative research can help you craft your own. However, even with the examples, you may need some guidelines for personalizing your research project or proposal topics.

So, here are some tips for getting a title for quantitative research:

Consider your area of studies
Look out for relevant subjects in the area
Expert advice may come in handy
Check out some sample quantitative research titles

Making a quantitative research title is easy if you know the qualities of a good title in quantitative research. Reading about how to make a quantitative research title may not help as much as looking at some samples. Looking at a quantitative research example title will give you an idea of where to start.

However, let’s look at some tips for how to make a quantitative research title:

The title should seem interesting to readers
Ensure that the title represents the content of the research paper
Reflect on the tone of the writing in the title
The title should contain important keywords in your chosen subject to help readers find your paper
The title should not be too lengthy
It should be grammatically correct and creative
It must generate curiosity

An excellent quantitative title should be clear, which implies that it should effectively explain the paper and what readers can expect. A research title for quantitative research is the gateway to your article or proposal. So, it should be well thought out. Additionally, it should give you room for extensive topic research.

A sample of quantitative research titles will give you an idea of what a good title for quantitative research looks like. Here are some examples:

What is the correlation between inflation rates and unemployment rates?
Has climate adaptation influenced the mitigation of funds allocation?
Job satisfaction and employee turnover: What is the link?
A look at the relationship between poor households and the development of entrepreneurship skills
Urbanization and economic growth: What is the link between these elements?
Does education achievement influence people’s economic status?
What is the impact of solar electricity on the wholesale energy market?
Debt accumulation and retirement: What is the relationship between these concepts?
Can people with psychiatric disorders develop independent living skills?
Children’s nutrition and its impact on cognitive development

Quantitative research applies to various subjects in the natural and social sciences. Therefore, depending on your intended subject, you have numerous options. Below are some good quantitative research topics for students:

The difference between the colorific intake of men and women in your country
Top strategies used to measure customer satisfaction and how they work
Black Friday sales: are they profitable?
The correlation between estimated target market and practical competitive risk assignment
Are smartphones making us brighter or dumber?
Nuclear families Vs. Joint families: Is there a difference?
What will society look like in the absence of organized religion?
A comparison between carbohydrate weight loss benefits and high carbohydrate diets?
How does emotional stability influence your overall well-being?
The extent of the impact of technology in the communications sector

Creativity is the key to creating a good research topic in quantitative research. Find a good quantitative research topic below:

How much exercise is good for lasting physical well-being?
A comparison of the nutritional therapy uses and contemporary medical approaches
Does sugar intake have a direct impact on diabetes diagnosis?
Education attainment: Does it influence crime rates in society?
Is there an actual link between obesity and cancer rates?
Do kids with siblings have better social skills than those without?
Computer games and their impact on the young generation
Has social media marketing taken over conventional marketing strategies?
The impact of technology development on human relationships and communication
What is the link between drug addiction and age?

Need more quantitative research title examples to inspire you? Here are some quantitative research title examples to look at:

Habitation fragmentation and biodiversity loss: What is the link?
Radiation has affected biodiversity: Assessing its effects
An assessment of the impact of the CORONA virus on global population growth
Is the pandemic truly over, or have human bodies built resistance against the virus?
The ozone hole and its impact on the environment
The greenhouse gas effect: What is it and how has it impacted the atmosphere
GMO crops: are they good or bad for your health?
Is there a direct link between education quality and job attainment?
How have education systems changed from traditional to modern times?
The good and bad impacts of technology on education qualities

Your examiner will give you excellent grades if you come up with a unique title and outstanding content. Here are some quantitative research examples titles.

Online classes: are they helpful or not?
What changes has the global CORONA pandemic had on the population growth curve?
Daily habits influenced by the global pandemic
An analysis of the impact of culture on people’s personalities
How has feminism influenced the education system’s approach to the girl child’s education?
Academic competition: what are its benefits and downsides for students?
Is there a link between education and student integrity?
An analysis of how the education sector can influence a country’s economy
An overview of the link between crime rates and concern for crime
Is there a link between education and obesity?

Research title example quantitative topics when well-thought guarantees a paper that is a good read. Look at the examples below to get started.

What are the impacts of online games on students?
Sex education in schools: how important is it?
Should schools be teaching about safe sex in their sex education classes?
The correlation between extreme parent interference on student academic performance
Is there a real link between academic marks and intelligence?
Teacher feedback: How necessary is it, and how does it help students?
An analysis of modern education systems and their impact on student performance
An overview of the link between academic performance/marks and intelligence
Are grading systems helpful or harmful to students?
What was the impact of the pandemic on students?

Irrespective of the course you take, here are some titles that can fit diverse subjects pretty well. Here are some creative quantitative research title ideas:

A look at the pre-corona and post-corona economy
How are conventional retail businesses fairing against eCommerce sites like Amazon and Shopify?
An evaluation of mortality rates of heart attacks
Effective treatments for cardiovascular issues and their prevention
A comparison of the effectiveness of home care and nursing home care
Strategies for managing effective dissemination of information to modern students
How does educational discrimination influence students’ futures?
The impacts of unfavorable classroom environment and bullying on students and teachers
An overview of the implementation of STEM education to K-12 students
How effective is digital learning?

If your paper addresses a problem, you must present facts that solve the question or tell more about the question. Here are examples of quantitative research titles that will inspire you.

An elaborate study of the influence of telemedicine in healthcare practices
How has scientific innovation influenced the defense or military system?
The link between technology and people’s mental health
Has social media helped create awareness or worsened people’s mental health?
How do engineers promote green technology?
How can engineers raise sustainability in building and structural infrastructures?
An analysis of how decision-making is dependent on someone’s sub-conscious
A comprehensive study of ADHD and its impact on students’ capabilities
The impact of racism on people’s mental health and overall wellbeing
How has the current surge in social activism helped shape people’s relationships?

Are you looking for an example of a quantitative research title? These ten examples below will get you started.

The prevalence of nonverbal communication in social control and people’s interactions
The impacts of stress on people’s behavior in society
A study of the connection between capital structures and corporate strategies
How do changes in credit ratings impact equality returns?
A quantitative analysis of the effect of bond rating changes on stock prices
The impact of semantics on web technology
An analysis of persuasion, propaganda, and marketing impact on individuals
The dominant-firm model: what is it, and how does it apply to your country’s retail sector?
The role of income inequality in economy growth
An examination of juvenile delinquents’ treatment in your country

Excellent Topics For Quantitative Research

Here are some titles for quantitative research you should consider:

Does studying mathematics help implement data safety for businesses
How are art-related subjects interdependent with mathematics?
How do eco-friendly practices in the hospitality industry influence tourism rates?
A deep insight into how people view eco-tourisms
Religion vs. hospitality: Details on their correlation
Has your country’s tourist sector revived after the pandemic?
How effective is non-verbal communication in conveying emotions?
Are there similarities between the English and French vocabulary?
How do politicians use persuasive language in political speeches?
The correlation between popular culture and translation

Here are some quantitative research titles examples for your consideration:

How do world leaders use language to change the emotional climate in their nations?
Extensive research on how linguistics cultivate political buzzwords
The impact of globalization on the global tourism sector
An analysis of the effects of the pandemic on the worldwide hospitality sector
The influence of social media platforms on people’s choice of tourism destinations
Educational tourism: What is it and what you should know about it
Why do college students experience math anxiety?
Is math anxiety a phenomenon?
A guide on effective ways to fight cultural bias in modern society
Creative ways to solve the overpopulation issue

An example of quantitative research topics for 12 th -grade students will come in handy if you want to score a good grade. Here are some of the best ones:

The link between global warming and climate change
What is the greenhouse gas impact on biodiversity and the atmosphere
Has the internet successfully influenced literacy rates in society
The value and downsides of competition for students
A comparison of the education system in first-world and third-world countries
The impact of alcohol addiction on the younger generation
How has social media influenced human relationships?
Has education helped boost feminism among men and women?
Are computers in classrooms beneficial or detrimental to students?
How has social media improved bullying rates among teenagers?

High school students can apply research titles on social issues or other elements, depending on the subject. Let’s look at some quantitative topics for students:

What is the right age to introduce sex education for students
Can extreme punishment help reduce alcohol consumption among teenagers?
Should the government increase the age of sexual consent?
The link between globalization and the local economy collapses
How are global companies influencing local economies?

There are numerous possible quantitative research topics you can write about. Here are some great quantitative research topics examples:

The correlation between video games and crime rates
Do college studies impact future job satisfaction?
What can the education sector do to encourage more college enrollment?
The impact of education on self-esteem
The relationship between income and occupation

You can find inspiration for your research topic from trending affairs on social media or in the news. Such topics will make your research enticing. Find a trending topic for quantitative research example from the list below:

How the country’s economy is fairing after the pandemic
An analysis of the riots by women in Iran and what the women gain to achieve
Is the current US government living up to the voter’s expectations?
How is the war in Ukraine affecting the global economy?
Can social media riots affect political decisions?

A proposal is a paper you write proposing the subject you would like to cover for your research and the research techniques you will apply. If the proposal is approved, it turns to your research topic. Here are some quantitative titles you should consider for your research proposal:

Military support and economic development: What is the impact in developing nations?
How does gun ownership influence crime rates in developed countries?
How can the US government reduce gun violence without influencing people’s rights?
What is the link between school prestige and academic standards?
Is there a scientific link between abortion and the definition of viability?

You can never have too many sample titles. The samples allow you to find a unique title you’re your research or proposal. Find a sample quantitative research title here:

Does weight loss indicate good or poor health?
Should schools do away with grading systems?
The impact of culture on student interactions and personalities
How can parents successfully protect their kids from the dangers of the internet?
Is the US education system better or worse than Europe’s?

If you’re a business major, then you must choose a research title quantitative about business. Let’s look at some research title examples quantitative in business:

Creating shareholder value in business: How important is it?
The changes in credit ratings and their impact on equity returns
The importance of data privacy laws in business operations
How do businesses benefit from e-waste and carbon footprint reduction?
Organizational culture in business: what is its importance?

We Are A Call Away

Interesting, creative, unique, and easy quantitative research topics allow you to explain your paper and make research easy. Therefore, you should not take choosing a research paper or proposal topic lightly. With your topic ready, reach out to us today for excellent research paper writing services .

Experimental Quantitative Research Topics For Stem Students

1. impact of variable x on y: examine how changes in x affect y using controlled experiments., 2. algorithm efficiency in different conditions: test algorithm performance under varying data loads., 3. material strength under stress: measure how different materials withstand stress and strain., 4. effects of temperature on reaction rates: analyze how temperature variations influence chemical reactions., 5. simulation of particle dynamics: study how particles interact in different simulated environments., 6. energy consumption of renewable sources: compare energy output and efficiency from various renewables., growth rates of plants with different nutrients: investigate how plant growth varies with nutrient types., 8. accuracy of machine learning models: assess how well different models predict outcomes using real data., discover more stories.

199+ Engaging Quantitative Research Topics for STEM Students

quantitative research topics for stem students (2)

Discover engaging quantitative research topics for STEM students, spanning energy solutions, medical advancements, and cutting-edge technology. Explore hands-on ideas to sharpen skills and make a tangible impact on the future.

Hey STEM buddy! Ready for some awesome research ideas? We’ve handpicked cool topics just for you.

Inside, you’ll find stuff from new energy solutions to medical breakthroughs. They’re chosen to let you shine with your skills in numbers, problems, and creativity.

You’ll get better at stats, experiments, and telling a great story with your results.

Best part? These ideas make a real impact. Whether it’s saving the environment or inventing something new, let’s dive in and shape the future together!

Why Choose Quantitative Research for STEM Students?

Here’s why quantitative research rocks for STEM students:

Boosts Core Skills

Crunching Numbers: It helps you analyze big data, spot trends, and draw solid conclusions, super important in any STEM gig.
Problem-Solving with Math: You’ll ace making research questions that need number answers, a must-have skill in physics, engineering, and computer science.
Smart Thinking: By sussing out good data and drawing logical conclusions, you’ll become a pro problem-solver in STEM.

Preps You for More

Grad School Ready: Quantitative research sets you up for success in advanced STEM studies.
R&D Pros: Get ready to contribute big time to scientific breakthroughs and cool research projects.

Keeps It Real

Less Bias, More Facts: Quantitative research sticks to the facts, making your findings solid and reliable.
Tangible Results: You’ll get real numbers and clear outcomes, making your research super easy to understand and compare.

Works in All Kinds of STEM

Engineering: Perfect for testing designs and improving processes.
Computer Science: From writing code to analyzing networks, you’ll need those numbers.
Life Sciences: It’s vital for everything from drug trials to understanding how living things work.

So while qualitative research is cool, focusing on numbers sets you up with the skills you need to rock STEM like a pro!

Quantitative Research Topics for STEM Students

Check out quantiative research topics for STEM students:-

Impact of environment on bacteria growth.
Effects of fertilizers on plant growth.
Genetic mutations and disease prevalence.
Strategies for biodiversity preservation.
Temperature’s influence on enzyme activity.
Ecological footprint of a species.
Spread of infectious diseases.
Pollution’s effects on aquatic ecosystems.
Diet’s correlation with lifespan.
Climate change’s impact on bird migration.
Catalyst efficiency in reactions.
Chemical reaction kinetics.
Nanoparticle properties in drug delivery.
pH levels and metal corrosion.
Pollution analysis in air and water.
Water purification methods.
Polymer behavior under conditions.
Thermodynamics of chemical compounds.
Additives’ impact on food stability.
Solar cell efficiency.
Superconductor properties at temperatures.
Mass-acceleration relationship.
Energy transfer in collisions.
Material behavior under pressure.
Projectile trajectory modeling.
Renewable energy source efficiency.
Aerodynamics of wing designs.
Electromagnetic wave properties.
Doppler effect in astronomy.
Temperature’s effect on conductivity.

Mathematics

Resource allocation algorithms.
Prime number distribution analysis.
Fractals in mathematical modeling.
Numerical equation-solving methods.
Geometry-topology relationship.
Chaotic system modeling.
Convergence properties of methods.
Graph theory in networks.
Financial market risk analysis.
Population growth prediction models.

Engineering

Structural material performance.
Heat exchanger efficiency.
Stability of bridges and buildings.
Vehicle propulsion system efficiency.
Fluid behavior in hydraulic systems.
Vibration effects on mechanics.
Electrical circuit reliability.
Manufacturing process energy efficiency.
Traffic flow optimization.

Environmental Science

Deforestation’s impact on climate.
Soil erosion rate quantification.
Conservation strategy effectiveness.
Air pollution’s effects on health.
Ocean acidification’s impact.
Waste management technique analysis.
Carbon footprint analysis.
Urbanization’s effect on water quality.
Renewable energy policy effectiveness.
Species distribution analysis.

Computer Science

Image recognition algorithm development.
Sorting algorithm efficiency.
Machine learning model performance.
Scalability of distributed systems.
Encryption method vulnerability.
Network behavior under cyber-attacks.
Routing protocol efficiency.
Social media user behavior analysis.
Data compression technique performance.
Algorithmic bias impact analysis.

Agricultural Science

Irrigation method impact on crop yield.
Climate change effects on productivity.
Pest control method efficiency.
Crop nutritional content analysis.
Plant disease spread modeling.
Soil composition and crop growth.
Water usage in farming systems.
Genetically modified crop efficacy.
Land use change biodiversity impact.
Organic farming economic viability.

Material Science

Graphene-based material mechanical properties.
Ceramic thermal conductivity.
Metal alloy corrosion resistance.
Semiconductor electrical properties.
Composite material behavior modeling.
Nanomaterial optical property analysis.
Material hardness quantification.
Polymer biocompatibility assessment.
Material self-healing property study.
Manufacturing process environmental impact.

Health Sciences

Exercise impact on cardiovascular health.
Diet’s correlation with obesity.
Air quality’s effects on respiratory health.
Genetic predisposition to diseases.
Infectious disease spread modeling.
Rehabilitation technique effectiveness.
Psychological factors in substance abuse.
Treatment efficacy for mental disorders.
Sleep patterns’ effect on cognition.
Telemedicine’s healthcare access impact.

Neuroscience

Neural mechanisms of learning and memory.
Neurotransmitter effects on brain function.
Neuroplasticity’s brain injury recovery impact.
Brain structure’s cognitive ability correlation.
Decision-making neural network modeling.
Stress effects on brain development.
Brain activity analysis with EEG or fMRI.
Intervention efficacy for neurodegenerative diseases.
Neural basis of addiction.
Aging’s effect on brain health.
Dark matter distribution analysis.
Exoplanet properties using transit photometry.
Galaxy cluster dynamics.
Star formation and evolution.
Celestial body gravitational interaction modeling.
Asteroid and comet composition analysis.
Stellar spectra analysis.
Pulsating star variability study.
Black hole properties analysis.
Cosmic microwave background radiation analysis.

Earth Science

Tectonic activity and seismicity relationship.
Climate change’s glacier retreat impact.
Tornadoes and hurricanes formation factors.
Ocean currents’ climate effect.
Groundwater system behavior modeling.
Deforestation’s local climate impact.
Soil erosion rate analysis.
Landslide risk assessment.
Volcanic eruption impact on atmosphere.
Historical climate data analysis.

Social Sciences

Socioeconomic status and education correlation.
Early childhood intervention impact.
Social media’s mental health effect.
Voter behavior influencing factors.
Misinformation spread modeling.
Crime rate intervention effectiveness.
Urban area income inequality analysis.
Immigration’s labor market impact.
Social support’s health outcome impact.
Residential segregation patterns analysis.
Teaching method impact on learning.
Class size’s academic achievement impact.
Student retention and graduation analysis.
Parental involvement and student success correlation.
Socioeconomic status’ educational attainment impact.
Educational technology effectiveness analysis.
Gender gap in STEM fields analysis.
Early childhood education program effectiveness.
Standardized testing’s educational equity impact.
Teacher training’s student achievement impact.

Business and Economics

Consumer behavior influencing factors.
Advertising’s product sales impact.
Pricing strategy effectiveness analysis.
Inflation’s economic growth correlation.
Financial market dynamics modeling.
Government policy economic impact analysis.
Income inequality’s economic stability impact.
International trade agreement impact analysis.
Entrepreneurship program economic growth impact.
Corporate governance’s firm performance correlation.

Linguistics

Bilingual children’s language acquisition patterns.
Linguistic diversity’s communication impact.
Language structure and cognitive process correlation.
Language evolution modeling.
Cultural trait spread modeling.
Colonialism’s impact on indigenous languages.
Phonological variation analysis.
Technology’s language impact.
Language processing neural basis study.
Language’s cultural identity shaping role.
Decision-making in social dilemmas analysis.
Stress impact on cognitive performance.
Personality traits’ academic achievement impact.
Emotion regulation neural mechanism study.
Attachment style development modeling.
Sleep deprivation’s mood and behavior impact.
Psychotherapy intervention effectiveness analysis.
Social support’s mental health outcome impact.
Social media’s self-esteem impact.
Online community behavior analysis.

Anthropology

Cultural transmission patterns in indigenous communities.
Globalization’s impact on traditional cultures.
Kinship structures and social organization relationship.
Cooperative behavior evolution in societies.
Cultural trait diffusion modeling.
Colonialism’s impact on indigenous populations.
Material culture’s social identity implications.
Migration’s cultural diversity impact.
Rituals and ceremonies’ social cohesion role.
Cultural contact’s language evolution impact.

Political Science

Voter turnout influencing factors analysis.
Gerrymandering’s political representation impact.
Media bias and public opinion correlation.
Campaign finance law electoral outcome impact.
Political polarization modeling.
International diplomacy’s conflict resolution effectiveness.
Government policy income distribution impact.
Electoral system political stability impact.
Identity politics’ political movement impact.
Political participation and activism patterns analysis.

These simplified points should provide a clear overview of research topics in each category for STEM students.

What is quantitative research related to stem students?

Quantitative research is like a secret weapon for STEM students. It helps them explore science using numbers and stats, making their findings solid and reliable. Here’s why:-

It’s Objective: Numbers don’t lie, so it keeps things fair and unbiased.
Testing Ideas: Got a hunch? Quantitative research helps you test your theories properly.
Big Picture: With lots of data, you can make conclusions that matter to more than just your study group.

Now, here are some cool areas where STEM students can get into it:

Environmental Science: Measure climate change impacts or track biodiversity loss.
Public Health: Look at how diets affect diseases or see if vaccines really work.
Engineering: Figure out which materials make things stronger or find the best renewable energy sources.

And a few tips for STEM students diving into quantitative research:

Find the Data: Check out government databases or online sources for info on your topic.
Think Beyond Science: Consider how your research might affect society or the environment.
Get Advice: Talk to your professors for guidance on your project.

With quantitative research, STEM students can unlock new discoveries and make a real impact on the world.

What are the best topics for quantitative research for STEM?

Selecting the “best” topic hinges on your specific STEM interests, but here are some general paths to guide your quantitative research:

Considering Your STEM Field

Data Trends: Explore existing data to uncover patterns. For instance, analyze how different fertilizers impact crop yield or the relationship between exercise and heart rate across demographics.

Efficiency Evaluation: Assess effectiveness through quantitative methods. Research topics could include comparing insulation materials or evaluating algorithm performance for specific tasks.

Design Optimization: Enhance designs by studying material properties’ impact on structural integrity or how design features influence aerodynamic efficiency.

Phenomena Modeling: Use mathematical models to understand real-world scenarios. Investigate disease spread models or analyze financial markets through time series analysis.

General Tips for Strong Quantitative Research:

Data Accessibility: Ensure you can access necessary data, whether from existing sources or through your own experiments.
Real-World Relevance: Address pertinent issues with your research.
Feasibility: Consider time and resource constraints.

Finding Inspiration

Current Events: Stay informed about recent breakthroughs or technological advancements.
Personal Interests: Blend academic pursuits with personal hobbies for added motivation.
Review Papers: Explore current challenges researchers are tackling in your field.

Additional Tips

Focus on Specificity: Craft a precise research question for targeted data collection and analysis.
Consider Study Design: Choose between correlational or experimental approaches to suit your research goals.

Remember, the ideal topic should spark your enthusiasm and enable you to contribute meaningfully to your field.

How can you apply quantitative research in STEM?

Quantitative research is like the engine driving progress in STEM fields. Here’s how it works:

Understanding Phenomena

Modeling and Prediction: We use math to create models of complex systems, making better predictions about real-world behavior. For example, epidemiological models forecast disease spread.
Spotting Relationships: Crunching numbers helps us find connections between things. In ecology, we might study how ocean temperatures affect fish populations.

Testing and Evaluating

Experimenting: We design experiments with controlled variables to discover new stuff. Quantitative research ensures our tests are fair and help us pinpoint what works, like developing new technologies or drugs.
Performance Boost: Engineers use numbers to make things better, like testing different car designs to improve fuel efficiency.

Making Smart Choices

Data-Driven Decisions: From building roads to treating illnesses, STEM fields rely on data to make smart decisions. For instance, traffic data helps plan efficient road networks, while clinical trial results inform medical treatments.
Spotting Trends: Big datasets reveal hidden patterns, helping us predict future events or manage resources. Quantitative weather analysis, for instance, helps forecast climate patterns.

In a nutshell, quantitative research gives us the tools to test ideas, analyze data, and make informed decisions in STEM, driving innovation and understanding in our world.

How do you choose a research topic in STEM?

Choosing an exciting STEM research topic is like embarking on a thrilling quest, blending your passions, feasibility, and potential impact. Here’s your guide:

Ignite Your Passion

Follow What Interests You: Dive into what excites you most in STEM. Whether it’s the mysteries of the human body, the elegance of math, or the potential of renewable energy, let your interests guide your journey.
Stay Current: Keep an eye on the latest breakthroughs in AI or pressing environmental issues. Researching these hot topics can give your project relevance and timeliness.
Classroom Insights: Reflect on standout moments from your STEM classes. Did a concept or experiment leave you eager to explore further? Use that as a starting point for your research.

Refine Your Focus

From Broad to Specific: Begin with a broad STEM area of interest, then zoom into specific subtopics that intrigue you. Whether it’s a particular gene’s role or the ethical dilemmas of AI, sharpen your focus.
Background Check: Dive into preliminary research to understand what’s already known in your chosen subtopic. Look for gaps or unanswered questions that your research can tackle.

Consider Feasibility

Data Access: Can you get your hands on the data you need? Explore existing datasets or plan to gather your own. Also, consider the resources available, like lab equipment or software.
Time and Resources: Be realistic about what’s achievable with your available time and resources. Some topics might demand more effort and advanced tools than others.

Sharpen Your Focus

Craft Your Question: Develop a clear and focused research question. It should guide your data collection and analysis, specific enough to answer but broad enough to intrigue.
Design Decision: Decide on your research approach—correlational or experimental. Choose the one that best fits your question and goals.
Seek Guidance: Discuss your ideas with professors or researchers. They can offer insights to refine your topic and ensure its feasibility.
Find Your Fit: Your ideal topic should excite you, allow for meaningful contribution, and align with your academic goals and resources.

By following these steps, you’ll uncover a captivating STEM research topic that not only ignites your curiosity but also propels you toward making a valuable scientific contribution. Let the adventure begin!

Absolutely, selecting research topics for STEM scholars resembles navigating a vast ocean of opportunities.

From unraveling the mysteries of the cosmos to devising solutions for mundane hurdles, an entire realm awaits exploration.

Simply pursue your passions, maintain a pragmatic approach, and embrace collaboration when needed.

Whether you’re crunching numbers, conducting experiments, or dissecting data, always keep in mind the essence: unearthing novel insights and effecting tangible change.

Therefore, plunge into the depths, relish the journey, and allow your inquisitiveness to chart the course!

STEM Research Topics for an Educational Paper

STEM stands for Science, Technology, Engineering, and Math. It is essential for learning and discovery, helping us understand the world, solve problems, and think critically. STEM research goes beyond classroom learning, allowing us to explore specific areas in greater detail. But what is a good topic for research STEM?

Here are a few examples to get you thinking:

Can computers be used to help doctors diagnose diseases?
How can we build houses that are strong and don't hurt the environment?
What are the mysteries of space that scientists haven't figured out yet?

Why is STEM important? STEM is everywhere—from the phones we use to the medicine that keeps us healthy. Learning about these fields helps us build a better future by developing new technologies, protecting our environment, and solving critical problems.

Now that you understand the basics, let's dive into some of the most interesting and important research topics you can choose from.

The List of 260 STEM Research Topics

The right topic will keep you engaged and motivated throughout the writing process. However, with so many areas to explore and problems to solve, finding a unique topic can seem a bit tough. To help you with this, we have compiled a list of 260 STEM research topics. This list aims to guide your decision-making and help you discover a subject that holds significant potential for impact. And if you need further help writing about your chosen topic, feel free to hire someone to write a paper on our professional platform!

Feeling Overwhelmed by Your STEM Research Paper?

Don't go it alone! Our team of seasoned STEM Ph.D.s is here to be your assistant!

Physics Research Topics

Physics, the study of matter, energy, and their interactions, is the foundation for understanding our universe. Here are 20 topics to ignite your curiosity:

Can we develop more efficient solar panels to capture and utilize solar energy for a sustainable future?
How can we further explore the fundamental building blocks of matter, like quarks and leptons, to understand the nature of our universe?
How can we detect and understand dark matter and dark energy, which make up most of the universe's mass and energy but remain a mystery?
What happens to matter and energy when they enter a black hole?
How can we reconcile the theories of quantum mechanics and general relativity to understand gravity at the atomic level?
How can materials with zero electrical resistance be developed and used for more efficient power transmission and next-generation technologies?
What were the conditions of the universe moments after the Big Bang?
How can we manipulate and utilize sound for applications in areas like medical imaging and communication?
How does light behave as both a wave and a particle?
Can we harness the power of nuclear fusion, the process that powers stars, to create a clean and sustainable energy source for the future?
How can physics principles be used to understand and predict the effects of climate change and develop solutions to mitigate its impact?
Can we explore new physics concepts to design more efficient and sustainable aircraft?
What is the fundamental nature of magnetism?
How can we develop new materials with specific properties like superconductivity, high strength, or self-healing capabilities?
How do simple toys like pendulums or gyroscopes demonstrate fundamental physics concepts like motion and energy transfer?
How do physics principles like aerodynamics, momentum, and force transfer influence the performance of athletes and sports equipment?
What is the physics behind sound waves that allow us to hear and appreciate music?
How do technologies like X-rays, MRIs, and CT scans utilize physics principles to create images of the human body for medical diagnosis?
How do waves, currents, and tides behave in the ocean?
How do basic physics concepts like friction, gravity, and pressure play a role in everyday activities like walking, riding a bike, or playing sports?

Use our physics helper to write a paper on any of these topics of your choice!

Chemistry Research Topics

If you're curious about the world around you at the molecular level, here are 20 intriguing topic questions for you:

Can we create chemical reactions that are kinder to the environment?
How can we design new drugs to fight diseases more effectively?
Is it possible to develop materials with properties never seen before?
Can we store energy using chemical reactions for a sustainable future?
What's the chemistry behind creating delicious and nutritious food?
Can chemistry help us analyze evidence and solve crimes more efficiently?
Are there cleaner ways to power our vehicles using chemistry?
How can we reduce plastic pollution with innovative chemical solutions?
What chemicals influence our brain function and behavior?
What exciting new applications can we discover for versatile polymers?
What's the science behind the fascinating world of scents?
How can we develop effective methods for purifying water for safe consumption?
Can we explore the potential of nanochemistry to create revolutionary technologies?
What chemicals are present in the air we breathe, and how do they affect our health?
Why do objects have different colors? Can we explain it through the lens of chemistry?
Do natural catalysts like enzymes hold the key to more efficient chemical processes?
Can we use chemistry to analyze historical objects and uncover their stories?
What's the science behind the beauty products we use every day?
Are artificial sweeteners and flavors safe for consumption?
What chemicals are present in space, and how do they contribute to our universe's composition?

Engineering Research Topics

The world of engineering is all about applying scientific knowledge to solve practical problems. Here are some thought-provoking questions to guide you:

Can we design robots that can assist us in complex surgeries?
How can we create self-driving cars that are safe and reliable?
Is it possible to build sustainable cities that minimize environmental impact?
What innovative materials can we develop for stronger and more resilient buildings?
How can we harness renewable energy sources like wind and solar more efficiently?
Can we design more sustainable and eco-friendly water treatment systems?
What technologies can improve communication and connectivity, especially in remote areas?
How can we create next-generation prosthetics that provide a natural feel and function?
Is it possible to engineer solutions for food security and sustainable agriculture?
What innovative bridges and transportation systems can we design for smarter cities?
How can we engineer safer and more efficient methods for space exploration?
Can we develop robots that can perform hazardous tasks in dangerous environments?
Is it possible to create new manufacturing processes that minimize waste and pollution?
How can we engineer smarter and more efficient power grids to meet our energy demands?
What innovative solutions can we develop to mitigate the effects of climate change?
Can we design more accessible technologies that improve the lives of people with disabilities?
How can we engineer better disaster preparedness and response systems?
Is it possible to create sustainable and efficient methods for waste management?
What innovative clothing and protective gear can we engineer for extreme environments?
Can we develop new technologies for faster and more accurate medical diagnostics?

Mathematics Research Topics

Mathematics, the language of patterns and relationships, offers endless possibilities for exploration. While you ask us to do my math homework for me online , you can choose the topic for your math paper below.

Can we develop new methods to solve complex mathematical problems more efficiently?
Is there a hidden mathematical structure behind seemingly random events?
How can we apply mathematical models to understand and predict real-world phenomena?
Are there undiscovered prime numbers waiting to be found, stretching the boundaries of number theory?
Can we develop new methods for data encryption and security based on advanced mathematical concepts?
How can we utilize game theory to understand competition, cooperation, and decision-making?
Can we explore the fascinating world of fractals and their applications in various fields?
Is it possible to solve long standing mathematical problems like the Goldbach conjecture?
How can we apply topology to understand the properties of shapes and spaces?
Can we develop new mathematical models for financial markets and risk analysis?
What role does cryptography play in the future of secure communication?
How can abstract algebra help us solve problems in other areas of mathematics and science?
Is it possible to explore the connections between mathematics and computer science for groundbreaking discoveries?
Can we utilize calculus to optimize processes and solve problems in engineering and physics?
How can mathematical modeling help us understand and predict weather patterns?
Is it possible to develop new methods for solving differential equations?
Can we explore the applications of set theory in various branches of mathematics?
How can mathematical logic help us analyze arguments and ensure their validity?
Is it possible to apply graph theory to model complex networks like social media or transportation systems?
Can we explore the fascinating world of infinity and its implications for our understanding of numbers and sets?

STEM Topics for Research in Biology

Biology is the amazing study of living things, from the tiniest creatures to giant ecosystems. If you're curious about the world around you, here are 20 interesting research topics to explore:

Can we change plants to catch more sunlight and grow better, helping us get food in a more eco-friendly way?
How do animals like whales or bees use sounds or dances to chat with each other?
Can tiny living things in our gut be used to improve digestion, fight sickness, or even affect our mood?
How can special cells called stem cells be used to repair damaged organs or tissues, leading to brand-new medical treatments?
What happens inside our cells that makes us age, and can we possibly slow it down?
How do internal clocks in living things influence sleep, how their body works, and overall health?
How does pollution from things like tiny plastic pieces harm sea creatures and maybe even us humans?
Can we understand how our brains learn and remember things to create better ways of teaching?
Explore the relationships between different species, like clownfish and anemones, where both creatures benefit.
Can we use living things like bacteria to make new, eco-friendly materials like bioplastics for different uses?
How similar or different are identical twins raised in separate environments, helping us understand how genes and surroundings work together?
Can changing crops using science be a solution to hunger and not having enough healthy food in some countries?
How do viruses change and spread, and how can we develop better ways to fight new viruses that appear?
Explore how amazing creatures like fireflies make their own light and see if there are ways to use this knowledge for other things.
What is the purpose of play in animals' lives, like helping them grow, socialize, or even learn?
How can tools like drones, special cameras from a distance, or other new technology be used to help protect wildlife?
How can we crack the code of DNA to understand how genes work and their role in different diseases?
As a new science tool called CRISPR lets us change genes very precisely, what are the ethical concerns and possible risks involved?
Can spending time in nature, like forests, improve how we feel mentally and physically?
What signs could we look for to find planets with potential life on them besides Earth?

STEM Topics for Research in Robotics

Robotics is a great area for exploration. Here is the topics list that merely scratches the surface of the exciting possibilities in robotics research.

How can robots be programmed to make their own decisions, like self-driving cars navigating traffic?
How can robots be equipped with sensors to "see" and understand their surroundings?
How can robots be programmed to move with precision and coordination, mimicking human actions or performing delicate tasks?
Can robots be designed to learn and improve their skills over time, adapting to new situations?
How can multiple robots work together seamlessly to achieve complex tasks?
How can robots be designed to assist people with disabilities?
How can robots be built to explore the depths of oceans and aid in underwater endeavors?
How can robots be designed to fly for tasks like search and rescue or environmental monitoring?
Can robots be built on an incredibly tiny scale for medical applications or super-precise manufacturing?
How can robots be used to assist surgeons in operating rooms?
How can robots be designed to explore space and assist astronauts?
How can robots be used in everyday life, helping with chores or providing companionship?
How can robots be designed by mimicking the movement and abilities of animals?
What are the ethical considerations in the development and use of robots?
How can robots be designed to interact with humans in a safe and user-friendly way?
How can robots be used in agriculture to automate tasks?
How can robots be used in educational settings to enhance learning?
How will the rise of robots impact the workforce?
How can robots be made more affordable and accessible?
What exciting advancements can we expect in the future of robotics?

Experimental Research Topics for STEM Students

Here are some great topics that can serve as your starting point.

