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Behavior Analysis in School and Education Settings

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Articles in this issue

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• Introduction to the Special Issue: Behavior Analysis in Educational Settings Duane A. Lundervold and Claire St. Peter
• Effects of the Good Behavior Game on Individual Student Behavior Jeanne M. Donaldson, Alyssa B. Fisher, and SungWoo Kahng
• Preschool Life Skills Using the Response to Intervention Model With Preschoolers With Developmental Disabilities John Michael Falligant and Sacha T. Pence
• Using Behavioral Skills Training to Teach High School Students to Implement Discrete Trial Training Evan H. Dart, Keith C. Radley, Christopher M. Furlow, and Ashley N. Murphy
• Increasing Positive and Decreasing Negative Teacher Responses to Student Behavior Through Training and Feedback Alicia A. Mrachko, Douglas E. Kostewicz, and William P. Martin
• Evaluation of Response Interruption and Redirection During School and Community Activities Kimberly N. Sloman, Rebecca K. Schulman, Mariana Torres-Viso, and Matthew L. Edelstein
• Self-and-Match System Suppresses Vocal Stereotypy During Independent Work Andrew J. Bulla and Jessica E. Frieder
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Handbook of Applied Behavior Analysis

Integrating Research into Practice

• © 2023
• Johnny L. Matson 0

Department of Psychology, Louisiana State University, Baton Rouge, USA

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• Examines the history and training methods of applied behavioral analysis (ABA)
• Discusses ethical and legal issues of applied behavioral analysis
• Explores ABA assessment, treatment, and health issues

Part of the book series: Autism and Child Psychopathology Series (ACPS)

141k Accesses

24 Citations

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About this book

This book provides comprehensive coverage of applied behavioral analysis (ABA). It examines the history and training methods of ABA as well as related ethical and legal issues. The book discusses various aspects of reinforcement, including social reinforcers, tangible reinforcers, automatic reinforcement, thinning reinforcers, and behavioral momentum. It addresses basic training strategies, such as prompts and fadings, stimulus fading, and stimulus pairing and provides insights into auditory/visual discrimination, instructional feedback, generalization, error correction procedures, and response interruption. In addition, the book addresses the use of ABA in education and explores compliance training, on-task behavior, teaching play and social skills, listening and academic skills, technology, remembering and cognitions, picture-based instruction, foreign language instruction, teaching verbal behavior, public speaking, and vocational skills. In addition, the book covers treatments for tics, trichotillomania, stereotypies, self-injurious behavior, aggression, and toe walking. It also addresses ABA for special populations, including individuals with autism, ADHD, substance abuse, and intellectual disabilities.

Featured areas of coverage include:

• Basic assessment methods, such as observing behavior, treatment integrity, social validation, evaluating physical activity, measuring sleep disturbances, preference assessment, and establishing criteria for skill mastery.
• Functional assessment, including how to quantify outcomes and evaluate results, behaviors that precede and are linked to target behaviors, and treatments.
• Treatment methods, such as token economies, discrete trial instruction, protective equipment, group-based and parent training as well as staff training and self-control procedures.
• Health issues, including dental and self-care, life skills, mealtime and feeding, telehealth, smoking reduction and cessation, and safety training.
• Leisure and social skills, such as cellphone use, gambling, teaching music, sports and physical fitness.

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An Introduction to Applied Behavior Analysis

Applied Behavior Analysis: Foundations and Applications

Applied Behavior Analysis: Conceptual Foundations, Defining Characteristics, and Behavior-Change Elements

• Academic skills and applied behavior analysis
• Autism, ADHD and applied behavior analysis
• Automatic reinforcement and ABA
• Cell phone use and ABA
• Dental and medical care and ABA
• Functional assessment and ABA
• Geriatric populations and ABA
• Go/no go and applied behavior analysis
• Intellectual and developmental disabilities and ABA
• Mealtime and feeding behavior and ABA
• Musical skills, good behavior game, and ABA
• Prompts, fading, modeling and ABA
• Response interruption and ABA
• Single case designs and applied behavior analysis
• Sleep disturbance and applied behavior analysis
• Smoking reduction and cessation and ABA
• Social skills and validation and ABA
• Substance abuse and applied behavior analysis
• Tacts and mands and ABA
• Vocational skills and applied behavior analysis

Table of contents (67 chapters)

Front matter, foundations, history of applied behavior analysis.

