why is critical thinking so hard

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Critical Thinking: A Simple Guide and Why It’s Important

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Critical Thinking: A Simple Guide and Why It’s Important was originally published on Ivy Exec .

Strong critical thinking skills are crucial for career success, regardless of educational background. It embodies the ability to engage in astute and effective decision-making, lending invaluable dimensions to professional growth.

At its essence, critical thinking is the ability to analyze, evaluate, and synthesize information in a logical and reasoned manner. It’s not merely about accumulating knowledge but harnessing it effectively to make informed decisions and solve complex problems. In the dynamic landscape of modern careers, honing this skill is paramount.

The Impact of Critical Thinking on Your Career

☑ problem-solving mastery.

Visualize critical thinking as the Sherlock Holmes of your career journey. It facilitates swift problem resolution akin to a detective unraveling a mystery. By methodically analyzing situations and deconstructing complexities, critical thinkers emerge as adept problem solvers, rendering them invaluable assets in the workplace.

☑ Refined Decision-Making

Navigating dilemmas in your career path resembles traversing uncertain terrain. Critical thinking acts as a dependable GPS, steering you toward informed decisions. It involves weighing options, evaluating potential outcomes, and confidently choosing the most favorable path forward.

☑ Enhanced Teamwork Dynamics

Within collaborative settings, critical thinkers stand out as proactive contributors. They engage in scrutinizing ideas, proposing enhancements, and fostering meaningful contributions. Consequently, the team evolves into a dynamic hub of ideas, with the critical thinker recognized as the architect behind its success.

☑ Communication Prowess

Effective communication is the cornerstone of professional interactions. Critical thinking enriches communication skills, enabling the clear and logical articulation of ideas. Whether in emails, presentations, or casual conversations, individuals adept in critical thinking exude clarity, earning appreciation for their ability to convey thoughts seamlessly.

☑ Adaptability and Resilience

Perceptive individuals adept in critical thinking display resilience in the face of unforeseen challenges. Instead of succumbing to panic, they assess situations, recalibrate their approaches, and persist in moving forward despite adversity.

☑ Fostering Innovation

Innovation is the lifeblood of progressive organizations, and critical thinking serves as its catalyst. Proficient critical thinkers possess the ability to identify overlooked opportunities, propose inventive solutions, and streamline processes, thereby positioning their organizations at the forefront of innovation.

☑ Confidence Amplification

Critical thinkers exude confidence derived from honing their analytical skills. This self-assurance radiates during job interviews, presentations, and daily interactions, catching the attention of superiors and propelling career advancement.

So, how can one cultivate and harness this invaluable skill?

✅ developing curiosity and inquisitiveness:.

Embrace a curious mindset by questioning the status quo and exploring topics beyond your immediate scope. Cultivate an inquisitive approach to everyday situations. Encourage a habit of asking “why” and “how” to deepen understanding. Curiosity fuels the desire to seek information and alternative perspectives.

✅ Practice Reflection and Self-Awareness:

Engage in reflective thinking by assessing your thoughts, actions, and decisions. Regularly introspect to understand your biases, assumptions, and cognitive processes. Cultivate self-awareness to recognize personal prejudices or cognitive biases that might influence your thinking. This allows for a more objective analysis of situations.

✅ Strengthening Analytical Skills:

Practice breaking down complex problems into manageable components. Analyze each part systematically to understand the whole picture. Develop skills in data analysis, statistics, and logical reasoning. This includes understanding correlation versus causation, interpreting graphs, and evaluating statistical significance.

✅ Engaging in Active Listening and Observation:

Actively listen to diverse viewpoints without immediately forming judgments. Allow others to express their ideas fully before responding. Observe situations attentively, noticing details that others might overlook. This habit enhances your ability to analyze problems more comprehensively.

✅ Encouraging Intellectual Humility and Open-Mindedness:

Foster intellectual humility by acknowledging that you don’t know everything. Be open to learning from others, regardless of their position or expertise. Cultivate open-mindedness by actively seeking out perspectives different from your own. Engage in discussions with people holding diverse opinions to broaden your understanding.

✅ Practicing Problem-Solving and Decision-Making:

Engage in regular problem-solving exercises that challenge you to think creatively and analytically. This can include puzzles, riddles, or real-world scenarios. When making decisions, consciously evaluate available information, consider various alternatives, and anticipate potential outcomes before reaching a conclusion.

✅ Continuous Learning and Exposure to Varied Content:

Read extensively across diverse subjects and formats, exposing yourself to different viewpoints, cultures, and ways of thinking. Engage in courses, workshops, or seminars that stimulate critical thinking skills. Seek out opportunities for learning that challenge your existing beliefs.

✅ Engage in Constructive Disagreement and Debate:

Encourage healthy debates and discussions where differing opinions are respectfully debated.

This practice fosters the ability to defend your viewpoints logically while also being open to changing your perspective based on valid arguments. Embrace disagreement as an opportunity to learn rather than a conflict to win. Engaging in constructive debate sharpens your ability to evaluate and counter-arguments effectively.

✅ Utilize Problem-Based Learning and Real-World Applications:

Engage in problem-based learning activities that simulate real-world challenges. Work on projects or scenarios that require critical thinking skills to develop practical problem-solving approaches. Apply critical thinking in real-life situations whenever possible.

This could involve analyzing news articles, evaluating product reviews, or dissecting marketing strategies to understand their underlying rationale.

In conclusion, critical thinking is the linchpin of a successful career journey. It empowers individuals to navigate complexities, make informed decisions, and innovate in their respective domains. Embracing and honing this skill isn’t just an advantage; it’s a necessity in a world where adaptability and sound judgment reign supreme.

So, as you traverse your career path, remember that the ability to think critically is not just an asset but the differentiator that propels you toward excellence.

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Critical Thinking and Decision-Making  - Why is it So Hard to Make Decisions?

Critical thinking and decision-making  -, why is it so hard to make decisions, critical thinking and decision-making why is it so hard to make decisions.

Critical Thinking and Decision-Making: Why is it So Hard to Make Decisions?

Lesson 2: why is it so hard to make decisions.

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The challenge of making decisions

No matter who you are or what you do for a living, you make thousands of minor decisions every day . Most are relatively inconsequential; for example, what do you want for breakfast? Do you want coffee, tea, or something else?

Other decisions are much more complex . Should you accept a new job? Should you move to a different city? What about buying a house, or starting a family? These decisions weigh more heavily because they can impact your life in many ways.

You might feel like you're bad at making decisions (or not good at making good ones). However, it's something we all struggle with due to the way our brains are made. Behind every decision, there are secret psychological factors that shape the way we think and act. Understanding these factors can make them easier to overcome.

Watch the video below to learn more about the psychology of decision-making.

Status quo bias

Many missteps in decision-making can be chalked up to cognitive bias . That's our tendency to think a certain way without even realizing it. Here's a simple example: Have you ever avoided switching Internet providers, even though you were unhappy with your current service?

Something called status quo bias might be to blame. That's our tendency to stick with what we know, instead of choosing something new and different. We see the alternative as a risk or just not worth the trouble, even if it might be better. Without realizing it, we can become overly resistant to change.

Anchoring bias

Anchoring bias can also affect the choices we make. To understand how anchoring works, imagine you're shopping for a used car at a local dealership. The model you like is priced at $9,999.

Next, imagine the dealer offers you a discount. The car is now $8,999, a full thousand dollars less. Sounds like a can't-miss opportunity, right? Not necessarily.

Anchoring suggests that we rely too heavily on the first thing we hear (in this case, the initial price of the car). That's what makes the discount so appealing, but it shouldn't be the deciding factor. There are also more objective things to consider, like how much the car is really worth, and whether you can find a better price elsewhere. If you're not careful, the anchoring effect can weigh you down.

Choice overload

Cognitive biases aren't the only things that can affect decision-making. More and more studies show that stress can have an impact—both on the quality of our decisions and on our ability to make them. Take this well-known study about jam.

At an upscale food market, researchers set up two displays offering free samples of jam . One gave customers six different flavors to choose from; the other gave them 24.

The larger display attracted more people, but they were six times less likely to actually buy a jar of jam (compared to those who visited the smaller display). The reason for this is a phenomenon now known as choice overload .

Choice overload can happen any time we feel overwhelmed by the sheer number of options. We have such a hard time comparing them that we're less likely to choose anything at all. As in the jam example, many of us would sooner walk away empty-handed than deal with the stress of choosing from such a large selection.

Decision fatigue

A similar thing happens when we're forced to make multiple decisions one after another—a common occurrence in everyday life. We experience an effect psychologists call decision fatigue .

Decision fatigue suggests that making a large number of decisions over a prolonged period of time can be a significant drain on our willpower. The result? We have a harder time saying no —to things like junk food, impulse buys, and other tempting offers.

On the flip side, fatigue can also make it harder to say yes , especially to decisions that would upset the status quo.

Fatigue makes it difficult to even think about making decisions, let alone what's right or wrong, correct or incorrect. We follow the path of least resistance because it's the easiest thing to do.

The upside of uncertainty

Making decisions will always be difficult because it takes time and energy to weigh your options. Things like second-guessing yourself and feeling indecisive are just a part of the process.

In many ways, they're a good thing—a sign that you're thinking about your choices instead of just going with the flow. That's the first step to making better, more thoughtful decisions.

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Why is critical thinking difficult?

05 nov why is critical thinking difficult, students struggle to think critically.

85% of teachers thought critical thinking skills were inadequate when students reached post-16 education (TES). Our own qualitative research in schools revealed typical worries that students have such as: losing track of the argument; not planning before starting an essay; including irrelevant information. Examiners’ reports consistently point out the lack of a good argument in exam entries. Moreover, teachers express concern with regards to teaching of critical thinking skills. Students are often much better at learning facts than making a good argument, but there is no time to teach this properly in a content-heavy curriculum. The requirements to think critically have increased, but the textbooks and training have not always kept up.

Arguments are hidden in textbook prose

In school, students are introduced to critical thinking by reading and writing arguments in prose. The textbooks, articles and original sources they read are usually in prose, as are the essays they write. Prose is a very flexible medium, but it is not the optimal way to represent an argument.

Firstly, students cannot look at argumentative prose and immediately find the argument. Prose makes no distinction between the sentences which are part of the argument and those that do other things, such as supporting facts and context. So the argument is hidden amongst other information, much of which is distracting.

Prose is linear, but arguments are branched

Prose is written in a way that makes it hard to understand the structure of the argument. This is a problem, because the whole structure has to be kept in mind when evaluating the argument. For example, if they find a counter-example to one step of an argument, they need to know the structure to realise whether this defeats the whole argument or just a part of it.

Poor critical thinking leads to poor arguments

For these reasons, argumentative prose imposes a heavy cognitive load on the reader. Students are obliged to work hard to discover how an argument works before they can even begin to critique it. This is especially difficult for those who have reading difficulties such as dyslexia.

School students normally create their own arguments by writing essays. Even if they are well-informed they often write a lot of facts without pulling them together into an argument. The very flexibility of prose allows essays to be unrigorous, ambiguous, and irrelevant. Moreover, essays are slow for students to write and slow for teachers to check and mark, limiting the amount of arguments that can be studied in detail. For these reasons, learning critical thinking through school work is difficult and its results are patchy.

At Endoxa Learning, we design resources that make it easier for students to read, understand and create arguments.

What is critical thinking?

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• Apr 1, 2022

Why is critical thinking so hard?

…and how we can teach it.

Hello! Welcome to the 8th edition of Things in Education, the fortnightly newsletter through which we hope to share the latest in education research and developments in the form of accessible summaries and stories to help you in the classroom and at home.

Let’s be frank. Thinking critically is hard. It’s so hard that most adults struggle to think critically. Take the example of the millions who are convinced that world leaders and award-winning actors are actually power-hungry aliens, or that the COVID-19 vaccines contain microchips so that the government can track every second of our every day…

And so, education boards all over the world have made the teaching of critical thinking skills compulsory in school. But if learning how to think critically is hard, teaching it is a hundred times harder. After all, there is no set definition of critical thinking. And unlike the steps of long division, there is no set process to teach critical thinking either.

Though all is not lost. With some understanding of how the mind likes to think and what thinking critically really requires, we can begin to build these skills in ourselves and in our students. What better day to begin than today?

Surface Structure and Deep Structure

So let’s begin by exercising our own critical thinking skills.

In 1980, researchers Mary Gick and Keith Holyoke conducted an experiment. In this experiment, they gave some college students a story to read:

The students were asked to memorise this story. Then, they were asked to solve a problem:

Here’s the solution: Just like the army general broke up his soldiers into small groups to converge on the fortress at the same time, the doctor can send several low-intensity rays towards the tumour. These rays won’t destroy healthy tissue, but when they all converge on the tumour, their intensity will be high enough to destroy it.

In the experiment, only 20% of the students were able to solve the problem, in spite of memorising the first story. Why do you think most of them were not able to see the similarities in the structures of the two problems?

Here’s why: Our mind tends to prefer the surface structure of new information – the specific, concrete details and particulars. In this case, these concrete details were the fortress and the general and the roads in the first problem, and the tumour and the tissue and the rays in the second problem.

In order to think critically, we must also understand the deep structure of new information – the general underlying principle. In this case, the underlying principle was “the dispersal and regathering of strength” – of the soldiers and of the rays!

So what does this look like in the classroom? Let’s take an example from English. Ask your middle- or high-school students the following question: Why will Grade 7 students not enjoy the story of the lion and the mouse as much as a mystery set on Mars?

Students will most likely begin by thinking about the surface structures of the stories: Mars is more exciting; lions and mice cannot talk; and so on. However, students have not thought critically in giving these responses – they have not gone down to the deep structure.