Test how different light intensities affect plant growth rate.
Compare the effectiveness of compost and fertilizer on plant growth.
Experiment with different materials for water filtration and compare their efficiency.
Does playing specific types of music affect plant growth rate?
Test the strength of different bridge designs using readily available materials.
Find the optimal angle for solar panels to maximize energy production.
Compare the insulating properties of different building materials.
Test the effectiveness of different materials (straw, feathers) in absorbing oil spills.
Explore the impact of social media algorithms on user behavior.
Evaluate the effectiveness of different cybersecurity awareness training methods.
Develop and test a mobile app for learning a new language through interactive exercises.
Experiment with different blade shapes to optimize wind turbine energy generation.
Test different techniques to improve website loading speed.
Build a simple air quality monitoring system using low-cost sensors.
Investigate how different light wavelengths affect the growth rate of algae.
Compare the effectiveness of different food preservation methods (drying, salting) on food spoilage.
Test the antibacterial properties of common spices.
Investigate the impact of sleep duration on learning and memory retention.
Research the development of biodegradable packaging materials from natural resources like cellulose or mushroom mycelium.
Compare the effectiveness of different handwashing techniques in reducing bacteria.

Qualitative Research Topics for STEM Students

Qualitative research delves into the experiences, perceptions, and opinions surrounding STEM fields.

How do stellar STEM teachers inspire students to become scientists, engineers, or math whizzes?
As artificial intelligence advances, what are people's biggest concerns and hopes?
What are the hurdles women in engineering face, and how can we make the field more welcoming?
Why do some students freeze up during math tests, and how can we build their confidence?
How do different cultures approach protecting the environment?
What makes scientists passionate about their work, and what keeps them motivated?
When creating new technology, what are the ethical dilemmas developers face?
What are the best ways to explain complex scientific concepts to everyday people?
What fuels people's fascination with exploring space and sending rockets beyond Earth?
How are STEM jobs changing, and what skills will be crucial for the future workforce?
Would people be comfortable with robots becoming our companions, not just machines?
How can we create products that everyone can use, regardless of their abilities?
What makes some people hesitant about vaccines while others readily get them?
What motivates people to volunteer their time and contribute to scientific research?
Does learning to code early on give kids an edge in problem-solving?
Can games and activities make learning math less intimidating and more enjoyable?
What are people's thoughts on the ethical implications of using new technology to change genes?
What motivates people to adopt sustainable practices and protect the environment?
What are people's hopes and anxieties about using technology in medicine and healthcare?
Why do students choose to pursue careers in science, technology, engineering, or math?

Consider using our research paper writer online to create a perfectly-researched and polished paper.

Quantitative Research Topics for STEM Students

Quantitative research uses data and statistics to uncover patterns and relationships in STEM fields.

Does the type of music played affect plant growth rate?
Investigate the relationship between light intensity and the rate of photosynthesis in plants.
Test the impact of bridge design on its weight-bearing capacity.
Analyze how the angle of solar panels affects their energy production.
Quantify the impact of different website optimization techniques on loading speed.
Explore the correlation between social media use and user engagement metrics (likes, shares).
Test the effectiveness of various spices in inhibiting bacterial growth.
Investigate the relationship between sleep duration and memory retention in students.
Compare the effectiveness of different handwashing techniques in reducing bacterial count.
Quantify the impact of play-based learning on children's problem-solving skills.
Measure the efficiency of different materials in filtering microplastics from water samples.
Compare the impact of compost and traditional fertilizer on plant growth yield.
Quantify the insulating properties of various building materials for energy efficiency.
Evaluate the effectiveness of a newly designed learning app through user performance data.
Develop and test a low-cost sensor system to measure air quality parameters.
Quantify the impact of different light wavelengths on the growth rate of algae cultures.
Compare the effectiveness of different food preservation methods (drying, salting) on food spoilage rates.
Analyze the impact of a website redesign on user engagement and retention metrics.
Quantify the effectiveness of different cybersecurity awareness training methods through simulated hacking attempts.
Investigate the relationship between website color schemes and user conversion rates (purchases, sign-ups).

Environmental Sciences Research Topics for STEM students

These environmental science topics explore the connections between our planet's ecosystems and the influence of humans.

Can we track microplastic movement (water, soil, organisms) to understand environmental accumulation?
How can we seamlessly integrate renewable energy (solar, wind) into existing power grids?
Green roofs, urban forests, permeable pavements: their impact on cityscapes and environmental health.
Sustainable forest management: balancing timber production with biodiversity conservation.
Rising CO2: impact on ocean acidity and consequences for marine ecosystems.
Nature's clean-up crew: plants/microbes for decontaminating polluted soil and water.
Evaluating conservation strategies (protected areas, patrols) for endangered species.
Citizen science: potential and limitations for environmental monitoring and data collection.
Circular economy: reducing waste, promoting product reuse/recycling in an eco-friendly framework.
Water conservation strategies: rainwater harvesting, wastewater treatment for a sustainable future.
Agricultural practices (organic vs. conventional): impact on soil health and water quality.
Lab-grown meat: environmental and ethical implications of this alternative protein source.
A potential solution for improving soil fertility and carbon sequestration.
Mangrove restoration: effectiveness in mitigating coastal erosion and providing marine habitat.
Air pollution control technologies: investigating efficiency in reducing emissions.
Climate change and extreme weather events: the link between a warming planet and weather patterns.
Responsible disposal and recycling solutions for electronic waste.
Environmental education: effectiveness in fostering pro-environmental attitudes and behaviors.
Sustainable fashion: exploring alternatives like organic materials and clothing recycling.
Smart cities: using technology to improve environmental sustainability and resource management.

Check out more science research topics in our special guide!

Health Sciences Research Topic Ideas for STEM Students

If you're curious about how the body works and how to stay healthy, these research topics are for you:

Can changing your diet affect your happiness by influencing gut bacteria?
Can your genes help doctors create a treatment plan just for you?
Can viruses that attack bacteria be a new way to fight infections?
Does getting enough sleep help students remember things better?
Can listening to music help people feel less pain during medical procedures?
Can wearable devices warn people about health problems early?
Can doctors use technology to treat people who live far away?
Can meditation techniques help people feel calmer?
Can staying active keep your brain healthy as you age?
Can computers help doctors make better diagnoses?
Can looking at social media make people feel bad about their bodies?
Why are some people hesitant to get vaccinated, and how can we encourage them?
Can scientists create materials for implants that the body won't reject?
Can we edit genes to cure diseases caused by faulty genes?
Does dirty air make it harder to breathe?
Can therapy offered online be just as helpful as in-person therapy?
Can what you eat affect your chances of getting cancer?
Can we use 3D printing to create organs for transplant surgeries?
Do artificial sweeteners harm the good bacteria in your gut?
Can laughter actually be good for your body and mind?

Interdisciplinary STEM Research Topics

Here are 20 thought-provoking questions that explore the exciting intersections between different areas of science, technology, engineering, and math:

Can video games become educational tools, boosting memory and learning for all ages?
Can artificial intelligence compose music that evokes specific emotions in listeners?
Could robots be designed to assist surgeons in complex operations with greater precision?
Does virtual reality therapy hold promise for treating phobias and anxiety?
Can big data analysis predict and prevent natural disasters, saving lives?
Is there a link between dirty air and the rise of chronic diseases in cities?
Can we develop strong, eco-friendly building materials for a sustainable future?
Could wearable tech monitor athletes' performance and prevent injuries?
Will AI advancements lead to the creation of conscious machines, blurring the line between humans and technology?
Can social media platforms be designed to promote positive interactions and reduce online bullying?
Can personalized learning algorithms improve educational outcomes for all students?
Could neuroimaging technologies unlock the secrets of human consciousness?
Will advancements in gene editing allow us to eradicate inherited diseases?
Is there a connection between gut bacteria and mental health issues like depression?
Can drones be used for efficient and safe delivery of medical supplies in remote areas?
Is there potential for using artificial intelligence to design life-saving new drugs?
Could advances in 3D printing revolutionize organ transplantation procedures?
Will vertical farming techniques offer a sustainable solution to food security concerns?
Can we harness the power of nanotechnology to create self-cleaning and self-repairing materials?
Will advancements in space exploration technology lead to the discovery of life on other planets?

STEM Topics for Research in Technology

These research topics explore how technology can solve problems, make life easier, and unlock new possibilities:

How can self-driving cars navigate busy roads safely, reducing accidents?
In what ways can robots explore the deep ocean and unlock its mysteries?
How might technology automate tasks in our homes, making them more efficient and comfortable?
What advancements are possible for directly controlling computers with our thoughts using brain-computer interfaces?
How can we develop stronger cybersecurity solutions to protect our online information and devices from hackers?
What are the methods for harnessing natural resources like wind and sun for clean energy through renewable energy sources?
How can wearable translators instantly translate languages, breaking down communication barriers?
In what ways can virtual reality allow us to explore amazing places without leaving home?
How can games and apps make learning more engaging and effective through educational tools?
What technologies can help us reduce the amount of food that gets thrown away?
How can online platforms tailor education to each student's needs with personalized learning systems?
What new technologies can help us travel farther and learn more about space?
How can desalination techniques turn saltwater into clean drinking water for everyone?
What are the ways drones can deliver aid and supplies quickly and efficiently in emergencies?
How can robots allow doctors to remotely examine and treat patients in distant locations?
What possibilities exist for 3D printers to create customized medical devices and prosthetics?
How can technology overlay information onto the real world, enhancing our learning and experiences with augmented reality tools?
What methods can we use for secure access to devices and information with biometric security systems?
How can AI help us develop strategies to combat climate change?
In what ways can we ensure technology benefits everyone and is used ethically?

While you're researching these STEM topics, learn more about how to get better at math in our dedicated article.

How Do You Choose a Research Topic in STEM?

Choosing research topics for STEM students can be an exciting task. Here are several tips to help you find a topic that is both unique and meaningful:

Identify Your Interests: Start by considering what areas of STEM excite you the most. Do you have a passion for renewable energy, artificial intelligence, biomedical engineering, or environmental science? Your interest in the subject will keep you motivated throughout the research process.
Review Current Research: Conduct a thorough review of existing research in your field. Read recent journal articles, attend seminars, and follow relevant news. This will help you understand what has already been studied and where there might be gaps or opportunities for new research.
Consult with Experts: Talking to professors, advisors, or professionals in your field can provide valuable insights. They can help you identify important research questions, suggest resources, and guide you toward a feasible and impactful topic.
Consider Real-World Problems: Think about the practical applications of your research. Focus on real-world problems that need solutions. This not only makes your research more relevant but also increases its potential impact.
Narrow Down Your Focus: A broad topic can be overwhelming and difficult to manage. Narrow down your focus to a specific question or problem. This will make your research more manageable and allow you to delve deeper into the subject.
Assess Feasibility: Consider the resources and time available to you. Ensure that you have access to the necessary equipment, data, and expertise to complete your research. A feasible topic will help you stay on track and complete your project successfully.
Stay Flexible: Be open to adjusting your topic as you delve deeper into your research. Sometimes, initial ideas may need refinement based on new findings or practical constraints.

These research topics have shown us a glimpse of the exciting things happening in science, technology, engineering, and math (STEM). From understanding our planet to figuring out how the human body works, STEM fields are full of new things to learn and problems to solve.

Don't be afraid to challenge ideas and work with others to find answers. The future of STEM belongs to people who think carefully, try new things, and want to make the world a better place. Remember the famous scientist Albert Einstein, who said, "It is important never to stop asking questions. Curiosity has its own reason for existing."

Drowning in Data Analysis or Struggling to Craft a Strong Argument?

Don't let a challenging STEM research paper derail your academics!

What is STEM in Research?

What are the keys to success in stem fields, what should women in stem look for in a college.

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

Trending Topic Research: STEM . (n.d.). Www.aera.net. Retrieved July 15, 2024, from https://www.aera.net/Newsroom/Trending-Topic-Research-Files/Trending-Topic-Research-STEM

Social Studies Topics: Philosophy, Economics, Culture & More!

200 Quantitative Research Title for Stem Students

Are you a STEM (Science, Technology, Engineering, and Mathematics) student looking for inspiration for your next research project? You’re in the right place! Quantitative research involves gathering numerical data to answer specific questions, and it’s a fundamental part of STEM fields. To help you get started on your research journey, we’ve compiled a list of 200 quantitative research title for stem students. These titles span various STEM disciplines, from biology to computer science. Whether you’re an undergraduate or graduate student, these titles can serve as a springboard for your research ideas.

Biology and Life Sciences

The Impact of pH Levels on Microbial Growth
Examining the Impact of Temperature on Enzyme Activity.
Investigating the Relationship Between Genetics and Obesity
Exploring the Diversity of Microorganisms in Soil Samples
Quantifying the Impact of Pesticides on Aquatic Ecosystems
Studying the Effect of Light Exposure on Plant Growth
Analyzing the Efficiency of Antibiotics on Bacterial Infections
Investigating the Relationship Between Blood Type and Disease Susceptibility
Evaluating the Effects of Different Diets on Lifespan in Fruit Flies
Evaluating the Influence of Air Pollution on Respiratory Health.
Determining the Kinetics of Chemical Reactions
Investigating the Conductivity of Various Ionic Solutions
Analyzing the Effects of Temperature on Gas Solubility
Studying the Corrosion Rate of Metals in Different Environments
Quantifying the Concentration of Heavy Metals in Water Sources
Evaluating the Efficiency of Photocatalytic Materials in Water Purification
Examining the Thermodynamics of Electrochemical Cells
Investigating the Effect of pH on Acid-Base Titrations
Analyzing the Composition of Natural and Synthetic Polymers
Assessing the Chemical Properties of Nanoparticles
Measuring the Speed of Light Using Interferometry
Studying the Behavior of Electromagnetic Waves in Different Media
Investigating the Relationship Between Mass and Gravitational Force
Analyzing the Efficiency of Solar Cells in Energy Conversion
Examining Quantum Entanglement in Photon Pairs
Quantifying the Heat Transfer in Different Materials
Evaluating the Efficiency of Wind Turbines in Energy Production
Studying the Elasticity of Materials Through Stress-Strain Analysis
Analyzing the Effects of Magnetic Fields on Particle Motion
Investigating the Behavior of Superconductors at Low Temperatures

Mathematics

Exploring Patterns in Prime Numbers
Analyzing the Distribution of Random Variables
Investigating the Properties of Fractals in Geometry
Evaluating the Efficiency of Optimization Algorithms
Studying the Dynamics of Differential Equations
Quantifying the Growth of Cryptocurrency Markets
Analyzing Network Theory and its Applications
Investigating the Complexity of Sorting Algorithms
Assessing the Predictive Power of Machine Learning Models
Examining the Distribution of Prime Factors in Large Numbers

Computer Science

Evaluating the Performance of Encryption Algorithms
Analyzing the Efficiency of Data Compression Techniques
Investigating Cybersecurity Threats in IoT Devices
Quantifying the Impact of Code Refactoring on Software Quality
Studying the Behavior of Neural Networks in Image Recognition
Analyzing the Effectiveness of Natural Language Processing Models
Investigating the Relationship Between Software Bugs and Development Methods
Evaluating the Efficiency of Blockchain Consensus Mechanisms
Assessing the Privacy Implications of Social Media Data Mining
Studying the Dynamics of Online Social Networks

Engineering

Analyzing the Structural Integrity of Bridges Under Load
Investigating the Efficiency of Renewable Energy Systems
Quantifying the Performance of Water Filtration Systems
Evaluating the Durability of 3D-Printed Materials
Studying the Aerodynamics of Drone Design
Analyzing the Impact of Noise Pollution on Urban Environments
Investigating the Efficiency of Heat Exchangers in HVAC Systems
Assessing the Safety of Autonomous Vehicles in Real-world Scenarios
Exploring the Applications of Artificial Intelligence in Robotics
Investigating Material Behavior in Extreme Conditions.

Environmental Science

Assessing the Effect of Climate Change on Wildlife Migration.
Analyzing the Effect of Deforestation on Carbon Sequestration
Investigating the Relationship Between Air Quality and Human Health
Quantifying the Rate of Soil Erosion in Different Landscapes
Analyzing the Impacts of Ocean Acidification on Coral Reefs.
Assessing the Efficiency of Waste-to-Energy Conversion Technologies
Analyzing the Impact of Urbanization on Local Microclimates
Investigating the Effect of Oil Spills on Aquatic Ecosystems
Assessing the Effectiveness of Endangered Species Conservation Initiatives.
Studying the Dynamics of Ecological Communities

Astronomy and Space Sciences

Measuring the Orbits of Exoplanets Using Transit Photometry
Investigating the Formation of Stars in Nebulae
Analyzing the Characteristics of Black Holes
Exploring the Characteristics of Cosmic Microwave Background Radiation.
Quantifying the Distribution of Dark Matter in Galaxies
Assessing the Effects of Space Weather on Satellite Communications
Evaluating the Potential for Asteroid Mining
Investigating the Habitability of Exoplanets in the Goldilocks Zone
Analyzing Gravitational Waves from Neutron Star Collisions
Investigating the Evolution of Galaxies Across Cosmic Eras.

Health Sciences

Evaluating the Impact of Exercise on Cardiovascular Health
Analyzing the Relationship Between Diet and Diabetes
Investigating the Efficacy of Vaccination Programs
Quantifying the Psychological Effects of Social Media Use
Studying the Genetics of Neurodegenerative Diseases
Analyzing the Effects of Meditation on Stress Reduction
Investigating the Correlation Between Sleep Patterns and Mental Health
Assessing the Influence of Environmental Factors on Allergies
Evaluating the Effectiveness of Telemedicine in Patient Care
Studying the Health Disparities Among Different Demographic Groups

Materials Science

Analyzing the Properties of Carbon Nanotubes for Nanoelectronics
Investigating the Thermal Conductivity of Advanced Ceramics
Quantifying the Strength of Composite Materials
Studying the Optical Properties of Quantum Dots
Evaluating the Biocompatibility of Biomaterials for Implants
Investigating the Phase Transitions in Perovskite Materials
Analyzing the Mechanical Behavior of Shape Memory Alloys
Assessing the Corrosion Resistance of Coatings on Metals
Studying the Electrical Conductivity of Polymer Blends
Exploring the Superconducting Properties of High-Temperature Superconductors

Earth Sciences

Assessing the Influence of Volcanic Eruptions on Climate.
Analyzing the Geological Processes Shaping Earth’s Surface
Investigating the Seismic Activity in Subduction Zones
Quantifying the Rate of Glacial Retreat in Polar Regions
Studying the Formation of Earthquakes Along Fault Lines
Analyzing the Changes in Ocean Circulation Due to Climate Change
Investigating the Effects of Urbanization on Groundwater Quality
Assessing the Risk of Landslides in Hilly Terrain
Evaluating the Impact of Coastal Erosion on Communities
Studying the Behavior of Hurricanes in Different Oceanic Basins

Social Sciences and Economics

Analyzing the Economic Impact of Natural Disasters
Investigating the Relationship Between Education and Income
Quantifying the Effects of Public Health Policies on Disease Spread
Studying the Demographic Changes in Aging Populations
Evaluating the Effects of Gender Diversity on Corporate Performance
Analyzing the Influence of Social Media on Political Behavior
Investigating the Correlation Between Happiness and Economic Growth
Assessing the Factors Affecting Consumer Buying Behavior
Studying the Dynamics of International Trade Flows
Exploring the Effects of Income Inequality on Social Mobility

Robotics and Artificial Intelligence

Evaluating the Performance of Reinforcement Learning Algorithms in Robotics
Analyzing the Efficiency of Autonomous Navigation Systems
Investigating Human-Robot Interaction in Collaborative Environments
Quantifying the Accuracy of Object Detection Algorithms
Studying the Ethics of Autonomous AI Decision-Making
Analyzing the Robustness of Machine Learning Models to Adversarial Attacks
Investigating the Use of AI in Healthcare Diagnosis
Assessing the Impact of AI on Job Markets
Evaluating the Efficiency of Natural Language Processing in Chatbots
Studying the Potential for AI to Enhance Education

Energy and Sustainability

Examining the Environmental Consequences of Renewable Energy Sources.
Investigating the Efficiency of Energy Storage Systems
Quantifying the Benefits of Green Building Technologies
Studying the Effects of Carbon Pricing on Emissions Reduction
Examining the Prospect for Carbon Capture and Storage
Assessing the Sustainability of Food Production Systems
Investigating the Impact of Electric Vehicles on Urban Air Quality
Analyzing the Energy Consumption Patterns in Smart Cities
Studying the Feasibility of Hydrogen as a Clean Energy Carrier
Exploring Sustainable Agriculture Practices for Crop Yield Improvement

Neuroscience and Psychology

Evaluating the Cognitive Effects of Video Game Play
Analyzing Brain Activity During Decision-Making Processes
Investigating the Neural Correlates of Emotional Regulation
Quantifying the Impact of Music on Brain Function
Analyzing the Outcomes of Mindfulness Meditation on Anxiety
Analyzing Sleep Patterns and Memory Consolidation
Investigating the Relationship Between Neurotransmitters and Mood
Assessing the Neural Basis of Addiction
Evaluating the Effects of Trauma on Brain Structure
Studying the Brain’s Response to Virtual Reality Environments

Mechanical Engineering

Analyzing the Efficiency of Heat Exchangers in Power Plants
Investigating the Wear and Tear of Mechanical Bearings
Quantifying the Vibrations in Mechanical Systems
Studying the Aerodynamics of Wind Turbine Blades
Evaluating the Frictional Properties of Lubricants
Assessing the Efficiency of Cooling Systems in Electronics
Investigating the Performance of Internal Combustion Engines
Analyzing the Impact of Additive Manufacturing on Product Development
Studying the Dynamics of Fluid Flow in Pipelines
Exploring the Behavior of Composite Materials in Aerospace Structures

Biomedical Engineering

Evaluating the Biomechanics of Human Joint Replacements
Analyzing the Performance of Wearable Health Monitoring Devices
Investigating the Biocompatibility of 3D-Printed Medical Implants
Quantifying the Drug Release Rates from Biodegradable Polymers
Studying the Efficiency of Drug Delivery Systems
Assessing the Use of Nanoparticles in Cancer Therapies
Investigating the Biomechanics of Tissue Engineering Constructs
Analyzing the Effects of Electrical Stimulation on Nerve Regeneration
Evaluating the Mechanical Properties of Artificial Heart Valves
Studying the Biomechanics of Human Movement

Civil and Environmental Engineering

Analyzing the Structural Behavior of Tall Buildings in Seismic Zones
Investigating the Efficiency of Stormwater Management Systems
Quantifying the Impact of Green Infrastructure on Urban Flooding
Studying the Behavior of Soils in Slope Stability Analysis
Evaluating the Performance of Water Treatment Plants
Assessing the Sustainability of Transportation Systems
Investigating the Effects of Climate Change on Infrastructure Resilience
Analyzing the Environmental Impact of Construction Materials
Studying the Dynamics of River Sediment Transport
Exploring the Use of Smart Materials in Civil Engineering Applications

Chemical Engineering

Evaluating the Efficiency of Chemical Reactors in Pharmaceutical Production
Analyzing the Mass Transfer Rates in Membrane Separation Processes
Investigating the Effects of Catalysis on Chemical Reactions
Quantifying the Kinetics of Polymerization Reactions
Studying the Thermodynamics of Gas-Liquid Absorption Processes
Assessing the Efficiency of Adsorption-Based Carbon Capture
Investigating the Rheological Properties of Non-Newtonian Fluids
Analyzing the Effects of Surfactants on Foam Stability
Studying the Mass Transport in Microfluidic Devices
Exploring the Synthesis of Nanomaterials for Energy Applications

Electrical and Electronic Engineering

Analyzing the Efficiency of Power Electronics in Electric Vehicles
Investigating the Performance of Wireless Communication Systems
Quantifying the Power Consumption of IoT Devices
Studying the Reliability of Printed Circuit Boards
Evaluating the Efficiency of Photovoltaic Inverters
Assessing the Electromagnetic Compatibility of Electronic Devices
Investigating the Behavior of Antenna Arrays in Beamforming
Analyzing the Power Quality in Electrical Grids
Studying the Security of IoT Networks
Exploring the Use of Machine Learning in Signal Processing

These 200 quantitative research titles offer a diverse array of options to inspire your next STEM research endeavor. Always remember to select a subject that truly captivates your interest and curiosity, as your enthusiasm and curiosity will drive your research to new heights. Good luck with your research journey, STEM student!

189+ Experimental Quantitative Research Topics For STEM Students

Are you looking for incredible experimental quantitative research topics for STEM students? Then you are in the right place. Here, we’ll explore the fantastic experimental research topics for STEM students and others you want to learn. That will help you to increase your knowledge in your field.

Experimental quantitative research plays a pivotal role in STEM. These students explore a broad range of multidisciplinary experimental quantitative research subjects. STEM students take on challenges that push the boundaries of knowledge, whether by studying the complexities of ecological systems, creating novel technologies, delving into the workings of the human brain, or investigating the subtleties of subatomic particles.

Before jumping to our main topic, experimental quantitative research topics for STEM students. Let’s learn about what STEM is.

What Is STEM?

STEM is an acronym that stands for Science, Technology, Engineering, and Mathematics. It is an interdisciplinary approach to learning and problem-solving that combines these four main areas. Scientists, technicians, engineers, and mathematicians collaborate to address challenging real-world challenges and generate novel solutions in the STEM fields.

Let’s know about how to do experimental research. Before starting the experimental quantitative research topics for STEM students.

How To Do Experimental Research

Here are 8 key points on how to do experimental research effectively.

1. Clear Research Focus

Begin by defining a clear and focused research question. A well-defined question provides a purpose and direction for your experiment, guiding your choices in variables and methodology.

2. Thorough Literature Review

Conduct a comprehensive literature review to understand the existing knowledge in your field. This step helps you identify gaps in research and ensures your experiment contributes meaningfully to the scientific community.

3. Precise Variable Definition

Carefully define the variables you will manipulate (independent variable) and measure (dependent variable). Precise definitions are crucial for the validity of your experiment, ensuring you measure what you intend to study.

Also read: 199+ Quantitative Research Topics For STEM Students to Try Now

4. Randomization and Control

Use randomization to assign participants randomly to experimental and control groups. Control all other variables that might influence the outcome, creating a controlled environment. This minimizes biases and enhances the reliability of your results.

5. Standardized Procedures

Develop standardized procedures for conducting the experiment. Consistency in methods across participants and groups is essential to ensure that any observed effects are due to the manipulated variables and not external factors.

6. Accurate Data Collection

Employ accurate and reliable methods to collect data. Be meticulous in recording observations and measurements. Utilize appropriate tools and technologies to minimize errors and enhance the precision of your data.

7. Thorough Data Analysis

Use appropriate statistical techniques to analyze the collected data. Statistical analysis helps you identify patterns, relationships, and significant differences between groups. Proper analysis is key to drawing valid conclusions from your experiment.

8. Clear Communication of Results

Effectively communicate your research findings through clear and concise writing. Present your results, methods, and conclusions in a structured manner, adhering to the standards of scientific reporting. Transparent communication ensures that others can understand, evaluate, and build upon your research.

By following these 8 points, you can conduct experimental research in a systematic, reliable, and impactful manner, leading to valuable contributions to your field of study. Now, let’s move to the main topic, experimental quantitative research topics for STEM students.

Experimental Quantitative Research Topics For STEM Students

Certainly, there are more than 189+ experimental quantitative research topics for STEM students, categorized into different fields:

Biology and Life Sciences

Effects of Different Fertilizers on Plant Growth
Impact of Light Intensity on Photosynthesis
Influence of Temperature on Enzyme Activity
Relationship Between Diet and Animal Behavior
Efficacy of Antibiotics on Bacterial Cultures
Effects of Microplastics on Aquatic Ecosystems
Impact of pH Levels on Microbial Growth
The Role of Genetics in Disease Susceptibility
Influence of Pollution on Soil Microbes
The Effect of Radiation on Cellular DNA

Chemistry and Chemical Engineering

Kinetics of Chemical Reactions at Various Temperatures
Efficiency of Various Catalysts in Chemical Processes
Influence of pH on Chemical Equilibrium
Study of Electrochemical Cells and Voltage
Impact of Different Solvents on Reaction Rates
Properties of Various Polymers in Material Science
Effects of Different Oxidizing Agents on Reactions
The Relationship Between Pressure and Gas Behavior
The Influence of Concentration on Reaction Rate
The Efficacy of Water Purification Methods

Physics and Engineering

The Impact of Different Materials on Magnet Strength
Efficiency of Wind Turbines at Different Wind Speeds
Influence of Friction on Motion and Speed
Relationship Between Light Wavelengths and Energy
Effects of Different Insulation Materials on Heat Transfer
Impact of Material Properties on Bridge Strength
Efficiency of Solar Panels in Different Light Conditions
Influence of Temperature on Electrical Conductivity
Study of Fluid Dynamics in Various Geometries
The Role of Geometric Shapes in Sound Resonance

Environmental Science

Effects of Land Use on Local Climate Patterns
Influence of Air Pollution on Plant Health
Impact of Climate Change on Ocean Acidification
The Relationship Between Soil Erosion and Agricultural Productivity
Efficacy of Biodegradable Materials in Reducing Plastic Pollution
Study of Water Quality Parameters in Urban vs. Rural Areas
Effects of Renewable Energy Sources on Carbon Footprint
Influence of Pesticides on Honeybee Population Decline
Impact of Soil Contaminants on Groundwater Quality
The Role of Algae in Wastewater Treatment

Computer Science and Technology

Effects of Algorithm Complexity on Execution Time
Influence of Data Structures on Software Performance
Impact of Different Programming Languages on Code Efficiency
The Relationship Between Internet Speed and User Experience
Efficacy of Different Machine Learning Models in Data Analysis
Effects of Cybersecurity Measures on Network Vulnerabilities
Influence of Mobile App Features on User Engagement
Impact of Virtual Reality in Education on Learning Outcomes
The Use of Nanomaterials in Data Storage Devices
The Role of Artificial Intelligence in Natural Language Processing

Mathematics and Statistics

Effects of Teaching Methods on Math Skill Acquisition
Influence of Classroom Size on Student Performance
Impact of Tutoring Programs on Math Proficiency
The Relationship Between Homework and Test Scores
Efficacy of Different Teaching Strategies in Probability Education
Effects of Math Anxiety on Test Performance
Influence of Gender on Mathematical Problem-Solving
Impact of Early Math Education on Later Achievement
The Role of Game-Based Learning in Mathematics
The Use of Data Visualization in Statistical Analysis

Medicine and Healthcare

Effects of Medication on Heart Rate Variability
Influence of Different Therapies on Pain Management
Impact of Sleep Duration on Cognitive Performance
The Relationship Between Diet and Weight Loss
Efficacy of Telemedicine in Remote Healthcare Delivery
Effects of Telehealth on Patient Engagement
Influence of Lifestyle on Blood Pressure
Impact of Exercise on Stress Reduction
The Role of Telemedicine in Mental Health Support
The Use of Wearable Health Devices in Disease Monitoring

Materials Science and Nanotechnology

Effects of Nanomaterials on Solar Cell Efficiency
Influence of Nanoparticles on Drug Delivery
Impact of Nanotechnology on Water Filtration
The Relationship Between Nanomaterial Size and Strength
Efficacy of Nanoparticles in Targeted Cancer Therapy
Effects of Nanotechnology on Wearable Electronics
Influence of Nanomaterials in Energy Storage
Impact of Nanomaterials on Sensor Technologies
The Role of Nanomaterials in Environmental Remediation
The Use of Nanotechnology in Biomedical Imaging

Astronomy and Space Science

Effects of Stellar Types on Planetary Formation
Influence of Dark Matter on Galactic Dynamics
Impact of Solar Activity on Earth’s Climate
The Relationship Between Asteroids and Space Weather
Efficacy of Space Telescopes in Exoplanet Discovery
Effects of Cosmic Radiation on Space Travelers
Influence of Gravitational Waves on Black Hole Research
Impact of Satellite Data on Weather Prediction
The Role of Telescopes in Exoplanet Characterization
The Use of Space Probes in Solar System Exploration

Geology and Earth Sciences

Effects of Plate Tectonics on Earthquakes
Influence of Rock Types on Coastal Erosion
Impact of Soil Composition on Landslide Risk
The Relationship Between Geothermal Activity and Volcanic Eruptions
Efficacy of Geological Maps in Hazard Prediction
Effects of Climate Change on Glacier Movement
Influence of Seismic Waves on Building Resilience
Impact of Mineral Properties on Geological Exploration
The Role of Ground-Penetrating Radar in Archaeological Surveys
The Use of LiDAR in Topographic Mapping

Social Sciences

Effects of Social Media Use on Mental Health
Influence of Parenting Styles on Child Behavior
Impact of Education Levels on Income Disparities
The Relationship Between Income and Job Satisfaction
Efficacy of Diversity Training in Workplace Inclusion
Effects of Media Violence on Aggressive Behavior
Influence of Music on Stress Reduction
Impact of Family Structure on Child Development
The Role of Gender Stereotypes in Career Choices
The Use of Virtual Reality in Empathy Training

Economics and Finance

Effects of Fiscal Policy Changes on Economic Growth
Influence of Interest Rates on Investment Decisions
Impact of Inflation on Consumer Spending
The Relationship Between Stock Market Volatility and Investor Behavior
Efficacy of Financial Education on Saving Habits
Effects of Tax Policies on Small Business Growth
Influence of Exchange Rates on International Trade
Impact of Government Regulation on Industry Profitability
The Role of Behavioral Economics in Decision-Making
The Use of Cryptocurrencies in Global Transactions

Environmental Engineering

Effects of Wetland Restoration on Water Quality
Influence of Green Building Techniques on Energy Efficiency
Impact of Renewable Energy Integration on Grid Stability
The Relationship Between Land Use Planning and Flood Resilience
Efficacy of Environmental Impact Assessments in Construction
Effects of Water Treatment Methods on Contaminant Removal
Influence of Erosion Control Measures on Coastal Preservation
Impact of Watershed Management on Aquatic Ecosystem Health
The Role of Stormwater Management in Urban Sustainability
The Use of Biodegradable Materials in Waste Reduction

Also read: 139+ Creative SK Projects Ideas: Your Key to Creative Achievement

Robotics and Automation

Effects of Different Algorithms on Robot Navigation
Influence of Sensor Technologies on Autonomous Vehicles
Impact of Machine Learning on Robotic Object Recognition
The Relationship Between Human-Robot Interaction and User Satisfaction
Efficacy of Robot-Assisted Surgery in Medical Procedures
Effects of Robotics on Disaster Response and Recovery
Influence of Automation on Manufacturing Efficiency
Impact of AI in Autonomous Drones for Environmental Monitoring
The Role of Robotics in Space Exploration
The Use of AI in Predictive Maintenance for Industrial Equipment

Agricultural Sciences

Effects of Crop Rotation on Soil Nutrient Levels
Influence of Pest Control Methods on Crop Yields
Impact of Irrigation Techniques on Water Conservation
The Relationship Between Genetic Modification and Crop Resilience
Efficacy of Precision Agriculture in Resource Optimization
Effects of Soil Microbes on Plant Health
Influence of Organic Farming on Soil Biodiversity
Impact of Sustainable Practices on Farming Profitability
The Role of Drought-Resistant Crops in Food Security
The Use of Drones in Precision Farming

Energy Engineering

Effects of Different Energy Storage Systems on Grid Reliability
Influence of Renewable Energy Integration on Energy Independence
Impact of Building Insulation on Energy Efficiency
The Relationship Between Energy-Efficient Appliances and Household Savings
Efficacy of Smart Grid Technologies in Energy Management
Effects of Solar Thermal Systems on Water Heating
Influence of Geothermal Heat Pumps on HVAC Efficiency
Impact of Hydropower on Renewable Energy Portfolios
The Role of Energy-Efficient Lighting in Green Building
The Use of Biofuels in Reducing Carbon Emissions

Telecommunications and Networking

Effects of Network Topologies on Data Transmission Speed
Influence of Encryption Protocols on Data Security
Impact of 5G Technology on Mobile Network Performance
The Relationship Between Network Load and Bandwidth Allocation
Efficacy of Network Redundancy in Data Backup
Effects of Internet Traffic on Quality of Service
Influence of Routing Algorithms on Packet Delivery
Impact of Firewall Configurations on Network Protection
The Role of Network Virtualization in Scalability
The Use of IoT Devices in Smart Home Connectivity

Materials Engineering

Effects of Heat Treatment on Material Strength
Influence of Alloy Composition on Metal Durability
Impact of Coating Materials on Corrosion Resistance
The Relationship Between Material Properties and Wear Resistance
Efficacy of Composite Materials in Structural Applications
Effects of Surface Treatments on Material Hardness
Influence of Polymers in Biodegradable Packaging
Impact of Nanomaterials on Lightweight Materials
The Role of Smart Materials in Shape Memory Applications
The Use of Superconductors in Energy Transmission

Renewable Energy Technologies

Effects of Wind Turbine Blade Design on Energy Efficiency
Influence of Solar Panel Orientation on Energy Output
Impact of Biofuel Feedstock on Bioenergy Production
The Relationship Between Geothermal Heat Extraction and Sustainability
Efficacy of Tidal Energy Systems in Marine Environments
Effects of Concentrated Solar Power on Thermal Storage
Influence of Energy-Efficient Lighting in Building Sustainability
Impact of Biomass Gasification on Bioenergy Generation
The Role of Ocean Thermal Energy Conversion in Renewable Energy
The Use of Piezoelectric Materials in Energy Harvesting

Urban Planning and Architecture

Effects of Urban Green Spaces on Air Quality
Influence of Building Design on Indoor Air Quality
Impact of Transportation Systems on Urban Accessibility
The Relationship Between Noise Pollution and Building Acoustics
Efficacy of Low-Impact Development in Urban Stormwater Management
Effects of Smart Cities Technologies on Energy Efficiency
Influence of Green Building Materials on Sustainable Construction
Impact of Walkability in Urban Planning and Health
The Role of Urban Farms in Food Security
The Use of Building Automation Systems in Energy Management

Psychology and Behavioral Science

Effects of Stress Management Techniques on Well-Being
Influence of Cognitive Behavioral Therapy on Anxiety Reduction
Impact of Behavioral Interventions on Autism Spectrum Disorder
The Relationship Between Color Psychology and Retail Sales
Efficacy of Mindfulness Meditation in Stress Reduction
Effects of Music Therapy on Dementia Patients’ Behavior
Influence of Social Media Use on Self-Esteem
Impact of Positive Psychology on Employee Well-Being
The Impact of Video Games on Cognitive Skills

Here, we discussed the list of incredible experimental quantitative research topics for STEM students.