• Megan M. Callahan, Jill C. Fodstad, James W. Moore

Ethics and Legal Issues

• Sara Gershfeld Litvak, Darren J. Sush

Social Reinforcers

• Makenzie W. Bayles, Claudia L. Dozier, Amy H. Briggs, Sara Diaz de Villegas

Tangible Reinforcers: Conceptual Overview and Considerations for Practice

• Andrea M. Stephens, Jacqueline A. Pachis, Kayla M. Rinna, Eleah A. Sunde, Adam M. Briggs

Automatic Reinforcement

• Catia Cividini-Motta, Hannah MacNaul, Natalie R. Mandel, Alyssa Rojas, William H. Ahearn

Reinforcer Thinning: General Approaches and Considerations for Maintaining Skills and Mitigating Relapse

• Adam M. Briggs, Daniel R. Mitteer, Samantha Bergmann, Brian D. Greer

Behavioral Momentum Theory

• Sean W. Smith, Brian D. Greer

Differential Reinforcement Procedures

• Catalina N. Rey, Kaitlynn M. Gokey

Applied Behavior Analysis

Prompt and prompt-fading procedures.

• Lauren K. Schnell, Mirela Cengher, April N. Kisamore

Stimulus-Stimulus Pairing

• Natalia A. Baires, Mitch Fryling

Psychological Modeling and the Treatment of Obsessive-Compulsive and Related Disorders

• Michael Upston, Matthew Jacofsky, Fugen Neziroglu

Conditional Discrimination: What’s in a Name?

• Joseph H. Cihon, Julia L. Ferguson, Justin B. Leaf

Auditory–Visual Discriminations: Stimulus Control, Teaching Procedures, and Considerations

• Samantha Bergmann, Tiffany Kodak

Instructive Feedback: Applications in Applied Behavior Analysis

• Julia L. Ferguson, Shannon Arthur, Justin B. Leaf, Joseph H. Cihon

Generalization

• Patricio Erhard, Terry S. Falcomata

Response Interruption and Redirection

• Haley M. K. Steinhauser, William H. Ahearn

Error-Correction Procedures

• Tom Cariveau, Alexandria Brown, Delanie F. Platt

Editors and Affiliations

Johnny L. Matson

About the editor

Johnny L. Matson, Ph.D., is Professor and Distinguished Research Master in the Department of Psychology at LSU. He has served as major professor for 71 doctoral students during a 43-year career and he has more than 850 publications, including 51 books. He is founding editor for the Review Journal of Autism and Developmental Disorders.

Bibliographic Information

Book Title : Handbook of Applied Behavior Analysis

Book Subtitle : Integrating Research into Practice

Editors : Johnny L. Matson

Series Title : Autism and Child Psychopathology Series

DOI : https://doi.org/10.1007/978-3-031-19964-6

Publisher : Springer Cham

eBook Packages : Behavioral Science and Psychology , Behavioral Science and Psychology (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

Hardcover ISBN : 978-3-031-19963-9 Published: 30 April 2023

Softcover ISBN : 978-3-031-19966-0 Published: 01 May 2024

eBook ISBN : 978-3-031-19964-6 Published: 29 April 2023

Series ISSN : 2192-922X

Series E-ISSN : 2192-9238

Edition Number : 1

Number of Pages : XI, 1337

Topics : Child and School Psychology , Psychiatry , Clinical Psychology , Education, general , Developmental Psychology

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Department of Special Education

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M.Ed. in Special Education with emphasis in Applied Behavior Analysis

Applied behavior analysis (ABA) is the application of behavioral principles to change behavior in meaningful ways. Assessment and intervention strategies based in ABA are implemented with individuals of all ages, with and without disabilities, in clinics, schools, and community settings.

At the University of Utah, the M.Ed. in Special Education with emphasis in ABA prepares behavior analysts who are skilled, knowledgeable, and ethical practitioners. Students who are accepted into the program will complete all coursework requirements needed to apply to sit for the  Board Certified Behavior Analyst® exam . ABA coursework equips students with understanding of basic and advanced behavioral principles and applications of those principles, while providing ethical and theoretical frameworks for decision making as behavior analysts.