Thinking about the deep structure in this case begins with thinking about the genres of the two stories – one is a fable, and the other is science fiction. Fables are written keeping the developmental stage of young children in mind; science fiction is for teenagers and young adults. Fables have simple storylines; the plotline of science fiction is much more complicated. Fables present everything as black and white, good and bad; while science fiction usually poses moral dilemmas that teenagers are beginning to grapple with… The deep structures of stories give us a much more critical understanding of the question posed!

Background Knowledge

Here’s another task for you: Do you agree with the following tweet by Web3 Coin? Support your opinion with 2 pieces of evidence.

Here were my thoughts when I first read this: 543 retweets? And 525 people liked this tweet? I don’t even know what it means… What is web3 in the first place? Is web3 something we can “have”? Huh? What does it have to do with crypto? I’m not intelligent enough for this…

Does this mean that I have no critical thinking skills? Absolutely not. What it does mean is that background knowledge is the first and most important requirement for critical thinking. I can’t think critically about something I don’t know enough about. A doctor can think critically about the oxygen requirements of her patients, but that does not mean that she can think critically about the construction of an oxygen cylinder. A lawyer can think critically about the legal nuances of a mental harassment lawsuit, but that does not mean that she can think critically about the medical requirements of mental health. Background knowledge is the foundation of critical thinking.

So what does this mean for the classroom? One of the best ways to build students’ background knowledge is to adopt a multi-disciplinary approach. A multi-disciplinary approach analyses a concept or a topic through the lens of various disciplines. For example:

Such an approach would require teachers of different subjects to plan well in advance and collaborate on the progress of the curriculum. At the same time, individual subject teachers can also implement the multi-disciplinary approach in simpler ways by incorporating one other subject into their curriculum, like asking students to use their knowledge of language to break down and understand new scientific terms, or having students research the history of the place in which a famous author lived. Slowly but surely, teachers will see students begin to think critically in these subjects.

There is no set definition of critical thinking because different areas of life and different problems require different types of thinking skills. “Critical thinking skills” is an umbrella term for many different skills. What we do know, however, is that going beyond the surface structure of information and to the deep structure as well as building background knowledge are important steps towards developing critical thinking skills. Let’s start there!

Useful Links:

Multi-disciplinary learning : In this blog, we explain how multi-disciplinary learning leads to deep understanding by increasing and strengthening connections in the brain.

Critical thinking : In this periodical, cognitive psychologist Daniel Willingham explains why critical thinking is so hard to teach.

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13 Easy Steps To Improve Your Critical Thinking Skills

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With the sheer volume of information that we’re bombarded with on a daily basis – and with the pervasiveness of fake news and social media bubbles – the ability to look at evidence, evaluate the trustworthiness of a source, and think critically is becoming more important than ever. This is why, for me, critical thinking is one of the most vital skills to cultivate for future success.

Critical thinking isn’t about being constantly negative or critical of everything. It’s about objectivity and having an open, inquisitive mind. To think critically is to analyze issues based on hard evidence (as opposed to personal opinions, biases, etc.) in order to build a thorough understanding of what’s really going on. And from this place of thorough understanding, you can make better decisions and solve problems more effectively.

To put it another way, critical thinking means arriving at your own carefully considered conclusions instead of taking information at face value. Here are 13 ways you can cultivate this precious skill:

1. Always vet new information with a cautious eye. Whether it’s an article someone has shared online or data that’s related to your job, always vet the information you're presented with. Good questions to ask here include, "Is this information complete and up to date?” “What evidence is being presented to support the argument?” and “Whose voice is missing here?”

2. Look at where the information has come from. Is the source trustworthy? What is their motivation for presenting this information? For example, are they trying to sell you something or get you to take a certain action (like vote for them)?

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3. Consider more than one point of view. Everyone has their own opinions and motivations – even highly intelligent people making reasonable-sounding arguments have personal opinions and biases that shape their thinking. So, when someone presents you with information, consider whether there are other sides to the story.

4. Practice active listening. Listen carefully to what others are telling you, and try to build a clear picture of their perspective. Empathy is a really useful skill here since putting yourself in another person's shoes can help you understand where they're coming from and what they might want. Try to listen without judgment – remember, critical thinking is about keeping an open mind.

5. Gather additional information where needed. Whenever you identify gaps in the information or data, do your own research to fill those gaps. The next few steps will help you do this objectively…

6. Ask lots of open-ended questions. Curiosity is a key trait of critical thinkers, so channel your inner child and ask lots of "who," "what," and "why" questions.

7. Find your own reputable sources of information, such as established news sites, nonprofit organizations, and education institutes. Try to avoid anonymous sources or sources with an ax to grind or a product to sell. Also, be sure to check when the information was published. An older source may be unintentionally offering up wrong information just because events have moved on since it was published; corroborate the info with a more recent source.

8. Try not to get your news from social media. And if you do see something on social media that grabs your interest, check the accuracy of the story (via reputable sources of information, as above) before you share it.

9. Learn to spot fake news. It's not always easy to spot false or misleading content, but a good rule of thumb is to look at the language, emotion, and tone of the piece. Is it using emotionally charged language, for instance, and trying to get you to feel a certain way? Also, look at the sources of facts, figures, images, and quotes. A legit news story will clearly state its sources.

10. Learn to spot biased information. Like fake news, biased information may seek to appeal more to your emotions than logic and/or present a limited view of the topic. So ask yourself, “Is there more to this topic than what’s being presented here?” Do your own reading around the topic to establish the full picture.

11. Question your own biases, too. Everyone has biases, and there’s no point pretending otherwise. The trick is to think objectively about your likes and dislikes, preferences, and beliefs, and consider how these might affect your thinking.

12. Form your own opinions. Remember, critical thinking is about thinking independently. So once you’ve assessed all the information, form your own conclusions about it.

13. Continue to work on your critical thinking skills. I recommend looking at online learning platforms such as Udemy and Coursera for courses on general critical thinking skills, as well as courses on specific subjects like cognitive biases.

Read more about critical thinking and other essential skills in my new book, Future Skills: The 20 Skills & Competencies Everyone Needs To Succeed In A Digital World . Written for anyone who wants to surf the wave of digital transformation – rather than be drowned by it – the book explores why these vital future skills matter and how to develop them.

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Why Is Critical Thinking Difficult to Teach?

Nimblywise team.

• April 25, 2017

So, why is critical thinking so hard to teach?

Daniel T. Willingham’s seminal 2007 article on the subject is often cited in the materials published over the past decade. In it, Willingham draws on extensive research pointing to the fact that critical thinking is difficult to define, hard to transfer from one setting to another, and challenging to measure and assess.

His point about assessment is echoed in this 2014 report on the state of critical thinking assessment in higher ed. After all, when definitions of critical thinking vary from school to school, or even department to department, setting institutional benchmarks can be a tricky endeavor.

After reviewing the research, it appears there are a few agreed-upon best practices that can lead to the most effective deployment of critical thinking instruction.

• Practice, Practice, Practice: Critical thinking is not a one-off skill. It requires practice to master, and it works best when introduced in a variety of settings so that students can learn how to transfer the skill to novel situations.
• Reinforce Critical Thinking with other Skills: Willingham makes the point that critical thinking is ineffective without an individual’s factual knowledge of a given subject, and their ability to find/evaluate information to help them solve problems and make decisions.
• Consistent Assessment Helps Improve Instruction: 60% of provosts said that getting faculty to use assessment results was their top priority. However, the lack of consistency in both instruction and assessment makes faculty buy-in a challenge. Accurate measurements with actionable results are key to closed-loop assessment. 

Improving critical thinking instruction is an obvious benefit for students, academic institutions, and businesses looking to hire graduates with a broad range of skills. New technology is making it easier to teach critical thinking consistently, as well as assess student gains accurately across departments. For a practical example of how schools are accomplishing this, watch this webinar recording featuring Dr. Stephanie Dance-Barnes, who was able to teach strong critical thinking skills in her biology courses, while also meeting her university’s focus on measurable, real-time assessment throughout the curriculum.

Further reading:

• Kuh, G. D., Jankowski, N., Ikenberry, S. O., & Kinzie, J. (2014). Knowing what students know and can do: The current state of student learning outcomes assessment in U.S. colleges and universities . Champaign, IL: National Institute for Learning Outcomes Assessment.
• Liu, O. L., Frankel, L. and Roohr, K. C. (2014), Assessing Critical Thinking in Higher Education: Current State and Directions for Next-Generation Assessment. ETS Research Reports Series, 2014: 1–23. doi:10.1002/ets2.12009

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Critical Thinking Skills: Why They Are So Difficult To Acquire

Critical thinking skills are difficult to acquire because the mind is a believing machine, as this classic psychology study demonstrates.

What is the mind’s default position to critical thinking: are we naturally critical or naturally gullible?

As a species do we have a tendency to behave like Agent Mulder from the X-Files who always wanted to believe in mythical monsters and alien abductions?

Or are we like his partner Agent Scully who applied critical thinking, generating alternative explanations, trying to understand and evaluate the strange occurrences they encountered rationally?

Do we believe what the TV, the newspapers, blogs even, tell us at first blush or do we use critical thinking processes?

Can we ignore the claims of adverts, do we lap up what politicians tell us, do we believe our lover’s promises?

It’s not just that some people do think critically and some people don’t think critically; in fact all our minds are built with the same first instinct, the same first reaction to new information.

But what is it: do we believe first or do we first understand, so that belief (or disbelief) comes later?

Critical thinking skills: Descartes vs. Spinoza

This argument about whether belief is automatic when we are first exposed to an idea or whether belief is a separate process that follows understanding has been going on for at least 400 years.

The French philosopher, mathematician and physicist René Descartes (below, right) argued that understanding and believing are two separate processes.

First, people take in some information by paying attention to it, then they decide what to do with that information, which includes believing or disbelieving it.

Descartes’ view is intuitively attractive and seems to accord with the way our minds work, or at least the way we would like our minds to work.

The Dutch philosopher Baruch Spinoza, a contemporary of Descartes, took a quite different view.

He thought that the very act of understanding information was believing it.

We may, he thought, be able to change our minds afterwards, say when we come across evidence to the contrary, but until that time we believe everything.

Spinoza’s approach is unappealing because it suggests we have to waste our energy using critical thinking to root out falsities that other people have randomly sprayed in our direction, whether by word of mouth, TV, the internet or any other medium of communication.

So who was right, Spinoza or Descartes?

Research on critical thinking skills

Daniel Gilbert and colleagues put these two theories head-to-head in a series of experiments to test whether understanding and belief operate together or whether belief (or disbelief) comes later ( Gilbert et al., 1993 ).

In their classic social psychology experiment on critical thinking, seventy-one participants read statements about two robberies then gave the robber a jail sentence.

Some of the statements were designed to make the crime seem worse, for example the robber had a gun, and others to make it look less serious, for example the robber had starving children to feed.

The twist was that only some of the statements were true, while others were false.

Participants were told that all the statements that were true would be displayed in green type, while the false statement would be in red.

Here’s the clever bit: half the participants where purposefully distracted while they were reading the false statements while the other half weren’t.

In theory, if Spinoza was correct, then those who were distracted while reading the false statements wouldn’t have time to process the additional fact that the statement was written in red and therefore not true, and consequently would be influenced by it in the jail term they gave to the criminal.

On the other hand, if Descartes was right then the distraction would make no difference as participants wouldn’t have time to believe or not believe the false statements so it wouldn’t make any difference to the jail term.

The reason critical thinking is difficult

The results showed that when the false statements made the crime seem much worse rather than less serious, the participants who were interrupted gave the criminals almost twice as long in jail, up from about 6 years to around 11 years.

In contrast, the group in which participants hadn’t been interrupted managed to ignore the false statements.

Consequently, there was no significant difference between jail terms depending on whether false statements made the crime seem worse or less serious.

This meant that only when given time to think about it did people behave as though the false statements were actually false.

On the other hand, without time for reflection, people simply believed what they read.

Gilbert and colleagues carried out further experiments to successfully counter some alternative explanations of their results.

These confirmed their previous findings and led them to the rather disquieting conclusion that Descartes was in error and Spinoza was right.

Believing is not a two-stage process involving first understanding then believing.

Instead understanding is believing, a fraction of a second after reading it, you believe it until some other critical faculty kicks in to change your mind.

We really do want to believe, just like Agent Mulder and naturally lack the critical thinking skills of Agent Scully.

Believe first, ask questions later

Not only that, but their conclusions, and those of Spinoza, also explain other behaviours that people regularly display:

• The fundamental attribution error : this is people’s assumption that others’ behaviour reflects their personality, when really it reflects the situation.
• Truthfulness bias: people tend to assume that others are telling the truth, even when they are lying.
• The persuasion effect: when people are distracted it increases the persuasiveness of a message.
• Denial-innuendo effect: people tend to positively believe in things that are being categorically denied.
• Hypothesis testing bias: when testing a theory, instead of trying to prove it wrong people tend to look for information that confirms it. This, of course, isn’t very effective hypothesis testing!

When looked at in light of Spinoza’s claim that understanding is believing, these biases and effects could result from our tendency to believe first and ask questions later.

Take the fundamental attribution error: when meeting someone who is nervous we may assume they are a nervous person because this is the most obvious inference to make.

It only occurs to us later, when applying critical thinking skills, that they might have been worried because they were waiting for important test results.

If all this is making your feel rather uncomfortable then you’re not alone.

Gilbert and colleagues concede that our credulous mentality seems like bad news.

It may even be an argument for limiting freedom of speech.

After all, if people automatically believe everything they see and hear, we have to be very careful about what people see and hear.

Disadvantages of too much critical thinking

Gilbert and colleagues counter this by arguing that too much critical thinking or even cynicism is not a good thing.