Some Experimental Research Topics For High School Students

Above, we discussed the list of experimental quantitative research topics for STEM students. Now, let’s discuss some experimental research topics suitable for high school students.

Exploring Alternative Energy Sources
Investigating the Effects of Climate Change on Local Ecosystems
Testing the Impact of Different Fertilizers on Plant Growth
Studying the Genetics of Inherited Traits
Measuring the Impact of Music on Concentration and Productivity
Examining the Relationship Between Exercise and Academic Performance
Investigating the Effects of Different Cooking Methods on Food Nutrient Levels
Testing the Efficiency of Water Filtration Methods
Studying the Behavior of Insects in Various Environments
Exploring the Chemistry of Food Preservation
Investigating the Physics of Simple Machines
Testing the Effect of Light on Plant Growth
Studying the Impact of Color on Human Mood and Perception
Measuring the Effect of Different Cleaning Products on Bacterial Growth
Investigating the Physics of Projectile Motion

These research topics cover a wide range of disciplines, allowing high school students to engage in exciting and educational experiments while nurturing their scientific curiosity and passion.

6 Mistakes To Avoid While Choosing an Experimental Research Topic

Selecting the right experimental research topic is an essential step to scoring in academic life. However, some common mistakes can hinder your research progress. Let’s explore six pitfalls to avoid:

1. Lack of Personal Interest

Choosing a topic solely based on its popularity or perceived prestige can lead to a lack of personal connection—your emotional investment matters. Select a subject that genuinely intrigues and excites you, as your enthusiasm will be your driving force throughout the research journey.

2. Overambitious Goals

Setting unrealistic expectations can lead to frustration and burnout. Remember, you’re not expected to solve the world’s most complex problems with a single experiment. Start with manageable, well-defined objectives that align with your resources and timeframe.

3. Ignoring Your Skill Level

Overestimating your skills can be disheartening. Choose a topic that matches your current knowledge and expertise. Gradual growth is emotionally rewarding, and as you gain proficiency, you can tackle more complex challenges.

4. Neglecting Resources

Research can be emotionally draining if you lack the necessary resources, be it equipment, materials, or mentorship. Before diving in, ensure you have access to the tools and guidance required for your chosen topic.

5. Failure to Consider the Bigger Picture

Focusing solely on your topic’s microcosm may lead to a lack of context. Remember to examine how your research fits into the larger scientific landscape. This perspective can be emotionally fulfilling, knowing that your work contributes to a broader understanding.

Also read: 21+ Best Paying Jobs In Computer Software Prepackaged Software

6. Ignoring Ethical and Emotional Implications

Some topics may have ethical considerations or evoke emotional responses. Be aware of the potential emotional toll and moral dilemmas that your research may entail. Ensure that you’re emotionally prepared to address these issues responsibly.

Here, we discussed the mistakes to avoid while choosing the experimental research topics.

In this blog, we discussed the experimental quantitative research topics for STEM students, how to do research, what is STEM, some research topics for high school, and mistakes that should be avoided while choosing the experimental research topics.

In conclusion, an experimental research topic is valuable for STEM students to increase their practical knowledge. Each research topic we choose in this blog will definitely help you to achieve your academic goals. Experimental quantitative research gives STEM students concrete insights to deepen their scientific understanding.

STEM students, addressing what STEM is and why research matters in this field. The key takeaway is to choose a topic that resonates with your passion and aligns with your goals, ensuring a successful journey in STEM research. Choose the best Experimental Quantitative Research Topics For STEM students today!

Frequently Asked Questions

Q1. why is experimental quantitative research important for stem students .

It is important because it fosters critical thinking, problem-solving skills, and hands-on learning. It allows STEM students to explore real-world questions, make evidence-based discoveries, and contribute to advancements in their chosen fields.

Q2. What Skills Will I Develop Through Experimental Research?

STEM students will develop skills in critical thinking, data analysis, problem-solving, project management, and effective communication. These skills are valuable in both academia and the workplace.

Q3. What are the Key Elements of a Good Research Question?

A good research question should be specific, clear, measurable, and relevant. It should also be focused on testing a hypothesis or addressing a knowledge gap in your field.

500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

“Statistical Analysis of Supply Chain Disruptions on Retail Sales”
“Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
“Predicting Stock Market Trends Using Machine Learning Algorithms”
“Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
“Impact of Economic Policies on Small Businesses: A Regression Analysis”
“Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
“Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
“Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
“Price Elasticity of Demand for Luxury Goods: A Case Study”
“The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
“Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
“Examining the Correlation Between Interest Rates and Consumer Spending”
“Standardized Testing and Academic Performance: A Quantitative Evaluation”
“Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
“The Relationship Between Extracurricular Activities and Academic Success”
“Influence of Parental Involvement on Children’s Educational Achievements”
“Digital Literacy in Primary Schools: A Quantitative Assessment”
“Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
“Correlation Between Teacher Experience and Student Success Rates”
“Analyzing the Impact of Classroom Technology on Reading Comprehension”
“Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
“The Relationship Between Homework Load and Academic Burnout”
“Assessment of Special Education Programs in Public Schools”
“Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

“The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
“Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
“Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
“Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
“Correlations Between Stress Levels and Immune System Functioning”
“Smoking and Lung Function: A Quantitative Analysis”
“Influence of Physical Activity on Mental Health in Older Adults”
“Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
“The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
“Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
“Impact of Hospital Design on Patient Recovery Rates”
“Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

“Examining Social Inequality through Wage Distribution: A Quantitative Study”
“Impact of Parental Divorce on Child Development: A Longitudinal Study”
“Social Media and its Effect on Political Polarization: A Quantitative Analysis”
“The Relationship Between Religion and Social Attitudes: A Statistical Overview”
“Influence of Socioeconomic Status on Educational Achievement”
“Quantifying the Effects of Community Programs on Crime Reduction”
“Public Opinion and Immigration Policies: A Quantitative Exploration”
“Analyzing the Gender Representation in Political Offices: A Quantitative Study”
“Impact of Mass Media on Public Opinion: A Regression Analysis”
“Influence of Urban Design on Social Interactions in Communities”
“The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
“Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

“Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
“Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
“Impacts of Data Center Cooling Solutions on Energy Consumption”
“Analyzing the Reliability of Renewable Energy Sources in Grid Management”
“Optimization of 5G Network Performance: A Quantitative Assessment”
“Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
“The Relationship Between Software Complexity and Bug Frequency”
“Machine Learning in Predictive Maintenance: A Quantitative Analysis”
“Wearable Technologies and their Impact on Healthcare Monitoring”
“Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
“Analysis of Noise Pollution from Urban Transportation Systems”
“The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

The effects of social media on self-esteem among teenagers.
A comparative study of academic achievement among students of single-sex and co-educational schools.
The impact of gender on leadership styles in the workplace.
The correlation between parental involvement and academic performance of students.
The effect of mindfulness meditation on stress levels in college students.
The relationship between employee motivation and job satisfaction.
The effectiveness of online learning compared to traditional classroom learning.
The correlation between sleep duration and academic performance among college students.
The impact of exercise on mental health among adults.
The relationship between social support and psychological well-being among cancer patients.
The effect of caffeine consumption on sleep quality.
A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
The relationship between physical attractiveness and job opportunities.
The correlation between smartphone addiction and academic performance among high school students.
The impact of music on memory recall among adults.
The effectiveness of parental control software in limiting children’s online activity.
The relationship between social media use and body image dissatisfaction among young adults.
The correlation between academic achievement and parental involvement among minority students.
The impact of early childhood education on academic performance in later years.
The effectiveness of employee training and development programs in improving organizational performance.
The relationship between socioeconomic status and access to healthcare services.
The correlation between social support and academic achievement among college students.
The impact of technology on communication skills among children.
The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
The relationship between employee turnover and organizational culture.
The correlation between job satisfaction and employee engagement.
The impact of video game violence on aggressive behavior among children.
The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
The relationship between bullying and academic performance among middle school students.
The correlation between teacher expectations and student achievement.
The impact of gender stereotypes on career choices among high school students.
The effectiveness of anger management programs in reducing violent behavior.
The relationship between social support and recovery from substance abuse.
The correlation between parent-child communication and adolescent drug use.
The impact of technology on family relationships.
The effectiveness of smoking cessation programs in promoting long-term abstinence.
The relationship between personality traits and academic achievement.
The correlation between stress and job performance among healthcare professionals.
The impact of online privacy concerns on social media use.
The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
The relationship between teacher feedback and student motivation.
The correlation between physical activity and academic performance among elementary school students.
The impact of parental divorce on academic achievement among children.
The effectiveness of diversity training in improving workplace relationships.
The relationship between childhood trauma and adult mental health.
The correlation between parental involvement and substance abuse among adolescents.
The impact of social media use on romantic relationships among young adults.
The effectiveness of assertiveness training in improving communication skills.
The relationship between parental expectations and academic achievement among high school students.
The correlation between sleep quality and mood among adults.
The impact of video game addiction on academic performance among college students.
The effectiveness of group therapy in treating eating disorders.
The relationship between job stress and job performance among teachers.
The correlation between mindfulness and emotional regulation.
The impact of social media use on self-esteem among college students.
The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
The impact of renewable energy policies on carbon emissions
The relationship between employee motivation and job performance
The effectiveness of psychotherapy in treating eating disorders
The correlation between physical activity and cognitive function in older adults
The effect of childhood poverty on adult health outcomes
The impact of urbanization on biodiversity conservation
The relationship between work-life balance and employee job satisfaction
The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
The correlation between parenting styles and child behavior
The effect of social media on political polarization
The impact of foreign aid on economic development
The relationship between workplace diversity and organizational performance
The effectiveness of dialectical behavior therapy in treating borderline personality disorder
The correlation between childhood abuse and adult mental health outcomes
The effect of sleep deprivation on cognitive function
The impact of trade policies on international trade and economic growth
The relationship between employee engagement and organizational commitment
The effectiveness of cognitive therapy in treating postpartum depression
The correlation between family meals and child obesity rates
The effect of parental involvement in sports on child athletic performance
The impact of social entrepreneurship on sustainable development
The relationship between emotional labor and job burnout
The effectiveness of art therapy in treating dementia
The correlation between social media use and academic procrastination
The effect of poverty on childhood educational attainment
The impact of urban green spaces on mental health
The relationship between job insecurity and employee well-being
The effectiveness of virtual reality exposure therapy in treating anxiety disorders
The correlation between childhood trauma and substance abuse
The effect of screen time on children’s social skills
The impact of trade unions on employee job satisfaction
The relationship between cultural intelligence and cross-cultural communication
The effectiveness of acceptance and commitment therapy in treating chronic pain
The correlation between childhood obesity and adult health outcomes
The effect of gender diversity on corporate performance
The impact of environmental regulations on industry competitiveness.
The impact of renewable energy policies on greenhouse gas emissions
The relationship between workplace diversity and team performance
The effectiveness of group therapy in treating substance abuse
The correlation between parental involvement and social skills in early childhood
The effect of technology use on sleep patterns
The impact of government regulations on small business growth
The relationship between job satisfaction and employee turnover
The effectiveness of virtual reality therapy in treating anxiety disorders
The correlation between parental involvement and academic motivation in adolescents
The effect of social media on political engagement
The impact of urbanization on mental health
The relationship between corporate social responsibility and consumer trust
The correlation between early childhood education and social-emotional development
The effect of screen time on cognitive development in young children
The impact of trade policies on global economic growth
The relationship between workplace diversity and innovation
The effectiveness of family therapy in treating eating disorders
The correlation between parental involvement and college persistence
The effect of social media on body image and self-esteem
The impact of environmental regulations on business competitiveness
The relationship between job autonomy and job satisfaction
The effectiveness of virtual reality therapy in treating phobias
The correlation between parental involvement and academic achievement in college
The effect of social media on sleep quality
The impact of immigration policies on social integration
The relationship between workplace diversity and employee well-being
The effectiveness of psychodynamic therapy in treating personality disorders
The correlation between early childhood education and executive function skills
The effect of parental involvement on STEM education outcomes
The impact of trade policies on domestic employment rates
The relationship between job insecurity and mental health
The effectiveness of exposure therapy in treating PTSD
The correlation between parental involvement and social mobility
The effect of social media on intergroup relations
The impact of urbanization on air pollution and respiratory health.
The relationship between emotional intelligence and leadership effectiveness
The effectiveness of cognitive-behavioral therapy in treating depression
The correlation between early childhood education and language development
The effect of parental involvement on academic achievement in STEM fields
The impact of trade policies on income inequality
The relationship between workplace diversity and customer satisfaction
The effectiveness of mindfulness-based therapy in treating anxiety disorders
The correlation between parental involvement and civic engagement in adolescents
The effect of social media on mental health among teenagers
The impact of public transportation policies on traffic congestion
The relationship between job stress and job performance
The effectiveness of group therapy in treating depression
The correlation between early childhood education and cognitive development
The effect of parental involvement on academic motivation in college
The impact of environmental regulations on energy consumption
The relationship between workplace diversity and employee engagement
The effectiveness of art therapy in treating PTSD
The correlation between parental involvement and academic success in vocational education
The effect of social media on academic achievement in college
The impact of tax policies on economic growth
The relationship between job flexibility and work-life balance
The effectiveness of acceptance and commitment therapy in treating anxiety disorders
The correlation between early childhood education and social competence
The effect of parental involvement on career readiness in high school
The impact of immigration policies on crime rates
The relationship between workplace diversity and employee retention
The effectiveness of play therapy in treating trauma
The correlation between parental involvement and academic success in online learning
The effect of social media on body dissatisfaction among women
The impact of urbanization on public health infrastructure
The relationship between job satisfaction and job performance
The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
The correlation between early childhood education and social skills in adolescence
The effect of parental involvement on academic achievement in the arts
The impact of trade policies on foreign investment
The relationship between workplace diversity and decision-making
The effectiveness of exposure and response prevention therapy in treating OCD
The correlation between parental involvement and academic success in special education
The impact of zoning laws on affordable housing
The relationship between job design and employee motivation
The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
The correlation between early childhood education and social-emotional learning
The effect of parental involvement on academic achievement in foreign language learning
The impact of trade policies on the environment
The relationship between workplace diversity and creativity
The effectiveness of emotion-focused therapy in treating relationship problems
The correlation between parental involvement and academic success in music education
The effect of social media on interpersonal communication skills
The impact of public health campaigns on health behaviors
The relationship between job resources and job stress
The effectiveness of equine therapy in treating substance abuse
The correlation between early childhood education and self-regulation
The effect of parental involvement on academic achievement in physical education
The impact of immigration policies on cultural assimilation
The relationship between workplace diversity and conflict resolution
The effectiveness of schema therapy in treating personality disorders
The correlation between parental involvement and academic success in career and technical education
The effect of social media on trust in government institutions
The impact of urbanization on public transportation systems
The relationship between job demands and job stress
The correlation between early childhood education and executive functioning
The effect of parental involvement on academic achievement in computer science
The effectiveness of cognitive processing therapy in treating PTSD
The correlation between parental involvement and academic success in homeschooling
The effect of social media on cyberbullying behavior
The impact of urbanization on air quality
The effectiveness of dance therapy in treating anxiety disorders
The correlation between early childhood education and math achievement
The effect of parental involvement on academic achievement in health education
The impact of global warming on agriculture
The effectiveness of narrative therapy in treating depression
The correlation between parental involvement and academic success in character education
The effect of social media on political participation
The impact of technology on job displacement
The relationship between job resources and job satisfaction
The effectiveness of art therapy in treating addiction
The correlation between early childhood education and reading comprehension
The effect of parental involvement on academic achievement in environmental education
The impact of income inequality on social mobility
The relationship between workplace diversity and organizational culture
The effectiveness of solution-focused brief therapy in treating anxiety disorders
The correlation between parental involvement and academic success in physical therapy education
The effect of social media on misinformation
The impact of green energy policies on economic growth
The relationship between job demands and employee well-being
The correlation between early childhood education and science achievement
The effect of parental involvement on academic achievement in religious education
The impact of gender diversity on corporate governance
The relationship between workplace diversity and ethical decision-making
The correlation between parental involvement and academic success in dental hygiene education
The effect of social media on self-esteem among adolescents
The impact of renewable energy policies on energy security
The effect of parental involvement on academic achievement in social studies
The impact of trade policies on job growth
The relationship between workplace diversity and leadership styles
The correlation between parental involvement and academic success in online vocational training
The effect of social media on self-esteem among men
The impact of urbanization on air pollution levels
The effectiveness of music therapy in treating depression
The correlation between early childhood education and math skills
The effect of parental involvement on academic achievement in language arts
The impact of immigration policies on labor market outcomes
The effectiveness of hypnotherapy in treating phobias
The effect of social media on political engagement among young adults
The impact of urbanization on access to green spaces
The relationship between job crafting and job satisfaction
The effectiveness of exposure therapy in treating specific phobias
The correlation between early childhood education and spatial reasoning
The effect of parental involvement on academic achievement in business education
The impact of trade policies on economic inequality
The effectiveness of narrative therapy in treating PTSD
The correlation between parental involvement and academic success in nursing education
The effect of social media on sleep quality among adolescents
The impact of urbanization on crime rates
The relationship between job insecurity and turnover intentions
The effectiveness of pet therapy in treating anxiety disorders
The correlation between early childhood education and STEM skills
The effect of parental involvement on academic achievement in culinary education
The impact of immigration policies on housing affordability
The relationship between workplace diversity and employee satisfaction
The effectiveness of mindfulness-based stress reduction in treating chronic pain
The correlation between parental involvement and academic success in art education
The effect of social media on academic procrastination among college students
The impact of urbanization on public safety services.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

300+ Science Research Topics

500+ Educational Research Topics

1100+ Research Paper Topics

500+ Economics Research Topics

350+ Biology Research Topics

500+ Music Research Topics

2023 AERA in the News
2022 AERA in the News
2021 AERA In the News
2020 AERA In the News
2019 AERA In the News
2018 AERA In the News
2017 AERA In the News
2016 AERA In the News
2015 AERA In the News
2014 AERA In the News
2013 AERA In the News
2023 AERA News Releases
2022 AERA News Releases
2021 AERA News Releases
2020 AERA News Releases
2019 AERA News Releases
2018 AERA News Releases
2017 AERA News Releases
2016 AERA News Releases
2015 AERA News Releases
2014 AERA News Releases
2013 AERA News Releases
2012 AERA News Releases
2011 News Releases
2010 News Releases
2009 News Releases
2008 News Releases
2007 News Releases
2006 News Releases
2005 News Releases
2004 News Releases
AERA Research Archive
Trending Topic Research Files
Communication Resources for Researchers
AERA Highlights Archival Issues
AERA Video Gallery

STEM

Science, Technology Engineering, and Mathematics (STEM) is one of the most talked about topics in education, emphasizing research, problem solving, critical thinking, and creativity.

The following compendium of open-access articles are inclusive of all substantive AERA journal content regarding STEM published since 1969. This page will be updated as new articles are published.

Jason Jabbari, Yung Chun, Wenrui Huang, Stephen Roll
October 2023
Researchers found that program acceptance was significantly associated with increased earnings and probabilities of working in a science, technology, engineering, and math (STEM) profession.

Robert R. Martinez, Jr., James M. Ellis
September 2023
Researchers found that STEM-CR involves four related yet distinct dimensions of Think, Know, Act, and Go. Results also demonstrated soundness of these STEM-CR dimensions by race and gender (key learning skills and techniques/Act).

Rosemary J. Perez, Rudisang Motshubi, Sarah L. Rodriguez
April 2023
Researchers found that because participants did not attend to how racism and White supremacy fostered negative climate, their strategies (e.g., increased recruitment, committees, workshops) left systemic racism intact and (un)intentionally amplified labor for racially minoritized graduate students and faculty champions who often led change efforts with little support.

Kathleen Lynch, Lily An, Zid Mancenido
, July 2022
Researchers found an average weighted impact estimate of +0.10 standard deviations on mathematics achievement outcomes.

Luis A. Leyva, R. Taylor McNeill, B R. Balmer, Brittany L. Marshall, V. Elizabeth King, Zander D. Alley
, May 2022
Researchers address this research gap by exploring four Black queer students’ experiences of oppression and agency in navigating invisibility as STEM majors.

Angela Starrett, Matthew J. Irvin, Christine Lotter, Jan A. Yow
, May 2022
Researchers found that the more place-based workforce development adolescents reported, the higher their expectancy beliefs, STEM career interest, and rural community aspirations.

Matthew H. Rafalow, Cassidy Puckett
May 2022
Researchers found that educational resources, like digital technologies, are also sorted by schools.

Pamela Burnard, Laura Colucci-Gray, Carolyn Cooke
April 2022
This article makes a case for repositioning STEAM education as democratized enactments of transdisciplinary education, where arts and sciences are not separate or even separable endeavors.

Salome Wörner, Jochen Kuhn, Katharina Scheiter
, April 2022
Researchers conclude that for combining real and virtual experiments, apart from the individual affordances and the learning objectives of the different experiment types, especially their specific function for the learning task must be considered.

Seung-hyun Han, Eunjung Grace Oh, Sun “Pil” Kang
April 2022
Researchers found that the knowledge sharing mechanism and student learning outcomes can be explained in terms of their social capital within social networks.

Barbara Schneider, Joseph Krajcik, Jari Lavonen, Katariina Salmela-Aro, Christopher Klager, Lydia Bradford, I-Chien Chen, Quinton Baker, Israel Touitou, Deborah Peek-Brown, Rachel Marias Dezendorf, Sarah Maestrales, Kayla Bartz
March 2022
Researchers found that improving secondary school science learning is achievable with a coherent system comprising teacher and student learning experiences, professional learning, and formative unit assessments that support students in “doing” science.

Paulo Tan, Alexis Padilla, Rachel Lambert
, March 2022
Researchers found that studies continue to avoid meaningful intersectional considerations of race and disability.

Ta-yang Hsieh, Sandra D. Simpkins
March 2022
Researchers found patterns with overall high/low beliefs, patterns with varying levels of motivational beliefs, and patterns characterized by domain differentiation.

Jonté A. Myers, Bradley S. Witzel, Sarah R. Powell, Hongli Li, Terri D. Pigott, Yan Ping Xin, Elizabeth M. Hughes
, February 2022
Findings of meta-regression analyses showed several moderators, such as sample composition, group size, intervention dosage, group assignment approach, interventionist, year of publication, and dependent measure type, significantly explained heterogeneity in effects across studies.

Grace A. Chen, Ilana S. Horn
, January 2022
The findings from this review highlight the interconnectedness of structures and individual lives, of the material and ideological elements of marginalization, of intersectionality and within-group heterogeneity, and of histories and institutions.

Victor R. Lee, Michelle Hoda Wilkerson, Kathryn Lanouette
December 2021
Researchers offer an interdisciplinary framework based on literature from multiple bodies of educational research to inform design, teaching and research for more effective, responsible, and inclusive student learning experiences with and about data.

Ido Davidesco, Camillia Matuk, Dana Bevilacqua, David Poeppel, Suzanne Dikker
December 2021
This essay critically evaluates the value added by portable brain technologies in education research and outlines a proposed research agenda, centered around questions related to student engagement, cognitive load, and self-regulation.

Guan K. Saw, Charlotte A. Agger
December 2021
Researchers found that during high school rural and small-town students shifted away from STEM fields and that geographic disparities in postsecondary STEM participation were largely explained by students’ demographics and precollege STEM career aspirations and academic preparation.

Kyle M. Whitcomb, Sonja Cwik, Chandralekha Singh
November 2021
Researchers found that on average across all years of study, underrepresented minority (URM) students experience a larger penalty to their mean overall and STEM GPA than even the most disadvantaged non-URM students.

Lana M. Minshew, Amanda A. Olsen, Jacqueline E. McLaughlin
, October 2021
Researchers found that the CA framework is a useful and effective model for supporting faculty in cultivating rich learning opportunities for STEM graduate students.

Xin Lin, Sarah R. Powell
, October 2021
Findings suggested fluency in both mathematics and reading, as well as working memory, yielded greater impacts on subsequent mathematics performance.

Christine L. Bae, Daphne C. Mills, Fa Zhang, Martinique Sealy, Lauren Cabrera, Marquita Sea
, September 2021
This systematic literature review is guided by a complex systems framework to organize and synthesize empirical studies of science talk in urban classrooms across individual (student or teacher), collective (interpersonal), and contextual (sociocultural, historical) planes.

Toya Jones Frank, Marvin G. Powell, Jenice L. View, Christina Lee, Jay A. Bradley, Asia Williams
August/September 2021
Researchers found that teachers’ experiences of microaggressions accounted for most of the variance in our modeling of teachers’ thoughts of leaving the profession.

Ebony McGee, Yuan Fang, Yibin (Amanda) Ni, Thema Monroe-White
August 2021
Researchers found that 40.7% of the respondents reported that their career plans have been affected by Trump’s antiscience policies, 54.5% by the COVID-19 pandemic.

Martha Cecilia Bottia, Roslyn Arlin Mickelson, Cayce Jamil, Kyleigh Moniz, Leanne Barry
, May 2021
Consistent with cumulative disadvantage and critical race theories, findings reveal that the disproportionality of racially minoritized students in STEM is related to their inferior secondary school preparation; the presence of racialized lower quality educational contexts; reduced levels of psychosocial factors associated with STEM success; less exposure to inclusive and appealing curricula and instruction; lower levels of family social, cultural, and financial capital that foster academic outcomes; and fewer prospects for supplemental STEM learning opportunities. Policy implications of findings are discussed.

Iris Daruwala, Shani Bretas, Douglas D. Ready
April 2021
Researchers describe how teachers, school leaders, and program staff navigated institutional pressures to improve state grade-level standardized test scores while implementing tasks and technologies designed to personalize student learning.

Michael A. Gottfried, Jay Plasman, Jennifer A. Freeman, Shaun Dougherty
March 2021
Researchers found that students with learning disabilities were more likely to earn more units in CTE courses compared with students without disabilities.

Ebony Omotola McGee
December 2020
This manuscript also discusses how universities institutionalize diversity mentoring programs designed mostly to fix (read “assimilate”) underrepresented students of color while ignoring or minimizing the role of the STEM departments in creating racially hostile work and educational spaces.

Miray Tekkumru-Kisa, Mary Kay Stein, Walter Doyle
November 2020
The purpose of this article is to revisit theory and research on tasks, a construct introduced by Walter Doyle nearly 40 years ago.

Elizabeth S. Park, Federick Ngo
November 2020
Researchers found that lower math placement may have supported women, and to a lesser extent URM students, in completing transferable STEM credits.

Karisma Morton, Catherine Riegle-Crumb
August/September 2020
Results of regression analyses reveal that, net of school, teacher, and student characteristics, the time that teachers report spending on algebra and more advanced content in eighth grade algebra classes is significantly lower in schools that are predominantly Black compared to those that are not predominantly minority. Implications for future research are discussed.

Qi Zhang, Jessaca Spybrook, Fatih Unlu
, July 2020
Researchers consider strategies to maximize the efficiency of the study design when both student and teacher effects are of primary interest.

Jennifer Lin Russell, Richard Correnti, Mary Kay Stein, Ally Thomas, Victoria Bill, Laurie Speranzo
, July 20, 2020
Analysis of videotaped coaching conversations and teaching events suggests that model-trained coaches improved their capacity to use a high-leverage coaching practice—deep and specific prelesson planning conversations—and that growth in this practice predicted teaching improvement, specifically increased opportunities for students to engage in conceptual thinking.

Maithreyi Gopalan, Kelly Rosinger, Jee Bin Ahn
, April 21, 2020
The overarching purpose of this chapter is to explore and document the growth, applicability, promise, and limitations of quasi-experimental research designs in education research.

Thomas M. Philip, Ayush Gupta
, April 21, 2020
By bringing this collection of articles together, this chapter provides collective epistemic and empirical weight to claims of power and learning as co-constituted and co-constructed through interactional, microgenetic, and structural dynamics.

Steve Graham, Sharlene A. Kiuhara, Meade MacKay
, March 19, 2020
This meta-analysis examined if students writing about content material in science, social studies, and mathematics facilitated learning.

Janina Roloff, Uta Klusmann, Oliver Lüdtke, Ulrich Trautwein
, January 2020
Multilevel regression analyses revealed that agreeableness, high school GPA, and the second state examination grade predicted teachers’ instructional quality.

: Contemporary Views on STEM Subjects and Language With English Learners
Okhee Lee, Amy Stephens
, 2020
With the release of the consensus report , the authors highlight foundational constructs and perspectives associated with STEM subjects and language with English learners that frame the report.

Angela Calabrese Barton and Edna Tan
, 2020
This essay presents a rightful presence framework to guide the study of teaching and learning in justice-oriented ways.

Day Greenberg, Angela Calabrese Barton, Carmen Turner, Kelly Hardy, Akeya Roper, Candace Williams, Leslie Rupert Herrenkohl, Elizabeth A. Davis, Tammy Tasker
, 2020
Researchers report on how one community builds capacity for disrupting injustice and supporting each other during the COVID-19 crisis.

Tatiana Melguizo, Federick Ngo
, 2020
This study explores the extent to which “college-ready” students, by high school standards, are assigned to remedial courses in college.

Karisma Morton and Catherine Riegle-Crumb
, 2020
Results of regression analyses reveal that, net of school, teacher, and student characteristics, the time that teachers report spending on algebra and more advanced content in eighth grade algebra classes is significantly lower in schools that are predominantly Black compared to those that are not predominantly minority. Implications for future research are discussed.

Jonathan D. Schweig, Julia H. Kaufman, and V. Darleen Opfer
, 2020
Researchers found that there are both substantial fluctuations in students’ engagement in these practices and reported cognitive demand from day to day, as well as large differences across teachers.

David Blazar and Casey Archer
, 2020
Researchers found that exposure to “ambitious” mathematics practices is more strongly associated with test score gains of English language learners compared to those of their peers in general education classrooms.

Megan Hopkins, Hayley Weddle, Maxie Gluckman, Leslie Gautsch
, December 2019
Researchers show how both researchers and practitioners facilitated research use.

Adrianna Kezar, Samantha Bernstein-Sierra
, October 2019
Findings suggest that Association of American Universities’ influence was a powerful motivator for institutions to alter deeply ingrained perceptions and behaviors.

Denis Dumas, Daniel McNeish, Julie Sarama, Douglas Clements
, October 2019
While students who receive a short-term intervention in preschool may not differ from a control group in terms of their long-term mathematics outcomes at the end of elementary school, they do exhibit significantly steeper growth curves as they approach their eventual skill level.

Jessica Thompson, Jennifer Richards, Soo-Yean Shim, Karin Lohwasser, Kerry Soo Von Esch, Christine Chew, Bethany Sjoberg, Ann Morris
, September 2019
Researchers used data from professional learning communities to analyze pathways into improvement work and reflective data to understand practitioners’ perspectives.

Ross E. O’Hara, Betsy Sparrow
, September 2019
Results indicate that interventions that target psychosocial barriers experienced by community college STEM students can increase retention and should be considered alongside broader reforms.

Ran Liu, Andrea Alvarado-Urbina, Emily Hannum
, September 2019
Findings reveal disparate national patterns in gender gaps across the performance distribution.

Adam Kirk Edgerton
, September 2019
Through an analysis of 52 interviews with state, regional, and district officials in California, Texas, Ohio, Pennsylvania, and Massachusetts, the author investigates the decline in the popularity of K–12 standards-based reform.

Amy Noelle Parks
, September 2019
The study suggests that more research needs to represent mathematics lessons from the perspectives of children and youth, particularly those students who engage with teachers infrequently or in atypical ways.

Rajeev Darolia, Cory Koedel, Joyce B. Main, J. Felix Ndashimye, Junpeng Yan
, September 30, 2019
Researchers found that differential access to high school courses does not affect postsecondary STEM enrollment or degree attainment.

Laura A. Davis, Gregory C. Wolniak, Casey E. George, Glen R. Nelson
, August 2019
The findings point to variation in informational quality across dimensions ranging from clarity of language use and terminology, to consistency and coherence of visual displays, which accompany navigational challenges stemming from information fragmentation and discontinuity across pages.

Juan E. Saavedra, Emma Näslund-Hadley, Mariana Alfonso
, August 12, 2019
Researchers present results from the first randomized experiment of a remedial inquiry-based science education program for low-performing elementary students in a developing country.

F. Chris Curran, James Kitchin
, July 2019
Researchers found suggestive evidence in some models (student fixed effects and regression with observable controls) that time on science instruction is related to science achievement but little evidence that the number of science topics/skills covered are related to greater science achievement.

Kathleen Lynch, Heather C. Hill, Kathryn E. Gonzalez, Cynthia Pollard
, June 2019
Programs saw stronger outcomes when they helped teachers learn to use curriculum materials; focused on improving teachers’ content knowledge, pedagogical content knowledge, and/or understanding of how students learn; incorporated summer workshops; and included teacher meetings to troubleshoot and discuss classroom implementation. We discuss implications for policy and practice.

Elizabeth Stearns, Martha Cecilia Bottia, Jason Giersch, Roslyn Arlin Mickelson, Stephanie Moller, Nandan Jha, Melissa Dancy
, June 2019
Researchers found that relative advantages in college academic performance in STEM versus non-STEM subjects do not contribute to the gender gap in STEM major declaration.

Nicole Shechtman, Jeremy Roschelle, Mingyu Feng, Corinne Singleton
, May 2019
As educational leaders throughout the United States adopt digital mathematics curricula and adaptive, blended approaches, the findings provide a relevant caution.

Colleen M. Ganley, Robert C. Schoen, Mark LaVenia, Amanda M. Tazaz
, March 2019
Factor analyses support a distinction between components of general math anxiety and anxiety about teaching math.

Felicia Moore Mensah
, February 2019
The implications for practice in both teacher education and science education show that educational and emotional support for teachers of color throughout their educational and professional journey is imperative to increasing and sustaining Black teachers.

Herbert W. Marsh, Brooke Van Zanden, Philip D. Parker, Jiesi Guo, James Conigrave, Marjorie Seaton
, February 2019
Researchers evaluated STEM coursework selection by women and men in senior high school and university, controlling achievement and expectancy-value variables.

Yasemin Copur-Gencturk, Debra Plowman, Haiyan Bai
, January 2019
The results showed that a focus on curricular content knowledge and examining students’ work were significantly related to teachers’ learning.