Program Mission and Values

The mission of the University of Utah ABA program is to support the professional development of behavior analysts who are skilled, knowledgeable, compassionate, and ethical through high-quality training and mentorship.

• We are committed to the continuous improvement of the science and practice of behavior analysis with a focus on socially significant behavior change.
• We believe that the effectiveness of interventions is defined by meaningful impact on the consumer and that interventions must be centered on consumer needs.
• We center social validity as a core tenet of applied behavior analysis and believe that seeking consumer feedback to inform research and practice is an essential value of the field.
• We believe in centering the autonomy of individuals with disabilities and designing interventions that promote quality of life as defined by people with disabilities and their supporters.
• We value the flexibility of applied behavior analysis and the application of behavioral principles to meet the needs of diverse consumers through contextually relevant practice.
• We are committed to the ethical and compassionate practice of applied behavior analysis, centering well-being and positive behavioral supports in our research and in our practice.
• We acknowledge harm when caused and work with consumers to improve our field and our practice to avoid future harm.

Students Will Learn

BASIC AND ADVANCED CONCEPTS IN ABA

STRATEGIES FOR IMPLEMENTATION IN DIVERSE CONTEXTS

CONTEXTUALLY RELEVANT AND INDIVIDUALIZED ASSESSMENT AND INTERVENTION

PERSON-CENTERED AND QUALITY-OF-LIFE FOCUSED APPROACHES

• M.ED. without a License
• Additional ABA Offerings and Opportunities  

M.Ed. Coursework Requirements

36 total credit hours 

ABA Coursework Sequence

Students who are accepted to the University of Utah ABA program will enroll in coursework that includes the required courwork necessary to apply to sit for the BCBA exam, as well as a core coursework that prepares students to implement ABA in school contexts. Additionally, students are required to enroll in a practicum sequences that enhances and extends the core curriculum to promote professional and interpersonal skill development essential for success as a practicing behavior analyst. In the second year of the program, students will complete a two-semester experiential learning sequence to demonstrate essential skills and knowledge.

Year 1 Fall Semester

Year 1 Spring Semester

Year 2 Fall Semester

Year 2 Spring Semester

Supervised Fieldwork Experience (0-5 credit hours)

Students working in clinical and/or applied contexts may enroll in supervision credits to access individualized supervision from program faculty and/or staff that meets the requirements for supervised fieldwork experience as defined by the BACB and required to apply to sit for the BCBA exam. Students interested in registering for supervision credits should contact the faculty program coordinator to determine the appropriate number of credits to register for, given individual needs and context.

M.S. in Special Education with Emphasis in Applied Behavior Analysis

Students who are interested in pursuing an M.S. in Special Education with Emphasis in Applied Behavior Analysis should contact their advisor no later than the end of the Spring Semester of their first year. The M.S. option requires that students work with their advisor to complete a thesis project (e.g., single case research design study, systematic literature review).

Professional Online M.Ed. in Special Education with Emphasis in Applied Behavior Analysis

The University of Utah offers an asynchronous online M.Ed. program that meets coursework requirements to apply to sit for the BCBA exam, as well as a two-semester experiential learning sequence.

ABA Coursework Only Option

 Students who already hold a master's degree may apply to enroll in some or all coursework required to apply to sit for the BCBA exam.

Other Study Requirements

Besides the courses above, other study requirements must be completed to earn the M.S. degree. 

• Establish a Supervisory Committee
• Qualifying Examination
• Continuous Enrollment
• Master's Degree Residency Requirement

Questions? Ask our Program Contacts

Kaitlin Lindsey

Academic Advisor

[email protected] 801-581-4764

Kathleen Strickland-Cohen

Assistant Professor Program Coordinator

[email protected]   801-581-3260

Applied Behavior Analysis

Applied Behavior Analysis (ABA) is a growing discipline with a presence in both psychology and education that improves the lives of children and adults with disabilities. We offer on-campus and online option ABA programs. Both options allow students to earn a master’s degree in special education and complete the ABA coursework necessary to apply to become a Board Certified Behavior Analyst (BCBA).