Minds working on a Descartian model would only believe things for which they had hard evidence.

Everything else would be neither believed or not believed, but in a state of limbo.

The problem is that a lot of the information we are exposed to is actually true, and some of it is vital for our survival.

If we had to go around applying critical thinking to our beliefs all the time, we’d never get anything done and miss out on some great opportunities.

Minds that work on a Spinozan model, however, can happily believe as a general rule of thumb, then check out anything that seems dodgy later.

Yes, they will often believe things that aren’t true, but it’s better to believe too much and be caught out once in a while than be too cynical and fail to capitalise on the useful and beneficial information that is actually true.

Or maybe by going along with this argument I’m being gullible and the harsh truth is that it’s a basic human failing that we are all too quick to take things at face value and too slow to engage our critical thinking.

I’ll leave you to ponder that one.

Author: Dr Jeremy Dean

Psychologist, Jeremy Dean, PhD is the founder and author of PsyBlog. He holds a doctorate in psychology from University College London and two other advanced degrees in psychology. He has been writing about scientific research on PsyBlog since 2004. View all posts by Dr Jeremy Dean

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Critical Thinking: Why Is It So Hard to Teach?

Learning critical thinking skills can only take a student so far. Critical thinking depends on knowing relevant content very well and thinking about it, repeatedly. Here are five strategies, consistent with the research, to help bring critical thinking into the everyday classroom.

On this page:

Why is thinking critically so hard, thinking tends to focus on a problem's "surface structure", with deep knowledge, thinking can penetrate beyond surface structure, looking for a deep structure helps, but it only takes you so far, is thinking like a scientist easier, why scientific thinking depends on scientific knowledge.

Virtually everyone would agree that a primary, yet insufficiently met, goal of schooling is to enable students to think critically. In layperson’s terms, critical thinking consists of seeing both sides of an issue, being open to new evidence that disconfirms your ideas, reasoning dispassionately, demanding that claims be backed by evidence, deducing and inferring conclusions from available facts, solving problems, and so forth. Then too, there are specific types of critical thinking that are characteristic of different subject matter: That’s what we mean when we refer to “thinking like a scientist” or “thinking like a historian.”

This proper and commonsensical goal has very often been translated into calls to teach “critical thinking skills” and “higher-order thinking skills” and into generic calls for teaching students to make better judgments, reason more logically, and so forth. In a recent survey of human resource officials 1 and in testimony delivered just a few months ago before the Senate Finance Committee, 2 business leaders have repeatedly exhorted schools to do a better job of teaching students to think critically. And they are not alone. Organizations and initiatives involved in education reform, such as the National Center on Education and the Economy, the American Diploma Project, and the Aspen Institute, have pointed out the need for students to think and/or reason critically. The College Board recently revamped the SAT to better assess students’ critical thinking and ACT, Inc. offers a test of critical thinking for college students.

These calls are not new. In 1983, A Nation At Risk , a report by the National Commission on Excellence in Education, found that many 17-year-olds did not possess the “ ‘higher-order’ intellectual skills” this country needed. It claimed that nearly 40 percent could not draw inferences from written material and only onefifth could write a persuasive essay.

Following the release of A Nation At Risk , programs designed to teach students to think critically across the curriculum became extremely popular. By 1990, most states had initiatives designed to encourage educators to teach critical thinking, and one of the most widely used programs, Tactics for Thinking, sold 70,000 teacher guides. 3 But, for reasons I’ll explain, the programs were not very effective — and today we still lament students’ lack of critical thinking.

After more than 20 years of lamentation, exhortation, and little improvement, maybe it’s time to ask a fundamental question: Can critical thinking actually be taught? Decades of cognitive research point to a disappointing answer: not really. People who have sought to teach critical thinking have assumed that it is a skill, like riding a bicycle, and that, like other skills, once you learn it, you can apply it in any situation. Research from cognitive science shows that thinking is not that sort of skill. The processes of thinking are intertwined with the content of thought (that is, domain knowledge). Thus, if you remind a student to “look at an issue from multiple perspectives” often enough, he will learn that he ought to do so, but if he doesn’t know much about an issue, he can’t think about it from multiple perspectives. You can teach students maxims about how they ought to think, but without background knowledge and practice, they probably will not be able to implement the advice they memorize. Just as it makes no sense to try to teach factual content without giving students opportunities to practice using it, it also makes no sense to try to teach critical thinking devoid of factual content.

In this article, I will describe the nature of critical thinking, explain why it is so hard to do and to teach, and explore how students acquire a specific type of critical thinking: thinking scientifically. Along the way, we’ll see that critical thinking is not a set of skills that can be deployed at any time, in any context. It is a type of thought that even 3-year-olds can engage in — and even trained scientists can fail in. And it is very much dependent on domain knowledge and practice.

Educators have long noted that school attendance and even academic success are no guarantee that a student will graduate an effective thinker in all situations. There is an odd tendency for rigorous thinking to cling to particular examples or types of problems. Thus, a student may have learned to estimate the answer to a math problem before beginning calculations as a way of checking the accuracy of his answer, but in the chemistry lab, the same student calculates the components of a compound without noticing that his estimates sum to more than 100%. And a student who has learned to thoughtfully discuss the causes of the American Revolution from both the British and American perspectives doesn’t even think to question how the Germans viewed World War II. Why are students able to think critically in one situation, but not in another? The brief answer is: Thought processes are intertwined with what is being thought about. Let’s explore this in depth by looking at a particular kind of critical thinking that has been studied extensively: problem solving.

Imagine a seventh-grade math class immersed in word problems. How is it that students will be able to answer one problem, but not the next, even though mathematically both word problems are the same, that is, they rely on the same mathematical knowledge? Typically, the students are focusing on the scenario that the word problem describes (its surface structure) instead of on the mathematics required to solve it (its deep structure). So even though students have been taught how to solve a particular type of word problem, when the teacher or textbook changes the scenario, students still struggle to apply the solution because they don’t recognize that the problems are mathematically the same.

To understand why the surface structure of a problem is so distracting and, as a result, why it’s so hard to apply familiar solutions to problems that appear new, let’s first consider how you understand what’s being asked when you are given a problem. Anything you hear or read is automatically interpreted in light of what you already know about similar subjects. For example, suppose you read these two sentences: “After years of pressure from the film and television industry, the President has filed a formal complaint with China over what U.S. firms say is copyright infringement. These firms assert that the Chinese government sets stringent trade restrictions for U.S. entertainment products, even as it turns a blind eye to Chinese companies that copy American movies and television shows and sell them on the black market.” Background knowledge not only allows you to comprehend the sentences, it also has a powerful effect as you continue to read because it narrows the interpretations of new text that you will entertain. For example, if you later read the word “Bush,” it would not make you think of a small shrub, nor would you wonder whether it referred to the former President Bush, the rock band, or a term for rural hinterlands. If you read “piracy,” you would not think of eye-patched swabbies shouting “shiver me timbers!” The cognitive system gambles that incoming information will be related to what you’ve just been thinking about. Thus, it significantly narrows the scope of possible interpretations of words, sentences, and ideas. The benefit is that comprehension proceeds faster and more smoothly; the cost is that the deep structure of a problem is harder to recognize.

The narrowing of ideas that occurs while you read (or listen) means that you tend to focus on the surface structure, rather than on the underlying structure of the problem. For example, in one experiment, 4 subjects saw a problem like this one:

Members of the West High School Band were hard at work practicing for the annual Homecoming Parade. First they tried marching in rows of 12, but Andrew was left by himself to bring up the rear. Then the director told the band members to march in columns of eight, but Andrew was still left to march alone. Even when the band marched in rows of three, Andrew was left out. Finally, in exasperation, Andrew told the band director that they should march in rows of five in order to have all the rows filled. He was right. Given that there were at least 45 musicians on the field but fewer than 200 musicians, how many students were there in the West High School Band?

Earlier in the experiment, subjects had read four problems along with detailed explanations of how to solve each one, ostensibly to rate them for the clarity of the writing. One of the four problems concerned the number of vegetables to buy for a garden, and it relied on the same type of solution necessary for the band problem-calculation of the least common multiple. Yet, few subjects — just 19 percent — saw that the band problem was similar and that they could use the garden problem solution. Why?

When a student reads a word problem, her mind interprets the problem in light of her prior knowledge, as happened when you read the two sentences about copyrights and China. The difficulty is that the knowledge that seems relevant relates to the surface structure — in this problem, the reader dredges up knowledge about bands, high school, musicians, and so forth. The student is unlikely to read the problem and think of it in terms of its deep structure — using the least common multiple. The surface structure of the problem is overt, but the deep structure of the problem is not. Thus, people fail to use the first problem to help them solve the second: In their minds, the first was about vegetables in a garden and the second was about rows of band marchers.

If knowledge of how to solve a problem never transferred to problems with new surface structures, schooling would be inefficient or even futile — but of course, such transfer does occur. When and why is complex, 5 but two factors are especially relevant for educators: familiarity with a problem’s deep structure and the knowledge that one should look for a deep structure. I’ll address each in turn. When one is very familiar with a problem’s deep structure, knowledge about how to solve it transfers well. That familiarity can come from long-term, repeated experience with one problem, or with various manifestations of one type of problem (i.e., many problems that have different surface structures, but the same deep structure). After repeated exposure to either or both, the subject simply perceives the deep structure as part of the problem description. Here’s an example:

A treasure hunter is going to explore a cave up on a hill near a beach. He suspected there might be many paths inside the cave so he was afraid he might get lost. Obviously, he did not have a map of the cave; all he had with him were some common items such as a flashlight and a bag. What could he do to make sure he did not get lost trying to get back out of the cave later?

The solution is to carry some sand with you in the bag, and leave a trail as you go, so you can trace your path back when you’re ready to leave the cave. About 75% of American college students thought of this solution — but only 25% of Chinese students solved it. 6 The experimenters suggested that Americans solved it because most grew up hearing the story of Hansel and Gretel which includes the idea of leaving a trail as you travel to an unknown place in order to find your way back. The experimenters also gave subjects another puzzle based on a common Chinese folk tale, and the percentage of solvers from each culture reversed. www.aft.org/pubs-reports/american_educator/index.htm”>Read the puzzle based on the Chinese folk tale, and the tale itself.

It takes a good deal of practice with a problem type before students know it well enough to immediately recognize its deep structure, irrespective of the surface structure, as Americans did for the Hansel and Gretel problem. American subjects didn’t think of the problem in terms of sand, caves, and treasure; they thought of it in terms of finding something with which to leave a trail. The deep structure of the problem is so well represented in their memory, that they immediately saw that structure when they read the problem.

Now let’s turn to the second factor that aids in transfer despite distracting differences in surface structure — knowing to look for a deep structure. Consider what would happen if I said to a student working on the band problem, “this one is similar to the garden problem.” The student would understand that the problems must share a deep structure and would try to figure out what it is. Students can do something similar without the hint. A student might think “I’m seeing this problem in a math class, so there must be a math formula that will solve this problem.” Then he could scan his memory (or textbook) for candidates, and see if one of them helps. This is an example of what psychologists call metacognition, or regulating one’s thoughts. In the introduction, I mentioned that you can teach students maxims about how they ought to think. Cognitive scientists refer to these maxims as metacognitive strategies. They are little chunks of knowledge — like “look for a problem’s deep structure” or “consider both sides of an issue” — that students can learn and then use to steer their thoughts in more productive directions.

Helping students become better at regulating their thoughts was one of the goals of the critical thinking programs that were popular 20 years ago. These programs are not very effective. Their modest benefit is likely due to teaching students to effectively use metacognitive strategies. Students learn to avoid biases that most of us are prey to when we think, such as settling on the first conclusion that seems reasonable, only seeking evidence that confirms one’s beliefs, ignoring countervailing evidence, overconfidence, and others. 7 Thus, a student who has been encouraged many times to see both sides of an issue, for example, is probably more likely to spontaneously think “I should look at both sides of this issue” when working on a problem.

Unfortunately, metacognitive strategies can only take you so far. Although they suggest what you ought to do, they don’t provide the knowledge necessary to implement the strategy. For example, when experimenters told subjects working on the band problem that it was similar to the garden problem, more subjects solved the problem (35% compared to 19% without the hint), but most subjects, even when told what to do, weren’t able to do it. Likewise, you may know that you ought not accept the first reasonable-sounding solution to a problem, but that doesn’t mean you know how to come up with alterative solutions or weigh how reasonable each one is. That requires domain knowledge and practice in putting that knowledge to work.

Since critical thinking relies so heavily on domain knowledge, educators may wonder if thinking critically in a particular domain is easier to learn. The quick answer is yes, it’s a little easier. To understand why, let’s focus on one domain, science, and examine the development of scientific thinking.

Teaching science has been the focus of intensive study for decades, and the research can be usefully categorized into two strands. The first examines how children acquire scientific concepts; for example, how they come to forgo naive conceptions of motion and replace them with an understanding of physics. The second strand is what we would call thinking scientifically, that is, the mental procedures by which science is conducted: developing a model, deriving a hypothesis from the model, designing an experiment to test the hypothesis, gathering data from the experiment, interpreting the data in light of the model, and so forth.† Most researchers believe that scientific thinking is really a subset of reasoning that is not different in kind from other types of reasoning that children and adults do. 8 What makes it scientific thinking is knowing when to engage in such reasoning, and having accumulated enough relevant knowledge and spent enough time practicing to do so.