Rebecca Colina Neri, Maritza Lozano, Louis M. Gomez
, 2019
Researchers found that teacher resistance to CRE as a multilevel learning problem stems from (a) limited understanding and belief in the efficacy of CRE and (b) a lack of know-how needed to execute it.

Russell T. Warne, Gerhard Sonnert, and Philip M. Sadler
, 2019
Researchers investigated the relationship between participation in AP mathematics courses (AP Calculus and AP Statistics) and student career interest in STEM.

Catherine Riegle-Crumb, Barbara King, and Yasmiyn Irizarry
, 2019
Results reveal evidence of persistent racial/ethnic inequality in STEM degree attainment not found in other fields.

Eben B. Witherspoon, Paulette Vincent-Ruz, and Christian D. Schunn
, 2019
Researchers found that high-performing women often graduate with lower paying, lower status degrees.

Bruce Fuller, Yoonjeon Kim, Claudia Galindo, Shruti Bathia, Margaret Bridges, Greg J. Duncan, and Isabel García Valdivia
, 2019
This article details the growing share of Latino children from low-income families populating schools, 1998 to 2010.

Rebekka Darner
, 2019
Drawing from motivated reasoning and self-determination theories, this essay builds a theoretical model of how negative emotions, thwarting of basic psychological needs, and the backfire effect interact to undermine critical evaluation of evidence, leading to science denial.

Okhee Lee
, 2019
As the fast-growing population of English learners (ELs) is expected to meet college- and career-ready content standards, the purpose of this article is to highlight key issues in aligning ELP standards with content standards.

Mark C. Long, Dylan Conger, and Raymond McGhee, Jr.
, 2019
The authors offer the first model of the components inherent in a well-implemented AP science course and the first evaluation of AP implementation with a focus on public schools newly offering the inquiry-based version of AP Biology and Chemistry courses.

Yasemin Copur-Gencturk, Joseph R. Cimpian, Sarah Theule Lubienski, and Ian Thacker
, 2019
Results indicate that teachers are not free of bias, and that teachers from marginalized groups may be susceptible to bias that favors stereotype-advantaged groups.

Geoffrey B. Saxe and Joshua Sussman
, 2019
Multilevel analysis of longitudinal data on a specialized integers and fractions assessment, as well as a California state mathematics assessment, revealed that the ELs in LMR classrooms showed greater gains than comparison ELs and gained at similar rates to their EP peers in LMR classrooms.

Jordan Rickles, Jessica B. Heppen, Elaine Allensworth, Nicholas Sorensen, and Kirk Walters
, 2019
The authors discuss whether it would have been appropriate to test for nominally equivalent outcomes, given that the study was initially conceived and designed to test for significant differences, and that the conclusion of no difference was not solely based on a null hypothesis test.

Soobin Kim, Gregory Wallsworth, Ran Xu, Barbara Schneider, Kenneth Frank, Brian Jacob, Susan Dynarski
, 2019
Using detailed Michigan high school transcript data, this article examines the effect of the MMC on various students’ course-taking and achievement outcomes.

Dario Sansone
, December 2018
Researchers found that students were less likely to believe that men were better than women in math or science when assigned to female teachers or to teachers who valued and listened to ideas from their students.

Ebony McGee
, December 2018
The authors argues that both racial groups endure emotional distress because each group responds to its marginalization with an unrelenting motivation to succeed that imposes significant costs.

Barbara Means, Haiwen Wang, Xin Wei, Emi Iwatani, Vanessa Peters
, November 2018
Students overall and from under-represented groups who had attended inclusive STEM high schools were significantly more likely to be in a STEM bachelor’s degree program two years after high school graduation.

Paulo Tan, Kathleen King Thorius
, November 2018
Results indicate identity and power tensions that worked against equitable practices.

Caesar R. Jackson
, November 2018
This study investigated the validity and reliability of the Motivated Strategies for Learning Questionnaire (MSLQ) for minority students enrolled in STEM courses at a historically black college/university (HBCU).

Tuan D. Nguyen, Christopher Redding
, September 2018
The results highlight the importance of recruiting qualified STEM teachers to work in high-poverty schools and providing supports to help them thrive and remain in the classroom.

Joseph A. Taylor, Susan M. Kowalski, Joshua R. Polanin, Karen Askinas, Molly A. M. Stuhlsatz, Christopher D. Wilson, Elizabeth Tipton, Sandra Jo Wilson
, August 2018
The meta-analysis examines the relationship between science education intervention effect sizes and a host of study characteristics, allowing primary researchers to access better estimates of effect sizes for a priori power analyses. The results of this meta-analysis also support programmatic decisions by setting realistic expectations about the typical magnitude of impacts for science education interventions.

Brian A. Burt, Krystal L. Williams, Gordon J. M. Palmer
, August 2018
Three factors are identified as helping them persist from year to year, and in many cases through completion of the doctorate: the role of family, spirituality and faith-based community, and undergraduate mentors.

Anna-Lena Rottweiler, Jamie L. Taxer, Ulrike E. Nett
, June 2018
Suppression improved mood in exam-related anxiety, while distraction improved mood only in non-exam-related anxiety.

Gabriel Estrella, Jacky Au, Susanne M. Jaeggi, Penelope Collins
, April 2018
Although an analysis of 26 articles confirmed that inquiry instruction produced significantly greater impacts on measures of science achievement for ELLs compared to direct instruction, there was still a differential learning effect suggesting greater efficacy for non-ELLs compared to ELLs.

Heather C. Hill, Mark Chin
, April 2018
In this article, evidence from 284 teachers suggests that accuracy can be adequately measured and relates to instruction and student outcomes.

Darrell M. Hull, Krystal M. Hinerman, Sarah L. Ferguson, Qi Chen, Emma I. Näslund-Hadley
, April 20, 2018
Both quantitative and qualitative evidence suggest students within this culture respond well to this relatively simple and inexpensive intervention that departs from traditional, expository math instruction in many developing countries.

Erika C. Bullock
, April 2018
The author reviews CME studies that employ intersectionality as a way of analyzing the complexities of oppression.

Angela Calabrese Barton, Edna Tan
, March 2018
Building a conceptual argument for an equity-oriented culture of making, the authors discuss the ways in which making with and in community opened opportunities for youth to project their communities’ rich culture knowledge and wisdom onto their making while also troubling and negotiating the historicized injustices they experience.

Sabrina M. Solanki, Di Xu
, March 2018
Researchers found that having a female instructor narrows the gender gap in terms of engagement and interest; further, both female and male students tend to respond to instructor gender.

Susanne M. Jaeggi, Priti Shah
, February 2018
These articles provide excellent examples for how neuroscientific approaches can complement behavioral work, and they demonstrate how understanding the neural level can help researchers develop richer models of learning and development.

Danyelle T. Ireland, Kimberley Edelin Freeman, Cynthia E. Winston-Proctor, Kendra D. DeLaine, Stacey McDonald Lowe, Kamilah M. Woodson
, 2018
Researchers found that (1) identity; (2) STEM interest, confidence, and persistence; (3) achievement, ability perceptions, and attributions; and (4) socializers and support systems are key themes within the experiences of Black women and girls in STEM education.

Ann Y. Kim, Gale M. Sinatra, Viviane Seyranian
, 2018
Findings indicate that young women experience challenges to their participation and inclusion when they are in STEM settings.

Guan Saw, Chi-Ning Chang, and Hsun-Yu Chan
, 2018
Results indicated that female, Black, Hispanic, and low SES students were less likely to show, maintain, and develop an interest in STEM careers during high school years.

Di Xu, Sabrina Solanki, Peter McPartlan, and Brian Sato
, 2018
This paper estimates the causal effects of a first-year STEM learning communities program on both cognitive and noncognitive outcomes at a large public 4-year institution.

Christina S. Chhin, Katherine A. Taylor, and Wendy S. Wei
, 2018
Data showed that IES has not funded any direct replications that duplicate all aspects of the original study, but almost half of the funded grant applications can be considered conceptual replications that vary one or more dimensions of a prior study.

Okhee Lee
, 2018
As federal legislation requires that English language proficiency (ELP) standards are aligned with content standards, this article addresses issues and concerns in aligning ELP standards with content standards in English language arts, mathematics, and science.

Jordan Rickles, Jessica B. Heppen, Elaine Allensworth, Nicholas Sorensen, and Kirk Walters
, 2018
Researchers found no statistically significant differences in longer term outcomes between students in the online and face-to-face courses. Implications of these null findings are discussed.

Colleen M. Ganley, Casey E. George, Joseph R. Cimpian, Martha B. Makowski
, December 2017
Researchers found that perceived gender bias against women emerges as the dominant predictor of the gender balance in college majors.

James P. Spillane, Megan Hopkins, Tracy M. Sweet
, December 2017
This article examines the relationship between teachers’ instructional ties and their beliefs about mathematics instruction in one school district working to transform its approach to elementary mathematics education.

Susan A. Yoon, Sao-Ee Goh, Miyoung Park
, December 6, 2017
Results revealed needs in five areas of research: a need to diversify the knowledge domains within which research is conducted, more research on learning about system states, agreement on the essential features of complex systems content, greater focus on contextual factors that support learning including teacher learning, and a need for more comparative research.

Candace Walkington, Virginia Clinton, Pooja Shivraj
, November 2017
Textual features that make problems more difficult to process appear to differentially negatively impact struggling students, while features that make language easier to process appear to differentially positively impact struggling students.

Rebecca L. Matz, Benjamin P. Koester, Stefano Fiorini, Galina Grom, Linda Shepard, Charles G. Stangor, Brad Weiner, Timothy A. McKay
, November 2017
Biology, chemistry, physics, accounting, and economics lecture courses regularly exhibit gendered performance differences that are statistically and materially significant, whereas lab courses in the same subjects do not.

Adam V. Maltese, Christina S. Cooper
, August 2017
The results reveal that although there is no singular pathway into STEM fields, self-driven interest is a large factor in persistence, especially for males, and females rely more heavily on support from others.

Brian R. Belland, Andrew E. Walker, Nam Ju Kim
, August 2017
Scaffolding has a consistently strong effect across student populations, STEM disciplines, and assessment levels, and a strong effect when used with most problem-centered instructional and educational levels.

Di Xu, Shanna Smith Jaggars
, July 2017
The findings indicate a robust negative impact of online course taking for both subjects.

Maisie L. Gholson, Charles E. Wilkes
, June 2017
This chapter reviews two strands of identity-based research in mathematics education related to Black children, exemplified by Martin (2000) and Nasir (2002).

Sarah Theule Lubienski, Emily K. Miller, and Evthokia Stephanie Saclarides
, November 2017
Using data from a survey of doctoral students at one large institution, this study finds that men submitted and published more scholarly works than women across many fields, with differences largest in natural/biological sciences and engineering.

David Blazar, Cynthia Pollard
, October 2017
Drawing on classroom observations and teacher surveys, researchers find that test preparation activities predict lower quality and less ambitious mathematics instruction in upper-elementary classrooms.

Nicole M. Joseph, Meseret Hailu, Denise Boston
, June 2017
This integrative review used critical race theory (CRT) and Black feminism as interpretive frames to explore factors that contribute to Black women’s and girls’ persistence in the mathematics pipeline and the role these factors play in shaping their academic outcomes.

Benjamin L. Wiggins, Sarah L. Eddy, Daniel Z. Grunspan, Alison J. Crowe
, May 2017
Researchers describe the results of a quasi-experimental study to test the apex of the ICAP framework (interactive, constructive, active, and passive) in this ecological classroom environment.

Sean Gehrke, Adrianna Kezar
, May 2017
This study examines how involvement in four cross-institutional STEM faculty communities of practice is associated with local departmental and institutional change for faculty members belonging to these communities.

Lawrence Ingvarson, Glenn Rowley
, May 2017
This study investigated the relationship between policies related to the recruitment, selection, preparation, and certification of new teachers and (a) the quality of future teachers as measured by their mathematics content and pedagogy content knowledge and (b) student achievement in mathematics at the national level.

Will Tyson, Josipa Roksa
, April 2017
This study examines how course grades and course rigor are associated with math attainment among students with similar eighth-grade standardized math test scores.

Anne K. Morris, James Hiebert
, March 2017
Researchers investigated whether the content pre-service teachers studied in elementary teacher preparation mathematics courses was related to their performance on a mathematics lesson planning task 2 and 3 years after graduation.

Laura M. Desimone, Kirsten Lee Hill
, March 2017
Researchers use data from a randomized controlled trial of a middle school science intervention to explore the causal mechanisms by which the intervention produced previously documented gains in student achievement.

Okhee Lee
, March 2017
This article focuses on how the Common Core State Standards (CCSS) and the Next Generation Science Standards (NGSS) treat “argument,” especially in Grades K–5, and the extent to which each set of standards is grounded in research literature, as claimed.

Cory Koedel, Diyi Li, Morgan S. Polikoff, Tenice Hardaway, Stephani L. Wrabel
, February 2017
Researchers estimate relative achievement effects of the four most commonly adopted elementary mathematics textbooks in the fall of 2008 and fall of 2009 in California.

Mary Kay Stein, Richard Correnti, Debra Moore, Jennifer Lin Russell, Katelynn Kelly
, January 2017
Researchers argue that large-scale, standards-based improvements in the teaching and learning of mathematics necessitate advances in theories regarding how teaching affects student learning and progress in how to measure instruction.

Alan H. Schoenfeld
, December 2016
The author begins by tracing the growth and change in research in mathematics education and its interdependence with research in education in general over much of the 20th century, with an emphasis on changes in research perspectives and methods and the philosophical/empirical/disciplinary approaches that underpin them.

Marcia C. Linn, Libby Gerard, Camillia Matuk, Kevin W. McElhaney
, December 2016
This chapter focuses on how investigators from varied fields of inquiry who initially worked separately began to interact, eventually formed partnerships, and recently integrated their perspectives to strengthen science education.

: Are Teachers’ Implicit Cognitions Another Piece of the Puzzle?
Almut E. Thomas
, December 2016
Drawing on expectancy-value theory, this study investigated whether teachers’ implicit science-is-male stereotypes predict between-teacher variation in males’ and females’ motivational beliefs regarding physical science.

: A By-Product of STEM College Culture?
Ebony O. McGee
, December 2016
The researcher found that the 38 high-achieving Black and Latino/a STEM study participants, who attended institutions with racially hostile academic spaces, deployed an arsenal of strategies (e.g., stereotype management) to deflect stereotyping and other racial assaults (e.g., racial microaggressions), which are particularly prevalent in STEM fields.

James Cowan, Dan Goldhaber, Kyle Hayes, Roddy Theobald
, November 2016
Researchers discuss public policies that contribute to teacher shortages in specific subjects (e.g., STEM and special education) and specific types of schools (e.g., disadvantaged) as well as potential solutions.

: A Sociological Analysis of Multimethod Data From Young Women Aged 10–16 to Explore Gendered Patterns of Post-16 Participation
Louise Archer, Julie Moote, Becky Francis, Jennifer DeWitt, Lucy Yeomans
, November 2016
Researchers draw on survey data from more than 13,000 year 11 (age 15/16) students and interviews with 70 students (who had been tracked from age 10 to 16), focusing in particular on seven girls who aspired to continue with physics post-16, discussing how the cultural arbitrary of physics requires these girls to be highly “exceptional,” undertaking considerable identity work and deployment of capital in order to “possibilize” a physics identity—an endeavor in which some girls are better positioned to be successful than others.

Jeremy Roschelle, Mingyu Feng, Robert F. Murphy, Craig A. Mason
, October 2016
In a randomized field trial with 2,850 seventh-grade mathematics students, researchers evaluated whether an educational technology intervention increased mathematics learning.

: Making Research Participation Instructionally Effective
Sherry A. Southerland, Ellen M. Granger, Roxanne Hughes, Patrick Enderle, Fengfeng Ke, Katrina Roseler, Yavuz Saka, Miray Tekkumru-Kisa
, October 2016
As current reform efforts in science place a premium on student sense making and participation in the practices of science, researchers use a close examination of 106 science teachers participating in Research Experiences for Teachers (RET) to identify, through structural equation modeling, the essential features in supporting teacher learning from these experiences.

Brian R. Belland, Andrew E. Walker, Nam Ju Kim, Mason Lefler
, October 2016
This review addresses the need for a comprehensive meta-analysis of research on scaffolding in STEM education by synthesizing the results of 144 experimental studies (333 outcomes) on the effects of computer-based scaffolding designed to assist the full range of STEM learners (primary through adult education) as they navigated ill-structured, problem-centered curricula.

Vaughan Prain, Brian Hand
, October 2016
Researchers claim that there are strong evidence-based reasons for viewing writing as a central but not sole resource for learning, drawing on both past and current research on writing as an epistemological tool and on their professional background in science education research, acknowledging its distinctive take on the use of writing for learning.

June Ahn, Austin Beck, John Rice, Michelle Foster
, September 2016
Researchers present analyses from a researcher-practitioner partnership in the District of Columbia Public Schools, where the researchers are exploring the impact of educational software on students’ academic achievement.

Barbara King
, September 2016
This study uses nationally representative data from a recent cohort of college students to investigate thoroughly gender differences in STEM persistence.

Ryan C. Svoboda, Christopher S. Rozek, Janet S. Hyde, Judith M. Harackiewicz, Mesmin Destin
, August 2016
This longitudinal study draws on identity-based and expectancy-value theories of motivation to explain the socioeconomic status (SES) and mathematics and science course-taking relationship.

Mathematics Course Placements in California Middle Schools, 2003–2013
Thurston Domina, Paul Hanselman, NaYoung Hwang, Andrew McEachin
, July 2016
Researchers consider the organizational processes that accompanied the curricular intensification of the proportion of California eighth graders enrolled in algebra or a more advanced course nearly doubling to 65% between 2003 and 2013.

Lina Shanley
, July 2016
Using a nationally representative longitudinal data set, this study compared various models of mathematics achievement growth on the basis of both practical utility and optimal statistical fit and explored relationships within and between early and later mathematics growth parameters.

Mimi Engel, Amy Claessens, Tyler Watts, George Farkas
, June 2016
Analyzing data from two nationally representative kindergarten cohorts, researchers examine the mathematics content teachers cover in kindergarten.

F. Chris Curran, Ann T. Kellogg
, June 2016
Researchers present findings from the recently released Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011 that demonstrate significant gaps in science achievement in kindergarten and first grade by race/ethnicity.

Rachel Garrett, Guanglei Hong
, June 2016
Analyzing the Early Childhood Longitudinal Study–Kindergarten cohort data, researchers find that heterogeneous grouping or a combination of heterogeneous and homogeneous grouping under relatively adequate time allocation is optimal for enhancing teacher ratings of language minority kindergartners’ math performance, while using homogeneous grouping only is detrimental.

Jennifer Gnagey, Stéphane Lavertu
, May 2016
This study is one of the first to estimate the impact of “inclusive” science, technology, engineering, and mathematics (STEM) high schools using student-level data.

Hanna Gaspard, Anna-Lena Dicke, Barbara Flunger, Isabelle Häfner, Brigitte M. Brisson, Ulrich Trautwein, Benjamin Nagengast
, May 2016
Through data from a cluster-randomized study in which a value intervention was successfully implemented in 82 ninth-grade math classrooms, researchers address how interventions on students’ STEM motivation in school affect motivation in subjects not targeted by the intervention.

Rebecca M. Callahan, Melissa H. Humphries
, April 2016
Researchers employ multivariate methods to investigate immigrant college going by linguistic status using the Educational Longitudinal Study of 2002.

Federick Ngo, Tatiana Melguizo
, March 2016
Researchers take advantage of heterogeneous placement policy in a large urban community college district in California to compare the effects of math remediation under different policy contexts.

: An Analysis of German Fourth- and Sixth-Grade Classrooms
Steffen Tröbst, Thilo Kleickmann, Kim Lange-Schubert, Anne Rothkopf, Kornelia Möller
, February 2016
Researchers examined if changes in instructional practices accounted for differences in situational interest in science instruction and enduring individual interest in science between elementary and secondary school classrooms.

: A Mixed-Methods Study
David F. Feldon, Michelle A. Maher, Josipa Roksa, James Peugh
, February 2016
Researchers offer evidence of a similar phenomenon to cumulative advantage, accounting for differential patterns of research skill development in graduate students over an academic year and explore differences in socialization that accompany diverging developmental trajectories.

: The Influence of Time, Peers, and Place
Luke Dauter, Bruce Fuller
, February 2016
Researchers hypothesize that pupil mobility stems from the (a) student’s time in school and grade; (b) student’s race, class, and achievement relative to peers; (c) quality of schooling relative to nearby alternatives; and (4) proximity, abundance, and diversity of local school options.

: How Workload and Curricular Affordances Shape STEM Faculty Decisions About Teaching and Learning
Matthew T. Hora
, January 2016
In this study the idea of the “problem space” from cognitive science is used to examine how faculty construct mental representations for the task of planning undergraduate courses.

Jessaca Spybrook, Carl D. Westine, Joseph A. Taylor
, January 2016
This article provides empirical estimates of design parameters necessary for planning adequately powered cluster randomized trials (CRTs) focused on science achievement.

Paul L. Morgan, George Farkas, Marianne M. Hillemeier, Steve Maczuga
, January 2016
Researchers examined the age of onset, over-time dynamics, and mechanisms underlying science achievement gaps in U.S. elementary and middle schools.

: Opportunity Structures and Outcomes in Inclusive STEM-Focused High Schools
Lois Weis, Margaret Eisenhart, Kristin Cipollone, Amy E. Stich, Andrea B. Nikischer, Jarrod Hanson, Sarah Ohle Leibrandt, Carrie D. Allen, Rachel Dominguez
, December 2015
Researchers present findings from a three-year comparative longitudinal and ethnographic study of how schools in two cities, Buffalo and Denver, have taken up STEM education reform, including the idea of “inclusive STEM-focused schools,” to address weaknesses in urban high schools with majority low-income and minority students.

: How Do They Interact in Promoting Science Understanding?
Jasmin Decristan, Eckhard Klieme, Mareike Kunter, Jan Hochweber, Gerhard Büttner, Benjamin Fauth, A. Lena Hondrich, Svenja Rieser, Silke Hertel, Ilonca Hardy
, December 2015
Researchers examine the interplay between curriculum-embedded formative assessment—a well-known teaching practice—and general features of classroom process quality (i.e., cognitive activation, supportive climate, classroom management) and their combined effect on elementary school students’ understanding of the scientific concepts of floating and sinking.

: An International Perspective
William H. Schmidt, Nathan A. Burroughs, Pablo Zoido, Richard T. Houang
, October 2015
In this paper, student-level indicators of opportunity to learn (OTL) included in the 2012 Programme for International Student Assessment are used to explore the joint relationship of OTL and socioeconomic status (SES) to student mathematics literacy.

Xueli Wang
, September 2015
This study examines the effect of beginning at a community college on baccalaureate success in science, technology, engineering, and mathematics (STEM) fields.

: Trends and Predictors
David M. Quinn, North Cooc
, August 2015
With research on science achievement disparities by gender and race/ethnicity often neglecting the beginning of the pipeline in the early grades, researchers address this limitation using nationally representative data following students from Grades 3 to 8.

Shaun M. Dougherty, Joshua S. Goodman, Darryl V. Hill, Erica G. Litke, Lindsay C. Page
, May 2015
Researchers highlight a collaboration to investigate one district’s effort to increase middle school algebra course-taking.

David F. Feldon, Michelle A. Maher, Melissa Hurst, Briana Timmerman
, April 2015
This mixed-method study investigates agreement between student mentees’ and their faculty mentors’ perceptions of the students’ developing research knowledge and skills in STEM.

: Reviving Science Education for Civic Ends
John L. Rudolph
, December 2014
This article revisits John Dewey’s now-well-known address “Science as Subject-Matter and as Method” and examines the development of science education in the United States in the years since that address.

Dermot F. Donnelly, Marcia C. Linn Sten Ludvigsen
, December 2014
The National Science Foundation–sponsored report Fostering Learning in the Networked World called for “a common, open platform to support communities of developers and learners in ways that enable both to take advantage of advances in the learning sciences”; we review research on science inquiry learning environments (ILEs) to characterize current platforms.

: A Longitudinal Case Study of America’s Chemistry Teachers
Gregory T. Rushton, Herman E. Ray, Brett A. Criswell, Samuel J. Polizzi, Clyde J. Bearss, Nicholas Levelsmier, Himanshu Chhita, Mary Kirchhoff
, November 2014
Researchers perform a longitudinal case study of U.S. public school chemistry teachers to illustrate a diffusion of responsibility within the STEM community regarding who is responsible for the teacher workforce.

: Relations Between Early Mathematics Knowledge and High School Achievement
Tyler W. Watts, Greg J. Duncan, Robert S. Siegler, Pamela E. Davis-Kean
, October 2014
Researchers find that preschool mathematics ability predicts mathematics achievement through age 15, even after accounting for early reading, cognitive skills, and family and child characteristics.

T. Jared Robinson, Lane Fischer, David Wiley, John Hilton, III
, October 2014
The purpose of this quantitative study is to analyze whether the adoption of open science textbooks significantly affects science learning outcomes for secondary students in earth systems, chemistry, and physics.

: 1968–2009
Robert N. Ronau, Christopher R. Rakes, Sarah B. Bush, Shannon O. Driskell, Margaret L. Niess, David K. Pugalee
, October 2014
We examined 480 dissertations on the use of technology in mathematics education and developed a Quality Framework (QF) that provided structure to consistently define and measure quality.

Andrew D. Plunk, William F. Tate, Laura J. Bierut, Richard A. Grucza
, June 2014
Using logistic regression with Census and American Community Survey (ACS) data ( = 2,892,444), researchers modeled mathematics and science course graduation requirement (CGR) exposure on (a) high school dropout, (b) beginning college, and (c) obtaining any college degree.

Corey Drake, Tonia J. Land, Andrew M. Tyminski
, April 2014
Building on the work of Ball and Cohen and that of Davis and Krajcik, as well as more recent research related to teacher learning from and about curriculum materials, researchers seek to answer the question, How can prospective teachers (PTs) learn to read and use educative curriculum materials in ways that support them in acquiring the knowledge needed for teaching?

Lorraine M. McDonnell, M. Stephen Weatherford
, December 2013
This article draws on theories of political and policy learning and interviews with major participants to examine the role that the Common Core State Standards (CCSS) supporters have played in developing and implementing the standards, supporters’ reasons for mobilizing, and the counterarguments and strategies of recently emerging opposition groups.

: Motivation, High School Learning, and Postsecondary Context of Support
Xueli Wang
, October 2013
This study draws upon social cognitive career theory and higher education literature to test a conceptual framework for understanding the entrance into science, technology, engineering, and mathematics (STEM) majors by recent high school graduates attending 4-year institutions.

Philip M. Sadler, Gerhard Sonnert, Harold P. Coyle, Nancy Cook-Smith, Jaimie L. Miller
, October 2013
This study examines the relationship between teacher knowledge and student learning for 9,556 students of 181 middle school physical science teachers.

: Teaching Critical Mathematics in a Remedial Secondary Classroom
Andrew Brantlinger
, October 2013
The researcher presents results from a practitioner research study of his own teaching of critical mathematics (CM) to low-income students of color in a U.S. context.

Jason G. Hill, Ben Dalton
, October 2013
This study investigates the distribution of math teachers with a major or certification in math using data from the National Center for Education Statistics’ High School Longitudinal Study of 2009 (HSLS:09).

Kristin F. Butcher, Mary G. Visher
, September 2013
This study uses random assignment to investigate the impact of a “light-touch” intervention, where an individual visited math classes a few times during the semester, for a few minutes each time, to inform students about available services.

Janet M. Dubinsky, Gillian Roehrig, Sashank Varma
, August 2013
Researchers argue that the neurobiology of learning, and in particular the core concept of , have the potential to directly transform teacher preparation and professional development, and ultimately to affect how students think about their own learning.

: The Impact of Undergraduate Research Programs
M. Kevin Eagan, Jr., Sylvia Hurtado, Mitchell J. Chang, Gina A. Garcia, Felisha A. Herrera, Juan C. Garibay
, August 2013
Researchers’ findings indicate that participation in an undergraduate research program significantly improved students’ probability of indicating plans to enroll in a STEM graduate program.

Okhee Lee, Helen Quinn, Guadalupe Valdés
, May 2013
This article addresses language demands and opportunities that are embedded in the science and engineering practices delineated in “A Framework for K–12 Science Education,” released by the National Research Council (2011).

Liliana M. Garces
, April 2013
This study examines the effects of affirmative action bans in four states (California, Florida, Texas, and Washington) on the enrollment of underrepresented students of color within six different graduate fields of study: the natural sciences, engineering, social sciences, business, education, and humanities.

: Learning Lessons From Research on Diversity in STEM Fields
Shirley M. Malcom, Lindsey E. Malcom-Piqueux
, April 2013
Researchers argue that social scientists ought to look to the vast STEM education research literature to begin the task of empirically investigating the questions raised in the case.

Roslyn Arlin Mickelson, Martha Cecilia Bottia, Richard Lambert
, March 2013
This metaregression analysis reviewed the social science literature published in the past 20 years on the relationship between mathematics outcomes and the racial composition of the K–12 schools students attend.

Jeffrey Grigg, Kimberle A. Kelly, Adam Gamoran, Geoffrey D. Borman
, March 2013
Researchers examine classroom observations from a 3-year large-scale randomized trial in the Los Angeles Unified School District (LAUSD) to investigate the extent to which a professional development initiative in inquiry science influenced teaching practices in in 4th and 5th grade classrooms in 73 schools.

:
Angela Calabrese Barton, Hosun Kang, Edna Tan, Tara B. O’Neill, Juanita Bautista-Guerra, Caitlin Brecklin
, February 2013
This longitudinal ethnographic study traces the identity work that girls from nondominant backgrounds do as they engage in science-related activities across school, club, and home during the middle school years.

: A Review of the State of the Field
Shuchi Grover, Roy Pea
, January 2013
This article frames the current state of discourse on computational thinking in K–12 education by examining mostly recently published academic literature that uses Jeannette Wing’s article as a springboard, identifies gaps in research, and articulates priorities for future inquiries.

Catherine Riegle-Crumb, Barbara King, Eric Grodsky, Chandra Muller
, December 2012
This article investigates the empirical basis for often-repeated arguments that gender differences in entrance into science, technology, engineering, and mathematics (STEM) majors are largely explained by disparities in prior achievement.

Richard M. Ingersoll, Henry May
, December 2012
This study examines the magnitude, destinations, and determinants of mathematics and science teacher turnover.

: How Families Shape Children’s Engagement and Identification With Science
Louise Archer, Jennifer DeWitt, Jonathan Osborne, Justin Dillon, Beatrice Willis, Billy Wong
, October 2012
Drawing on the conceptual framework of Bourdieu, this article explores how the interplay of family habitus and capital can make science aspirations more “thinkable” for some (notably middle-class) children than others.

Erin Marie Furtak, Tina Seidel, Heidi Iverson, Derek C. Briggs
, September 2012
This meta-analysis introduces a framework for inquiry-based teaching that distinguishes between cognitive features of the activity and degree of guidance given to students.

Jaekyung Lee, Todd Reeves
, June 2012
This study examines the impact of high-stakes school accountability, capacity, and resources under NCLB on reading and math achievement outcomes through comparative interrupted time-series analyses of 1990–2009 NAEP state assessment data.

: Toward a Theory of Teaching
Paola Sztajn, Jere Confrey, P. Holt Wilson, Cynthia Edgington
, June 2012
Researchers propose a theoretical connection between research on learning and research on teaching through recent research on students’ learning trajectories (LTs).

: The Perspectives of Exemplary African American Teachers
Jianzhong Xu, Linda T. Coats, Mary L. Davidson
, February 2012
Researchers argue both the urgency and the promise of establishing a constructive conversation among different bodies of research, including science interest, sociocultural studies in science education, and culturally relevant teaching.

Rebecca M. Schneider, Kellie Plasman
, December 2011
This review examines the research on science teachers’ pedagogical content knowledge (PCK) in order to refine ideas about science teacher learning progressions and how to support them.

Brian A. Nosek, Frederick L. Smyth
, October 2011
Researchers examined implicit math attitudes and stereotypes among a heterogeneous sample of 5,139 participants.

Libby F. Gerard, Keisha Varma, Stephanie B. Corliss, Marcia C. Linn
, September 2011
Researchers’ findings suggest that professional development programs that engaged teachers in a comprehensive, constructivist-oriented learning process and were sustained beyond 1 year significantly improved students’ inquiry learning experiences in K–12 science classrooms.

: Teaching and Learning Impacts of Reading Apprenticeship Professional Development
Cynthia L. Greenleaf, Cindy Litman, Thomas L. Hanson, Rachel Rosen, Christy K. Boscardin, Joan Herman, Steven A. Schneider, Sarah Madden, Barbara Jones
, June 2011
This study examined the effects of professional development integrating academic literacy and biology instruction on science teachers’ instructional practices and students’ achievement in science and literacy.

Paul Cobb, Kara Jackson
, May 2011
The authors comment on Porter, McMaken, Hwang, and Yang’s recent analysis of the Common Core State Standards for Mathematics by critiquing their measures of the focus of the standards and the absence of an assessment of coherence.

P. Wesley Schultz, Paul R. Hernandez, Anna Woodcock, Mica Estrada, Randie C. Chance, Maria Aguilar, Richard T. Serpe
, March 2011
This study reports results from a longitudinal study of students supported by a national National Institutes of Health–funded minority training program, and a propensity score matched control.

: Three Large-Scale Studies
Jeremy Roschelle, Nicole Shechtman, Deborah Tatar, Stephen Hegedus, Bill Hopkins, Susan Empson, Jennifer Knudsen, Lawrence P. Gallagher
, December 2010
The authors present three studies (two randomized controlled experiments and one embedded quasi-experiment) designed to evaluate the impact of replacement units targeting student learning of advanced middle school mathematics.

: Examining Disparities in College Major by Gender and Race/Ethnicity
Catherine Riegle-Crumb, Barbara King
, December 2010
The authors analyze national data on recent college matriculants to investigate gender and racial/ethnic disparities in STEM fields, with an eye toward the role of academic preparation and attitudes in shaping such disparities.

Mary Kay Stein, Julia H. Kaufman
, September 2010
This article begins to unravel the question, “What curricular materials work best under what kinds of conditions?” The authors address this question from the point of view of teachers and their ability to implement mathematics curricula that place varying demands and provide varying levels of support for their learning.

Andy R. Cavagnetto
, September 2010
This study of 54 articles from the research literature examines how argument interventions promote scientific literacy.

Victoria M. Hand
, March 2010
The researcher examined how the teacher and students in a low-track mathematics classroom jointly constructed opposition through their classroom interactions.

Terrence E. Murphy, Monica Gaughan, Robert Hume, S. Gordon Moore, Jr.
, March 2010
Researchers evaluate the association of a summer bridge program with the graduation rate of underrepresented minority (URM) students at a selective technical university.

Open access
Published: 22 April 2020

Research and trends in STEM education: a systematic analysis of publicly funded projects

Yeping Li 1 ,
Ke Wang 2 ,
Yu Xiao 1 ,
Jeffrey E. Froyd 3 &
Sandra B. Nite 1

International Journal of STEM Education volume 7 , Article number: 17 ( 2020 ) Cite this article

17k Accesses

32 Citations

7 Altmetric

Metrics details

Taking publicly funded projects in STEM education as a special lens, we aimed to learn about research and trends in STEM education. We identified a total of 127 projects funded by the Institute of Education Sciences (IES) of the US Department of Education from 2003 to 2019. Both the number of funded projects in STEM education and their funding amounts were high, although there were considerable fluctuations over the years. The number of projects with multiple principal investigators increased over time. The project duration was typically in the range of 3–4 years, and the goals of these projects were mostly categorized as “development and innovation” or “efficacy and replication.” The majority of the 127 projects focused on individual STEM disciplines, especially mathematics. The findings, based on IES-funded projects, provided a glimpse of the research input and trends in STEM education in the USA, with possible implications for developing STEM education research in other education systems around the world.