Recognizing when to engage in scientific reasoning is so important because the evidence shows that being able to reason is not enough; children and adults use and fail to use the proper reasoning processes on problems that seem similar. For example, consider a type of reasoning about cause and effect that is very important in science: conditional probabilities. If two things go together, it’s possible that one causes the other. Suppose you start a new medicine and notice that you seem to be getting headaches more often than usual. You would infer that the medication influenced your chances of getting a headache. But it could also be that the medication increases your chances of getting a headache only in certain circumstances or conditions. In conditional probability, the relationship between two things (e.g., medication and headaches) is dependent on a third factor. For example, the medication might increase the probability of a headache only when you’ve had a cup of coffee. The relationship of the medication and headaches is conditional on the presence of coffee.

Understanding and using conditional probabilities is essential to scientific thinking because it is so important in reasoning about what causes what. But people’s success in thinking this way depends on the particulars of how the question is presented. Studies show that adults sometimes use conditional probabilities successfully, 9 but fail to do so with many problems that call for it. 10 Even trained scientists are open to pitfalls in reasoning about conditional probabilities (as well as other types of reasoning). Physicians are known to discount or misinterpret new patient data that conflict with a diagnosis they have in mind, 11 and Ph.D.- level scientists are prey to faulty reasoning when faced with a problem embedded in an unfamiliar context. 12

And yet, young children are sometimes able to reason about conditional probabilities. In one experiment, 13 the researchers showed 3-year-olds a box and told them it was a “blicket detector” that would play music if a blicket were placed on top. The child then saw one of the two sequences shown below in which blocks are placed on the blicket detector. At the end of the sequence, the child was asked whether each block was a blicket. In other words, the child was to use conditional reasoning to infer which block caused the music to play.

Note that the relationship between each individual block (yellow cube and blue cylinder) and the music is the same in sequences 1 and 2. In either sequence, the child sees the yellow cube associated with music three times, and the blue cylinder associated with the absence of music once and the presence of music twice. What differs between the first and second sequence is the relationship between the blue and yellow blocks, and therefore, the conditional probability of each block being a blicket. Three-year-olds understood the importance of conditional probabilities.For sequence 1, they said the yellow cube was a blicket, but the blue cylinder was not; for sequence 2, they chose equally between the two blocks.

This body of studies has been summarized simply: Children are not as dumb as you might think, and adults (even trained scientists) are not as smart as you might think.What’s going on? One issue is that the common conception of critical thinking or scientific thinking (or historical thinking) as a set of skills is not accurate. Critical thinking does not have certain characteristics normally associated with skills — in particular, being able to use that skill at any time. If I told you that I learned to read music, for example, you would expect, correctly, that I could use my new skill (i.e., read music) whenever I wanted. But critical thinking is very different. As we saw in the discussion of conditional probabilities, people can engage in some types of critical thinking without training, but even with extensive training, they will sometimes fail to think critically. This understanding that critical thinking is not a skill is vital.‡ It tells us that teaching students to think critically probably lies in small part in showing them new ways of thinking, and in large part in enabling them to deploy the right type of thinking at the right time.

Returning to our focus on science, we’re ready to address a key question: Can students be taught when to engage in scientific thinking? Sort of. It is easier than trying to teach general critical thinking, but not as easy as we would like. Recall that when we were discussing problem solving, we found that students can learn metacognitive strategies that help them look past the surface structure of a problem and identify its deep structure, thereby getting them a step closer to figuring out a solution. Essentially the same thing can happen with scientific thinking. Students can learn certain metacognitive strategies that will cue them to think scientifically. But, as with problem solving, the metacognitive strategies only tell the students what they should do — they do not provide the knowledge that students need to actually do it. The good news is that within a content area like science, students have more context cues to help them figure out which metacognitive strategy to use, and teachers have a clearer idea of what domain knowledge they must teach to enable students to do what the strategy calls for.

For example, two researchers 14 taught second-, third-, and fourth-graders the scientific concept behind controlling variables; that is, of keeping everything in two comparison conditions the same, except for the one variable that is the focus of investigation. The experimenters gave explicit instruction about this strategy for conducting experiments and then had students practice with a set of materials (e.g., springs) to answer a specific question (e.g., which of these factors determine how far a spring will stretch: length, coil diameter, wire diameter, or weight?). The experimenters found that students not only understood the concept of controlling variables, they were able to apply it seven months later with different materials and a different experimenter, although the older children showed more robust transfer than the younger children. In this case, the students recognized that they were designing an experiment and that cued them to recall the metacognitive strategy, “When I design experiments, I should try to control variables.” Of course, succeeding in controlling all of the relevant variables is another matter-that depends on knowing which variables may matter and how they could vary.

Experts in teaching science recommend that scientific reasoning be taught in the context of rich subject matter knowledge. A committee of prominent science educators brought together by the National Research Council put it plainly: “Teaching content alone is not likely to lead to proficiency in science, nor is engaging in inquiry experiences devoid of meaningful science content.”

The committee drew this conclusion based on evidence that background knowledge is necessary to engage in scientific thinking. For example, knowing that one needs a control group in an experiment is important. Like having two comparison conditions, having a control group in addition to an experimental group helps you focus on the variable you want to study. But knowing that you need a control group is not the same as being able to create one. Since it’s not always possible to have two groups that are exactly alike, knowing which factors can vary between groups and which must not vary is one example of necessary background knowledge. In experiments measuring how quickly subjects can respond, for example, control groups must be matched for age, because age affects response speed, but they need not be perfectly matched for gender.

More formal experimental work verifies that background knowledge is necessary to reason scientifically. For example, consider devising a research hypothesis. One could generate multiple hypotheses for any given situation. Suppose you know that car A gets better gas mileage than car B and you’d like to know why. There are many differences between the cars, so which will you investigate first? Engine size? Tire pressure? A key determinant of the hypothesis you select is plausibility. You won’t choose to investigate a difference between cars A and B that you think is unlikely to contribute to gas mileage (e.g., paint color), but if someone provides a reason to make this factor more plausible (e.g., the way your teenage son’s driving habits changed after he painted his car red), you are more likely to say that this now-plausible factor should be investigated. 16 One’s judgment about the plausibility of a factor being important is based on one’s knowledge of the domain.

Other data indicate that familiarity with the domain makes it easier to juggle different factors simultaneously, which in turn allows you to construct experiments that simultaneously control for more factors. For example, in one experiment, 17 eighth-graders completed two tasks. In one, they were to manipulate conditions in a computer simulation to keep imaginary creatures alive. In the other, they were told that they had been hired by a swimming pool company to evaluate how the surface area of swimming pools was related to the cooling rate of its water. Students were more adept at designing experiments for the first task than the second, which the researchers interpreted as being due to students’ familiarity with the relevant variables. Students are used to thinking about factors that might influence creatures’ health (e.g., food, predators), but have less experience working with factors that might influence water temperature (e.g., volume, surface area). Hence, it is not the case that “controlling variables in an experiment” is a pure process that is not affected by subjects’ knowledge of those variables.

Prior knowledge and beliefs not only influence which hypotheses one chooses to test, they influence how one interprets data from an experiment. In one experiment, 18 undergraduates were evaluated for their knowledge of electrical circuits. Then they participated in three weekly, 1.5-hour sessions during which they designed and conducted experiments using a computer simulation of circuitry, with the goal of learning how circuitry works. The results showed a strong relationship between subjects’ initial knowledge and how much subjects learned in future sessions, in part due to how the subjects interpreted the data from the experiments they had conducted. Subjects who started with more and better integrated knowledge planned more informative experiments and made better use of experimental outcomes.

Other studies have found similar results, and have found that anomalous, or unexpected, outcomes may be particularly important in creating new knowledge-and particularly dependent upon prior knowledge. 19 Data that seem odd because they don’t fit one’s mental model of the phenomenon under investigation are highly informative. They tell you that your understanding is incomplete, and they guide the development of new hypotheses. But you could only recognize the outcome of an experiment as anomalous if you had some expectation of how it would turn out. And that expectation would be based on domain knowledge, as would your ability to create a new hypothesis that takes the anomalous outcome into account.

The idea that scientific thinking must be taught hand in hand with scientific content is further supported by research on scientific problem solving; that is, when students calculate an answer to a textbook-like problem, rather than design their own experiment. A meta-analysis 20 of 40 experiments investigating methods for teaching scientific problem solving showed that effective approaches were those that focused on building complex, integrated knowledge bases as part of problem solving, for example by including exercises like concept mapping. Ineffective approaches focused exclusively on the strategies to be used in problem solving while ignoring the knowledge necessary for the solution.

What do all these studies boil down to? First, critical thinking (as well as scientific thinking and other domain-based thinking) is not a skill. There is not a set of critical thinking skills that can be acquired and deployed regardless of context. Second, there are metacognitive strategies that, once learned, make critical thinking more likely. Third, the ability to think critically (to actually do what the metacognitive strategies call for) depends on domain knowledge and practice. For teachers, the situation is not hopeless, but no one should underestimate the difficulty of teaching students to think critically.

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May 1, 2012

How Critical Thinkers Lose Their Faith in God

Religious belief drops when analytical thinking rises

By Daisy Grewal

Why are some people more religious than others? Answers to this question often focus on the role of culture or upbringing.  While these influences are important, new research suggests that whether we believe may also have to do with how much we rely on intuition versus analytical thinking. In 2011 Amitai Shenhav, David Rand and Joshua Greene of Harvard University published a paper showing that people who have a tendency to rely on their intuition are more likely to believe in God.  They also showed that encouraging people to think intuitively increased people’s belief in God. Building on these findings, in a recent paper published in Science , Will Gervais and Ara Norenzayan of the University of British Columbia found that encouraging people to think analytically reduced their tendency to believe in God. Together these findings suggest that belief may at least partly stem from our thinking styles.

Gervais and Norenzayan’s research is based on the idea that we possess two different ways of thinking that are distinct yet related. Understanding these two ways, which are often referred to as System 1 and System 2, may be important for understanding our tendency towards having religious faith. System 1 thinking relies on shortcuts and other rules-of-thumb while System 2 relies on analytic thinking and tends to be slower and require more effort. Solving logical and analytical problems may require that we override our System 1 thinking processes in order to engage System 2. Psychologists have developed a number of clever techniques that encourage us to do this. Using some of these techniques, Gervais and Norenzayan examined whether engaging System 2 leads people away from believing in God and religion.

For example, they had participants view images of artwork that are associated with reflective thinking (Rodin’s The Thinker) or more neutral images (Discobulus of Myron). Participants who viewed The Thinker reported weaker religious beliefs on a subsequent survey. However, Gervais and Norenzayan wondered if showing people artwork might have made the connection between thinking and religion too obvious. In their next two studies, they created a task that more subtly primed analytic thinking. Participants received sets of five randomly arranged words (e.g. “high winds the flies plane”) and were asked to drop one word and rearrange the others in order to create a more meaningful sentence (e.g. “the plane flies high”). Some of their participants were given scrambled sentences containing words associated with analytic thinking (e.g. “analyze,” “reason”) and other participants were given sentences that featured neutral words (e.g. “hammer,” “shoes”). After unscrambling the sentences, participants filled out a survey about their religious beliefs. In both studies, this subtle reminder of analytic thinking caused participants to express less belief in God and religion. The researchers found no relationship between participants’ prior religious beliefs and their performance in the study. Analytic thinking reduced religious belief regardless of how religious people were to begin with.

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In a final study, Gervais and Norenzayan used an even more subtle way of activating analytic thinking: by having participants fill out a survey measuring their religious beliefs that was printed in either clear font or font that was difficult to read. Prior research has shown that difficult-to-read font promotes analytic thinking by forcing participants to slow down and think more carefully about the meaning of what they are reading. The researchers found that participants who filled out a survey that was printed in unclear font expressed less belief as compared to those who filled out the same survey in the clear font.

These studies demonstrate yet another way in which our thinking tendencies, many of which may be innate, have contributed to religious faith. It may also help explain why the vast majority of Americans tend to believe in God. Since System 2 thinking requires a lot of effort , the majority of us tend to rely on our System 1 thinking processes when possible. Evidence suggests that the majority of us are more prone to believing than being skeptical. According to a 2005 poll by Gallup, 3 out of every 4 Americans hold at least one belief in the paranormal. The most popular of these beliefs are extrasensory perception (ESP), haunted houses, and ghosts. In addition, the results help explain why some of us are more prone to believe that others. Previous research has found that people differ in their tendency to see intentions and causes in the world. These differences in thinking styles could help explain why some of us are more likely to become believers.

Why and how might analytic thinking reduce religious belief? Although more research is needed to answer this question, Gervais and Norenzayan speculate on a few possibilities. For example, analytic thinking may inhibit our natural intuition to believe in supernatural agents that influence the world. Alternatively, analytic thinking may simply cause us to override our intuition to believe and pay less attention to it. It’s important to note that across studies, participants ranged widely in their religious affiliation, gender, and race. None of these variables were found to significantly relate to people’s behavior in the studies.

Gervais and Norenzayan point out that analytic thinking is just one reason out of many why people may or may not hold religious beliefs. In addition, these findings do not say anything about the inherent value or truth of religious beliefs—they simply speak to the psychology of when and why we are prone to believe. Most importantly, they provide evidence that rather than being static, our beliefs can change drastically from situation to situation, without us knowing exactly why.

Are you a scientist who specializes in neuroscience, cognitive science, or psychology? And have you read a recent peer-reviewed paper that you would like to write about? Please send suggestions to Mind Matters editor Gareth Cook, a Pulitzer prize-winning journalist at the Boston Globe. He can be reached at garethideas AT gmail.com or Twitter @garethideas .

Think Critically Before Thinking Critically

The source of information is often as important as the information itself..

Posted February 11, 2020 | Reviewed by Daniel Lyons M.A.

This post is by Jeffrey A. Greene and Brian M. Cartiff of the University of North Carolina at Chapel Hill.

The Internet’s superabundance of information (Lankshear et al., 2000) has led to a “data smog” (Shenk, 1998) of mis- and dis-information (Wardle, 2019). This vast proliferation of dangerous information is particularly concerning given more and more people are primarily getting their news online (Fedeli & Matsa, 2018).

To help people cut through the smog, policy-makers, educators, and parents have called for a greater focus on teaching critical thinking in schools. But what is critical thinking, and can we really expect people to engage in such thinking consistently and successfully across the many topics they encounter every day? In short, the answer is no.

Concerns about people’s critical thinking, or lack thereof, extend back to the time of Plato and his stories of Socrates as the gadfly of the Athenian state and marketplace, stinging and questioning people to make them aware of their lazy and complacent thought processes. In the early 20th century, the pragmatic philosopher John Dewey pointed out that American schools were not helping students learn how to think deeply and reflectively about ideas; instead, he argued they overemphasized specific content knowledge.

Dewey claimed that the major aim of schools should be to teach critical thinking, which he defined as the “active, persistent, and careful consideration of any belief or supposed form of knowledge in the light of the grounds that support it and the further conclusions to which it tends” (Dewey, 1933, p. 9). Modern scholars including Paul (1992), Facione (1990), and Ennis (1991, 1996) have argued that critical thinking involves dispositions such as being open-minded and intellectually flexible (i.e., being willing to look at ideas from multiple perspectives) and skills such as being able to reflect on ideas and one’s own biases.

Other definitions of critical thinking focus on the “ability to engage in purposeful, self-regulatory judgment” necessary for problem-solving, reasoning, and conceptual understanding (Abrami et al., 2008, p. 1102). Regardless of the definition used, researchers have shown that people struggle to learn how to think critically, particularly when they are taught those skills outside of an academic discipline or setting, such as in “general critical thinking” courses (Abrami et al., 2015; Willingham, 2007).

Why is critical thinking so difficult for people to do well? Perhaps it is because critically evaluating information requires a tremendous amount of prior knowledge and a disposition for questioning the data and oneself, neither of which is easy to acquire. Even relatively knowledgeable people can struggle to think critically. Medical students are prone to start diagnosing themselves with the illnesses they are learning about (i.e., medical student disease; Hunter et al., 1964; Woods et al., 1966). With extensive training and experience, medical students gain knowledge to appropriately contextualize and interpret symptoms and other related health information; that is, they learn to think critically about the evidence to make appropriate diagnoses.

Similarly, the proliferation of medical information sites like WebMD has led people to diagnose themselves in ways similar to medical students (Starcevic & Berle, 2013). However, research has shown that online symptom checkers are accurate only about one-third of the time (Semigran et al., 2015), leading doctors and scholars to recommend that most people avoid using the Internet for researching illness-related information altogether (Doherty-Torstrick et al., 2016). Thus, expecting people to think critically about medical or technical, scientific information is unrealistic because most people are not medical experts; they do not have the appropriate training nor the necessary vast amounts of specific, medical knowledge.

The modern world requires critical thinking about a large variety of topics, ranging from biology (e.g., vaccines) to political science (e.g., constitutional procedures) to psychology (e.g., confirmation bias ). Yet, research has shown that it is difficult to become an expert in even one area, let alone many (Collins, 2014; Ericsson et al., 2018). So, how can we help people successfully deal with all the information they encounter, and often seek out, online and elsewhere?

The answer lies in redefining critical thinking. Good critical thinkers know when they have the disciplinary knowledge necessary to directly evaluate reasoning and evidence (i.e., first-order reasoning; Chinn & Duncan, 2018). Likewise, good critical thinkers have the self-knowledge and metacognitive skills to know when they do not possess the necessary knowledge, skills, or training to directly evaluate the evidence, and instead should shift to determining which experts or sources to believe about the topic (i.e., second-order reasoning; Chinn & Duncan, 2018).

Thus, good critical thinking sometimes requires only first-order reasoning but more often needs both the metacognitive skills to determine when second-order reasoning is required instead (Barzilai & Chinn, 2018), as well as the skills to determine reliable sources (Brante & Strømsø, 2018; Greene, 2016). Second-order reasoning skills can be taught and learned. As but one example, the Stanford History Education Group has developed a Civic Online Reasoning website with tools and curricula.

In sum, many modern scholars, employers, policymakers, and educators (e.g., Tsui, 2002) agree with Dewey that critical thinking should be a “fundamental aim and an overriding ideal of education” (Bailin & Siegel, 2003, p. 188). However, the “data smog” created by the vast amounts of often contradictory information found on the Internet calls for new views of what critical thinking involves. If people happen to have the disciplinary expertise, background knowledge, and skills to competently evaluate information and evidence about a particular topic, then they can engage first-order reasoning, which includes enacting the dispositions and cognitive skills that many critical thinking scholars have discussed in the past.

At the same time, when people do not possess such knowledge and skills, which describes most of us much of the time, apt critical thinking would involve realizing the need to switch to second-order reasoning: comparing and evaluating the sources of the information using these same dispositions and skills (Barzilai & Chinn, 2018; Wineburg & McGrew, 2017). Thus, people should think critically about thinking critically, and in many cases, evaluate the sources of information rather than the information itself.

Abrami, P. C., Bernard, R. M., Borokhovski, E., Waddington, D. I., Wade, C. A., & Persson, T. (2015). Strategies for teaching students to think critically: A meta-analysis. Review of Educational Research, 85 (2), 275-314. https://doi.org/10.3102/0034654314551063

Bailin, S., & Siegel, H. (2003). Critical thinking. In N. Blake, P. Smeyers, R. Smith, & P. Standish (Eds.), The Blackwell guide to the philosophy of education (pp. 181–193). Oxford, UK: Blackwell.

Barzilai, S., & Chinn, C. A. (2018). On the goals of epistemic education: Promoting apt epistemic performance. Journal of the Learning Sciences, 27 (3), 353–389. doi:10.1080/10508406.2017.1392968

Brante, E. W., & Strømsø, H. I. (2018). Sourcing in text comprehension: A review of interventions targeting sourcing skills. Educational Psychology Review, 30 (3), 773-799.

Chinn, C. A., & Duncan, R. G. (2018). What is the value of general knowledge of scientific reasoning? In K. Engelmann, F. Fischer, J. Osborne, & C. A. Chinn (Eds.), Scientific reasoning and argumentation: The role of domain-specific and domain-general knowledge (pp. 460-478). New York, NY: Routledge.

Collins, H. (2014). Are we all scientific experts now? Cambridge, UK: Polity.

Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to the educative process. Boston, MA: D.C. Heath and company.

Doherty-Torstrick, E. R., Walton, K. E., & Fallon, B. A. (2016). Cyberchondria: Parsing health anxiety from online behavior. Psychosomatics, 57 (4), 390–400. https://doi.org/10/ggcm5z

Ennis, R. H. (1991). Critical thinking: A streamlined conception. Teaching Philosophy, 14 (1), 5-24. https://doi.org/10.5840/teachphil19911412

Ennis, R. H. (1996). Critical thinking dispositions: Their nature and assessability. Informal Logic, 18 (2-3), 165-182. https://doi.org/10.22329/il.v18i2.2378

Ericsson, K. A., Hoffman, R. R., Kozbelt, A., & Williams, A. M. (Eds.). (2018). The Cambridge handbook of expertise and expert performance. Cambridge, UK: Cambridge University Press.

Facione, P. A. (1990). The Delphi report: Committee on pre-college philosophy. Millbrae, CA: California Academic Press.

Fedeli, S., & Matsa, K. E. (2018, July 17). Use of mobile devices for news continues to grow, outpacing desktops and laptops. Retrieved from https://www.pewresearch.org/fact-tank/2018/07/17/use-of-mobile-devices-…

Greene, J. A. (2016). Interacting epistemic systems within and beyond the classroom. In J. A. Greene, W. A. Sandoval, & I. Bråten (Eds.). Handbook of epistemic cognition (pp. 265-278). New York: Routledge.

Hunter, R. C. A., Lohrenz, J. G., & Schwartzman, A. E. (1964). Nosophobia and hypochondriasis in medical students. The Journal of Nervous and Mental Disease, 139 (2), 147-152. https://doi.org/10.1097/00005053-196408000-00008

Lankshear, C., Peters, M., & Knobel, M. (2000). Information, knowledge and learning: Some issues facing epistemology and education in a digital age. Journal of the Philosophy of Education, 34 (1), 17–39. https://doi.org/10/bkn52d

Paul, R. (1992). Critical thinking: What every person needs to survive in a rapidly changing world (2nd edition). Rohnert Park, CA: Foundation for Critical Thinking.

Semigran, H. L., Linder, J. A., Gidengil, C., & Mehrotra, A. (2015). Evaluation of symptom checkers for self diagnosis and triage: Audit study. The BMJ , h3480. https://doi.org/10/gb3sw7

Shenk, D. (1997). Data smog: Surviving the information glut . San Francisco, CA: Harper Edge.

Starcevic, V., & Berle, D. (2013). Cyberchondria: Towards a better understanding of excessive health-related Internet use. Expert Review of Neurotherapeutics, 13 (2), 205–213. https://doi.org/10/f4pknn

Tsui, L. (2002). Fostering critical thinking through effective pedagogy: Evidence from four institutional case studies. Journal of Higher Education, 73 (6), 740–763. https://doi.org/10.1080/00221546.2002.11777179

Wardle, C. (2019, September). Misinformation has created a new world disorder. Scientific American , 88-93.

Willingham, D. T. (2007). Critical thinking: Why is it so hard to teach? American Educator , 8-19.

Wineburg, S., & McGrew, S. (2017). Lateral reading: Reading less and learning more when evaluating digital information. SSRN Electronic Journal . doi:10.2139/ssrn.3048994

Woods, S. M., Natterson, J., & Silverman, J. (1966). Medical students’ disease: Hypochondriasis in medical education. Journal of Medical Education, 41 (8), 785-790. https://doi.org/10.1097/00001888-196608000-00006

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Why is Critical Thinking so Hard to Teach?

ORIGINAL RESEARCH article

Analysis of the contribution of critical thinking and psychological well-being to academic performance.

• 1 Faculty of Human Sciences, Universidad Icesi, Cali, Colombia
• 2 Department of Basic Psychology, Psychobiology and Methodology of Behavioral Sciences, University of Salamanca, Salamanca, Spain
• 3 Psychology Research Centre (CIPsi/UM), Department of Basic Psychology, School of Psychology, University of Minho, Braga, Portugal

This study examines the influence of critical thinking and psychological well-being on the academic performance of first-year college students. It emphasizes the importance of a model of psychological well-being focused on self-acceptance, environmental mastery and purpose in life, along with a critical thinking approach oriented to problem solving and decision making. A total of 128 first-year psychology students from a Spanish public university participated, assessed by means of Ryff’s psychological well-being scale (PWBS) and the PENCRISAL critical thinking test, complemented with grades obtained in a critical thinking course. The results show positive correlations between psychological well-being, critical thinking and academic performance, with a stronger relationship between critical thinking and academic performance. However, psychological well-being also plays a significant role in academic performance. The findings highlight the need for holistic pedagogical approaches that combine cognitive skills and personal development to enhance first-year students’ learning.

1 Introduction

In the context of the increasing demands of contemporary societies, in this study we address how critical thinking (CT) and psychological well-being (PWB) influence academic performance within the university setting. Upon entering university, first-year students are faced with the challenge of adapting to new academic dynamics and demands, which they must balance with the pursuit of personal satisfaction ( Acee et al., 2012 ; Casanova et al., 2018 ). The adaptation process, which involves the achievement of academic goals and the projection of long-term life objectives, is fundamental to academic performance, considered a key indicator of successful adaptation and a reflection of the competencies required in the professional environment ( Alonso-Borrego and Romero-Medina, 2016 ; Frick and Maihaus, 2016 ).

The goal of this research is to show the link between CT, which is characterized by analyzing and evaluating information, making evidence-based inferences, and reflecting on one’s own thought process for decision making and problem solving ( Bailin et al., 1999 ; Ennis, 2015 ; Jahn and Kenner, 2018 ; Saiz, 2020 ; Halpern and Dunn, 2023 ), and the PWB, which focuses on personal development ( Ryff, 1989 , 2013 ; Ryff and Keyes, 1995 ); and analyze how both contribute to academic performance. Despite the complexity of the factors that can influence academic performance, in this study we want to combine cognitive and socio-affective variables to better understand these dynamics. Based on The Ryff Psychological Well-Being Scale (PWBS), we examine how well-being, especially through self-acceptance, environmental mastery, and purpose in life impacts academic performance. As a starting point we recognize that CT may have an even greater effect on academic performance. This holistic approach seeks to contribute to the debate on the competencies needed for the 21st century through the relevance of CT and PWB in university education and their role in the formation of individuals capable of coping with contemporary demands.

1.1 Contextualization and characterization of academic performance

In the university context, academic performance is influenced by a series of factors ranging from pedagogical practices and student satisfaction with them to more personal and intrinsic elements. These include the student’s motivation and emotional state, academic background, IQ, personality traits and level of psychological maturity. This multi-layered approach focuses the complexity underlying academic performance and emphasizes the interaction between the educational environment and the individual qualities of each student.

A study by Oliván Blázquez et al. (2019) highlights the flipped classroom (FC) method in comparison to traditional lecture-based learning (LB) and shows that FC not only improves students’ grades, but also maintains their satisfaction with learning without increasing their perceived workload. Although FC was initially perceived as more difficult, this did not have a negative impact on satisfaction or long-term learning, underscoring the importance of student perceptions and involvement in the learning process. These results support the introduction of FC in higher education and point to the need for continuous adjustments based on student feedback to maximize academic performance and develop critical and practical skills.