Introduction

The rapid development of science, technology, engineering, and mathematics (STEM) education and research since the beginning of this century has benefited from strong, ongoing support from many different entities, including government agencies, professional organizations, industries, and education institutions (Li, 2014 ). Typically, studies that summarized the status of research in STEM education have used publications as the unit of their analyses (e.g., Li et al., 2019 ; Li et al., 2020 ; Margot & Kettler, 2019 ; Minichiello et al., 2018 ; Otten, Van den Heuvel-Panhuizen, & Veldhuis, 2019 ; Schreffler et al., 2019 ). Another approach, which has been used less frequently, is to study research funding. Although not all research publications were generated from funded projects and not all funded projects have been equally productive, as measured by publications, research funding and publications present two different, but related perspectives on the state of research in STEM education. Our review focuses on research funding.

Types of funding support to education research

There are different types of sources and mechanisms in place to allocate, administer, distribute, and manage funding support to education. In general, there are two sources of funding: public and private.

Public funding sources are commonly government agencies that support education program development and training, project evaluation, and research. For example, multiple state and federal agencies in the USA provide and manage funding support to education research, programs and training, including the US Department of Education (ED), the National Science Foundation (NSF), and the National Endowment for the Humanities—Division of Education Programs. Researchers seeking support from public funding sources often submit proposals that are vetted through a well-structured peer-review process. The process is competitive, and the decision to fund a project validates both its importance and alignment with the funding agency’s development agenda. Changes in the agencies’ agendas and funding priorities can reflect governmental intentions and priorities for education and research.

Private funding sources have played a very important role in supporting education programs and research with a long history. Some private funding sources in the USA can be sizeable, such as the Bill & Melinda Gates Foundation ( https://www.gatesfoundation.org ), while many also have specific foci, such as the Howard Hughes Medical Institute ( https://www.hhmi.org ) that is dedicated to advancing science through research and science education. At the same time, private funding sources often have their own development agendas, flexibility in deciding funding priorities, and specific mechanisms in making funding decisions, including how funds can be used, distributed, and managed. Indeed, private funding sources differ from public funding sources in many ways. Given many special features associated with private funding sources, including the lack of transparency, we chose to examine projects that were supported by public funding sources in this review.

Approaches to examining public research funding support

One approach to studying public research funding support to STEM education would be to examine requests-for-proposals (RFPs) issued by different government agencies. However, those RFPs tend to provide guidelines, which are not sufficiently concrete to learn about specific research that is funded. In contrast, reviewing those projects selected for funding can provide more detailed information on research activity. Figure 1 shows a flowchart of research activity and distinguishes how funded projects and publications might provide different perspectives on research. In this review, we focus on the bolded portion of the flowchart, i.e., projects funded to promote STEM education.

A general flowchart of RFPs to publications

Current review

Why focus on research funding in the usa.

Recent reviews of journal publications in STEM education have consistently revealed that scholars in the USA played a leading role in producing and promoting scholarship in STEM education, with about 75% of authorship credits for all publications in STEM education either in the International Journal of STEM Education alone from 2014 to 2018 (Li et al., 2019 ) or in 36 selected journals published from 2000 to 2018 (Li et al., 2020 ). The strong scholarship development in the USA is likely due to a research environment that is well supported and conducive to high research output. Studying public funding support for STEM education research in the USA will provide information on trends and patterns, which will be valuable both in the USA and in other countries.

The context of policy and public funding support to STEM education in the USA

The tremendous development of STEM education in the USA over the past decades has benefited greatly from both national policies and strong funding support from the US governmental agencies as well as private funding sources. Federal funding for research and development in science, mathematics, technology, and engineering-related education in the USA was restarted in the late 1980s, in the latter years of the Reagan administration, which had earlier halted funding. In recent years, the federal government has strongly supported STEM education research and development. For example, the Obama administration in the USA (The White House, 2009 ) launched the “Educate to Innovate” campaign in November 2009 for excellence in STEM education as a national priority, with over 260 million USD in financial and in-kind support commitment. The Trump administration has continued to emphasize STEM education. For example, President Trump signed a memorandum in 2017 to direct ED to spend 200 million USD per year on competitive grants promoting STEM (The White House, 2017 ). In response, ED awarded 279 million USD in STEM discretionary grants in Fiscal Year 2018 (US Department of Education, 2018 ). The Trump administration took a step further to release a report in December 2018 detailing its five-year strategic plan of boosting STEM education in the USA (The White House, 2018 ). The strategic plan envisions that “All Americans will have lifelong access to high-quality STEM education and the USA will be the global leader in STEM literacy, innovation, and employment.” (Committee on STEM Education, 2018 , p. 1). Consistently, current Secretory of Education DeVos in the Trump administration has taken STEM as a centerpiece of her comprehensive education agenda (see https://www.ed.gov/stem ). The consistency in national policies and public funding support shows that STEM education continues to be a strategic priority in the USA.

Among many federal agencies that funded STEM education programs, the ED and NSF have functioned as two primary agencies. For ED, the Institute of Education Sciences (Institute of Education Sciences (IES), n.d. , see https://ies.ed.gov/aboutus/ ) was created by the Education Sciences Reform Act of 2002 as its statistics, research, and evaluation arm. ED’s support to STEM education research has been mainly administered and managed by IES since 2003. In contrast to the focus of ED on education, NSF (see https://www.nsf.gov/about/ ) was created by Congress in 1950 to support basic research in many fields such as mathematics, computer sciences, and social sciences. Education and Human Resources is one of its seven directorates that provides important funding support to STEM education programs and research. In addition to these two federal agencies, some other federal agencies also provide funding support to STEM education programs and research from time to time.

Any study of public funding support to STEM education research in the USA would need to limit its scope, given the complexity of various public funding sources available in the system, the ambiguity associated with the meaning of STEM education across different federal agencies (Li et al., 2020 ), and the number of programs that have funded STEM education research over the years. For the purpose of this review, we have chosen to focus on the projects in STEM education funded by IES.

Research questions

Given the preceding research approach decision to focus on research projects funded by IES, we generated the following questions:

What were the number of projects, total project funding, and the average funding per project from 2003 to 2019 in STEM education research?

What were the trends of having single versus multiple principal investigator(s) in STEM education?

What were the types of awardees of the projects?

What were the participant populations in the projects?

What were the types of projects in terms of goals for program development and research in STEM education?

What were the disciplinary foci of the projects?

What research methods did projects tend to use in conducting STEM education research?

Based on the above discussion to focus on funding support from IES, we first specified the time period, and then searched the IES website to identify STEM education research projects funded by IES within the specified time period.

Time period

As discussed above, IES was established in 2002 and it did not start to administer and manage research funding support for ED until 2003. Therefore, we considered IES funded projects from 2003 to the end of 2019.

Searching and identifying IES funded projects in STEM education

Given the diverse perspectives about STEM education across different agencies and researchers (Li et al., 2020 ), we did not discuss and define the meaning of STEM education. Instead, we used the process described in the following paragraph to identify STEM education research projects funded by IES.

On the publicly accessible IES website ( https://ies.ed.gov ), one menu item is “FUNDING OPPORTUNITIES”, and there is a list of choices within this menu item. One choice is “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS.” On this web search page, we can choose “Program” under “ADDITIONAL SEARCH OPTIONS.” There are two program categories related to STEM under the option of “Program.” One is “Science, Technology, Engineering, and Mathematics (STEM) Education” under one large category of “Education Research” and the other is “Science, Technology, Engineering, and Mathematics” under another large category of “Special Education Research.” We searched for funded projects under these two program categories, and the process returned 98 funded projects in “Science, Technology, Engineering, and Mathematics (STEM) Education” under “Education Research” and 29 funded projects in “Science, Technology, Engineering, and Mathematics” under “Special Education Research,” for a total of 127 funded projects in these two programs designated for STEM education by IES Footnote 1 .

Data analysis

To address questions 1, 2, 3, and 4, we collected the following information about these projects identified using above procedure: amount of funding, years of duration, information about the PI, types of awardees that received and administered the funding (i.e., university versus those non-university including non-profit organization such as WestEd, Educational Testing Service), and projects’ foci on school level and participants. When a project’s coverage went beyond one category, the project was then coded in terms of its actual number of categories being covered. For example, we used the five categories to classify project’s participants: Pre–K, grades 1–4, grades 5–8, grades 9–12, and adult. If a funded project involved participants from Pre-school to grade 8, then we coded the project as having participants in three categories: Pre-K, grades 1–4, and grades 5–8.

To address question 5, we analyzed projects based on goal classifications from IES. IES followed the classification of research types that was produced through a joint effort between IES and NSF in 2013 (Institute of Education Sciences (IES) and National Science Foundation (NSF), 2013 ). The effort specified six types of research that provide guidance on the goals and level of funding support: foundational research, early-stage or exploratory research, design and development research, efficacy research, effectiveness research, and scale-up research. Related to these types, IES classified goals for funded projects: development and innovation, efficacy and replication, exploration, measurement, and scale-up evaluation, as described on the IES website.

To address question 6, we coded the disciplinary focus using the following five categories: mathematics, science, technology, engineering, and integrated (meaning an integration of any two or more of STEM disciplines). In some cases, we coded a project with multiple disciplinary foci into more than one category. The following are two project examples and how we coded them in terms of disciplinary foci:

The project of “A Randomized Controlled Study of the Effects of Intelligent Online Chemistry Tutors in Urban California School Districts” (2008, https://ies.ed.gov/funding/grantsearch/details.asp?ID=601 ) was to test the efficacy of the Quantum Chemistry Tutors, a suite of computer-based cognitive tutors that are designed to give individual tutoring to high school students on 12 chemistry topics. Therefore, we coded this project as having three categories of disciplinary foci: science because it was chemistry, technology because it applied instructional technology, and integrated because it integrated two or more of STEM disciplines.

The project of “Applications of Intelligent Tutoring Systems (ITS) to Improve the Skill Levels of Students with Deficiencies in Mathematics” (2009, https://ies.ed.gov/funding/grantsearch/details.asp?ID=827 ) was coded as having three categories of disciplinary foci: mathematics, technology because it used intelligent tutoring systems, and integrated because it integrated two or more of STEM disciplines.

To address question 7, all 127 projects were coded using a classification category system developed and used in a previous study (Wang et al., 2019 ). Specifically, each funded project was coded in terms of research type (experimental, interventional, longitudinal, single case, correlational) Footnote 2 , data collection method (interview, survey, observation, researcher designed tests, standardized tests, computer data Footnote 3 ), and data analysis method (descriptive statistics, ANOVA*, general regression, HLM, IRT, SEM, others) Footnote 4 . Based on a project description, specific method(s) were identified and coded following a procedure similar to what we used in a previous study (Wang et al., 2019 ). Two researchers coded each project’s description, and the agreement between them for all 127 projects was 88.2%. When method and disciplinary focus-coding discrepancies occurred, a final decision was reached after discussion.

Results and discussion

In the following sections, we report findings as corresponding to each of the seven research questions.

Question 1: the number of projects, total funding, and the average funding per project from 2003 to 2019

Figure 2 shows the distribution of funded projects over the years in each of the two program categories, “Education Research” and “Special Education Research,” as well as combined (i.e., “STEM” for projects funded under “Education Research,” “Special STEM” for projects funded under “Special Education Research,” and “Combined” for projects funded under both “Education Research” and “Special Education Research”). As Fig. 2 shows, the number of projects increased each year up to 2007, with STEM education projects started in 2003 under “Education Research” and in 2006 under “Special Education Research.” The number of projects in STEM under “Special Education Research” was generally less than those funded under the program category of “Education Research,” especially before 2011. There are noticeable decreases in combined project counts from 2009 to 2011 and from 2012 to 2014, before the number count increased again in 2015. We did not find a consistent pattern across the years from 2003 to 2019.

The distribution of STEM education projects over the years. (Note: STEM refers to projects funded under “Education Research,” Special STEM refers to projects funded under “Special Education Research,” and “Combined” refers to projects funded under both “Education Research” and “Special Education Research.” The same annotations are used in the rest of the figures.)

A similar trend can be observed in the total funding amount for STEM education research (see Fig. 3 ). The figure shows noticeably big year-to-year swings from 2003 to 2019, with the highest funding amount of more than 33 million USD in 2007 and the lowest amount of 2,698,900 USD in 2013 from these two program categories. Although it is possible that insufficient high-quality grant proposals were available in one particular year to receive funding, the funded amount and the number of projects (Fig. 2 ) provide insights about funding trends over the time period of the review.

Annual funding totals

As there are diverse perspectives and foci about STEM education, we also wondered if STEM education research projects might be funded by IES but in program options other than those designated options of “Science, Technology, Engineering, and Mathematics (STEM) Education.” We found a total of 54 funded projects from 2007 to 2019, using the acronym “STEM” as a search term under the option of “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS” without any program category restriction. Only 2 (3.7%) out of these 54 projects were in the IES designated program options of STEM education in the category of “Education Research.” Further information about these 54 projects and related discussion can be found as additional notes at the end of this review.

Results from two different approaches to searching for IES-funded projects will likely raise questions about what kinds of projects were funded in the designated program option of “Science, Technology, Engineering, and Mathematics (STEM) Education,” if only two funded projects under this option contained the acronym “STEM” in a project’s title and/or description. We shall provide further information in the following sub-sections, especially when answering question 6 related to projects’ disciplinary focus.

Figure 4 illustrates the trend of average funding amount per project each year in STEM education research from 2003 to 2019. The average funding per project varied considerably in the program category “Special Education Research,” and no STEM projects were funded in 2014 and 2017 in this category. In contrast, average funding per project was generally within the range of 1,132,738 USD in 2019 to 3,475,975 USD in 2014 for the projects in the category of “Education Research” and also for project funding in the combined category.

The trend of average funding amount per project funded each year in STEM education research

Figure 5 shows the number of projects in different funding amount categories (i.e., less than 1 million USD, 1–2 million USD, 2–3 million USD, 3 million USD or more). The majority of the 127 projects obtained funding of 1–2 million USD (77 projects, 60.6%), with 60 out of 98 projects (61.2%) under “Education Research” program and 17 out of 29 projects (58.6%) in the program category “Special Education Research.” The category with second most projects is funding of 3 million USD or more (21 projects, 16.5%), with 15 projects (15.3% of 98 projects) under “Education Research” and 6 projects (20.7% of 29 projects) under “Special Education Research.”

The number of projects in terms of total funding amount categories

Figure 6 shows the average amount of funding per project funded across these different funding amount and program categories. In general, the projects funded under “Education Research” tended to have a higher average amount than those funded under “Special Education Research,” except for those projects in the total funding amount category of “less than 1 million USD.” Considering all 127 funded projects, the average amount of funding was 1,960,826.3 USD per project.

The average amount of funding per project across different total funding amount and program categories

Figure 7 shows that the vast majority of these 127 projects were 3- or 4-year projects. In particular, 59 (46.5%) projects were funded as 4-year projects, with 46 projects (46.9%) under “Education Research” and 13 projects (44.8%) under “Special Education Research.” This category is followed closely by 3-year projects (54 projects, 42.5%), with 41 projects (41.8%) under “Education Research” and 13 projects (44.8%) under “Special Education Research.”

The number of projects in terms of years of project duration. (Note, 2: 2-year projects; 3: 3-year projects; 4: 4-year projects; 5: 5-year projects)

Question 2: trends of single versus multiple principal investigator(s) in STEM education

Figure 8 shows the distribution of projects over the years grouped by a single PI or multiple PIs where the program categories of “Education Research” and “Special Education Research” have been combined. The majority of projects before 2009 had a single PI, and the trend has been to have multiple PIs for STEM education research projects since 2009. The trend illustrates the increased emphases on collaboration in STEM education research, which is consistent with what we learned from a recent study of journal publications in STEM education (Li et al., 2020 ).

The distribution of projects with single versus multiple PIs over the years (combined)

Separating projects by program categories, Fig. 9 shows projects funded in the program category “Education Research.” The trends of single versus multiple PIs in Fig. 9 are similar to the trends shown in Fig. 8 for the combined programs. In addition, almost all projects in STEM education funded under this regular research program had multiple PIs since 2010.

The distribution of projects with single versus multiple PIs over the years (in “Education Research” program)

Figure 10 shows projects funded in the category “Special Education Research.” The pattern in Fig. 10 , where very few projects funded under this category had multiple PIs before 2014, is quite different from the patterns in Figs. 8 and 9 . We did not learn if single PIs were appropriate for the nature of these projects. The trend started to change in 2015 as the number of projects with multiple PIs increased and the number of projects with single PIs declined.

The distribution of projects with single versus multiple PIs over the years (in “Special Education Research” program)

Question 3: types of awardees of these projects

Besides the information about the project’s PI, the nature of the awardees can help illustrate what types of entity or organization were interested in developing and carrying out STEM education research. Figure 11 shows that the university was the main type of awardee before 2012, with 80 (63.0%) projects awarded to universities from 2003 to 2019. At the same time, non-university entities received funding support for 47 (37.0%) projects and they seem to have become even more active and successful in obtaining research funding in STEM education over the past several years. The result suggests that diverse organizations develop and conduct STEM education research, another indicator of the importance of STEM education research.

The distribution of projects funded to university versus non-university awardees over the years

Question 4: participant populations in the projects

Figure 12 indicates that the vast majority of projects were focused on student populations in preschool to grade 12. This is understandable as IES is the research funding arm of ED. Among those projects, middle school students were the participants in the most projects (70 projects), followed by student populations in elementary school (48 projects), and high school (38 projects). The adult population (including post-secondary students and teachers) was the participant group in 36 projects in a combined program count.

The number of projects in STEM education for different groups of participants (Note: Pre-K: preschool-kindergarten; G1–4: grades 1–4; G5–8: grades 5–8; G9–12: grades 9–12; adult: post-secondary students and teachers)

If we separate “Education Research” and “Special Education Research” programs, projects in the category “Special Education Research” focused on student populations in elementary and middle school most frequently, and then adult population. In contrast, projects in the category “Education Research” focused most frequently on middle school student population, followed by student populations in high school and elementary school.

Given the importance of funded research in special education Footnote 5 at IES, we considered projects focused on participants with disabilities. Figure 13 shows there were 28 projects in the category “Special Education Research” for participants with disabilities. There were also three such projects funded in the category “Education Research,” which together accounted for a total of 31 (24.4%) projects. In addition, some projects in the category “Education Research” focused on other participants, including 11 projects focused on ELL students (8.7%) projects and 37 projects focused on low SES students (29.1%).

The number of funded projects in STEM education for three special participant populations (Note: ELL: English language learners, Low SES: low social-economic status)

Figure 14 shows the trend of projects in STEM education for special participant populations. Participant populations with ELL and/or Low SES gained much attention before 2011 among these projects. Participant populations with disabilities received relatively consistent attention in projects on STEM education over the years. Research on STEM education with special participant populations is important and much needed. However, related scholarship is still in an early development stage. Interested readers can find related publications in this journal (e.g., Schreffler et al., 2019 ) and other journals (e.g., Lee, 2014 ).

The distribution of projects in STEM education for special participant populations over the years

Question 5: types of projects in terms of goals for program development and research

Figure 15 shows that “development and innovation” was the most frequently funded type of project (58 projects, 45.7%), followed by “efficacy and replication” (34 projects, 26.8%), and “measurement” (21 projects, 16.5%). The pattern is consistent across “Education Research,” “Special Education Research,” and combined. However, it should be noted that all five projects with the goal of “scale-up evaluation” were in the category “Education Research” Footnote 6 and funding for these projects were large.

The number of projects in terms of the types of goals

Examining the types of projects longitudinally, Fig. 16 shows that while “development and innovation” and “efficacy and replication” types of projects were most frequently funded in the “Education Research” program, the types of projects being funded changed longitudinally. The number of “development and innovation” projects was noticeably fewer over the past several years. In contrast, the number of “measurement” projects and “efficacy and replication” projects became more dominant. The change might reflect a shift in research development and needs.

The distribution of projects in terms of the type of goals over the years (in “Education Research” program)

Figure 17 shows the distribution of project types in the category “Special Education Research.” The pattern is different from the pattern shown in Fig. 16 . The types of “development and innovation” and “efficacy and replication” projects were also the dominant types of projects under “Special Education Research” program category in most of these years from 2007 to 2019. Projects in the type “measurement” were only observed in 2010 when that was the only type of project funded.

The distribution of projects in terms of goals over the years (in “Special Education Research” program)

Question 6: disciplinary foci of projects in developing and conducting STEM education research

Figure 18 shows that the majority of the 127 projects under such specific programs included disciplinary foci on individual STEM disciplines: mathematics in 88 projects, science in 51 projects, technology in 43 projects, and engineering in 2 projects. The tremendous attention to mathematics in these projects is a bit surprising, as mathematics was noted as being out of balance in STEM education (English, 2016 ) and also in STEM education publications (Li, 2018b , 2019 ). As noted above, each project can be classified in multiple disciplinary foci. However, of the 88 projects with a disciplinary focus on mathematics, 54 projects had mathematics as the only disciplinary focus (38 under “Education Research” program and 16 under “Special Education Research” program). We certainly hope that there will be more projects that further scholarship where mathematics is included as part of (integrated) STEM education (see Li & Schoenfeld, 2019 ).

The number of projects in terms of disciplinary focus

There were also projects with specific focus on integrated STEM education (i.e., combining any two or more disciplines of STEM), with a total of 55 (43.3%) projects in a combined program count. The limited number of projects on integrated STEM in the designated STEM funding programs further confirms the common perception that the development of integrated STEM education and research is still in its initial stage (Honey et al., 2014 ; Li, 2018a ).

In examining possible funding trends, Fig. 19 shows that mathematics projects were more frequently funded before 2012. Engineering was a rare disciplinary focus. Integrated STEM was a disciplinary focus from time to time among these projects. No other trends were observed.

The distribution of projects in terms of disciplinary focus over the years

Question 7: research types and methods that projects used

Figure 20 indicates that “interventional” (in 104 projects, 81.9%) and “experimental research” (in 89 projects, 70.1%) were the most frequently funded types of research. The percentages of projects funded under the regular education research program were similar to those funded under “Special Education Research” program, except that projects funded under “Special Education Research” tended to utilize correlational research more often.

The number of projects in terms of the type of research conducted

Research in STEM education uses diverse data collection and analysis methods; therefore, we wanted to study types of methods (Figs. 21 and 22 , respectively). Among the six types of methods used for data collection, Fig. 21 indicates that “standardized tests” and “designed tests” were the most commonly used methods for data collection, followed by “survey,” “observation,” and “interview.” The majority of projects used three quantitative methods (“standardized tests,” “researcher designed tests,” and “survey”). The finding is consistent with the finding from analysis of journal publications in STEM education (Li et al., 2020 ). Data collected through “interview” and “observation” were more likely to be analyzed using qualitative methods as part of a project’s research methodology.

The number of projects categorized by the type of data collection methods

The number of projects categorized by the type of data analysis methods

Figure 22 shows the use of seven (including others) data analysis methods among these projects. The first six methods (i.e., descriptive, ANOVA*, general regression, HLM, IRT, and SEM) as well as some methods in “others” are quantitative data analysis methods. The number of projects that used these quantitative methods is considerably larger than the number of projects that used qualitative methods (i.e., included in “others” category).

Concluding remarks

The systematic analysis of IES-funded research projects in STEM education presented an informative picture about research support for STEM education development in the USA, albeit based on only one public funding agency from 2003 to 2019. Over this 17-year span, IES funded 127 STEM education research projects (an average of over seven projects per year) in two designated STEM program categories. Although we found no discernable longitudinal funding patterns in these two program categories, both the number of funded projects in STEM education and their funding amounts were high. If we included an additional 52 projects with the acronym “STEM” funded by many other programs from 2007 to 2019 (see “ Notes ” section below), the total number of projects in STEM education research would be even higher, and the number of projects with the acronym “STEM” would also be larger. The results suggested the involvement of many researchers with diverse expertise in STEM education research was supported by a broad array of program areas in IES.

Addressing the seven questions showed several findings. Funding support for STEM education research was strong, with an average of about 2 million USD per project for a typical 3–4 year duration. Also, our analysis showed that the number of projects with multiple PIs over the years increased over the study time period, which we speculate was because STEM education research increasingly requires collaboration. STEM education research is still in early development stage, evidenced by the predominance of project goals in either “development and innovation” or “efficacy and replication” categories. We found very few projects (5 out of 127 projects, 4.0%) that were funded for “scale-up evaluation.” Finally, as shown by our analysis of project participants, IES had focused on funding projects for students in grades 1–12. Various quantitative research methods were frequently used by these projects for data collection and analyses.

These results illustrated how well STEM education research was supported through both the designated STEM education and many other programs during the study time period, which helps to explain why researchers in the USA have been so productive in producing and promoting scholarship in STEM education (Li et al., 2019 ; Li et al., 2020 ). We connected several findings from this study to findings from recent reviews of journal publications in STEM education. For example, publications in STEM education appeared in many different journals as many researchers with diverse expertise were supported to study various issues related to STEM education, STEM education publications often have co-authorship, and there is heavy use of quantitative research methods. The link between public funding and significant numbers of publications in STEM education research from US scholars offers a strong argument for the importance of providing strong funding support to research and development in STEM education in the USA and also in many other countries around the world.

The systematic analysis also revealed that STEM education, as used by IES in naming the designated programs, did not convey a clear definition or scope. In fact, we found diverse disciplinary foci in these projects. Integrated STEM was not a main focus of these designated programs in funding STEM education. Instead, many projects in these programs had clear subject content focus in individual disciplines, which is very similar to discipline-based education research (DBER, National Research Council, 2012 ). Interestingly enough, STEM education research had also been supported in many other programs of IES with diverse foci Footnote 7 , such as “Small Business Innovation Research,” “Cognition and Student Learning,” and “Postsecondary and Adult Education.” This funding reality further suggested the broad scope of issues associated with STEM education, as well as the growing need of building STEM education research as a distinct field (Li, 2018a ).

Inspired by our recent review of journal publications as research output in STEM education, this review started with an ambitious goal to study funding support as research input for STEM education. However, we had to limit the scope of the study for feasibility. We limited funding sources to one federal agency in the USA. Therefore, we did not analyze funding support from private funding sources including many private foundations and corporations. Although public funding sources have been one of the most important funding supports available for researchers to develop and expand their research work, the results of this systematic analysis suggest the importance future studies to learn more about research support and input to STEM education from other sources including other major public funding agencies, private foundations, and non-profit professional organizations.

Among these 54 funded projects containing the acronym “STEM” from 2007 to 2019, Table 1 shows that only 2 (3.7%) were in the IES designated program option of STEM education in the category of “Education Research.” Forty-nine projects were in 13 other program options in the category of “Education Research,” with surprisingly large numbers of projects under the “Small Business Innovation Research” option (17, 31.5%) and “Cognition and Student Learning” (11, 20.4%). Three of the 54 funded projects were in the program category of “Special Education Research.” To be specific, two of the three were in the program of “Small Business Innovation Research in Special Education,” and one was in the program of “Special Topic: Career and Technical Education for Students with Disabilities.”

The results suggest that many projects, focusing on various issues and questions directly associated with STEM education, were funded even when researchers applied for funding support in program options not designated as “Science, Technology, Engineering, and Mathematics (STEM) Education.” It implies that issues associated with STEM education had been generally acknowledged as important across many different program areas in education research and special education research. The funding support available in diverse program areas likely allowed numerous scholars with diverse expertise to study many different questions and publish their research in diverse journals, as we noted in the recent review of journal publications in STEM education (Li et al., 2020 ).

A previous study identified and analyzed a total of 46 IES funded projects from 2007 to 2018 (with an average of fewer than 4 projects per year) that contain the acronym “STEM” in a project’s title and/or description (Wang et al., 2019 ). Finding eight newly funded projects in 2019 suggested a growing interest in research on issues directly associated with STEM education in diverse program areas. In fact, five out of these eight newly funded projects specifically included the acronym “STEM” in the project’s title to explicitly indicate the project’s association with STEM education.

Availability of data and materials

The data and materials used and analyzed for the review are publicly available at the IES website, White House website, and other government agency websites.

In a previous study (Wang, Li, & Xiao, 2019), we used the acronym “STEM” as a search term under the option of “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS” without any program category restriction, and identified and analyzed 46 funded projects from 2007 to 2018 that contain “STEM” in a project’s title and/or description after screening out unrelated key words containing “stem” such as “system”. To make comparisons when needed, we did the same search using the acronym “STEM” and found 8 more funded projects in 2019 for a total of 54 funded projects across many different program categories from 2007 to 2019.

The project of “A Randomized Controlled Study of the Effects of Intelligent Online Chemistry Tutors in Urban California School Districts” (2008). In the project description, its subtitle shows intervention information. We coded this project as “interventional.” Then, the project also included the treatment group and the control group. We coded this project as “experimental.” Finally, this project was to test the efficacy of computer-based cognitive tutors. This was a correlational study. We thus coded it as “correlational.”

Computer data means that the project description indicated this kind of information, such as log data on students.

Descriptive means “descriptive statistics.” General regression means multiple regression, linear regression, logistical regression, except hierarchical linear regression model. ANOVA* is used here as a broad term to include analysis of variance, analysis of covariance, multivariate analysis of variance, and/or multivariate analysis of variance. Others include factor analysis, t tests, Mann-Whitney tests, and binomial tests, log data analysis, meta-analysis, constant comparative data analysis, and qualitative analysis.

Special education originally was about students with disabilities. It has broadened in scope over the years.

The number of students under Special Education was 14% of students in public schools in the USA in 2017–2018. https://nces.ed.gov/programs/coe/indicator_cgg.asp

For example, “Design Environment for Educator-Student Collaboration Allowing Real-Time Engineering-centric, STEM (DESCARTES) Exploration in Middle Grades” (2017) was funded as a 2-year project to Parametric Studios, Inc. (awardee) under the program option of “Small Business Innovation Research” (here is the link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=1922 ). “Exploring the Spatial Alignment Hypothesis in STEM Learning Environments” (2017) was funded as a 4-year project to WestEd (awardee) under the program option of “Cognition and Student Learning” (link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=2059 ). “Enhancing Undergraduate STEM Education by Integrating Mobile Learning Technologies with Natural Language Processing” (2018) was funded as a 4-year project to Purdue University (awardee) under the program option of “Postsecondary and Adult Education” (link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=2130 ).

Abbreviations

Analysis of variance

Discipline-based education research

Department of Education

Hierarchical linear modeling

Institute of Education Sciences

Item response theory

National Science Foundation

Pre-school–grade 12

Requests-for-proposal

Structural equation modeling

Science, technology, engineering, and mathematics

Committee on STEM Education, National Science & Technology Council, the White House (2018). Charting a course for success: America’s strategy for STEM education . Washington, DC. https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf Accessed on 18 Jan 2019.

English, L. D. (2016). STEM education K-12: perspectives on integration. International Journal of STEM Education, 3 , 3 https://doi.org/10.1186/s40594-016-0036-1 .

Article Google Scholar

Honey, M., Pearson, G., & Schweingruber, A. (2014). STEM integration in K-12 education: status, prospects, and an agenda for research . Washington DC: National Academies Press.

Google Scholar

Institute of Education Sciences (IES) (n.d.). About IES: connecting research, policy and practice. Retrieved from https://ies.ed.gov/aboutus/ Accessed on 2 Feb 2020.

Institute of Education Sciences (IES) & National Science Foundation (NSF). (2013). Common guidelines for education research and development. Washington, DC: The authors. Retrieved from https://www.nsf.gov/pubs/2013/nsf13126/nsf13126.pdf Accessed on 2 Feb 2020.

Lee, A. (2014). Students with disabilities choosing science technology engineering and math (STEM) majors in postsecondary institutions. Journal of Postsecondary Education and Disability, 27 (3), 261–272.

Li, Y. (2014). International journal of STEM education – a platform to promote STEM education and research worldwide. International Journal of STEM Education, 1 , 1 https://doi.org/10.1186/2196-7822-1-1 .

Li, Y. (2018a). Journal for STEM Education Research – promoting the development of interdisciplinary research in STEM education. Journal for STEM Education Research, 1 (1-2), 1–6 https://doi.org/10.1007/s41979-018-0009-z .

Li, Y. (2018b). Four years of development as a gathering place for international researchers and readers in STEM education. International Journal of STEM Education, 5 , 54 https://doi.org/10.1186/s40594-018-0153-0 .

Li, Y. (2019). Five years of development in pursuing excellence in quality and global impact to become the first journal in STEM education covered in SSCI. International Journal of STEM Education, 6 , 42 https://doi.org/10.1186/s40594-019-0198-8 .

Li, Y., Froyd, J. E., & Wang, K. (2019). Learning about research and readership development in STEM education: a systematic analysis of the journal’s publications from 2014 to 2018. International Journal of STEM Education, 6 , 19 https://doi.org/10.1186/s40594-019-0176-1 .

Li, Y., & Schoenfeld, A. H. (2019). Problematizing teaching and learning mathematics as ‘given’ in STEM education. International Journal of STEM Education, 6 , 44 https://doi.org/10.1186/s40594-019-0197-9 .

Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020). Research and trends in STEM education: a systematic review of journal publications. International Journal of STEM Education, 7 , 11 https://doi.org/10.1186/s40594-020-00207-6 .

Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: a systematic literature review. International Journal of STEM Education, 6 , 2 https://doi.org/10.1186/s40594-018-0151-2 .

Minichiello, A., Hood, J. R., & Harkness, D. S. (2018). Bring user experience design to bear on STEM education: a narrative literature review. Journal for STEM Education Research, 1 (1-2), 7–33.

National Research Council. (2012). Discipline-based education research: understanding and improving learning in undergraduate science and engineering . Washington DC: National Academies Press.

Otten, M., Van den Heuvel-Panhuizen, M., & Veldhuis, M. (2019). The balance model for teaching linear equations: a systematic literature review. International Journal of STEM Education, 6 , 30 https://doi.org/10.1186/s40594-019-0183-2 .

Schreffler, J., Vasquez III, E., Chini, J., & James, W. (2019). Universal design for learning in postsecondary STEM education for students with disabilities: a systematic literature review. International Journal of STEM Education, 6 , 8 https://doi.org/10.1186/s40594-019-0161-8 .

The White House (2009). President Obama launches “Educate to Innovate” campaign for excellence in science, technology, engineering & math (Stem) education. Retrieved from https://obamawhitehouse.archives.gov/the-press-office/president-obama-launches-educate-innovate-campaign-excellence-science-technology-en Accessed on 2 Feb 2020.

The White House (2017). Presidential memorandum for the secretary of Education. Retrieved from https://www.whitehouse.gov/presidential-actions/presidential-memorandum-secretary-education/ Accessed on 2 Feb 2020.

The White House (2018). President Donald J. Trump is working to ensure all Americans have access to STEM education. Retrieved from https://www.whitehouse.gov/briefings-statements/president-donald-j-trump-is-working-to-ensure-all-americans-have-access-to-stem-education/ Accessed on 2 Feb 2020.

U.S. Department of Education (2018). U.S. Department of Education fulfills administration promise to invest $200 million in STEM education. Retrieved from https://www.ed.gov/news/press-releases/us-department-education-fulfills-administration-promise-invest-200-million-stem-education Accessed on 2 Feb 2020.

Wang, K., Li, Y., & Xiao, Y. (2019). Exploring the status and development trends of STEM education research: the case of IES funded projects on STEM education in the U.S. 数学教育学报 . Journal of Mathematics Education, 28 (3), 53–61.

Download references

This review was supported by a grant from the National Science Foundation (DUE-1852942). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and affiliations.

Texas A&M University, College Station, TX, 77843-4232, USA

Yeping Li, Yu Xiao & Sandra B. Nite

Nicholls State University, Thibodaux, LA, 70310, USA

Ohio State University, Columbus, OH, 43210, USA

Jeffrey E. Froyd

You can also search for this author in PubMed Google Scholar

Contributions

YL conceptualized the study and drafted the manuscript. KW contributed with data collection, coding, analyses, and manuscript reviews. YX contributed to data collection, coding, and manuscript reviews. JEF and SBN contributed to manuscript improvement through manuscript reviews and revisions. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yeping Li .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Li, Y., Wang, K., Xiao, Y. et al. Research and trends in STEM education: a systematic analysis of publicly funded projects. IJ STEM Ed 7 , 17 (2020). https://doi.org/10.1186/s40594-020-00213-8

Download citation

Received : 18 March 2020

Accepted : 20 March 2020

Published : 22 April 2020

DOI : https://doi.org/10.1186/s40594-020-00213-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Scholarship
STEM education research
STEM funding

23+ Quantitative Research Topics For STEM Students In The Philippines

Post author By Ankit
February 6, 2024

“Did you know only 28% of college graduates in the Philippines get degrees in STEM fields? Finding good research topics is vital to getting more Filipino students curious about quantitative studies.