Beyond educational practices, Gilar-Corbi et al. (2020) investigated how motivational and emotional factors and prior academic performance influence college students’ success. The study used the Motivated Strategies Learning Questionnaire (MSLQ) and the Trait Meta-Mood Scale (TMMS) to measure motivational learning strategies and emotional intelligence. The findings show that scores obtained in the diagnostic tests have a strong influence on academic performance, while emotional attention has a minor influence. The study points out that prior performance, together with self-efficacy and appropriate emotional regulation, plays a crucial role in predicting academic success. Thus, the authors suggest that interventions focused on improving self-efficacy and emotional intelligence may be key to optimizing students’ academic outcomes.

In the same context, this time with more variables, Morales-Vives et al. (2020) investigate the influence of intelligence, psychological maturity and personality traits on the academic performance of adolescents, and find that these factors combined explain about 30% of their variability. Intelligence, especially in reasoning and numerical aptitude, emerges as the most significant predictor, while psychological maturity, reflected in work orientation, and traits such as conscientiousness and openness to experience, have an indirect influence. These findings show that, although intelligence plays a decisive role, maturity and personality are in a lesser proportion.

These conclusions and the recommendations derived from them resemble recent advances in academic research. One example is the work of Mammadov (2022) , which draws attention to cognitive ability as the main predictor of academic performance, but also points to the relevance of conscientiousness, a personality trait associated with self-discipline and organization, which explains a significant part of the variability in academic performance. Mammadov also suggests that the influence of personality on performance varies by educational level, showing the dynamics between a student’s personality and his or her educational context. These findings demonstrate the need for educational strategies that promote both cognitive development and the reinforcement of positive personality traits.

Recent research on academic performance shows two consensuses. First, there is a growing understanding of the influence of the interaction between intrinsic and extrinsic factors, including pedagogical methods and motivational, emotional and cognitive elements, in improving the performance and satisfaction of students in higher education. The studies reviewed highlight the relevance of cognitive ability and personality traits such as consciousness, and promote a holistic educational approach that integrates the development of cognitive and personality dimensions. Second, academic achievement is recognized as a multidimensional construct, objectively assessed through quantitative indicators such as grade point average (GPA) and standardized assessment scores. These reflect the attainment of educational objectives and the accumulation of knowledge and skills over time.

1.2 Contextualization and characterization of critical thinking

Halpern (1998 ) argues that intrinsic effort and a willingness to analyze and solve complex problems are key competencies for learning and adapting to a constantly changing environment. According to Halpern (1998) CT transcends the mere acquisition of analytical skills and requires the development of an active predisposition to question assumptions, consider diverse perspectives, and persist in cognitive effort. This disposition is by no means innate, but can be cultivated through a pedagogy that explicitly integrates the teaching of critical skills such as logical analysis, argument evaluation, and information synthesis, and that emphasizes problem structuring to facilitate skill transfer and metacognitive self-regulation. Halpern proposes an educational framework that promotes the acquisition of these skills and encourages reflection on the thinking process so that students are able to apply CT effectively in diverse contexts and continuously improve. This methodical and structured approach characterizes CT as a set of advanced cognitive skills and an exercise of conscious judgment that is essential for informed, evidence-based decision making, which integrates non-cognitive elements ( Halpern and Dunn, 2023 ).

Throughout the development of the discourse on CT, various theories and their empirical foundations have evolved into meaningful educational practices, recognized in diverse academic settings. Meta-analyses, particularly those by Abrami et al. (2008 , 2015) have contributed significantly to the understanding of effective teaching of CT and have emphasized the need for specific and tailored teaching strategies that incorporate clear CT objectives into educational programs. These studies demonstrate that CT, defined as a process of intentional, self-regulated judgment that includes interpretation, analysis, evaluation, and inference, is increasingly recognized as essential in the knowledge era. Abrami et al. (2008) note that critical skills and dispositions are developed through explicit pedagogical interventions, as opposed to spontaneous acquisition, which challenges traditional pedagogical paradigms and fosters a shift towards intentional educational practices, placing students at the center of learning.

In addition, a more detailed analysis by Abrami et al. (2015) identifies that strategies that encourage interactive dialogue, confrontation with real problems, and individual tutorials are particularly effective. This suggests that active and meaningful learning outperforms traditional methods in the development of critical skills. This approach not only enhances students’ analytical and synthesis skills, but also facilitates the transfer of knowledge to new contexts, a key skill for the 21st century. The research reinforces the view that CT is a cross-cutting competency, crucial for navigating the complexity of contemporary challenges, and argues for an education that integrates these skills into all areas of learning.

Despite in-depth analyses of the need for CT, the growing discrepancy between rapid progress, the availability of information and the ability to critically analyze it poses a major challenge. Dwyer et al. (2014) point out that the exponential increase in global information has outpaced the ability of traditional education systems to teach effective CT skills, creating a gap that may inadequately prepare students for the challenges of today’s world. The authors argue that the ability to critically evaluate, synthesize, and apply knowledge is crucial for academic success and survival in the 21st century. This approach highlights how CT, by fostering analytical and reflective skills, transcends academia to positively impact individual and collective well-being, and argues for educational strategies that bridge the gap between information acquisition and critical analytical skills.

Recent research on this topic points to the indisputable relevance of CT as an essential component of academic performance and points to its role as a key predictor of success in educational processes. Rivas et al. (2023) show that CT transcends conventional cognitive skills. This is because CT is characterized as a rigorous practice that fosters in-depth analysis, critical evaluation and synthesis of information oriented to decision making and problem solving, fundamental skills to understand and apply knowledge in complex contexts. Research shows that CT skills not only maintain a positive correlation with academic performance, but can be significantly improved through targeted educational programs. For this reason, the authors advocate their integration into curricula and educational assessment systems to prepare students for the challenges of the 21st century, especially when phenomena such as artificial intelligence acquire greater prominence in social and professional dynamics ( Saiz and Rivas, 2023 ).

The literature on CT identifies two fundamental consensuses: first, it defines it as an intentional and deep process, oriented to problem solving and decision making, based on meticulous analysis that goes beyond logical reasoning to include a critical evaluation of the basis for judgments. In addition, it involves detailed scrutiny and integration of new information in changing contexts, as well as metacognition, i.e., conscious self-regulation of thinking that facilitates adaptation and continuous improvement of cognitive strategies in accordance with the major demands and obstacles of our first half century ( Dwyer, 2023 ). In its practical application, CT enables daily challenges to be met through informed judgments and a willingness to question and adjust perspectives in response to new information. Characterized by curiosity and adaptability, CT is essential for making responsible decisions and achieving successful outcomes, underscoring its practical value in both personal and professional settings.

Second, CT, beyond its theoretical value, can be conceived as a key theory of action for academic performance and PWB ( Saiz, 2020 ; Saiz and Rivas, 2023 ), by enhancing in individuals the ability to face and solve problems in an effective and grounded manner. CT involves crucial skills such as analysis, evaluation and synthesis, indispensable for acquiring and retaining knowledge, and also for applying it in new contexts, which improves academic performance and has, in principle, positive effects on quality of life. Thus, CT emerges as an academic competence and an essential tool for everyday life ( Dumitru and Halpern, 2023 ; Guamanga et al., 2023 ). Therefore, to synthesize theoretical paths with a practical function, we understand that “to think critically is to arrive at the best explanation of a fact, phenomenon or problem in order to know how to solve it effectively” ( Saiz, 2024 , p. 19).

1.3 Contextualization and characterization of psychological well-being

The task of relating concepts that are difficult to operationalize, such as well-being, is a major challenge; but it is necessary to approach it, more within a framework of CT understood as a means to achieve broad objectives than as an end in itself. Thinking critically transcends the mere application of skills or the accumulation of goal-oriented knowledge. In fact, it requires a detailed examination of the effect that such management has on the environment and how the satisfaction derived from reaching certain achievements is related to subjective aspects.

CT by its very deliberative and goal-oriented nature goes beyond the search for how to reach effective solutions and addresses a wider range of human and social consequences resulting from these actions ( Facione, 1990 ; Elder, 1997 ; Jahn, 2019 ). The idea is to involve non-cognitive aspects that occupy a central place in academia, and that are crucial in the interaction between specific knowledge and skills, elements widely explored in the discourse of CT. In this sense, PWB has been selected as the focus of study, recognizing it as a desirable attribute in educational processes. The challenges this poses are not lost sight of, especially when it comes to quantifying transient, subjective and normatively mediated judgments about what states or conditions are considered good, healthy or desirable in the complexity of human experience, as detailed by Flanagan et al. (2023) .

Ryff (1989 , 2013) , Ryff and Keyes (1995) contribution to the conceptual understanding and dissemination of PWB is notorious and highly valued in different fields of knowledge ( Van Dierendonck and Lam, 2023 ). The imprint of his research has been marked by criticism of a reductionist conception of PWB that simplifies well-being to the presence of positive affective states ( Ryff, 1989 ). Consequently, Ryff defends a much more complex multidimensional concept that seeks to attune the attainment of goals with the development of potentialities. Ryff’s thesis is that PWB is a multidimensional construct that transcends happiness or mere life satisfaction ( Ryff and Keyes, 1995 ).

Carol Ryff’s theory of PWB, based on humanistic, clinical and developmental psychology, as well as Aristotelian eudaimonia, focuses on self-actualization, the search for meaning and purpose in life as the core of well-being. As detailed in the text Happiness is everything, or is it? Explorations on the meaning of psychological well-being ( Ryff, 1989 ) the model consists of six dimensions that converge in personal development: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance.

The first dimension, self-acceptance, implies a positive attitude towards oneself and an acceptance of all aspects of one’s identity, including both positive and negative qualities. As for positive relationships with others, Ryff states that these are interpersonal relationships characterized by warmth, trust and genuine concern for the well-being of others; this dimension is dominated by the value of empathy in human well-being. Autonomy is defined by an individual’s capacity to maintain independence and resist social pressures in order to regulate their behavior according to internal personal norms. This dimension emphasizes self-determination as a compass for the pursuit of well-being. On the other hand, environmental mastery emphasizes the ability to effectively manage and control the external environment, which implies a feeling of competence and control over personal and professional life. Finally, purpose in life and personal growth refer to the possession of goals, direction and a sense of development and fulfillment of one’s potential. These dimensions reflect the search for meaning and continuous personal evolution as fundamental components of PWB.

Ryff’s PWBS has established itself as a key instrument in positive psychology. Research after 1989 ( Ryff and Keyes, 1995 ; Ryff, 2013 ) has explored the variability of these dimensions with age and across genders. These studies showed the influence of sociodemographic factors on well-being, so the model has been extended to consider the development of PWB across the lifespan and determined by more contextual factors such as health. The approach enriches the understanding of PWB and denotes the practical relevance of the construct in fields such as mental health and social policy. Ryff’s work has inspired other researchers to discuss and extend its principles ( Van Dierendonck and Lam, 2023 ). For example, Huppert (2009) complements Ryff’s dimensions by emphasizing the management of negative emotions and resilience as key components of sustainable well-being; Huppert aligns this view with the World Health Organization (WHO) definition of health and adds a dynamic dimension on overcoming adversity. This theoretical and practical deepening demonstrates the robustness and adaptability of Ryff’s model. The synthesis of these contributions confirms the value and applicability of Ryff’s PWBS; they reveal how the eudaemonic model not only reinforces an academic discourse, but also guides practices that promote well-being in different contexts and consolidates itself as a vital field in human development.

However, due to the same complexity and extension of the PWB construct, Ryff’s PWBS has different observations that question its theoretical and statistical foundations. On the first aspect, the work of Disabato et al. (2016) , by examining the distinction between hedonic and eudaimonic well-being, problematizes the theoretical basis of this dichotomy. Through an analysis incorporating data from 7,617 individuals from 109 countries, the authors find that there is no clear distinction between hedonic well-being experiences, focused on pleasure, and eudaimonic ones, related to personal fulfillment. The results indicate a high correlation between the two types of well-being ( r  = 0.96). This suggests that people do not significantly differentiate between pleasure seeking and self-fulfillment in their perception of well-being. This implies that the hedonic-eudaimonic dichotomy may not hold empirically and, therefore, a unified model of well-being that reflects the current behavioral dynamics should be sought.

From a statistical perspective, Ryff and Keyes (1995) analyses show that the PWBS, composed of 18 items, meets psychometric criteria and shows strong internal and moderate correlations among different scales. Correlations between dimensions range from low to modest (0.13 to 0.46), suggesting that each dimension addresses unique aspects of well-being. From the theoretical model, this diversity underscores that, although interrelated, the dimensions represent unique aspects of psychological well-being. In terms of specific results, studies indicate that with age the dimensions of environmental mastery and autonomy increase, while purpose in life and personal growth tend to decrease, with no significant changes in self-acceptance and positive relationships with others. Women outperform men on positive relationships with others and personal growth, suggesting that changes in these dimensions reflect evolving priorities and perceptions of personal development across the life span ( Ryff and Keyes, 1995 ).

On the number of dimensions of PWBS, Blasco-Belled and Alsinet (2022) note that the six-dimensional theoretical model has generated debate even among experts in the field. Some suggest that a four-dimensional model-environmental mastery, personal growth, purpose in life, and self-acceptance-might represent a second-order PWB factor, indicating a possible conceptual overlap between Ryff’s original dimensions; others exclude positive relationships with others and autonomy from the model. The study of Ryff’s PWBS by network analysis conducted by Blasco-Belled and Alsinet (2022) shows four different dimensions, in one of these, the most important node of the network, self-acceptance, purpose in life and environmental mastery are grouped, with special emphasis on self-acceptance because of its centrality in the network at the item level.