With limited research money and resources, it can be hard for STEM students to find quantitative projects that are possible, new, and impactful. Often, researchers end up feeling apart from local issues and communities.

This blog post offers a unique collection of quantitative research topics for STEM students in the Philippines. Thus, drawing from current events, social issues, and the country’s needs, these project ideas will feel relevant and help students do research that creates positive change.

Philippines students can find inspiration for quantitative studies that make a difference at home through many examples across science, technology, engineering, and math.

Read Our Blog: 120+ Best Quantitative Research Topics for Nursing Students (2024 Edition)

Table of Contents

30 Great Quantitative Research Topics for STEM Students in The Philippines

Here are the top quantitative research topics for STEM students in the Philippines in 2024

1. Impact of Climate Change on Farming

Analyze how changing weather affects the growth of crops like rice and corn in different parts of the Philippines. Use numbers to find ways and suggest ways farmers can adapt.

2. Using Drones to Watch Nature

See how well drones with special sensors can watch over forests and coasts in the Philippines. Look at the data they gather to figure out how to save these places.

3. Making Solar Panels Work Better

Experiment with various ways to make more power with solar panels in sunny, humid places like the Philippines. Utilize math to guess how well they’ll work.

4. Checking How Pollution Hurts Coral Reefs

Count how much damage pollution does to coral reefs in the Philippines. Try to predict how bad it’ll get if we don’t stop polluting.

5. Watching Traffic to Fix Roads

Look at how cars move in big cities like Manila. Use math to figure out how to make traffic flow better and help people get around faster.

6. at Air and Sick People

Measure how clean the air is in various parts of the Philippines and see if it affects how many people get sick. Find out which areas need help to stay healthy.

7. Guessing When Earthquakes Might Happen

Look at data from sensors all over the Philippines to see if we can tell when earthquakes might come. Try to guess where they’ll occur next.

8. Making Water Pipes Better

Use math tricks to design cheap pipes that bring clean water to small towns in the Philippines. Think about things like hills and how many people need water.

9. Checking If Planting Trees Helps

Measure if planting trees helps stop the shore from washing away during storms. Use photos from far away and math to see if it’s working.

10. Teaching Computers to Find Sickness

Teach computers to look at pictures and records from hospitals to see if people are sick. Check if they’re good at spotting problems in the Philippines.

11. Finding Better Bags That Break Down

Test different materials like banana leaves to see which ones can be made into bags that don’t hurt the environment. Compare them to regular plastic bags.

12. Making Gardens in the City

See if we can grow vegetables in tall buildings in big cities like Manila. Use numbers to figure out if it’s a good idea.

13. Checking If Bugs Spread Easily in Crowded Places

Use computers to see if diseases spread fast in busy places in the Philippines. Look at how people move around to stop diseases from spreading.

14. Storing Energy for Islands Without Power

Think about ways to save power for small islands without electricity. Try out different ways to save energy and see which one works best.

15. Seeing How Much Storms Hurt Farms

Calculate how much damage storms do to farms in the Philippines. Use numbers to see how much money farmers lose.

16. Testing Ways to Stop Dirt from Washing Away

Try out different ways to stop dirt from being washed away when it rains. Use math to see which way works best on hills in the Philippines.

17. Checking How Healthy Local Food Is

Look at the vitamins and minerals in local foods like sweet potatoes and moringa leaves. See if eating them is good for people in the Philippines.

18. Making Cheap Water Cleaners

Build simple machines that clean dirty water in small towns. Notice if they work better than expensive ones.

19. Seeing How Hot Cities Get

Use satellites to see how hot cities like Manila get compared to places with more trees. Think about how this affects people.

20. Thinking About Trash in Cities

Look at how much trash cities in the Philippines make and find ways to deal with it. Consider what people can do to make less trash.

21. Checking If We Can Use Hot Rocks for Power

Look at rocks under the ground to see if we can get power from them. Consider whether it is beneficial for the environment.

22. Counting Animals in the Forest

Use cameras to count how many animals are in forests in the Philippines. Notice which places need the most help to keep animals safe.

23. Making Fishing Fair

Look at how many fish are caught in the Philippines and see if it’s fair. Think about ways to make sure there will always be enough fish to catch.

24. Making Power Lines Smarter

Design power lines that can change how much power they use. Try to make sure power goes where it’s needed most.

25. Looking at Dirty Water

Find out if chopping down trees and building things by rivers makes the water dirty. Think about what this means for people and animals.

26. Thinking About Big Waves

Use computers to see if big waves could hit the Philippines and what might happen. Think about how to keep people safe.

27. Seeing If Parks Help Cities

Ask people if they like having parks in their city and see what animals live there. Think about if parks make cities better.

28. Making Houses That Don’t Break in Storms

Make houses that don’t fall when there are big storms. Try to make them cheap so more people can have them.

29. Stopping Food from Going Bad

Look at how food gets from farms to people’s houses and see if we can stop it from going bad. Think about how to make sure people have enough to eat.

30. Seeing How Hot Cities Get

Put machines around cities to see how hot they get. Consider how this affects people and what we can do to help.

These topics will help you to make a good project that assists you in getting better scores.

Importance Of Quantitative Research For STEM Students

Read why quantitative research matters to Filipino students.

Helps us understand problems more clearly by revealing trends, patterns, and connections in the data
Provides an accurate picture by removing personal biases and opinions
Allows quantitative comparison of results if studies use the same methods
Enables testing hypotheses and theories through experiments that can prove/disprove predictions
Allows replication and verification as other researchers can redo experiments and study methods
Numbers give a more precise, factual understanding compared to qualitative data.
Removes subjectivity through quantitative data rather than opinions
A key part of the scientific process is that data helps confirm or reject proposed explanations.
Overall, collecting and analyzing quantitative data is crucial for gaining insights, testing ideas, ensuring consistency, and reducing bias

It’s time to see what challenges students face with their quantitative research.

Challenge Philippines Students Face With Their Quantitative Research

Here are the common challenges that students face with their quantitative research topics:

Lack of resources and funding

Doing quantitative research needs access to equipment, software , datasets etc, which can be costly. Many students lack funding and access to these resources.

Lack of background in mathematics and statistics

Quantitative research relies heavily on math and statistical skills. However, many students haven’t developed strong enough skills in these areas yet.

Difficulty accessing scholarly databases

Students need access to academic journals and databases for literature reviews. However, these can be costly for people to access.

Language barriers

Many of the academic literature is in English. This can make reading and learning complex statistical concepts more difficult.

Lack of mentorship

Having an experienced mentor to provide guidance is invaluable. However, not all students have access to mentorship in quantitative research.

Managing large datasets

Collecting, cleaning and analyzing large datasets requires advanced technical skills. Students may struggle without proper guidance.

Presenting results clearly

Learning how to visualize and communicate statistical findings effectively is an important skill that takes practice.

Ethical challenges

Ensuring quantitative studies are designed ethically can be difficult for novice researchers.

Writing scientifically

Adopting the formal, precise writing style required in quantitative research is challenging initially.

Maintaining motivation

Quantitative research is complex and time-consuming. Students may lose motivation without a strong support network.

While quantitative research presents many challenges, Philippines STEM students can overcome these through access to proper resources and support. With hard work, mentorship and collaborative opportunities, students can build essential skills and contribute to the quantitative research landscape.

Tips For Conducting Quantitative Research In The Philippines

When conducting research in a new cultural context like the Philippines, it is vital to take time to understand local norms and build trust. Approaching research openly and collaboratively will lead to more meaningful insights.

1. Get Required Approvals

Be sure to get any necessary ethics reviews or approvals from local governing boards before conducting the analysis. It is wise to follow proper protocols and permissions.

2. Hire Local Assistants

Hire local research helpers to help navigate logistics, translation, and cultural sensitivities. This provides jobs and insider insights.

3. Use Multiple Research Methods

Triangulate findings using interviews, focus groups, surveys, participant observation, etc. Multiple methods provide more potent and well-rounded results.

4. Verify Information

Politely verify information collected from interviews before publication. Follow up to ensure accurate representation and context.

5. Share Results

Report back to participants and communities on research findings and next steps. This shows respect and accountability for their contributions.

6. Acknowledge Limitations

Openly acknowledge the limitations of perspective and methods as an outside researcher. Remain humble and keep improving approaches.

Keep in mind, when entering a new community to conduct research, taking an open, patient, and collaborative approach leads to more ethical and meaningful results. Thus, making the effort to understand and work within cultural norms demonstrates respect.

STEM students in the Philippines have many possible research topics using numbers. They could look at renewable energy, sustainability, pollution, environment, disease prevention, farming improvements, preparing for natural disasters, building projects, transportation, and technology access.

By carefully analyzing statistics and creating mathematical models, young Filipino researchers can provide key ideas to guide future policies and programs. Quantitative research allows real observations and suggestions based on evidence to make the country better now and later.

Number-based methods help young researchers in the Philippines give tangible recommendations to improve their communities.

How can I limit my choices and pick the right research topic?

Think about what you enjoy and what you’re skilled at. Consider if your topic is meaningful and if you have the resources to study it. Get advice from teachers or friends to help you decide.

What are some common problems in doing math research in science, technology, engineering, and math?

Problems might include: 1. Finding data. 2. Make sure your measurements are correct. 3. Following rules about ethics. 4. Handling big sets of data.

How can I make sure my research is done well?

Plan your study carefully, use the correct methods and tools, write down everything you do, and think about the strengths and weaknesses of your work.

Tags Quantitative Research Topics For STEM Students In The Philippines
australia (2)
duolingo (13)
Education (284)
General (78)
How To (18)
IELTS (127)
Latest Updates (162)
Malta Visa (6)
Permanent residency (1)
Programming (31)
Scholarship (1)
Sponsored (4)
Study Abroad (187)
Technology (12)
work permit (8)

Trends and Hot Topics of STEM and STEM Education: a Co-word Analysis of Literature Published in 2011–2020

Published: 23 February 2023
Volume 33 , pages 1069–1092, ( 2024 )

Cite this article

Ying-Shao Hsu ORCID: orcid.org/0000-0002-1635-8213 1 , 2 ,
Kai-Yu Tang ORCID: orcid.org/0000-0002-3965-3055 3 &
Tzu-Chiang Lin ORCID: orcid.org/0000-0003-3842-3749 4 , 5

1223 Accesses

3 Citations

Explore all metrics

This study explored research trends in science, technology, engineering, and mathematics (STEM) education. Descriptive analysis and co-word analysis were used to examine articles published in Social Science Citation Index journals from 2011 to 2020. From a search of the Web of Science database, a total of 761 articles were selected as target samples for analysis. A growing number of STEM-related publications were published after 2016. The most frequently used keywords in these sample papers were also identified. Further analysis identified the leading journals and most represented countries among the target articles. A series of co-word analyses were conducted to reveal word co-occurrence according to the title, keywords, and abstract. Gender moderated engagement in STEM learning and career selection. Higher education was critical in training a STEM workforce to satisfy societal requirements for STEM roles. Our findings indicated that the attention of STEM education researchers has shifted to the professional development of teachers. Discussions and potential research directions in the field are included.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

Get 10 units per month
Download Article/Chapter or eBook
1 Unit = 1 Article or 1 Chapter
Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

An Integrative Review with Word Cloud Analysis of STEM Education

A review of STEM education with the support of visualizing its structure through the CiteSpace software

A systematic review of STEM education research in the GCC countries: trends, gaps and barriers

Data availability.

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Akgunduz, D. (2016). A Research about the placement of the top thousand students placed in STEM fields in Turkey between the years 2000 and 2014. EURASIA Journal of Mathematics, Science and Technology Education, 12 (5), 1365–1377.

Google Scholar

Appianing, J., & Van Eck, R. N. (2018). Development and validation of the Value-Expectancy STEM Assessment Scale for students in higher education. International Journal of STEM Education , 5 , article 24.

Assefa, S. G., & Rorissa, A. (2013). A bibliometric mapping of the structure of STEM education using co-word analysis. Journal of the American Society for Information Science and Technology, 64 (12), 2513–2536.

Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2017). Synthesizing results from empirical research on computer-based scaffolding in STEM education: A meta-analysis. Review of Educational Research, 87 (2), 309–344.

Brotman, J. S., & Moore, F. M. (2008). Girls and science: A review of four themes in the science education literature. Journal of Research in Science Teaching, 45 (9), 971–1002.

Brown, R. E., & Bogiages, C. A. (2019). Professional development through STEM integration: How early career math and science teachers respond to experiencing integrated STEM tasks. International Journal of Science and Mathematics Education, 17 (1), 111–128.

Burt, B. A., Williams, K. L., & Palmer, G. J. M. (2019). It takes a village: The role of emic and etic adaptive strengths in the persistence of black men in engineering graduate programs. American Educational Research Journal, 56 (1), 39–74.

Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22 (1), 155–205.

Carlisle, D. L. & Weaver, G. C. (2018). STEM education centers: Catalyzing the improvement of undergraduate STEM education. International Journal of STEM Education, 5 , article 47.

Chang, D. F., & ChangTzeng, H. C. (2020). Patterns of gender parity in the humanities and STEM programs: The trajectory under the expanded higher education system. Studies in Higher Education, 45 (6), 1108–1120.

Charleston, L. J. (2012). A qualitative investigation of African Americans’ decision to pursue computing science degrees: Implications for cultivating career choice and aspiration. Journal of Diversity in Higher Education, 5 (4), 222–243.

Charleston, L. J., George, P. L., Jackson, J. F. L., Berhanu, J., & Amechi, M. H. (2014). Navigating underrepresented STEM spaces: Experiences of black women in US computing science higher education programs who actualize success. Journal of Diversity in Higher Education, 7 (3), 166–176.

Chien, Y. H., & Chu, P. Y. (2018). The different learning outcomes of high school and college students on a 3D-printing STEAM engineering design curriculum. International Journal of Science and Mathematics Education, 16 (6), 1047–1064.

Dehdarirad, T., Villarroya, A., & Barrios, M. (2014). Research trends in gender differences in higher education and science: A co-word analysis. Scientometrics, 101 (1), 273–290.

Dickerson, D. L., Eckhoff, A., Stewart, C. O., Chappell, S., & Hathcock, S. (2014). The examination of a pullout STEM program for urban upper elementary students. Research in Science Education, 44 (3), 483–506.

Eccles, J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J., & Midgley, C. (1983). Expectancies, values and academic behaviors. In J. T. Spence (Ed.), Achievement and Achievement Motives . W. San Francisco: H. Freeman.

Ellison, S., & Allen, B. (2018). Disruptive innovation, labor markets, and Big Valley STEM School: Network analysis in STEM education. Cultural Studies of Science Education, 13 (1), 267–298.

Erdogan, N., Navruz, B., Younes, R., & Capraro, R. M. (2016). Viewing how STEM project-based learning influences students’ science achievement through the implementation lens: A latent growth modeling. Eurasia Journal of Mathematics, Science and Technology Education, 12 (8), 2139–2154.

European Commission, Directorate-General for Education, Youth, Sport and Culture (2016). Does the EU need more STEM graduates? Final report . Retrieve from https://data.europa.eu/doi/10.2766/000444

Fredricks, J. A., Hofkens, T., Wang, M. T., Mortenson, E., & Scott, P. (2018). Supporting girls’ and boys’ engagement in math and science learning: A mixed methods study. Journal of Research in Science Teaching, 55 (2), 271–298.

Fry, R., Kennedy, B., & Funk, C. (2021). Stem jobs see uneven progress in increasing gender, racial and ethnic diversity. Retrieve from https://www.pewresearch.org/science/wp-content/uploads/sites/16/2021/03/PS_2021.04.01_diversity-in-STEM_REPORT.pdf

Ganley, C. M., George, C. E., Cimpian, J. R., & Makowski, M. B. (2018). Gender equity in college majors: Looking beyond the STEM/non-STEM dichotomy for answers regarding female participation. American Educational Research Journal, 55 (3), 453–487.

Gehrke, S., & Kezar, A. (2019). Perceived outcomes associated with engagement in and design of faculty communities of practice focused on STEM reform. Research in Higher Education, 60 (4), 844–869.

Gilmore, J., Vieyra, M., Timmerman, B., Feldon, D., & Maher, M. (2015). The relationship between undergraduate research participation and subsequent research performance of early career STEM graduate students. Journal of Higher Education, 86 (6), 834–863.

Godwin, A., Potvin, G., Hazari, Z., & Lock, R. (2016). Identity, critical agency, and engineering: An affective model for predicting engineering as a career choice. Journal of Engineering Education, 105 (2), 312–340.

Han, S., Yalvac, B., Capraro, M. M., & Capraro, R. M. (2015). In-service teachers’ implementation and understanding of STEM project based learning. Eurasia Journal of Mathematics Science and Technology Education, 11 (1), 63–76.

Heras, M., Ruiz-Mallén, I., & Gallois, S. (2020). Staging science with young people: Bringing science closer to students through stand-up comedy. International Journal of Science Education, 42 (12), 1968–1987.

Hernandez, P. R., Estrada, M., Woodcock, A., & Schultz, P. W. (2017). Protégé perceptions of high mentorship quality depend on shared values more than on demographic match. Journal of Experimental Education, 85 (3), 450–468.

Hinojo Lucena, F. J., Lopez Belmonte, J., Fuentes Cabrera, A., Trujillo Torres, J. M., & Pozo Sanchez, S. (2020). Academic effects of the use of flipped learning in physical education. International journal of Environmental Research and Public Health , 17 (1), article 276.

Holmes, K., Gore, J., Smith, M., & Lloyd, A. (2018). An integrated analysis of school students’ aspirations for STEM careers: Which student and school factors are most predictive? International Journal of Science and Mathematics Education, 16 (4), 655–675.

Huang, X., & Qiao, C. (2022). Enhancing computational thinking skills through artificial intelligence education at a STEAM high school. Science & Education . https://doi.org/10.1007/s11191-022-00392-6

Article Google Scholar

Hughes, R. M., Nzekwe, B., & Molynearx, K. J. (2013). The single sex debate for girls in science: A comparison between two informal science programs on middle school students’ STEM identity formation. Research in Science Education, 43 , 1979–2007.

Hughes, B. S., Corrigan, M. W., Grove, D., Andersen, S. B., & Wong, J. T. (2022). Integrating arts with STEM and leading with STEAM to increase science learning with equity for emerging bilingual learners in the United States. International Journal of STEM Education , 9 , article 58.

Johnson, A. M. (2019). “I can turn it on when I need to”: Pre-college integration, culture, and peer academic engagement among black and Latino/a engineering students. Sociology of Education, 92 (1), 1–20.

Kayan-Fadlelmula, F., Sellami, A., Abdelkader, N., & Umer, S. (2022). A systematic review of STEM education research in the GCC countries: Trends, gaps and barriers. International Journal of STEM Education, 9 , article 2.

Kelly, R., Mc Garr, O., Leahy, K., & Goos, M. (2020). An investigation of university students and professionals’ professional STEM identity status. Journal of Science Education and Technology, 29 (4), 536–546.

Kezar, A., Gehrke, S., & Bernstein-Sierra, S. (2017). Designing for success in STEM communities of practice: Philosophy and personal interactions. The Review of Higher Education, 40 (2), 217–244.

Kezar, A., Gehrke, S., & Bernstein-Sierra, S. (2018). Communities of transformation: Creating changes to deeply entrenched issues. The Journal of Higher Education, 89 (6), 832–864.

Kricorian, K., Seu, M., Lpoez, D., Ureta, E., & Equils, O. (2020). Factors influencing participation of underrepresented students in STEM fields: Matched mentors and mindsets. International Journal of STEM Education, 7 , article 16.

Ku, C. J., Hsu, Y. S., Chang, M. C., & Lin, K. Y. (2022). A model for examining middle school students’ STEM integration behavior in a national technology competition. International Journal of STEM Education, 9 (1), 3.

Leydesdroff, L. (1989). Words and co-words as indicators of intellectual organization. Research Policy, 18 (4), 209–223.

Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020a). Research and trends in STEM education: A systematic review of journal publications. International Journal of STEM Education, 7 , article 11.

Li, Y., Wang, K., Xiao, Y., Froyd, J. E., Nite, S. B. (2020b). Research and trends in STEM education: A systematic analysis of publicly funded projects. International Journal of STEM Education, 7 , article 17.

Lin, T. C., Lin, T. J., & Tsai, C. C. (2014). Research trends in science education from 2008 to 2012: A systematic content analysis of publications in selected journals. International Journal of Science Education, 36 (8), 1346–1372.

Lin, T. J., Lin, T. C., Potvin, P., & Tsai, C. C. (2019). Research trends in science education from 2013 to 2017: A systematic content analysis of publications in selected journals. International Journal of Science Education, 41 (3), 367–387.

Lin, T. C., Tang, K. Y., Lin, S. S., Changlai, M. L., & Hsu, Y. S. (2022). A co-word analysis of selected science education literature: Identifying research trends of scaffolding in two decades (2000–2019). Frontiers in Psychology, 13 , 844425.

Liu, J. S., & Lu, L. Y. (2012). An integrated approach for main path analysis: Development of the Hirsch index as an example. Journal of the American Society for Information Science and Technology, 63 (3), 528–542.

Liu, C. Y., & Wu, C. J. (2022). STEM without art: A ship without a sail. Thinking Skills and Creativity, 43 , 100977.

Lou, S. H., Shih, R. C., Diez, C. R., & Tseng, K. H. (2011). The impact of problem-based learning strategies on STEM knowledge integration and attitudes: An exploratory study among female Taiwanese senior high school students. International Journal of Technology and Design Education, 21 (2), 195–215.

Lynch, S. J., Burton, E. P., Behrend, T., House, A., Ford, M., Spillane, N., Matray, S., Han, E., & Means, B. (2018). Understanding inclusive STEM high schools as opportunity structures for underrepresented students: Critical components. Journal of Research in Science Teaching, 55 (5), 712–748.

Maass, K., Geiger, V., Ariza, M. R., & Goos, M. (2019). The Role of mathematics in interdisciplinary STEM education. ZDM-Mathematics Education, 51 (6), 869–884.

Mansfield, K. C. (2014). How listening to student voices informs and strengthens social justice research and practice. Educational Administration Quarterly, 50 (3), 392–430.

Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: A systematic literature review. International Journal of STEM education , 6 , article 2.

Marín-Marín, J. A., Moreno-Guerrero, A. J., Dúo-Terrón, P., & López-Belmonte, J. (2021). STEAM in education: A bibliometric analysis of performance and co-words in Web of Science. International Journal of STEM Education , 8 , article 41.

Martín-Páez, T., Aguilera, D., Perales-Palacios, F. J., & Vílchez-González, J. M. (2019). What are we talking about when we talk about STEM education? A Review of Literature. Science Education, 103 (4), 799–822.

McGee, E. O. (2020). Interrogating structural racism in STEM higher education. Educational Researcher, 49 (9), 633–644.

Meho, L. I., & Yang, K. (2006). A new era in citation and bibliometric analyses: Web of Science, Scopus, and Google Scholar. arXiv preprint cs/0612132 .

Mejias, S., Thompson, N., Sedas, R. M., Rosin, M., Soep, E., Peppler, K., Roche, J., Wong, J., Hurley, M., Bell, P., & Bevan, B. (2021). The trouble with STEAM and why we use it anyway. Science Education, 105 (2), 209–231.

Micari, M., Van Winkle, Z., & Pazos, P. (2016). Among friends: The role of academic-preparedness diversity in individual performance within a small-group STEM learning environment. International Journal of Science Education, 38 (12), 1904–1922.

Millar, V. (2020). Trends, issues and possibilities for an interdisciplinary STEM curriculum. Science & Education, 29 (4), 929–948.

Nadelson, L. S., Callahan, J., Pyke, P., Hay, A., Dance, M., & Pfiester, J. (2013). Teacher STEM perception and preparation: Inquiry-based STEM professional development for elementary teachers. Journal of Educational Research, 106 (2), 157–168.

Nakatoh, T., & Hirokawa, S. (2019, July). Evaluation index to find relevant papers: Improvement of focused citation count. In International Conference on Human-Computer Interaction (pp. 555–566). Springer, Cham.

National Science Technology Council. (2012). Coordinating federal science, technology, engineering, and mathematics (STEM) education investments: Progress report. Retrieved from https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/nstc_federal_stem_education_coordination_report.pdf

National Science Technology Council. (2013). Federal Science, Technology, Engineering, and Mathematics (STEM) Education 5-Year Strategic Plan. Retrieved from https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/stem_stratplan_2013.pdf

Ong, M., Smith, J. M., & Ko, L. T. (2018). Counterspaces for women of color in STEM higher education: Marginal and central spaces for persistence and success. Journal of Research in Science Teaching, 55 (2), 206–245.

Organisation for Economic Cooperation and Development, OECD (2021). Education at A Glance 2021. Retrieve from https://read.oecd.org/10.1787/b35a14e5-en?format=pdf

Perez-Felkner, L., Felkner, J. S., Nix, S., & Magalhaes, M. (2020). The puzzling relationship between international development and gender equity: The case of STEM postsecondary education in Cambodia. International Journal of Educational Development, 72 , 102102.

Perignat, E., & Katz-Buonincontro, J. (2019). STEAM in practice and research: An integrative literature review. Thinking Skills and Creativity, 31 , 31–43.

Quigley, C. F., & Herro, D. (2016). “Finding the joy in the unknown”: Implementation of steam teaching practices in middle school science and math classrooms. Journal of Science Education and Technology, 25 (3), 410–426.

Ramey, K. E., & Stevens, R. (2019). Interest development and learning in choice-based, in-school, making activities: The case of a 3D printer. Learning, Culture and Social Interaction, 23 , 100262.

Salami, M. K., Makela, C. J., & de Miranda, M. A. (2017). Assessing changes in teachers’ attitudes toward interdisciplinary STEM teaching. International Journal of Technology and Design Education, 27 (1), 63–88.

Sanders, M. (2009). Integrative STEM education primer. The Technology Teacher, 68 (4), 20–26.

Saorín, J. L., Melian-Díaz, D., Bonnet, A., Carrera, C. C., Meier, C., & De La Torre-Cantero, J. (2017). Makerspace teaching-learning environment to enhance creative competence in engineering students. Thinking Skills and Creativity, 23 , 188–198.

Simon, R. M., Wagner, A., & Killion, B. (2017). Gender and choosing a STEM major in college: Femininity, masculinity, chilly climate, and occupational values. Journal of Research in Science Teaching, 54 (3), 299–323.

Stolle-McAllister, K., Domingo, M. R. S., & Carrillo, A. (2011). The Meyerhoff way: How the Meyerhoff scholarship program helps black students succeed in the sciences. Journal of Science Education and Technology, 20 (1), 5–16.

Thomas, B., & Watters, J. J. (2015). Perspectives on Australian, Indian and Malaysian approaches to STEM education. International Journal of Educational Development, 45 , 42–53.

Tosun, C. (2022). Analysis of the last 40 years of science education research via bibliometric methods. Science & Education . https://doi.org/10.1007/s11191-022-00400-9

Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84 (2), 523–538.

Vencent-Ruz, P., & Schunn, C. D. (2017). The increasingly important role of science competency beliefs for science learning in girls. Journal of Research in Science Teaching, 54 (6), 790–822.

Wang, S., Chen, Y., Lv, X., & Xu, J. (2022). Hot topics and frontier evolution of science education research: A bibliometric mapping from 2001 to 2020. Science & Education . https://doi.org/10.1007/s11191-022-00337-z

Weeden, K. A., Gelbgiser, D., & Morgan, S. L. (2020). Pipeline dreams: Occupational plans and gender differences in STEM major persistence and completion. Sociology of Education, 93 (4), 297–314.

Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25 (1), 68–81.

Download references

Author information

Authors and affiliations.

Graduate Institute of Science Education, National Taiwan Normal University, No. 88, Ting-Jou Rd., Sec. 4, Taipei City, 116, Taiwan

Ying-Shao Hsu

Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, No. 88, Ting-Jou Rd., Sec. 4, Taipei City, 116, Taiwan

Graduate Institute of Library & Information Science, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City, 402, Taiwan

Kai-Yu Tang

Center for Liberal Arts, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist, Kaohsiung City, 807618, Taiwan

Tzu-Chiang Lin

Center for Teacher Education, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist, Kaohsiung City, 807618, Taiwan

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tzu-Chiang Lin .

Ethics declarations

Ethical approval and consent to participate.

This study involves neither human participants’ data nor relevant biological material. Ethics approval and informed consent are hence not applicable.

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Hsu, YS., Tang, KY. & Lin, TC. Trends and Hot Topics of STEM and STEM Education: a Co-word Analysis of Literature Published in 2011–2020. Sci & Educ 33 , 1069–1092 (2024). https://doi.org/10.1007/s11191-023-00419-6

Download citation

Accepted : 26 January 2023

Published : 23 February 2023

Issue Date : August 2024

DOI : https://doi.org/10.1007/s11191-023-00419-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

STEM education
Co-word analysis
Research trends
Find a journal
Publish with us
Track your research

Open access
Published: 28 August 2024

Fatty acid oxidation is critical for the tumorigenic potential and chemoresistance of pancreatic cancer stem cells

Marta Mascaraque 1 , 2 na1 ,
Sarah Courtois 1 na1 ,
Alba Royo-García 1 ,
David Barneda 3 ,
Andrei M. Stoian 1 ,
Isabel Villaoslada 1 ,
Pilar Espiau-Romera 1 ,
Ansooya Bokil 1 ,
Andrés Cano-Galiano 3 ,
Petra Jagust 3 ,
Christopher Heeschen 4 &
Patricia Sancho ORCID: orcid.org/0000-0002-1092-5395 1

Journal of Translational Medicine volume 22 , Article number: 797 ( 2024 ) Cite this article

1 Altmetric

Metrics details

We have previously demonstrated the significant reliance of pancreatic Cancer Stem Cells (PaCSCs) on mitochondrial oxidative phosphorylation (OXPHOS), which enables versatile substrate utilization, including fatty acids (FAs). Notably, dysregulated lipid scavenging and aberrant FA metabolism are implicated in PDAC progression.

Methods & results

Our bioinformatics analyses revealed elevated expression of lipid metabolism-related genes in PDAC tissue samples compared to normal tissue samples, which correlated with a stemness signature. Additionally, PaCSCs exhibited heightened expression of diverse lipid metabolism genes and increased lipid droplet accumulation compared to differentiated progenies. Treatment with palmitic, oleic, and linolenic FAs notably augmented the self-renewal and chemotherapy resistance of CD133 + PaCSCs. Conversely, inhibitors of FA uptake, storage and metabolism reduced CSC populations both in vitro and in vivo. Mechanistically, inhibition of FA metabolism suppressed OXPHOS activity, inducing energy depletion and subsequent cell death in PaCSCs. Importantly, combining a FAO inhibitor and Gemcitabine treatment enhanced drug efficacy in vitro and in vivo, effectively diminishing the CSC content and functionality.

Targeting FAO inhibition represents a promising therapeutic strategy against this highly tumorigenic population.

Introduction

Pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, is a disease with an unfavorable prognosis due to its late diagnosis, as symptoms are nonspecific even at advanced disease stages, and its intrinsic resistance to established therapeutic options such as chemotherapy and radiotherapy [ 1 ]. Despite its relatively low incidence, PDAC is the seventh leading cause of cancer-related deaths worldwide and one of the most lethal solid tumors [ 2 ] with a poor long-term outcome: the estimated one-year overall survival rate of these patients is 24% [ 3 ].

The main malignant features of PDAC, i.e., intrinsic chemoresistance and elevated metastasis rate, can be partially attributed to specific subpopulations of cancer cells with tumor and metastasis-initiating properties, known as pancreatic cancer stem cells (PaCSCs) [ 4 , 5 ]. CSCs are characterized by the capacity to undergo unlimited cell division while retaining their stem cell identity (self-renewal) and the ability to differentiate into diverse specialized cell types [ 4 , 5 ]. Although migratory and invasive abilities are not restricted to CSCs, only metastatic stem-like cells would be able to initiate secondary lesions upon surviving in the bloodstream as circulating tumor cells (CTCs) [ 6 ]. Considering their intrinsic chemoresistance leading to tumor relapse, the design of combined treatments targeting both PaCSCs and non-CSC populations may represent a promising strategy for improving the long-term survival of PDAC patients.

In the recent years, our group reported that mitochondria are essential organelles for stemness maintenance and tumorigenicity, representing a key vulnerability for PaCSCs. Indeed, perturbations in various processes throughout the mitochondrial life cycle, ranging from biogenesis [ 7 ], fission [ 8 ] and recycling via mitophagy [ 9 ], to interference with mitochondrial activity via oxidative phosphorylation (OXPHOS) inhibition [ 7 ] and alteration of redox state [ 10 ], all of which significantly impair the tumorigenicity and chemoresistance of PaCSCs.

ATP production via OXPHOS requires a large amount of acetyl-CoA, which is commonly supplied by glycolysis, but can also be produced by β-oxidation of fatty acids (FAs). FAs from exogenous sources can be internalized via membrane transporters or lipid receptors such as CD36 or LRPs and then directly metabolized or stored in lipid droplets (LDs). Importantly, increased lipid uptake and aberrant FA metabolism have been linked to disease progression and poor prognosis in PDAC patients [ 11 , 12 , 13 ]. Additionally, studies in other cancers have suggested that lipid metabolism plays a critical role in CSC maintenance, thereby supporting cell membrane formation and energy production [ 14 , 15 ]. Accordingly, we hypothesized that lipid metabolism, particularly FA metabolism, represents a pharmacologically targetable vulnerability for PaCSCs.

Indeed, we showed that several lipid metabolism genes are upregulated in PaCSCs and are correlated with stemness and poor survival in PDAC patients. PaCSCs show increased lipid storage in LDs and FA oxidation (FAO), and FAO inhibition markedly impaired OXPHOS activity, leading to an energy crisis and cell death. Finally, FAO inhibition improved the response to Gemcitabine both in vitro and in vivo, suggesting a new therapeutic strategy that may help improve the outcome of PDAC patients.

Materials and methods

Human data analysis.

Expression data from human PDAC tissue and normal pancreatic tissue were analyzed using the webserver GEPIA2 (TCGA and the GTEx project databases; http://gepia2.cancer-pku.cn/ ) [ 16 ]. The Pearson correlation coefficient was calculated to study the association of the individual genes corresponding to lipid metabolism with a stemness signature defined by the combined expression of the pluripotency-related genes KLF4 , OCT4 , NANOG and SOX2 . Additionally, we calculated the overall survival for pancreatic cancer patients from the respective upper and lower quartiles of the expression of these specific lipid metabolism genes; the hazard ratio (HR) was calculated from GEPIA2 using the Cox proportional hazards model.

Cell culture

PDAC patient-derived xenografts (PDXs): A6L, 185, 215, 253, 265 and 354 were obtained from the Biobank of the Spanish National Cancer Research Centre (CNIO), Madrid, Spain (MTAs #CNIO20-027, #CNIO21-253). PDAC PDX-derived cultures were established as previously described [ 17 ]. Pancreatic circulating tumor cells (CTCs): The metastatic model CTCA was established from circulating tumor cells and obtained through the Barts Pancreas Tissue Bank of the Barts Cancer Institute ( https://www.bartspancreastissuebank.org.uk/ ; BCI, London, United Kingdom; 2019/02/IISA/PS/E/Cellcultures).

The cells were grown in RPMI 1640 medium (61870044) supplemented with 10% FBS and 50 U/mL penicillin/streptomycin (all from Gibco, Life Technologies, Carlsbad, CA, USA). The cells were cultured under standard conditions of 5% CO2, 95% humidity, and 37 °C, propagated by treatment with 1X trypsin with 0.2% EDTA (Corning, Oneonta, NY, USA) and subjected to a maximum of 15 passages. For experiments, the medium was changed to sphere medium [DMEM/F-12 (31331028) supplemented with 2% B27 (both from Gibco) and 20 ng/mL FGFbasic (Pan-Biotech, Aidenbach, Germany)], ensuring proper comparison of cells grown in adherent conditions with cells grown as spheres and minimizing any interference resulting from the different concentrations of glucose and other factors present in each media.