In the Spanish-speaking context, Nogueira et al. (2023) identified three main factors: autonomy, positive relationships with others, and competence. This suggests that PWBS may vary according to cultural and contextual factors. Furthermore, although it is not a study analyzing the dimensions of Ryff’s PWBS, the study by Páez-Gallego et al. (2020) applied the PWBS to Spanish adolescent students and found a strong positive correlation with the use of adaptive decision-making strategies. Specifically, the findings show that the adaptive approach is significantly associated with improvements in self-acceptance, environmental mastery, and purpose in life. In contrast, maladaptive strategies characterized by impulsivity and avoidance are associated with lower PWB. From this we infer that fostering effective decision-making skills is important for well-being and, in particular, we identify from empirical studies the dimensions of PWBS that correlate with post decisional skills.

Taken together, these findings suggest that Ryff’s PWBS, although pioneering and widely used, could benefit from revision to more accurately reflect the structure of PWB and its application in diverse cultural and educational contexts. The convergence of evidence from factorial and network analysis perspectives points to the need for a more integrated and adaptive model capable of capturing the complexity and dynamics of the underlying constructs. This underscores the continuing interest in PWB in research and practice. It is also an indication of the ongoing scholarly debate about its conceptualization and measurement. The recurrence of dimensions such as self-acceptance, environmental mastery, and purpose in life across analyses suggests a common core of PWB. This raises the question of whether these dimensions can be conceptually aligned with academic achievement and CT. In addition, questioning the boundaries between hedonic and eudaimonic raises the issue of whether a broader construct is needed to analyze well-being in educational settings. In this context, we start from the premise that self-acceptance, environmental mastery, and purpose in life are sufficient to explore college students’ PWB. These dimensions reflect students’ ability to recognize their strengths and weaknesses, set goals, and navigate effectively in their educational environment, aspects that could be considered part of the dispositional component necessary for the development of higher-level competencies such as those of the CT.

The research brings to empirical analysis the complex interplay between CT, PWB, and academic performance in the university context. We seek to answer how CT skills and PWB influence college students’ academic performance; and, how CT practices can be aligned with PWB to improve academic performance. We propose that the study variables converge in both a theoretical and an empirical model. The argumentative strategy consists of analyzing the direct impact of CT on academic performance, assessing whether PWB correlates with better academic outcomes, examining in detail the predictive factor of the relationship between CT and PWB on academic performance, and finally, according to the data obtained, proposing some dialogic bridges between cognitive and non-cognitive aspects of CT.

2 Methodology

2.1 participants.

The study involved 128 first-year psychology students from a Spanish public university. The vast majority were women (83.1%), with only 16.9% men, which is usual in social sciences and humanities degrees. Age ranged from 18 to 33 years, with a mean of 19.28 (SD = 1.73). The sample was essentially composed of students who had completed secondary education (75.3% of the students were 19 years old). Between the ages of the students according to sex — females ( M  = 19.09, SD = 0.814) and males ( M  = 20.20, SD = 3.78) — there were no statistical differences, but the age of the males was not only higher, but also more dispersed.

2.2 Instruments

The instruments applied were Ryff’s PWBS in its Spanish adaptation ( Díaz et al., 2006 ) and the PENCRISAL critical thinking test ( Saiz and Rivas, 2008 ; Rivas and Saiz, 2012 ). For academic performance, the academic records of the students participating in the critical thinking course in the first year of the psychology graduation were collected. The grades have an ascending interval from 1 to 10.

Ryff’s PWBS as mentioned in the previous discussion has different models. This instrument aims to measure psychological well-being, focusing on students’ own evaluations of their situations and perceived success in various aspects of life and personal development. It explores well-being through six main dimensions, self-acceptance (α: 0.83), positive relationships with others (α: 0.81), environmental mastery (α: 0.71), autonomy (α: 0.73), purpose in life (α: 0.83) and personal growth (α: 0.68). The questionnaire consists of 39 items, presented in a Likert scale format ranging from 1 (strongly disagree) to 6 (strongly agree) ( Díaz et al., 2006 ).

Consistent with the complexity of the scale and some data in common with other studies, we have chosen to consider only self-acceptance, environmental mastery and purpose in life. In support of this methodological decision, we have performed with our sample an exploratory factor analysis (principal components method) to see if these three dimensions converge in the same factor. The data confirm this convergence and show that this single factor has an eigenvalue of 2.43 and explains a very high value of the variance of its results (81.1%).

In the case of the PENCRISAL, the full version was applied, and the score was taken for each of the five dimensions and the total score. The PENCRISAL was applied to measure CT skills. This test consists of 35 problem situations that participants answer in an open-response format. The test is organized into five key areas: deductive reasoning, inductive reasoning, practical reasoning, decision making and problem solving.

The deductive and inductive component tests different forms of reasoning, such as propositional, categorical, causal, analogical and hypothetical. Decision-making measures the ability to make probabilistic judgments and to effectively use heuristics to identify potential biases. The problem-solving section poses participants with general and specific problems that require appropriate solution strategies. These sections are intended to encourage the application of strategies necessary for effective problem planning. The open-ended question format encourages participants to justify their answers, which are evaluated using a scoring system that rates the quality of their responses on a scale of 0 to 2. Responses are converted into numerical scores using item-specific criteria. These are used to describe and identify the thinking mechanisms underlying each response. A score of 0 indicates that the answer is incorrect, 1 indicates that the answer is correct but no or inadequate justification is provided, and 2 indicates that the answer is correct and adequate justification is provided. The PENCRISAL yields an overall score of the CT ranging between 0 and 70 and between 0 and 14 for each dimension. Reliability assessments show satisfactory accuracy, with a minimum Cronbach’s Alpha of 0.632 and a test–retest reliability of 0.786 ( Rivas and Saiz, 2012 ). The test is administered online through the SelectSurvey.NET V5 platform.

2.3 Procedures

Students gave their free and informed consent to participate in the study. The PWBS was carried out at the beginning of the semester of the CT course. The PENCRISAL test was taken at the beginning and at the end of the academic period. Only the results of students who completed both instruments are considered. Academic performance is represented by the grade obtained by students at the end of the course. Statistical analyses were performed with IBM/SPSS version 29.0. After performing the descriptive statistics, we proceeded to a correlation analysis and, finally, we evaluated the impact of the PWBS and the CT on the variance of academic performance by performing a regression analysis.

Table 1 presents the descriptive data of the students’ scores on the two instruments applied, and the measure of academic performance. In addition to the minimum and maximum values, the mean, standard deviation and indicators of skewness and kurtosis of the distribution of the results are presented.

Table 1 . Descriptive statistics for the measures used ( n  = 128).

Observing the results, we can see a distribution with a slight tendency towards values above the mean (m = 79.80) for the PWBS, which is reflected in a negative skewness (−0.437). With respect to the five dimensions of CT, it can be stated globally that the mean value of DR, IR and PS is moving away from the maximum value observed and towards the minimum value, which represents a positive symmetry. The opposite situation occurs with the RP dimension. Regarding the TCT, the data show a tendency to scores around the mean (m = 37.21), as can be deduced from the residual values of skewness and kurtosis. Regarding the AP, the data suggest a balanced distribution of academic scores around an intermediate value between 3.66 and 9.01 as scores at the lower and upper extremes (m = 6.10), with very low skewness and kurtosis.

In general, the results show good variability or dispersion, since the mean of each variable is located in the center of the data interval, which is desirable in research to adequately represent the population studied. Skewness and kurtosis indices close to zero for academic achievement are especially indicative of a normal or Gaussian distribution of values. The slightly higher kurtosis in the IR dimension of CT (2.248) is still acceptable.

Table 2 shows the correlations between the variables in this study. Since these were interval metric variables, Pearson’s product x moment method was used to calculate the correlations. For statistical significance, the two-tailed test was used and p  < 0.05 was set as the limit of significance.

Table 2 . Correlations between study variables.

According to the data, the highest correlation is found between TCT and AP, with the lowest correlation being between CT and PWBS measurement (no correlation). At an intermediate level is the correlation between PWBS and AP. Likewise, all the dimensions of the CT correlate with the AP with values between 0.183 (PS) and 0.337 (PR). As can be seen, there are variations in the correlations among the five dimensions of the CT, but all have high correlations with the total score (between 0.502 and 0.668). In this sense, only the TCT score is used for the regression statistical analysis.

In summary, the data suggest that there is a significant and positive relationship between PWBS and AP, as well as an even stronger and more significant relationship between TCT and AP. There is no evidence of a significant relationship between PWBS and TCT. To further explore the relationships between cognitive and noncognitive variables in AP, we turned to a regression analysis. We opted for a linear regression with PWBS and TCT as predictors and AP as the criterion or dependent variable. Table 3 presents the regression values obtained.

Table 3 . Impact of psychological well-being and critical thinking on academic performance.

The regression model was found to be statistically significant, with an F -value (2, 88) = 18.571, p  < 0.001. This indicates that, collectively, PWBS and TCT provide significant prediction of AP. The coefficient of determination (R 2 adj.) is 0.285, which means that approximately 30% of the variability in AP can be explained by the independent variables in the model. As can be seen from the t -values and significance, both variables have a significant impact on AP, although TCT has a greater impact.

In a complementary manner, with the objective of enriching the analysis of the influence of the CT on the PA, we have included additional measures to the grade obtained by the students in the course (NCT), such as the selectivity grade with which they entered the university (NEBAU), the average grade of the transcript (NMEXP), that is, the grades of the other courses that the students must take, and the pretest results obtained with the PENCRISAL (PCT). The data obtained are recorded in Table 4 .

Table 4 . Correlations between study variables and complementary measures.

Table 4 shows that the relationship between PWBS and NMEXP has a Pearson correlation of 0.075, with a p -value of 0.372. This low correlation indicates that the connection is minimal. In contrast, the relationship between TCT and NMEXP shows a stronger correlation of 0.464**, suggesting a moderate positive association. The significance of this correlation, less than 0.001, indicates a statistically significant relationship, which implies that this result is not likely to be a coincidence. A similar case occurs with the relationship between NEBAU and NMEXP.

Given this context, if we perform a multiple linear regression analysis with NMEXP as the dependent variable and PWBS and TCT as independent variables, we would expect TCT to have a more significant impact on NMEXP. This projection is based on the statistically significant correlation of these variables. On the other hand, NEBAU has a slightly lower correlation with NMEXP compared to TCT (0.455 vs. 0.464), but the difference is very small, indicating that both have similar impact capacity for NMEXP in terms of linear correlation.

Confirmation of these hypotheses by appropriate regression analysis will provide a more detailed and accurate understanding of how PWBS and TCT individually contribute to the prediction of NMEXP, considering the influence of interrelated variables. However, in performing this procedure, a reduction in sample size to only 64 cases were observed. This increases the risk of failing to detect significant differences or could lead to unstable effect estimates.

4 Discussion and conclusions

The CT seeks to understand and effectively solve problems, through a correct approach, the generation of solution alternatives filtered by the mechanism of explanation and the selection of a solution, all with the aim of achieving a desired change. The PENCRISAL test is based on this defining framework of the CT ( Saiz and Rivas, 2008 ; Rivas and Saiz, 2012 ). Therefore, if we start from this concept and look at the data, we can conclude that the CT is a good predictor of academic performance.

Table 2 shows a positive and moderate correlation (0.514) between the CT and academic performance, suggesting that an increase in the CT is associated with an improvement in academic performance. Meanwhile, Table 3 shows — with a B coefficient of 0.074 and a Beta of 0.473 — that CT has a stronger relationship with academic performance compared to PWBS. This means that for every unit increase in CT, academic performance increases on average 0.074 units, and this effect is considerably significant in the model. The robust correlation and the impact indicated as a dependent variable highlight that the CT is a determinant competence of academic performance and is suggested as a relevant diagnostic and formative tool in the educational field. Although it is not the only factor that influences academic performance, the CT is presented as a significant predictor and one that can be worked on or trained in the classroom.

Declaratively, the current study coincides with other results obtained and recorded in Rivas et al. (2023) . On that occasion, the authors found that CT is a predictor of academic performance and that the benefits of instruction can be sustained over time. The study showed a correlation between CT and academic performance of 0.32. The main difference between these two studies concerns the objectives. The previous study did not attend to the explicit discussion of how CT could influence well-being, or vice versa. The current work recovers this line and incorporates non-cognitive variables in the analysis framework to account for well-being, under the assumption that this construct should have a significant impact on academic performance.

More generally, if we consider that, although the construct intelligence is not the same as CT, they do have several points of convergence ( Butler et al., 2017 ), then we can establish a dialogue with other studies on the factors that influence academic performance. Intelligence represents the intrinsic capacity to learn, understand, reason, and meet challenges through problem solving to adapt to the environment ( Sternberg, 1985 ). This cognitive potentiality manifests itself in various ways, being the CT one of its most relevant expressions, particularly in situations that demand deep analysis, evaluation, and decisions based on logical reasoning ( Saiz, 2024 ). The CT, therefore, acts as an essential tool that intelligence employs to effectively navigate through complex and challenging real-world situations ( Halpern and Butler, 2018 ).

In this conceptual line, the current results partially coincide with studies that have shown that the best predictors of academic performance are cognitive components, such as measures of general intelligence, analogical reasoning, fluid intelligence, logical, verbal and quantitative reasoning ( Morales-Vives et al., 2020 ; Mammadov, 2022 ); as well as scores on the diagnostic and university entrance test ( Gilar-Corbi et al., 2020 ).

In our study the other factor of analysis was the PWB. Although due to its non-cognitive nature it would be per se at a disadvantage compared to cognitive factors, the data also show that its inclusion in educational research, especially to account for academic performance, is significant. In Table 2 , the analysis of the correlation between PWBS and academic performance reveals a positive relationship with a correlation coefficient of 0.336. Although the correlation is moderate and not as strong as that observed between CT and academic performance, it is still significant and should not be ignored in the pursuit of improving students’ academic performance. Table 3 shows that PWBS has a positive and significant influence on the dependent variable. The standardized coefficient (Beta) of 0.271 indicates that there is a positive relationship between PWBS and academic achievement. The unstandardized coefficient (B) shows that, holding all other variables constant, for each unit increase in PWBS, academic performance increases on average 0.022 units. This relationship, supported by a low standard error of 0.007, points to a moderate but significant contribution of PWBS compared to other variables.

These findings show that the integration of some aspects of PWBS could be an effective strategy to improve academic performance, evidencing a beneficial and significant relationship between both aspects. PWB can influence academic performance through non-cognitive conditions or factors involved in learning, such as motivation, academic satisfaction, effective coping with stress or anxiety, and the acceptance and management of limitations related to the process of appropriation and adaptation to one’s own identity.

However, it is important to emphasize that the PWB is a construct that requires careful theoretical and empirical review in the educational context, as the Ryff scale has open debates and the lack of uniqueness of criteria on the number of dimensions influences these results. To cite just one case, we have used three dimensions out of six, with statistical and literature support, but the data may be different with a different selection approach. This finding highlights the importance of students’ PWB as part of a comprehensive educational strategy, but also shows that the direct impact of PWB on academic performance may be less pronounced than the impact of cognitive skills, and that due to its very multidimensional and complex nature, it is not easy to converge in an instructional design. Despite this, higher education institutions can take care of the institutional and relational climate so that students feel good and take advantage of the formative and educational opportunities of the academic environment. In the case of CT, there are concrete and validated training strategies that make it possible to improve skills such as argumentation, explanation, problem solving and decision making ( Guamanga et al., 2023 ; Saiz, 2024 ). On the PWB side, the same cannot be said due to the lack of empirical support; however, some studies have proposed a path that incorporates socio-emotional competences in the training of CT, a proposal characterized by the cognitive-emotional methodology, with interesting results that still need to be explored and debated ( Hanna, 2013 ).

Table 2 shows low and non-significant correlations between PWBS and the different forms of reasoning (deductive, inductive and practical), as well as with decision making and problem solving. For example, the correlation between PWBS and deductive reasoning is −0.082, which is not only low, but also lacks statistical significance. Additionally, the correlation between PWBS and decision making is −0.132, which is also a low correlation and not significant. Although there is a positive correlation between PWBS and problem solving (0.040), it is very low and not statistically significant, so there is not enough evidence to claim a positive relationship between these variables. This reinforces the idea that there is not a direct and significant relationship between how a student feels psychologically and CT skills or, nuanced is not supported by the data from this sample. It is possible that there are unexamined mediating factors that influence these relationships or that the relationship exists in a different context or with different measures.

The results of the present study do not coincide with other research that has shown positive relationships between decision-making and PWBS, especially with self-acceptance, environmental mastery, and purpose in life. The study by Páez-Gallego et al. (2020) addresses this issue by exploring how the PWBS of adolescents in Madrid, Spain, is linked to their decision-making methods. The research concludes that there is a positive correlation between the use of adaptive decision-making strategies and PWBS. Adolescents who opt for a rational and systematic evaluation of available options report higher levels of well-being. Specifically, adaptive decision-making style correlates significantly with overall well-being (0.544) and with aspects such as self-acceptance (0.485), positive relationships with others (0.242), environmental mastery (0.472), autonomy (0.359), purpose in life (0.473), and personal growth (0.346). In contrast, those who resort to maladaptive strategies, marked by impulsivity or avoidance, show reduced PWBS (−0.458).

The discrepancy in results with this study could be due to the difference between the instruments used to assess decision making. While Páez-Gallego et al. (2020) used the Flinders Adolescent Decision Making Questionnaire (FADMQ), which focuses on personal perceptions and experiences of decision making, our study uses the PENCRISAL, which although not limited to decision making, does include this ability as an essential component of the CT. The latter measures the ability to identify, analyze and solve everyday problems through items that simulate real situations, assessing the ability to choose the best solution or action strategy. Because the PENCRISAL responses are open-ended, it allows for a detailed assessment of how participants describe or explain their decisions. Ultimately, the fundamental difference between these two measures is that one is a self-report of perceptions and experiences, while the other is a set of problems to be solved correctly; in other words, one collects impressions of decision making and the other collects realized decision making. Therefore, although both studies applied Ryff’s PWBS, the differences between instruments and approach to decision making explain the variations in the results. This divergence evidences the relevance of considering the context and the specific instrument when interpreting the relationship between the PWBS and decision making.

Despite these findings, the need to further explore these interactions persists, especially given that the three selected dimensions-self-acceptance, environmental mastery, and life purpose-theoretically align with CT approaches focused on explanation and the development of post decisional skills, such as decision making and problem solving ( Guamanga et al., 2023 ). A CT approach that emphasizes the development of these skills must consider effects that transcend immediate or tangible outcomes. Therefore, it is crucial to understand how the concept of PWB, as examined above, relates to CT. Specifically, it must be determined whether some of these dimensions align directly to foster effective CT, or whether they instead lean more towards a conception of well-being in a more general sense, which could include hedonic aspects.

The emphasis on CT oriented to decision making and problem solving through the analysis of explanations and causalities should be evaluated for its pragmatic effects on PWB. At first glance this idea seems to confront parallel concepts paradoxically united by the same diachronic nature. In the case of the CT, this nature explains the high demands placed on it. For example, it is not enough to say that it contributes to tangible improvements in academic performance, but its usefulness is expected to transcend beyond academia and materialize in skills of interest to organizations in all sectors of the economy ( Casner-Lotto and Barrington, 2006 ; Atanasiu, 2021 ). However, their practical impact still presents serious challenges, especially when students, as active subjects of learning, face limitations in anticipating the usefulness and applicability of these critical skills for the future. This is partly explained by the fact that the educational system prioritizes academic performance over the comprehensive development required later in the professional sphere ( Saiz, 2020 ). Which means that the CT can be interpreted as an unfulfilled or partial promise. It is certainly a reading that omits the particular contexts, interests, motivations and concerns of students while they are part of these instructional programs and then the same factors analyzed by a student who knows that he or she must make the transition to the professional field.

A similar case happens with PWB as a diachronic phenomenon. An instant in time is not enough to understand and analyze students’ PWB. It is necessary to focus on how it changes and evolves through different stages, including through feelings of achievement or frustration in the academic process. Thus, it is recognized that PWB is not static and, therefore, evolves through lived experiences, among them, those comprising the applicability of a series of learned skills. This implies that as diachronic phenomena they can evolve and influence each other over time. This approach requires longitudinal studies to follow the evolution of the impact of curricular interventions aimed at strengthening cognitive skills such as those of the CT, in order to understand how these may influence the PWB in the long term.

The limitations of this study, beyond having a small sample that prevents the generalization of the results or having examined only certain dimensions of the PWBS, added to the theoretical impossibility of performing regression analyses with other performance measures, lie in the diachronic nature of the constructs studied. This characteristic makes it difficult, as has been argued, to give a definitive answer on the relationship.

Within the framework of the PWBS triad model we are analyzing, it is possible to theoretically group several key concepts. The development of the CT involves a process of self-acceptance, which is crucial given our inherent tendency for error. This process allows us, through a reflective evaluation of our past and present, to recognize and accept beliefs that we have discarded as erroneous. This self-acceptance facilitates deeper introspection, allowing us to see these errors as essential learning opportunities in our lives. On the other hand, any model that emphasizes post-decisional skills must also consider the non-linear complexity of our reality, and provide solid criteria for problem solving and decision making to master our environment more effectively. This is what allows us to adapt better, both biologically and socially. Finally, this approach to TC inevitably values purpose in life by seeking to ensure that it is in part determined by integrating the best tools of science, philosophy and education for a more effective life orientation, grounded in the principles of rationality. The importance of setting clear goals, recognizing that their achievement requires effort, discipline and determination, is essential to being an effective critical thinker.

Therefore, although each dimension proposed by Ryff’s PWBS possesses a conceptual richness that requires empirical validation, the dimensions selected for this study are aligned with a model of CT focused on problem solving and real-world decision making. Although we aspired to discover stronger links between PWB and CT, and to deepen their interrelationship, the theoretical parallelism analyzed is also reflected in the empirical results. Moreover, PWB as an operational concept, due to its complexity and multidimensionality, is subject to continuous revisions or possible unifications into a broader notion of well-being.

In future research on this topic, it is essential to include a broader set of variables predictive of academic performance. This includes, but is not limited to, students’ selectivity record and cumulative grades in other subjects. In addition, a more solid and theoretically robust concept of well-being must be adopted, one that fits contemporary educational and professional demands. This concept must transcend the simple distinction between eudaemonic and hedonic well-being, and address its diachronic nature. It is important to explore how these dimensions of well-being are interrelated, either as cause or effect; and to examine whether CT fosters a virtuous circle with well-being.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.

Ethics statement

Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

MG: Writing – original draft, Conceptualization, Investigation, Methodology, Writing – review & editing. CS: Investigation, Methodology, Project administration, Validation, Writing – original draft. SR: Data curation, Investigation, Supervision, Validation, Writing – review & editing. LA: Formal analysis, Methodology, Validation, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Universidad de Salamanca, Spain.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Keywords: critical thinking, psychological well-being, academic performance, higher education, assessment

Citation: Guamanga MH, Saiz C, Rivas SF and Almeida LS (2024) Analysis of the contribution of critical thinking and psychological well-being to academic performance. Front. Educ . 9:1423441. doi: 10.3389/feduc.2024.1423441

Received: 25 April 2024; Accepted: 28 June 2024; Published: 16 July 2024.

Reviewed by:

Copyright © 2024 Guamanga, Saiz, Rivas and Almeida. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Carlos Saiz, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Why the Global CrowdStrike Outage Hit Airports So Hard

Early Friday morning, a flawed software update from the security firm CrowdStrike took down Windows computers across the world. For the aviation industry, the outage created the kind of chaos usually reserved for sudden, catastrophic weather—except all over the world, all at the same time.

The outage highlighted an assumed but sometimes obscured fact of the aviation industry: The systems that keep you moving in and out of airports are complex, optimized for efficiency and profit. For passengers, the upside of this system is lower ticket prices. But the downside is that if one part of the system fails, the industry can grind to a halt.

That played out in real-time on Friday. In the US, all three major airlines—Delta, American, and United—grounded flights for several hours. A handful of global airports, including Hong Kong International Airport , Kempegowda International Airport Bengaluru in India, and Liverpool’s John Lennon Airport, resorted to checking in passengers to flights by hand and urged fliers to show up well before takeoff time. By Friday afternoon, over 4,000 flights had been canceled and 35,500 delayed globally, according to the flight tracking firm FlightAware.

“Earlier today, a CrowdStrike update was responsible for bringing down a number of IT systems globally," said a Microsoft spokesperson in a statement. "We are actively supporting customers to assist in their recovery.”

Delta, American, and United may have suffered more cancellations than other airlines (including easyJet, Allegiant Air, and Southwest) because of their “hub and spoke” model. This strategy concentrates flights and crews in a few major airports—the hubs—and increases the likelihood that passengers traveling outside of the hubs will have to make connections through them. This centralization allows airlines to offer passengers more flight options, albeit through connections, and to concentrate their maintenance and ground-handling services in fewer places, saving them money.

Because the hub-and-spoke system is so dependent on quickly getting flights out of busy hub airports, airlines have come to rely on a number of automated systems to check passengers in, to update them on boarding planes or delays, to get baggage handlers in the right place at the right time, and so on, says Michael McCormick, a professor and coordinator of the Air Traffic Management program at Embry-Riddle Aeronautical University. “Automation is critical to airline operations,” he says.

But automation requires computers. When those computers go down at a given airport, the effects can cascade, and delays pile up. But when they go down at hubs, the entire aviation system gets throttled. This happens even if the technologies used to fly and direct planes while in the air are unaffected. For example: The US Federal Aviation Administration posted on X on Friday morning that it was “not impacted by the global IT issue.”

Aviation industry complexity also moves well beyond computers. Airports are sometimes likened to little cities, and for good reason: Though the airlines are the “brands” that fliers interact with most often, plenty of different businesses help get planes in the air. And some of them, it turns out, rely on CrowdStrike.

Unifi Aviation is one of the largest ground handling companies in the US and contracts with airlines to staff all sorts of airport operations, serving as gate agents, cabin cleaners, and ramp baggage handlers. Their systems also went down Friday morning, says CEO Gautam Thakkar, leaving the company unable to, for example, direct cabin cleaners where to go or have wheelchairs ready for passengers getting on and coming off flights. Fortunately for the company, there wasn’t much of that to do, because the airlines they work with weren’t flying. “Once flights could resume, we were ready to go,” says Thakkar.

The knock-on effects of the outage might linger for days, well after the aviation industry has updated its computers, says Michael McCormick, a professor and coordinator of the Air Traffic Management program at Embry-Riddle. The affected airlines “have flight crews, cabin crews, bags, aircraft scattered across the US,” he says. The time it will take to get them back into the right places to carry passengers to their scheduled destination might be measured in days, not hours, he says.

By Friday afternoon on the US East Coast, even after most global airlines had restarted flights, the flight tracking service FlightRadar24 reported that more than a third of scheduled departures out of Atlanta’s Hartsfield-Jackson International Airport—Delta Airlines’ hub—were canceled, and another 60 percent were delayed by an average of 116 minutes.

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