Cancer stem cell-enriching culture

For enrichment of CSCs, cells were grown as spheres as previously described [ 7 ]. Briefly, the cells were trypsinized, centrifuged at 1200 rpm for 5 min and resuspended in sphere medium [DMEM/F-12 supplemented with 2% B27 (both from Gibco) and 20 ng/mL FGFbasic (Pan-Biotech)]. The cells were then seeded at a density of 10 5 cells/mL in flasks covered with 10% poly-HEMA (2-hydroxyethyl methacrylate, Sigma–Aldrich, Saint Louis, MO, USA) in 96% ethanol. First generation spheres were grown for seven days. For serial passaging, spheres were harvested using a 40 μm cell strainer, dissociated with trypsin (Corning, Oneonta, NY, USA) and regrown at 10 5 cells/mL for five more days.

In vitro treatments

FA inhibitors included 200 µM Etomoxir (CPT1A inhibitor) [ 18 ] (E1905, Sigma–Aldrich), 100 µM Mildronate (carnitine synthesis inhibitor) [ 19 ] (15997, Cambridge Biosciences, Cayman, UK), 100 µM Ranolazine (3-ketoacyl-CoA thiolase inhibitor) [ 18 ] (15604, Cambridge Biosciences), all of which were dissolved in dH 2 O, and 1 µM Perhexiline (SML010, CPT1/CPT2 dual inhibitor) [ 18 ] (Sigma-Aldrich) which was dissolved in DMSO following the manufacturer’s instructions. The cells were treated for 24 to 72 h.

FA supplementation included 50 and 100 µM oleic acid (OA) (O3008, Sigma Aldrich), 50 µM sodium palmitate (P9767, Sigma Aldrich) or 200 µM linolenic acid (LNA) (L2376, Sigma Aldrich) conjugated with bovine serum albumin, Fraction V, Fatty Acid-Free, Nuclease-Free and Protease-Free (126609 Sigma-Aldrich). The cells were treated for 24 to 72 h.

Chemotherapy: Gemcitabine 0.9% sodium chloride (Eli Lilly and Company, IN, USA) was used at concentrations ranging from 10 to 5000 nM for 48 h.

Real time quantitative polymerase chain reaction (RTqPCR)

RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The RNA concentration and purity were determined by spectrophotometry (Nanodrop™ 2000, ThermoFisher Scientific). 1 µg of RNA was used for cDNA synthesis using Maxima H minus cDNA synthesis Master Mix with dsDNase kit (Thermo Fisher Scientific), followed by SYBR Green RTqPCR (PowerUp™ SYBR Green Master Mix, Applied Biosystems, Thermo Fisher Scientific) according to the manufacturer’s instructions. The primers used are detailed in Table 1 .

Droplet digital PCR (ddPCR)

Sample preparation.

Blood and pancreatic tissue samples from mice bearing orthotopic tumors were harvested and processed for FACS sorting. Total blood samples were centrifuged and resuspended in ACK lysing buffer (Thermo Fisher Scientific) for 5 min. Pancreatic tumors were minced mechanically and enzymatically digested with collagenase P for 15 min at 37 °C followed by trypsin for 3 min at 37 °C. Next, cell suspensions were blocked in Flebogamma (1:10 dilution; Grifols) for 15 min at 4 °C and incubated with an hEpCAM-APC antibody (Miltenyi Biotec) or an appropriate isotype-matched control antibody (IgG2a-APC, BD Bioscience) for 30 min at 4 °C. Then, 96 single cells double positive for hEpCAM-APC and GFP and negative for DAPI (Sigma-Aldrich), were sorted in a BD FACSAria™ II into a 96-well plate containing 100 µl of GTC mix [4.19 M Guanidine thiocyanate; 25 mM Na Citrate pH = 7.3; 15 mM Sarcosyl; 11 mM 2-Mercaptoetanol; 18 µM Glycogen] and plates stored at -80 °C.

RNA extraction, cDNA synthesis and pre-amplification reaction

Individual cells were mixed with 10 µl of 2 M NaOAc and 100 µl 100% Phenol/Chloroform (both at pH 4) and then centrifuged for 13,000 rpm at 4 °C for 10 min. The upper aqueous phase was transferred to low binding tubes and mixed with 2 µl of 5 mg/mL linear acrylamide (Amresco®). After precipitation with isopropanol and ethanol washes, the RNA pellets were resuspended in RNasefree water and immediately reverse-transcribed with SuperScript® VILO cDNA Synthesis Kit (Thermo Fisher Scientific) following manufacturer´s instructions. Then, the cDNAs were mixed with the appropriate primers (CPT1A and HPRT) and a standard PCR using AmpliTaq Gold® 360 Master Mix (Thermo Fisher) was performed.

The ddPCR was performed following the official ddPCR™ application guide performed in a QX100™ Droplet Digital™ PCR system (Bio-Rad). Briefly, mixes containing the pre-amplified cDNA, the specific primers used for each reaction, together with QX200™ ddPCR™ EvaGreen Supermix and QX200™ Droplet Generation Oil for EvaGreen (Bio-Rad), were plated into ddPCR™ cartridges (DG8™ Cartridges for QX200™/QX100™, Bio-Rad) and incubated in a QX100™ Droplet Generator (Bio-Rad). After droplet generation, 40 µl of the generated droplet emulsions were transferred into a new 96-well PCR plate (Eppendorf), foil sealed (PX1™ PCR Plate Sealer, Bio-Rad) and amplified into a C1000 Touch™ Thermal Cycler (Bio-Rad) following manufacturer´s instructions. Following PCR amplification, the plates were measured in a QX100™ Droplet Reader (Bio-Rad) and data analyzed using the software QuantaSoft 1.3.2.0 (Bio-Rad).

BODIPY staining

Cells grown on coverslips were fixed in 4% paraformaldehyde for 30 min at 4°C. Then, the cells were incubated with BODIPY® 493/503 (Thermo Fisher Scientific) at 2.5 µg/mL for 1 h at 4°C, inside a humid chamber. Coverslips were then washed with PBS and mounted with ProLong® with DAPI (1 µg/mL). Images were taken with a Zeiss LSM 710 confocal microscope.

Flow cytometry analysis

The cells were resuspended in blocking buffer (2% FBS, 0.5% BSA in PBS) for 15 min on ice under agitation. The cells were stained for 30 min at 4°C with APC-conjugated anti-CD133 or anti-Epcam antibodies (BioLegend, San Diego, USA) or the corresponding control immunoglobulin G1 antibody (IgG1, BioLegend). When indicated, the cells were also incubated for 15 min at 4°C (LD540, Nile red, LipidTOX™, Bodipy®) or 90 min at 37°C (FAO Blue) (Table 2 ). Annexin V-FITC staining was performed on attached and floating cells according to the manufacturer’s instructions (550474 & 556454, BD Biosciences, San Diego, CA, USA). Zombie Violet Dye (77477, Biolegend) or DAPI were used to exclude nonviable cells. A total of 50,000 cells per sample were analyzed using a FACSCanto II (BD, Franklin Lakes, NJ, USA) or ImageStream X Mark II (Amnis, Seattle, WA, USA) and analyzed with FlowJo 9.2 software (Ashland, OR, USA).

XF extracellular flux analyzer experiments

The oxygen consumption rate (OCR) was determined by using the XF Mito Cell Stress Test, XF Long Chain Fatty Acid Oxidation Stress Test, or Palmitate Oxidation Stress Test (Agilent Technologies, Santa Clara, CA, USA). A total of 30,000 cells per well were cultured in an XF 96-well cell culture microplate (Agilent Technologies) previously coated with Cell-Tak (BD Biosciences) in growth medium for 24 h. Then, the cells were incubated for 1 h in base assay medium (D5030, Sigma Aldrich) supplemented with 2 mM glutamine, 10 mM glucose, and 1 mM pyruvate at 37 °C. The concentrations of Oligomycin and FCCP were adjusted for each primary cell type as follows: Oligomycin, 1.2 mM for 215 and 253 cells; and 0.8 µM for 354 cells; FCCP 1.2 µM for 215 and 253; and 0.4 µM for 354 cells. Oligomycin, FCCP, Rotenone (1 µM) and Antimycin A (1 µM) were dissolved in DMSO (all from Sigma-Aldrich). For the Long Chain Fatty Acid Oxidation Stress Test, Etomoxir (40 or 100 µM) or Ranolazine (50 µM) were injected into port A prior to Oligomycin, FCCP and Rotenone + Antimycin. For the Palmitate Oxidation Stress Test, cells were incubated for 1 h with FAO medium containing 1 mM glutamine, 2.5 mM glucose and 0.5 mM carnitine, and BSA-palmitate (100 µM) was injected in port A prior to Oligomycin, FCCP and Rotenone + Antimycin.

The percentage of complex I inhibition was calculated as the percentage of OCR inhibited upon compound injection with respect to the inhibition obtained with Rotenone, the latter used as 100%, as described previously [ 7 ]. Experiments were run in a XF96 analyzer (Seahorse Bioscience, Agilent Technologies), and the raw data were normalized to the protein content using the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific).

IncuCyte live-cell assay

Cells were seeded at 20,000 cells/well in 96-well culture plates in 100 µL complete RPMI medium. After 24 h, the cells were subjected to different treatments together with LD540 (25 ng/ml) or Annexin V-FITC (1:100) and incubated for 15 min, after which the plate was inserted into an IncuCyte® Live-Cell Analysis System (Sartorius, Göttingen, Germany) for real-time imaging, with two fields imaged per well at 10x magnification every 2 h for a total of 4 days. The fluorescence and confluence data were analyzed using IncuCyte® Confluence version 2021 C software. The data shown represent fluorescence normalized to confluence at each time point.

For cytosolic ATP, PDX265 and 215 cells were infected with the Incucyte® CytoATP Lentivirus Reagent Kit following the manufacturer’s instructions and subjected to puromycin selection to generate a stably expressing population. PDX cells stably expressing CytoATP or the non-binding control were seeded at 10,000 cells/well in a 96-well microplate. After 24 h, the cells were exposed to different treatments and the ATP ratio was analyzed by luminescence using IncuCyte® Confluence version 2021 C software.

Sphere formation assay

A total of 10 4 cells were seeded in triplicate in sphere medium using polyHEMA-coated 24-well plates in the presence of different treatments. Seven days later, spheres were harvested for subsequent assays or counted with an inverted EVOS FL microscope (Thermo Fisher Scientific) using a 10x objective with phase contrast.

Colony formation assay

For the colony formation assay (CFA), 500 or 1000 cells were seeded per well in 2 mL of complete RPMI medium. After 24 h, the treatments were added to sphere medium. The media and treatments were refreshed every 7 days. After 21 days, the cells were stained with crystal violet (Acros Organics, Thermo Fisher Scientific) and the number of colonies was manually counted.

The cell pellets were washed with PBS, resuspended in ultrapure water (10977035, Invitrogen) and frozen for lysis. ATP was quantified using an ATP Determination Kit (A22066, Invitrogen) following the manufacturer’s instructions. Bioluminiscence was determined using a Synergy™ HT Multi-Mode Microplate Reader (BioTek, Winooski, Vermont, USA). The results were normalized to the protein concentration measured for the same samples with the Pierce™ BCA Protein Assay Kit.

Western blot

Cell pellets were lysed with RIPA buffer (Sigma Aldrich) supplemented with protease and phosphatase inhibitors (both from Alfa Aesar, Thermo Fisher Scientific). The protein concentration was determined with the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific). Proteins (30 µg protein/lane) were separated on Novex™ WedgeWell™ 10% Tris-Glycine precast gels using BenchMark™ pre-stained protein ladder (Invitrogen) and transferred to PVDF membranes (Thermo Fisher Scientific). The membranes were blocked in 5% BSA-1X PBS-0.1% Tween 20 (Thermo Fisher Scientific) for 1 h at room temperature and incubated overnight at 4°C with the following primary antibodies: pAMPK (Thr172), AMPK (both from Cell Signaling Technology, Inc, Danvers, MA, USA), NANOG, DGAT1, MGLL, PPARD (all from Santa Cruz Biotechnology, Inc, Dallas, Texas, USA) and β-actin as loading control (clone AC74, Sigma Aldrich). Afterwards, the membranes were subjected to peroxidase–conjugated secondary antibody (Invitrogen) and developed by chemiluminescence (Pierce™ ECL Western Blotting Substrate) using CL-XPosure™ (Thermo Fisher Scientific) films. The bands were analyzed using ImageJ software.

Metabolic activity

A total of 10 4 cells were seeded in triplicate in 96-well plates 24 h before treatment. 72 h after treatment, resazurin (Alfa Aesar) was added to the cells at a concentration of 10 µM in 1X PBS and the cells were incubated for 1 h at 37°C with 5% CO 2 . The fluorescence was assessed according to the manufacturer’s instructions by using a Synergy HT plate reader.

In vivo experiments

Mice were housed according to institutional guidelines and all experimental procedures were performed in compliance with the institutional guidelines for the welfare of experimental animals as approved by the University of Zaragoza Ethics Committee (CEICA PI22/17, PI35/19, PI41/20) and in accordance with the guidelines for Ethical Conduct in the Care and Use of Animals as stated in The International Guiding Principles for Biomedical Research involving Animals, developed by the Council for International Organizations of Medical Sciences (CIOMS).

For the ddPCR, a total of 5 × 10 4 PDX354-GFP cells were orthotopically injected into NU-Foxn1nu (Charles River, UK) nude mice ( n = 8). Once the mice showed signs of disease, they were humanely sacrificed, and pancreatic tumors and total blood were harvested.

For the tumorigenicity assay (Extreme-Limiting Dilution Assay, ELDA), cells were treated in vitro for 48 h, trypsinized and resuspended in sphere medium with Matrigel™ (Corning) (50:50). Two cell densities (10 4 and 10 3 ) were subcutaneously injected into both the top and bottom flanks of six week-old Foxn1 nu nude mice of both sexes ( n = 4 mice per group, n = 8 injections per group). Tumor size was monitored once a week using a digital caliper and tumor volume was calculated using the formula (length*width 2 )/2. All the mice were sacrificed at the same time, when one of the tumors had reached the humane endpoint. ELDA calculations were performed at https://bioinf.wehi.edu.au/software/elda/ .

For in vivo treatment, tumor pieces of approximately 15 mm 3 were soaked in Matrigel™ prior to subcutaneous implantation in both flanks of six week-old Foxn1 nu nude female mice (Envigo, IN, USA) ( n = 5 mice per group, n = 10 implants per group) under isofluorane-induced anesthesia. When the tumor size was approximately 300 mm 3 , the mice were treated with 70 mg/kg Gemcitabine (i.p.) once a week for three weeks with or without 130 mg/kg Ranolazine or the corresponding dose of vehicle (PBS) (oral gavage) once a day until the endpoint. Tumor size was monitored twice a week using a caliper and tumor volume was calculated using the formula (length*width 2 )/2. After 13 weeks, when the control tumors had reached the humane endpoint criteria (maximum 1000 mm 3 ), the mice were euthanized, the tumors were collected and weighed and pictures were taken. A small piece of tumor tissue was processed for RNA to assess lipid metabolism genes by RTqPCR as described above, or for protein extraction to assess p-AMPK/AMPK by Western blot. The remaining tumor tissues were dissociated as previously reported [ 7 ] and stained with EpCAM-FITC, CD133-PE and CD44-APC antibodies for FACS analysis as described above.

Statistical analysis

The data are expressed as the mean value of at least three experiments ± SEM. The statistical analysis was carried out with GraphPad Prism 8 (GraphPad Software Inc, USA). The significant differences were determined using, in general, analysis of variance (ANOVA, Chicago, IL, USA) and post hoc Bonferroni correction or Kruskal-Wallis tests, depending on the results of the Shapiro-Wilk normality test; p < 0.05 was considered to indicate statistical significant. Significant differences were classified as follows: ∗ : p < 0.05; ∗ ∗ : p < 0.01; ∗ ∗ ∗ : p < 0.001.

Lipid metabolism genes are linked to stemness in PDAC

To explore the possible association of lipid metabolism with aggressiveness and stemness in PDAC, we first carried out correlation analyses of human gene expression data from the TCGA and GTEx datasets (normal pancreas/PDAC). For these analyses, we included all the genes related to lipid metabolism (GO pathways: sphingolipid acid metabolic process, fatty acid oxidation, fatty acid biosynthetic process, fatty acid metabolic process, medium/long-chain fatty acid catabolic process, positive/negative regulation of fatty acid biosynthetic process and fatty acid transporters) and our established stemness signature composed of the combination of NANOG , KLF4 , OCT3/4 and SOX2 [ 7 ]. Among the genes with greater correlations with stemness, we identified lipoprotein receptors ( LDLR , APOBR , APOER2 ), fatty acid transporters ( SLC27A1 , SLC27A4 ), genes related to LD maintenance and metabolism ( LPCAT2 , LPCAT4 , MGLL , DGAT1 ), sphingolipid and arachidonic acid metabolism ( SPTLC2 , SGPP2 , PTGES3 , ALOX5 , PLA2G4 , TBXAS1 ), FAO ( PEX13 , HADHA , BDH2 , ASAH1 ) and key general lipid metabolism regulators ( PPARD , PPARG ) (Fig. 1 A). Importantly, all these genes were commonly overexpressed in PDAC tissue compared to normal tissue and we found that most of these overexpressed genes were predictive of poor overall survival in PDAC patients, which further suggests their important role in the aggressiveness of PDAC. Next, we further corroborated our finding that lipid metabolism genes with a greater correlation with stemness in PDAC patients were also significantly upregulated in PaCSC-enriched cultures (spheres, with increased expression of stemness genes and CD133 (Figure S1 )) from five primary PDAC PDXs compared to their non-CSC counterparts (Fig. 1 B). Figure 1 C depicts some of the genes commonly upregulated in PDAC vs. normal tissue and CSCs vs. differentiated cells in their corresponding cellular pathways.

Lipid metabolism genes correlate with stemness in PDAC. ( A ) Bioinformatics analyses of lipid metabolism genes in human data included in the TCGA and GTEx project databases, were carried out with the webserver GEPIA2 ( http://gepia2.cancer-pku.cn/ ). The genes were sorted by their correlation (R, first column) with a stemness signature composed of the genes NANOG , KLF4 , OCT3/4 and SOX2 . Data relative to expression in tumor vs. normal tissue and overall survival in the highest and lowest expression quartiles for each gene are shown in the second and third columns, respectively. ( B ) Heatmap of lipid metabolism genes overexpressed in PaCSC-enriched cultures (spheres) from five PDAC PDXs (A6L, 185, 215, 253, 354), evaluated by RTqPCR. In bold, genes determined in A with R > 0.7 or significantly correlated with poor survival. ( C ) Schematic representation of the main genes and pathways correlated with stemness in PDAC. Genes indicated in bold in A and B are highlighted in red in the drawing, indicating their corresponding pathways. FA: Fatty acid; LD: Lipid droplets; TAG: Triglycerides. The data are presented as the mean ± SEM of at least three independent experiments. * p < 0.05; *** p < 0.001

Increased lipid storage and metabolism in PaCSCs

To confirm that PaCSC overexpress genes related with lipid storage in LDs, we first analyzed the LD content in our PDX models. The LD content was heterogeneous, with only a minority of cells showing significant enrichment of LDs, both in vitro (Fig. 2 A) and in vivo (Fig. 2 B). Interestingly, PaCSC-enriched conditions (spheres or CD133 + cells) showed a significantly greater LD content than non-CSCs (adherent cells or CD133 − ) (Fig. 2 C and D), suggesting that the differential distribution of lipid content is dependent on stemness. We also confirmed this observation in a highly metastatic PDAC PDX model (CTCA), derived from circulating tumor cells of a stage IV PDAC patient (Fig. 2 D). Interestingly, the percentage of cells with high lipid content was significantly greater in CTCs isolated from the blood of mice bearing orthotopic PDX tumors than in cells isolated from the primary tumor (Fig. 2 E), implying a potential survival advantage in blood for cells with high lipid storage. Single-cell ddPCR analysis of CTCs demonstrated significant CPT1A overexpression, particularly in the CSC compartment of CTCs (Fig. 2 F). Considering the important regulatory role of CPT1A in FAO via mitochondrial uptake of long-chain FAs, our results suggest that PaCSCs with active FA metabolism could be capable of initiating metastasis.

Increased lipid storage in PaCSCs. ( A ) Confocal microscopy of PDX215 and 354 stained with BODIPY (green) and DAPI (blue). ( B ) Imaging flow cytometry of a fresh PDX215 tumor stained with BODIPY (neutral lipids, green), EpCAM (human PDAC cells, red) and DAPI (live/dead cells). Left, representative flow cytometry plot for BODIPY and EpCAM expression in DAPI negative cells. Right, representative images of two individual cells. ( C ) Flow cytometry of Nile red staining (lipid droplets) in the indicated PDXs cultured in monolayers (adherent) or spheres. Left, representative plots. Right, pooled data. ( D ) Flow cytometry of LD540 staining (lipid droplets) in CD133 - and CD133 + populations in the indicated PDXs. Left, representative histograms. Right, pooled data. ( E ) Imaging flow cytometry of cells isolated from the blood (CTCs) or primary tumors (Tumor) of mice bearing orthotopic PDX354-GFP tumors ( n = 4). The cells were stained with LipidTox (neutral lipids, magenta), EpCAM (red) and DAPI (blue). Left, representative images of two individual CTCs. Right, quantification of the percentage of LipidTox + cells in the blood and primary tumors of each mouse. ( F ) Single-cell mRNA expression by ddPCR of CPT1A in individual cells isolated from blood (CTCs), classified as non-CSCs and CSCs based on the expression of the pluripotency genes NANOG , KLF4 , OCT3/4 and SOX2 . The data are presented as mean ± SEM of at least three independent experiments. ** p < 0.01; *** p < 0.001

Next, we aimed to functionally validate our expression data, which suggested increased FAO activity in PaCSCs. FAO Blue staining assessed by flow cytometry confirmed that CD133 + cells exhibited increased FAO activity, which was abrogated by the CPT1A inhibitor Etomoxir (Fig. 3 A). In addition, we measured the oxygen consumption rate (OCR) upon sequential injections of Etomoxir in cells previously grown either in adherent or in CSC-enriched sphere conditions. We found a greater inhibition of the OCR in CSC-enriched cultures (spheres) upon treatment with Etomoxir than in adherent cells (Fig. 3 B). When cells were treated for Etomoxir for 30 min and subjected to the Long Chain FAO Stress Test, only sphere-derived cells exhibited a significant reduction in ATP-linked and maximal respiration, as well as spare respiratory capacity (Fig. 3 C), suggesting increased FAO-dependent mitochondrial respiration in PaCSCs. Moreover, injection of the FA palmitate led to a significant increase in mitochondrial respiration parameters in sphere-derived cells only (Fig. 3 D). These results suggest that PaCSCs exhibit enhanced FA metabolism compared with differentiated cells, both at the basal level and upon FA supplementation.

PaCSCs show enhanced mitochondrial FAO. ( A ) Median fluorescence intensity of FAO Blue staining as assessed by flow cytometry in cells gated for CD133 expression. Pooled data for PDX185 and 354 cells. ( B ) Oxygen consumption rate (OCR) by Seahorse analysis upon consecutive injections of 40 µM Etomoxir (Eto) and a final injection of the complex III inhibitor Antimycin A and the complex I inhibitor Rotenone (A + R, 1 µM). Left panel, kinetics in adherent vs. sphere-derived PDX215 cells. Right, percentage of mitochondrial OCR inhibition by Etomoxir, with respect to A + R, considered as 100% inhibition. ( C ) Long Chain FAO Stress Test in adherent and sphere-derived PDX215 cells pre-treated for 30 min with Etomoxir. Left, OCR kinetic profile. O, ATP synthase inhibitor Oligomycin; F, mitochondrial oxidative phosphorylation uncoupler FCCP. Right, ATP-linked and maximal respiration, and spare respiratory capacity, shown as percentages of the control for each condition. ( D ) Palmitate Oxidation Stress Test in adherent vs. sphere-derived PDX215 cells upon injection of palmitic acid conjugated with BSA (BSA-PA, 100 µM). Left panel, OCR kinetic profile. ATP-linked, maximal respiration and spare respiratory capacity are shown as percentages of the control for each condition. The data are presented as mean ± SEM of at least three independent experiments. * p < 0.05; *** p < 0.001

Fatty acid supplementation promotes CSC features in PDAC

Free FAs are essential sources of energy within cells. Among them, the saturated nonesterified FA palmitic acid (PA), the monounsaturated FA oleic acid (OA) and the polyunsaturated FA linolenic acid (LNA) are the most common. Interestingly, treatment for 48 h with PA, OA and LNA in 2D cultures increased the expression of CPT1A , PPARD, LPCAT4 , DGAT1 and MGLL (Fig. 4 A), suggesting that exogenous FA supplementation may be able to induce the expression of genes that we previously found to be correlated with a high stemness signature (Fig. 1 A). These changes were confirmed at the protein level, together with increased expression of the stemness marker NANOG (Fig. 4 B). Considering these results together with the increased OXPHOS in PaCSCs upon incubation with PA (Fig. 3 D), we next evaluated the effects of FA supplementation on CSC functionality. First, we confirmed by flow cytometry and live microscopy that treatment with exogenous FAs increased the LD content in PDAC cells (Fig. 4 C and D). Interestingly, lipid storage in LDs was particularly relevant in the CD133 + population (Fig. 4 E and data not shown). Treatment with exogenous FAs significantly increased the sphere and colony formation abilities of different PDX models (Fig. 4 F and G), indicating enhanced self-renewal capacity. Crucially, in vitro pretreatment with FAs also enhanced in vivo tumorigenicity, as assessed by ELDA (Fig. 4 H). Notably, the most consistent results were obtained with OA supplementation, while PA and LNA only showed significant results in some of the assays used. In summary, we concluded that supplementation with free FAs increases the accumulation of LDs and enhances self-renewal capacity in vitro and tumorigenicity in vivo.

Fatty acid supplementation enhances CSC functionality. ( A ) Gene expression determined by RTqPCR of the indicated genes after 48 h of treatment with palmitic acid (PA) (50 µM), oleic acid (OA 100 µM) or linolenic acid (LNA) (200 µM). ( B ) Western blot analysis of PPARD, DGAT1, MGLL and NANOG after 48 h of treatment with OA at 50 and 100 µM. Left, representative experiment in PDX185. Right, densitometric analyses of Western blots from PDX185 and PDX354. β-Αctin was used as loading control. ( C ) Representative flow cytometry plots of LD540 for PDX253 cells treated with increasing concentrations of OA (1 and 5%) for 24 h. ( D ) LD540 staining by IncuCyte imaging for PDX185 cells upon treatment with PA, OA or LNA as indicated in A. Left, representative images. Right, quantification, calculated as a percentage of the control conditions. ( E ) LD540 staining by flow cytometry in CD133 + cells from the indicated PDX models treated as described in A. ( F ) Sphere formation after seven days of treatment with PA, OA or LNA. Left, representative phase contrast images. Right, quantification, shown as a percentage of the control conditions. ( G ) Colony formation after 21 days of treatment with PA, OA or LNA. Left, representative images of colonies stained with crystal violet. Right, quantification of colony number, shown as compared percentage of the control conditions. ( H ) In vivo ELDA upon injection of the indicated number of PDX185 cells pretreated with PA, OA or LNA as indicated in A. Pictures of the tumors at the endpoint. ELDA calculations were performed on https://bioinf.wehi.edu.au/software/elda/ . The data are presented as mean ± SEM of at least three independent experiments. * p < 0.05; ** p < 0.01; *** p < 0.001

Suppression of FAO activity in PDAC disrupts self-renewal and tumorigenic potential through the induction of energy stress

Considering that CSC-enriched cultures showed enhanced FAO activity and that FA supplementation increased stemness in PDAC cells, we next evaluated whether stemness is critically related to FAO and whether the respective inhibitors may be a potential new treatment strategy. For this purpose, we treated PDAC cells for 48 h with Etomoxir [ 18 ], Mildronate (carnitine synthesis inhibitor) [ 19 ], Perhexiline (CPT1/CPT2 dual inhibitor) [ 18 ] and Ranolazine (3-ketoacyl-CoA thiolase inhibitor) [ 18 ]. Interestingly, while Mildronate and Perhexiline were not effective, Etomoxir and Ranolazine both significantly reduced the percentage of CD133 + cells (Fig. 5 A). However, this effect was not accompanied by a reduction in LD content, neither in the CSC population (Fig. 5 B) nor in the total cell population (Figure S2 A). Although only Etomoxir significantly increased apoptosis in the CSC population (Fig. 5 C), but not in the total cell population (Figure S2 B), treatment with either Etomoxir or Ranolazine consistently reduced sphere formation (Fig. 5 D) and colony formation in vitro (Fig. 5 E) and tumorigenicity in vivo (Fig. 5 F). Therefore, Etomoxir and Ranolazine are able to target CSC functionality in PDAC.

FAO inhibition impairs PaCSC functionality. Unless otherwise specified, the cells were treated for 48 h with Etomoxir (Eto) (200 µM), Mildronate (Mild) (100 µM), Perhexiline (Perh) (1 µM) or Ranolazine (Rano) (100 µM). ( A ) Percentage of CD133 + cells by FACS. ( B ) LD540 staining of CD133 + cells. ( C ) Annexin V staining in CD133 + cells. Left panels, mean value of each cell type. Right panels, pooled data. ( D ) Sphere formation assay after seven days of treatment with the inhibitors. ( E ) Colony formation assay after 21 days of treatment with inhibitors. Upper panel representative images of crystal violet staining of PDX185 cells. Lower panel, colony number quantification. ( F ) Tumors at the endpoint after the inoculation of pretreated PDX185 cells. ELDA calculations were performed at https://bioinf.wehi.edu.au/software/elda/ . The data are presented as mean ± SEM of at least three independent experiments. The dashed lines represent the control conditions. * p < 0.05; ** p < 0.01; *** p < 0.001

Interestingly, both compounds inhibited mitochondrial oxygen consumption rate when administered acutely, impacting mitochondrial respiration parameters (Fig. 6 A). These effects were durable, since we detected mitochondrial OCR inhibition even after 72 h of treatment (Fig. 6 B). As expected, the inhibition of mitochondrial activity resulted in a significant decrease in intracellular ATP (Fig. 6 C and D) and a subsequent significant increase in AMPK stress kinase phosphorylation (Fig. 6 E). In summary, FAO inhibition impairs CSC functionality by inhibiting mitochondrial respiration and consequently inducing energy stress.

Treatment with Etomoxir and Ranolazine impairs mitochondrial respiration, inducing energy stress. ( A ) Long Chain FAO Stress Test with acute injections of 100 µM Etomoxir (Eto) or 50 µM Ranolazine (Rano). Left, Oxygen Consumption Rate (OCR) kinetics. Right, ATP-linked, maximal and spare respiration. Pooled data for 354 and CTCA cells. O, Oligomycin; F, FCCP; A + R, Antimycin A + Rotenone. ( B ) Mito Stress after 72 h of treatment of PDX215 cells with Etomoxir or Ranolazine. Left, OCR kinetics. Right, ATP-linked, maximal and spare respiration. ( C ) Cytosolic ATP levels in PDX215 cells visualized by time-lapse fluorescence microscopy (0–96 h). Left, representative images at the indicated times. Right, quantification. ( D ) Total ATP levels quantified by bioluminescence at 48 h. ( E ) Western blot analysis of phospho-AMPK and total AMPK after 48 h of treatment. Left, representative experiment in PDX185. Right, densitometric analysis. β-Αctin was used as loading control. The data are presented as mean ± SEM of at least three independent experiments. * p < 0.05; ** p < 0.01; *** p < 0.001

Fatty acid metabolism and response to Gemcitabine

Finally, we explored whether modulating FAO activity also affects another key feature of CSCs, i.e., chemoresistance [ 4 , 5 ]. Indeed, pretreatment with exogenous FAs for 24 h increased the IC50 of the chemotherapeutic agent Gemcitabine (Fig. 7 A), thus attenuating the effects of the drug on self-renewal (Fig. 7 B). Consistently, OA supplementation protected PDAC cells from Gemcitabine-induced apoptosis, as measured by FACS or IncuCyte (Fig. 7 C and D, S3 A). In contrast, cotreatment with either Etomoxir or Ranolazine significantly enhanced the response to Gemcitabine (Fig. 7 D). Interestingly, the chemosensitizing effect of Etomoxir or Ranolazine was also observed when lipid inhibitors were applied after treatment with Gemcitabine for 48 h (Fig. 7 E and Figure S3 B). Importantly, in vivo treatment with Gemcitabine (3 weeks) combined with Ranolazine (until the endpoint) significantly delayed tumor growth rate, but only at late time points when the tumors reached approximately 2.5-3 times the initial tumor size (Fig. 7 F). At the endpoint, the number of CD44 + /CD133 + CSCs was significantly lower in the Ranolazine-treated tumors (Fig. 7 G). Interestingly, the expression profiles of lipid metabolism genes significantly changed in the Ranolazine-treated tumors (Fig. 7 H), and the p-AMPK/AMPK ratio increased (Fig. 7 I), suggesting that tumors were suffering metabolic stress similar to what we observed in vitro (Fig. 6 E). Therefore, combined treatment with FAO inhibitors and Gemcitabine improves the response to Gemcitabine alone in PDAC PDXs, providing a new perspective for a more effective treatment.

Fatty acid metabolism determines the response to Gemcitabine. ( A ) Percentage of metabolic activity determined by resazurin measurement and the IC50 of Gemcitabine after 72 h of treatment alone and in combination with palmitic acid (PA) (50 µM), oleic acid (OA) (100 µM) or linolenic acid (LNA) (200 µM) (pretreatment 24 h). ( B ) Sphere formation assay of cells pretreated with FAs for 24 h followed by treatment with Gemcitabine (1000 nM) under sphere forming conditions. ( C ) Annexin V levels were determined by flow cytometry after 48 h of treatment with PA, OA or LNA with or without 100 nM Gemcitabine. Pooled data of the indicated PDXs. ( D ) Annexin V levels were measured by IncuCyte imaging at 48 h in PDX185. ( E ) Annexin V measured by Incucyte imaging after 48 h treatment with Gemcitabine and 72 h additional hours in the presence of absence of FAO inhibitors in PDX185. In F-I, tumor pieces of PDX185 were subcutaneously implanted in nude mice, and when they reached around 300 mm 3 they were treated for 3 weeks with Gemcitabine (70 mg/kg) with or without Ranolazine (130 mg/kg), which was administered daily for the whole duration of the experiment. ( F ) Tumor size is shown as the fold change vs. day 1 of treatment. ( G ) CSC content was measured as the percentage of CD44 + /CD133 + double positive cells in tumors at the endpoint. ( H ) Lipid metabolism genes determined by RTqPCR. (I ) Western blot analysis of phospho-AMPK and total AMPK in total lysates from tumors at the endpoint. β-Αctin was used as loading control. The data are presented as mean ± SEM of at least three independent experiments. * p < 0.05; ** p < 0.01; *** p < 0.001

The concept of metabolic rewiring is now widely accepted as one of the hallmarks of cancer [ 20 ], but it has increasingly been recognized as a highly complex and intertwined process in recent years. Indeed, metabolic heterogeneity within tumors, related to fluctuating local microenvironments and distinct cellular populations, is becoming apparent. Specifically, we (and others) have described the unique metabolic features of the CSC population in PDAC compared to their more differentiated progenies: while CSCs acquire a more glycolytic metabolism in some cancer types [ 21 , 22 ], most CSCs, including PaCSCs, rely on mitochondrial OXPHOS for maintenance of stemness [ 7 , 23 , 24 ]. Additionally, recent reports also highlight lipid metabolism as one of the main metabolic pathways regulating CSC functions in several tumor types [ 25 ]. Here, we describe the significance of mitochondrial FAO activity in regulating the self-renewal, tumorigenicity and, most importantly, chemoresistance of PaCSCs. Our findings are in line with the essential role of mitochondrial respiration in stemness in PDAC, as OXPHOS requires acetyl-CoA as a substrate for ATP production, which can be obtained not only via glucose metabolism but also through lipid catabolism via FAO.

Increased lipid uptake and aberrant FA metabolism have previously been associated with tumor progression and poor prognosis in PDAC patients [ 11 , 12 , 13 ]. Indeed, we found that several genes related to FA transport, storage and metabolism are correlated with a stemness signature in PDAC patients, some of which are significantly correlated with overall survival (Fig. 1 ). These genes were overexpressed under CSC-enriched culture conditions in several PDAC PDX models, strongly suggesting a causal relationship between active lipid metabolism and the malignant properties of CSCs. Among these genes, MGLL [ 26 , 27 ], PPARD [ 28 ] and CPT1A [ 29 ] have been correlated with poor prognosis in various cancers. Indeed, increased CPT1A expression specifically in CSCs has been associated with worse outcomes in breast [ 30 ] and colon [ 31 ] cancer, suggesting a key role for mitochondrial FAO activity in CSC functionality. Our results demonstrate that this gene expression profile indeed translates into enhanced FAO activity in CD133 + and sphere-derived cells in PDAC, as previously demonstrated for CSCs in breast cancer [ 32 ] and hepatocellular carcinoma [ 33 ], where NANOG directly upregulates the expression of genes related to FAO.

Our results further indicated increased lipid accumulation in LDs of CD133 + PaCSCs (Fig. 2 ), corroborating our initial gene expression analyses. While similar results were previously reported in vitro for CSCs isolated from primary cultures of colorectal cancer [ 34 ] and breast and ovarian cancer cell lines [ 35 , 36 ], we now also demonstrate differential LD storage for CSCs isolated from fresh tumors. Interestingly, we also detected an enrichment in LD content and CPT1A expression in CTCs isolated from an orthotopic PDX model. CPT1A overexpression was particularly prominent in circulating PaCSCs compared to primary tumors, suggesting a survival advantage for cells with increased lipid storage and metabolism in the blood. In fact, LD staining has been reported to significantly improve CTC detection in a prostate cancer model [ 37 ], suggesting that a similar approach might improve CTC detection in liquid biopsies from PDAC patients.

The addition of exogenous FAs upregulated the expression of several genes involved in lipid transport, storage and metabolism similar to their upregulation in sphere-derived cells (Fig. 4 A and B). Interestingly, lipid droplet accumulation upon exposure to FAs was especially relevant for CD133 + PaCSCs (Fig. 4 C and D). Moreover, PaCSCs also showed a significant increase in FAO activity coupled to mitochondrial respiration in response to PA (Fig. 3 C). This preferential effect of exogenous FAs on CSC metabolism translated into significantly increased self-renewal (measured as sphere and colony formation; Fig. 4 F and G) and in vivo tumorigenicity (Fig. 4 H). Notably, incubation with OA most consistently enhanced self-renewal and chemoresistance, while the effects of incubation with PA and LNA were more variable (Figs. 4 F-H and 7 A-D) and incubation with linoleic acid did not have any significant effect (data not shown). In this sense, our data are in agreement with a previous report suggesting that a panel of FAs exerted differential effects on PDAC viability and growth in vitro and in vivo [ 38 ]. Nevertheless, our results strongly suggest that FA supplementation promoted stemness in PDAC, in agreement with previous reports in different cancer types. OA supplementation was previously shown to maintain self-renewal in breast CSCs treated with soraphen A, a de novo FA synthesis inhibitor [ 39 ]. A similar phenotype has been reported for metastasis initiation in oral carcinomas, melanoma and breast cancer where a high-fat diet or PA supplementation favored sphere formation and tumor initiation in metastatic sites [ 40 , 41 , 42 ]. However, epigenetic histone modifications and the activation of cellular signaling pathways independent of energy production seem to mediate the prometastatic effects of PA in these models.

In addition, FA supplementation, particularly OA, also protected PDAC cells from the cytotoxic effects of Gemcitabine treatment (Fig. 7 A-D, Fig. S3 A). Therefore, our data add to the growing body of evidence indicating that FAs support chemoresistance in different cancer types. Specifically, OA reduced the cytotoxic effects of docetaxel treatment in prostate cancer cells [ 43 ], while PA supplementation protected against cisplatin treatment in ovarian cancer [ 44 ], both via activation of ERK/AKT-mediated survival signals. On the other hand, saturated FAs modulate the response to 5-fluorouracil in colorectal cancer by regulating cell membrane fluidity [ 45 ].

Although we cannot formally exclude the relevance of other protective effects in our experimental settings, our results clearly indicate that FA-mediated chemoresistance in pancreatic PDX models depends on FA catabolism, as FAO inhibition restores response to Gemcitabine (Fig. 7 E, S2 B). This finding suggested that FAO inhibitors may be successful adjuvant drugs for improving PDAC chemotherapy efficacy. Considering that Etomoxir is not suitable for use in humans, we also tested alternative FAO inhibitors that are already approved in different countries for the treatment of angina pectoris: Mildronate (carnitine synthesis inhibitor) [ 46 ], Perhexiline (CPT1/CPT2 dual inhibitor) [ 18 ] and Ranolazine (3- ketoacyl -CoA thiolase inhibitor) [ 47 ]. Among these inhibitors, Ranolazine was the only compound that significantly reduced CD133 + expression, self-renewal and in vivo tumorigenicity, similar to Etomoxir (Fig. 5 ). We confirmed that the mechanism of action of these inhibitors was OXPHOS inhibition, which decreased oxygen consumption and mitochondrial spare respiratory capacity, resulting in a significant reduction in ATP production, and an increase in the pAMPK/AMPK ratio. Indeed, FAO has been shown to contribute to the mitochondrial spare respiratory capacity associated with survival under chemotherapeutic stress conditions [ 48 , 49 ]. Interestingly, Ranolazine sensitized PDAC cells to Gemcitabine both in vitro and in vivo (Fig. 7 ), similar to previous results for human leukemia cells [ 50 , 51 ], prostate cancer [ 47 ], and melanoma [ 52 ], where enhanced OXPHOS via FAO was also described as the main mechanism for resistance to apoptosis induced by chemotherapy and immunotherapy both in vitro and in vivo.

Interestingly, the fact that not only FA synthesis but also external supplementation of FA resulted in increased chemoresistance, suggests an interesting link between a high-fat diet/obesity and chemoresistance. Indeed, Incio et al. demonstrated that obesity reduced drug delivery and toxicity in PDAC [ 53 ]. Remarkably, a previous publication reported that cross-talk between adipose tissue and chronic myeloid leukemia cells results in lipolysis to fuel FAO, inducing chemoresistance in chronic myeloid leukemia cells [ 54 ]. Further research in this area may improve current treatment designs that also consider systemic metabolism, which has become a topic of particular interest in the recent years.

Conclusions

Our results demonstrated that PaCSCs accumulate lipids that serve as substrates for FAO to sustain mitochondrial respiration, which is necessary for maintaining stemness. CSC functionality such as self-renewal and tumorigenicity can be enhanced via FA supplementation or reduced by pharmacological inhibition of FAO. Importantly, we showed that existing FAO inhibitors approved for clinical use under other conditions (e.g. Ranolazine) could represent potential tools for overcoming pancreatic CSC-associated chemoresistance.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Cancer stem cell

Circulating tumor cells

Hazard ratio

Lipid droplet

Linolenic acid

Oxygen consumption rate

Oxidative phosphorylation

Palmitic acid

Pancreatic cancer stem cells

Pancreatic ductal adenocarcinoma

Patient-derived xenografts

Kleeff J, Korc M, Apte M, et al. Pancreatic cancer. Nat Rev Dis Primers. 2016;2(1):16022. https://doi.org/10.1038/nrdp.2016.22 .

Article PubMed Google Scholar

Sung H, Ferlay J, Siegel RL, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660 .

Article CAS PubMed Google Scholar

Rawla P, Sunkara T, Gaduputi V. Epidemiology of pancreatic Cancer: global trends, etiology and risk factors. World J Oncol. 2019;10(1):10–27. https://doi.org/10.14740/wjon1166 .

Article PubMed PubMed Central Google Scholar

Ishiwata T, Matsuda Y, Yoshimura H, et al. Pancreatic cancer stem cells: features and detection methods. Pathol Oncol Res. 2018;24(4):797–805. https://doi.org/10.1007/s12253-018-0420-x .

Li C, Heidt DG, Dalerba P, et al. Identification of pancreatic Cancer stem cells. Cancer Res. 2007;67(3):1030–7. https://doi.org/10.1158/0008-5472.CAN-06-2030 .

Amantini C, Morelli MB, Nabissi M, et al. Expression profiling of circulating Tumor cells in pancreatic ductal adenocarcinoma patients: biomarkers Predicting overall survival. Front Oncol. 2019;9. https://doi.org/10.3389/fonc.2019.00874 .

Sancho P, Burgos-Ramos E, Tavera A, et al. MYC/PGC-1α balance determines the metabolic phenotype and plasticity of pancreatic Cancer stem cells. Cell Metab. 2015;22(4):590–605. https://doi.org/10.1016/j.cmet.2015.08.015 .

Courtois S, de Luxán-Delgado B, Penin-Peyta L, et al. Inhibition of mitochondrial dynamics preferentially targets pancreatic Cancer cells with enhanced tumorigenic and invasive potential. Cancers (Basel). 2021;13(4):698. https://doi.org/10.3390/cancers13040698 .

Alcalá S, Sancho P, Martinelli P, et al. ISG15 and ISGylation is required for pancreatic cancer stem cell mitophagy and metabolic plasticity. Nat Commun. 2020;11(1):2682. https://doi.org/10.1038/s41467-020-16395-2 .

Article CAS PubMed PubMed Central Google Scholar

Jagust P, Alcalá S Jr, Heeschen BS, Sancho C. Glutathione metabolism is essential for self-renewal and chemoresistance of pancreatic cancer stem cells. World J Stem Cells. 2020;12(11):1410–28. https://doi.org/10.4252/wjsc.v12.i11.1410 .

Tadros S, Shukla SK, King RJ, et al. De NovoLipid Synthesis facilitates Gemcitabine Resistance through endoplasmic reticulum stress in pancreatic Cancer. Cancer Res. 2017;77(20):5503–17. https://doi.org/10.1158/0008-5472.CAN-16-3062 .

Swierczynski J. Role of abnormal lipid metabolism in development, progression, diagnosis and therapy of pancreatic cancer. World J Gastroenterol. 2014;20(9):2279. https://doi.org/10.3748/wjg.v20.i9.2279 .

Guillaumond F, Bidaut G, Ouaissi M, et al. Cholesterol uptake disruption, in association with chemotherapy, is a promising combined metabolic therapy for pancreatic adenocarcinoma. Proc Natl Acad Sci. 2015;112(8):2473–8. https://doi.org/10.1073/pnas.1421601112 .

Jagust P, de Luxán-Delgado B, Parejo-Alonso B, Sancho P. Metabolism-based therapeutic strategies targeting Cancer Stem cells. Front Pharmacol. 2019;10. https://doi.org/10.3389/fphar.2019.00203 .

Liu Q, Luo Q, Halim A, Song G. Targeting lipid metabolism of cancer cells: a promising therapeutic strategy for cancer. Cancer Lett. 2017;401:39–45. https://doi.org/10.1016/j.canlet.2017.05.002 .

Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–60. https://doi.org/10.1093/nar/gkz430 .

Mueller M, Hermann PC, Witthauer J, et al. Combined targeted treatment to Eliminate Tumorigenic Cancer Stem cells in human pancreatic Cancer. Gastroenterology. 2009;137(3):1102–13. https://doi.org/10.1053/j.gastro.2009.05.053 .

Ma Y, Wang W, Devarakonda T, et al. Functional analysis of molecular and pharmacological modulators of mitochondrial fatty acid oxidation. Sci Rep. 2020;10(1):1450. https://doi.org/10.1038/s41598-020-58334-7 .

Wang JX, Rahimnejad S, Zhang YY, et al. Mildronate triggers growth suppression and lipid accumulation in largemouth bass (Micropterus salmoides) through disturbing lipid metabolism. Fish Physiol Biochem. 2022;48(1):145–59. https://doi.org/10.1007/s10695-021-01040-6 .

Hanahan D. Hallmarks of Cancer: New dimensions. Cancer Discov. 2022;12(1):31–46. https://doi.org/10.1158/2159-8290.CD-21-1059 .

Mamouni K, Kim J, Lokeshwar BL, Kallifatidis G. ARRB1 regulates metabolic reprogramming to promote glycolysis in Stem Cell-Like bladder Cancer cells. Cancers (Basel). 2021;13(8):1809. https://doi.org/10.3390/cancers13081809 .

O’Neill S, Porter RK, McNamee N, Martinez VG, O’Driscoll L. 2-Deoxy-D-Glucose inhibits aggressive triple-negative breast cancer cells by targeting glycolysis and the cancer stem cell phenotype. Sci Rep. 2019;9(1):3788. https://doi.org/10.1038/s41598-019-39789-9 .

Raggi C, Taddei ML, Sacco E, et al. Mitochondrial oxidative metabolism contributes to a cancer stem cell phenotype in cholangiocarcinoma. J Hepatol. 2021;74(6):1373–85. https://doi.org/10.1016/j.jhep.2020.12.031 .

Valle S, Alcalá S, Martin-Hijano L, et al. Exploiting oxidative phosphorylation to promote the stem and immunoevasive properties of pancreatic cancer stem cells. Nat Commun. 2020;11(1):5265. https://doi.org/10.1038/s41467-020-18954-z .

Royo-García A, Courtois S, Parejo-Alonso B, Espiau-Romera P, Sancho P. Lipid droplets as metabolic determinants for stemness and chemoresistance in cancer. World J Stem Cells. 2021;13(9):1307–17. https://doi.org/10.4252/wjsc.v13.i9.1307 .

Ye L, Zhang B, Seviour EG, et al. Monoacylglycerol lipase (MAGL) knockdown inhibits tumor cells growth in colorectal cancer. Cancer Lett. 2011;307(1):6–17. https://doi.org/10.1016/j.canlet.2011.03.007 .

Zhang J, Liu Z, Lian Z, et al. Monoacylglycerol lipase: a novel potential therapeutic target and Prognostic Indicator for Hepatocellular Carcinoma. Sci Rep. 2016;6(1):35784. https://doi.org/10.1038/srep35784 .

Yoshinaga M, Taki K, Somada S, et al. The expression of both Peroxisome Proliferator-Activated Receptor Delta and Cyclooxygenase-2 in tissues is Associated with Poor Prognosis in Colorectal Cancer patients. Dig Dis Sci. 2011;56(4):1194–200. https://doi.org/10.1007/s10620-010-1389-9 .

Das M, Giannoudis A, Sharma V. The role of CPT1A as a biomarker of breast cancer progression: a bioinformatic approach. Sci Rep. 2022;12(1):16441. https://doi.org/10.1038/s41598-022-20585-x .

Han S, Wei R, Zhang X, et al. CPT1A/2-Mediated FAO Enhancement—A metabolic target in radioresistant breast Cancer. Front Oncol. 2019;9. https://doi.org/10.3389/fonc.2019.01201 .

Xiong X, Wen YA, Fairchild R, et al. Upregulation of CPT1A is essential for the tumor-promoting effect of adipocytes in colon cancer. Cell Death Dis. 2020;11(9):736. https://doi.org/10.1038/s41419-020-02936-6 .

Wang T, Fahrmann JF, Lee H, et al. JAK/STAT3-Regulated fatty acid β-Oxidation is critical for breast Cancer Stem Cell Self-Renewal and Chemoresistance. Cell Metab. 2018;27(1):136–e1505. https://doi.org/10.1016/j.cmet.2017.11.001 .

Chen CL, Uthaya Kumar DB, Punj V, et al. NANOG metabolically reprograms Tumor-initiating stem-like cells through tumorigenic changes in oxidative phosphorylation and fatty acid metabolism. Cell Metab. 2016;23(1):206–19. https://doi.org/10.1016/j.cmet.2015.12.004 .

Tirinato L, Liberale C, Di Franco S, et al. Lipid droplets: a New Player in Colorectal Cancer Stem cells unveiled by Spectroscopic Imaging. Stem Cells. 2015;33(1):35–44. https://doi.org/10.1002/stem.1837 .

Hershey BJ, Vazzana R, Joppi DL, Havas KM. Lipid droplets define a sub-population of breast Cancer stem cells. J Clin Med. 2019;9(1):87. https://doi.org/10.3390/jcm9010087 .

Li J, Condello S, Thomes-Pepin J, et al. Lipid desaturation is a metabolic marker and therapeutic target of ovarian Cancer stem cells. Cell Stem Cell. 2017;20(3):303–e3145. https://doi.org/10.1016/j.stem.2016.11.004 .

Mitra R, Goodman OB, Le TT. Enhanced detection of metastatic prostate cancer cells in human plasma with lipid bodies staining. BMC Cancer. 2014;14(1):91. https://doi.org/10.1186/1471-2407-14-91 .

Yu M, Liu H, Duan Y, Zhang D, Li S, Wang F. Four types of fatty acids exert differential impact on pancreatic cancer growth. Cancer Lett. 2015;360(2):187–94. https://doi.org/10.1016/j.canlet.2015.02.002 .

Corominas-Faja B, Cuyàs E, Gumuzio J, et al. Chemical inhibition of acetyl-CoA carboxylase suppresses self-renewal growth of cancer stem cells. Oncotarget. 2014;5(18):8306–16. https://doi.org/10.18632/oncotarget.2059 .

Pascual G, Domínguez D, Elosúa-Bayes M, et al. Dietary palmitic acid promotes a prometastatic memory via Schwann cells. Nature. 2021;599(7885):485–90. https://doi.org/10.1038/s41586-021-04075-0 .

Pascual G, Avgustinova A, Mejetta S, et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature. 2017;541(7635):41–5. https://doi.org/10.1038/nature20791 .

Palmitate oxidation drives a pro-metastatic post-translational modification. Nat Cancer . Published Online Febr 15, 2023. https://doi.org/10.1038/s43018-023-00514-1

Liotti A, Cosimato V, Mirra P, et al. Oleic acid promotes prostate cancer malignant phenotype via the G protein-coupled receptor FFA1/GPR40. J Cell Physiol. 2018;233(9):7367–78. https://doi.org/10.1002/jcp.26572 .

Bauerschlag DO, Maass N, Leonhardt P, et al. Fatty acid synthase overexpression: target for therapy and reversal of chemoresistance in ovarian cancer. J Transl Med. 2015;13(1):146. https://doi.org/10.1186/s12967-015-0511-3 .

MORITA HIRAIDET, HORIKAWA Y. Saturated fatty acids in cell membrane lipids induce resistance to 5-Fluorouracil in Colorectal Cancer cells. Anticancer Res. 2022;42(7):3313–24. https://doi.org/10.21873/anticanres.15819 .

Agency EM. SMH. Opinion of the Paediatric Committee on the Granting of a Product-Specific Waiver EMEA-002212-PIP01-17 .; 2017.

Flaig TW, Salzmann-Sullivan M, Su LJ, et al. Lipid catabolism inhibition sensitizes prostate cancer cells to antiandrogen blockade. Oncotarget. 2017;8(34):56051–65. https://doi.org/10.18632/oncotarget.17359 .

Pfleger J, He M, Abdellatif M. Mitochondrial complex II is a source of the reserve respiratory capacity that is regulated by metabolic sensors and promotes cell survival. Cell Death Dis. 2015;6(7):e1835–1835. https://doi.org/10.1038/cddis.2015.202 .

Marchetti P, Fovez Q, Germain N, Khamari R, Kluza J. Mitochondrial spare respiratory capacity: mechanisms, regulation, and significance in non-transformed and cancer cells. FASEB J. 2020;34(10):13106–24. https://doi.org/10.1096/fj.202000767R .

Farge T, Saland E, de Toni F, et al. Chemotherapy-resistant human acute myeloid leukemia cells are not enriched for leukemic stem cells but require oxidative metabolism. Cancer Discov. 2017;7(7):716–35. https://doi.org/10.1158/2159-8290.CD-16-0441 .

Samudio I, Harmancey R, Fiegl M, et al. Pharmacologic inhibition of fatty acid oxidation sensitizes human leukemia cells to apoptosis induction. J Clin Invest. 2010;120(1):142–56. https://doi.org/10.1172/JCI38942 .

Redondo-Muñoz M, Rodriguez-Baena FJ, Aldaz P, et al. Metabolic rewiring induced by ranolazine improves melanoma responses to targeted therapy and immunotherapy. Nat Metab. 2023;5(9):1544–62. https://doi.org/10.1038/s42255-023-00861-4 .

Incio J, Liu H, Suboj P, et al. Obesity-Induced inflammation and Desmoplasia promote pancreatic Cancer progression and resistance to Chemotherapy. Cancer Discov. 2016;6(8):852–69. https://doi.org/10.1158/2159-8290.CD-15-1177 .

Ye H, Adane B, Khan N, et al. Leukemic stem cells evade chemotherapy by metabolic adaptation to an adipose tissue niche. Cell Stem Cell. 2016;19(1):23–37. https://doi.org/10.1016/j.stem.2016.06.001 .

Download references

Acknowledgements

The authors would like to acknowledge the use of the CIBA (Centro de Investigación Biomédica de Aragón) Flow Cytometry and Animal Facilities (Servicios Científico-Técnicos, IACS Universidad de Zaragoza). We also thank Laura Sancho Andrés for proofreading the manuscript.

The research was supported by the Instituto de Salud Carlos III through the Miguel Servet Program (CP16/00121 and CPII21/00005, to P.S.), the pFIS program (FI21/00031, to P. E-R) and Fondo de Investigaciones Sanitarias (PI17/00082 and PI20/00921, to P.S.) (all co-financed by European funds (FSE: “El FSE invierte en tu futuro” and FEDER: “Una manera de hacer Europa”, respectively), the Worldwide Cancer Research (WCR) Charity together with Asociación Española contra el Cáncer (AECC) (19–0250, to P.S.), and a LAB AECC grant (LABAE223389SANC, to P.S.). M.M. was recipient of a Margarita Salas fellowship from the Universidad Autónoma de Madrid (CA1/RSUE/202100646). A.R-G. was a recipient of a predoctoral contract from the Spanish AECC (PRDAR222458ROYO). I.V. was a recipient of a predoctoral contract from the Aragon Government.

Author information

Marta Mascaraque and Sarah Courtois contributed equally to this work.

Authors and Affiliations

Instituto de Investigación Sanitaria Aragón (IIS Aragón), Hospital Universitario Miguel Servet, Zaragoza, Spain

Marta Mascaraque, Sarah Courtois, Alba Royo-García, Andrei M. Stoian, Isabel Villaoslada, Pilar Espiau-Romera, Ansooya Bokil & Patricia Sancho

Department of Biology, Universidad Autónoma de Madrid, Madrid, Spain

Marta Mascaraque

Centre for Stem Cells in Cancer & Ageing, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK

David Barneda, Andrés Cano-Galiano & Petra Jagust

Pancreatic Cancer Heterogeneity, Candiolo Cancer Institute – FPO – IRCCS, Candiolo, TO, Italy

Christopher Heeschen

You can also search for this author in PubMed Google Scholar

Contributions

MM: investigation, formal analysis, writing-original draft, writing-review and editing. SC: conceptualization, investigation, formal analysis and writing-review. AR-G: investigation and writing-editing. DB: conceptualization and investigation. AMS, IV, PE, AB, ACG, PJ: investigation. CH: conceptualization, resources, writing-review and editing. PS: conceptualization, project administration, supervision, funding acquisition, investigation, writing-original draft, writing-review and editing.

Corresponding authors

Correspondence to Christopher Heeschen or Patricia Sancho .

Ethics declarations

Ethical approval.

Human material: PDAC patient-derived xenografts (PDXs): A6L, 185, 215, 253, 265 and 354 were obtained from the Biobank of the Spanish National Cancer Research Centre (CNIO), Madrid, Spain (MTAs #CNIO20-027, #CNIO21-253). PDAC PDX-derived cultures were established as previously described [ 17 ]. Pancreatic circulating tumor cells (CTCs): The metastatic model CTCA was established from circulating tumor cells and obtained through the Barts Pancreas Tissue Bank of the Barts Cancer Institute ( https://www.bartspancreastissuebank.org.uk/ ; BCI, London, United Kingdom; 2019/02/IISA/PS/E/Cellcultures). In vivo experiments: Mice were housed according to institutional guidelines and all experimental procedures were performed in compliance with the institutional guidelines for the welfare of experimental animals as approved by the University of Zaragoza Ethics Committee (CEICA PI22/17, PI35/19, PI41/20) and in accordance with the guidelines for Ethical Conduct in the Care and Use of Animals as stated in The International Guiding Principles for Biomedical Research involving Animals, developed by the Council for International Organizations of Medical Sciences (CIOMS).

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Mascaraque, M., Courtois, S., Royo-García, A. et al. Fatty acid oxidation is critical for the tumorigenic potential and chemoresistance of pancreatic cancer stem cells. J Transl Med 22 , 797 (2024). https://doi.org/10.1186/s12967-024-05598-6

Download citation

Received : 24 April 2024

Accepted : 11 August 2024

Published : 28 August 2024

DOI : https://doi.org/10.1186/s12967-024-05598-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Pancreatic cancer
Cancer stem cells
Lipid metabolism
Chemoresistance

Journal of Translational Medicine

ISSN: 1479-5876

Submission enquiries: Access here and click Contact Us
General enquiries: [email protected]

IMAGES

Best 151+ Quantitative Research Topics for STEM Students
189+ Good Quantitative Research Topics For STEM Students
199+ Engaging Quantitative Research Topics for STEM Students
Quantitative-Research-Proposal-Topics-list.pdf
Quantitative Research Titles for STEM Students
100+ Best Quantitative Research Topics For Students In 2023

VIDEO

Quantitative Data in STEM Education
Quantitative Research Purposes: Updating the Previous Theories
Difference Between Quantitative and Qualitative Research #quantitativeresearch #qualitativeresearch
Shawn Chen 2024 Junior Scientist Awardee
Averages topic.... Quantitative Aptitude for all competitive exams (SSC,RRB,BANKINGS)
Quantitative Floer Theory and Coefficients

COMMENTS

200+ Experimental Quantitative Research Topics For Stem Students
Here are 10 qualitative research topics for STEM students: Exploring the experiences of female STEM students in overcoming gender bias in academia. Understanding the perceptions of teachers regarding the integration of technology in STEM education. Investigating the motivations and challenges of STEM educators in underprivileged schools.
Best 151+ Quantitative Research Topics for STEM Students
Chemistry. Let's get started with some quantitative research topics for stem students in chemistry: 1. Studying the properties of superconductors at different temperatures. 2. Analyzing the efficiency of various catalysts in chemical reactions. 3. Investigating the synthesis of novel polymers with unique properties. 4.
189+ Good Quantitative Research Topics For STEM Students
Following are the best Quantitative Research Topics For STEM Students in mathematics and statistics. Prime Number Distribution: Investigate the distribution of prime numbers. Graph Theory Algorithms: Develop algorithms for solving graph theory problems. Statistical Analysis of Financial Markets: Analyze financial data and market trends.
Best 101 Quantitative Research Topics for STEM Students
Quantitative research can be both challenging and rewarding, but finding the right topic is the first step to success. In this blog, we've gathered 101 quantitative research topics in the easiest language possible to help you kickstart your research journey. 101 Quantitative Research Topics for STEM Students Biology Research Topics
110+ Best Quantitative Research Topics for STEM Students 2024
Explore engaging quantitative research topics for STEM students. This guide covers the basics, popular areas, and tips for success to help you make an impact. Quantitative research uses data and numbers to uncover insights. Whether you're into computer science, engineering, or natural sciences, it's a powerful tool for discovery.
55 Brilliant Research Topics For STEM Students
There are several science research topics for STEM students. Below are some possible quantitative research topics for STEM students. A study of protease inhibitor and how it operates. A study of how men's exercise impacts DNA traits passed to children. A study of the future of commercial space flight.
210 Best Quantitative Research Topics For STEM Students
Here are the key characteristics of quantitative research topics for STEM Students: Measurable Data: Quantitative topics examine things that can be measured and quantified with numbers, allowing statistical analysis of the data. Statistical Analysis: Quantitative topics use mathematical statistics to analyze numerical data and spot patterns ...
Quantitative Research Topics for Stem Students
Choosing the right research topic is important for STEM (Science, Technology, Engineering, and Mathematics) students. Quantitative research focuses on numbers and data analysis, making it suitable ...
100+ Best Quantitative Research Topics For Students In 2023
An example of quantitative research topics for 12 th -grade students will come in handy if you want to score a good grade. Here are some of the best ones: The link between global warming and climate change. What is the greenhouse gas impact on biodiversity and the atmosphere.
Experimental Quantitative Research Topics For Stem Students
Experimental Quantitative Research Topics For Stem Students. 1. Impact of Variable X on Y: Examine how changes in X affect Y using controlled experiments. 2. Algorithm Efficiency in Different Conditions: Test algorithm performance under varying data loads. 3.
199+ Engaging Quantitative Research Topics for STEM Students
199+ Engaging Quantitative Research Topics for STEM Students. March 20, 2024 by Rupam. Discover engaging quantitative research topics for STEM students, spanning energy solutions, medical advancements, and cutting-edge technology. Explore hands-on ideas to sharpen skills and make a tangible impact on the future. Hey STEM buddy!
199+ Quantitative Research Topics For STEM Students to Try Now
30 Quantitative Research Topic Ideas For STEM Students. Investigating the properties and applications of novel materials created through 3D printing. Studying the effectiveness of virtual reality simulations for medical training programs. Analyzing the feasibility and methods for mineral extraction from asteroids.
STEM Research Topics: 200+ Great Choices
STEM Research Topics for an Educational Paper. STEM stands for Science, Technology, Engineering, and Math. It is essential for learning and discovery, helping us understand the world, solve problems, and think critically. STEM research goes beyond classroom learning, allowing us to explore specific areas in greater detail.
200 Quantitative Research Title for Stem Students
To help you get started on your research journey, we've compiled a list of 200 quantitative research title for stem students. These titles span various STEM disciplines, from biology to computer science. Whether you're an undergraduate or graduate student, these titles can serve as a springboard for your research ideas.
189+ Experimental Quantitative Research Topics For STEM Students
Here are 8 key points on how to do experimental research effectively. 1. Clear Research Focus. Begin by defining a clear and focused research question. A well-defined question provides a purpose and direction for your experiment, guiding your choices in variables and methodology. 2.
11 STEM Research Topics for High School Students
Topic 1: Artificial Intelligence (AI) AI stands at the forefront of technological innovation. Students can engage in research on AI applications in various sectors and the ethical implications of AI. This field is suitable for students with interests in computer science, AI, data analytics, and related areas. Topic 2: Applied Math and AI.
500+ Quantitative Research Titles and Topics
Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology, economics, and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas ...
Trending Topic Research: STEM
Trending Topic Research File. Science, Technology Engineering, and Mathematics (STEM) is one of the most talked about topics in education, emphasizing research, problem solving, critical thinking, and creativity. The following compendium of open-access articles are inclusive of all substantive AERA journal content regarding STEM published since ...
Research and trends in STEM education: a systematic review of journal
With the rapid increase in the number of scholarly publications on STEM education in recent years, reviews of the status and trends in STEM education research internationally support the development of the field. For this review, we conducted a systematic analysis of 798 articles in STEM education published between 2000 and the end of 2018 in 36 journals to get an overview about developments ...
A systematic review of high impact empirical studies in STEM education
The formation of an academic field is evidenced by many factors, including the growth of relevant research articles and the increasing impact of highly cited publications. Building upon recent scoping reviews of journal publications in STEM education, this study aimed to provide a systematic review of high impact empirical studies in STEM education to gain insights into the development of STEM ...
Research and trends in STEM education: a systematic analysis of
Taking publicly funded projects in STEM education as a special lens, we aimed to learn about research and trends in STEM education. We identified a total of 127 projects funded by the Institute of Education Sciences (IES) of the US Department of Education from 2003 to 2019. Both the number of funded projects in STEM education and their funding amounts were high, although there were ...
23+ Quantitative Research Topics For STEM Students In ...
Here are the top quantitative research topics for STEM students in the Philippines in 2024. 1. Impact of Climate Change on Farming. Analyze how changing weather affects the growth of crops like rice and corn in different parts of the Philippines. Use numbers to find ways and suggest ways farmers can adapt. 2.
Trends and Hot Topics of STEM and STEM Education: a Co-word ...
This study explored research trends in science, technology, engineering, and mathematics (STEM) education. Descriptive analysis and co-word analysis were used to examine articles published in Social Science Citation Index journals from 2011 to 2020. From a search of the Web of Science database, a total of 761 articles were selected as target samples for analysis. A growing number of STEM ...
Fatty acid oxidation is critical for the tumorigenic potential and
We have previously demonstrated the significant reliance of pancreatic Cancer Stem Cells (PaCSCs) on mitochondrial oxidative phosphorylation (OXPHOS), which enables versatile substrate utilization, including fatty acids (FAs). Notably, dysregulated lipid scavenging and aberrant FA metabolism are implicated in PDAC progression. Our bioinformatics analyses revealed elevated expression of lipid ...

Best 151+ Quantitative Research Topics for STEM Students

What is Quantitative Research in STEM?

Importance of Quantitative Research Topics for STEM Students

1. Empirical Evidence

2. Data-Driven Decision-Making

3. Innovation

4. Problem Solving

5. Interdisciplinary Applications

Quantitative Research Topics for STEM Students

Biology and Life Sciences

Engineering

Environmental Science

Computer Science

Mathematics

Earth Sciences

Health Sciences and Medicine

Related Posts

10 Online Tutoring Help Benefits

189+ Good Quantitative Research Topics For STEM Students

What Is Quantitative Research

How To Choose Quantitative Research Topics For STEM

Step 1:- Identify Your Interests and Passions

Step 2:- Review Coursework and Textbooks

Step 3:- Consult with Professors and Advisors

Step 4:- Read Recent Literature

Step 5:- Narrow Down Your Focus

Step 6:- Consider Resources and Access

Step 7:- Think About Practicality

Step 8:- Define Your Research Question

Step 9:- Conduct a Literature Review

Step 10:- Consider the Impact

Step 11:- Brainstorm Research Methods

Step 12:- Seek Feedback

Step 13:- Assess Ethical Considerations

Step 14:- Finalize Your Research Topic

Step 15:- Stay Open to Adjustments

Quantitative Research Topics In Physics and Astronomy

Quantitative Research Topics In Biology and Life Sciences

Engineering and Technology Quantitative Research Topics

Quantitative Research Topics In Mathematics and Statistics

Experimental Quantitative Research Topics In Science and Earth Sciences

Chemistry and Materials Science Quantitative Research Topics

Computer Science and Information Technology Topics

Good Quantitative Research Topics Students In Medicine and Healthcare

Agriculture and Food Sciences Topics

Social Sciences with Quantitative Approaches

Environmental Engineering Quantitative Research Topics

Quantitative Research Topics Robotics and Automation

Quantitative Research Topics Materials Engineering

Nuclear Engineering Quantitative Research Topics

Quantitative Research Topics In Biomedical Engineering

Good Quantitative Research Topics Chemical Engineering

Best Quantitative Research Topics In Renewable Energy

Brilliant Quantitative Research Topics In Astronomy and Space Sciences

Quantitative Research Topics In Psychology and Cognitive Science

Geology and Geological Engineering Quantitative Research Topics

Top Quantitative Research Topics In Forensic Science

Great Quantitative Research Topics In Cybersecurity

Mathematical Biology Quantitative Research Topics

Quantitative Research Topics In Chemical Analysis

Mathematics Education Quantitative Research Topics

Quantitative Research Topics In Social Research

Quantitative Research Topics In Computational Neuroscience

Best Topics In Transportation Engineering

Good Quantitative Research Topics In Energy Economics

Quantum Information Science

Human Genetics

Marine Biology

Data Science and Machine Learning

Environmental Engineering

1. Understanding a Phenomenon

2. Testing Hypotheses

3. Contributing to Knowledge

4. Informing Decision-Making

5. Enhancing Understanding

6. Application

7. Contributing to Theory

Conclusion – Quantitative Research Topics For STEM Students

What is quantitative research in STEM?

What are good examples of quantitative research?