The demand for highly qualified Board Certified Behavior Analysts (BCBAs) has been steadily increasing nationwide! A variety of settings and industries are competitively seeking BCBAs to fill a wide variety of positions in education, home services, clinical programs, health services and more. Our programs meet all the Behavior Analyst Certification Board coursework and supervision requirements and will qualify applicants to sit for the board exam.

Our goal is to prepare students to be competent, inclusive, ethical, and professional behavior analysts who work with persons with developmental disabilities and their families. Students coming out of our program will:

• Understand and fluently apply the principles of behavior analysis
• Have a working knowledge of current evidence-based practices for individuals with developmental disabilities 
• Select or create contextually appropriate, evidence-based interventions for individuals with whom they work and critically analyze and evaluate the effects of those interventions 
• Work collaboratively and openly with schools, families and other community stakeholders, always with an understanding of how culture and equity impact service delivery
• Ensure that the primary outcome of their work is to improve the quality of life for the individual and his or her family

Beneficence . Behavior analysts have a responsibility to engage in practices that maximize their clients' well-being and avoid those that cause harm. We understand that behavior analytic services are most likely to benefit our clients when they are provided in the context of a trusting and compassionate relationship. Where conflicts of interest arise between consumers of behavior analysis, we prioritize outcomes for the most vulnerable clients.

Inclusion. Behavior analysts have a responsibility to provide individuals of all backgrounds and abilities access to and authentic participation in meaningful activities that promote relationships, a sense of community, and an improved quality of life.

Professional excellence. Behavior analysts have a responsibility to be honest and transparent. We engage in ongoing professional development and analyze our own practices. Professional excellence requires respectful and effective collaboration with individuals from other disciplines while maintaining a commitment to data-based decision-making. Analyzing evidence from different methodologies is encouraged as a way of collaborating with others and improving practice.

Self-determination. Behavior analysts respect clients’ rights and promote client dignity, privacy, and autonomy. We assist clients to set and achieve their own goals, develop their own agency, and make decisions about their own lives.

Social Justice. Behavior analysts have a responsibility to attend to injustice where they see it, avoid perpetuating inequitable systems, and advocate for equitable systems change. We are uniquely qualified to identify controlling and contextual variables that contribute to inequitable educational and service-delivery systems and develop solutions to supplant them.

Download principles & preamble

Our Programs

Applied behavior analysis (on-campus), applied behavior analysis (online).

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Graduate Certificate in Applied Behavior Analysis

Degree: Graduate Certificate

Classroom Type: Fully Online

Offers an opportunity to become a board-certified behavior analyst (BCBA) to those who are passionate about identifying and championing the strengths and assets of children with differing abilities.

Application​ deadlines

Spring 2024: November 1 Fall 2024: June 1

Program Details

Interest Categories: Education & Counseling

Campus: CU Denver Online

What you'll learn

The demand for special education professionals with expertise in Applied Behavior Analysis is growing. Children with special needs require specific and optimized learning environments created by qualified professionals to aid in their growth and skill development. Our strengths-based/assets-based approach to teaching focuses on preparing you to understand the strengths of every child in every family.

Enhance your ability to work with individuals with special learning and behavior needs by earning your post baccalaureate certificate in Applied Behavior Analysis from the University of Colorado Denver. This certificate program prepares professionals to follow best practices for ABA in a variety of settings.

If you already have a master’s degree, earning this certificate will help you to advance or diversify in your field. You’ll build competencies in the science of ABA and advance your skills in working with individuals with special needs.

CU Denver’s Applied Behavior Analysis certificate will prepare you with knowledge of:

• Ethical and professional conduct considerations required for practicing ABA.
• How to understand and accurately describe the behavior of individuals.
• How to collect and interpret data as well as the importance of making data-driven decisions specifically for ABA.
• How to implement recommended, evidence-based practices with young children with autism.

The Association for Behavior Analysis International has verified that our course sequence contributes to the requirements for eligibility to take the Board Certified Behavior Analyst ®  examination. Applicants will need to meet additional requirements before they can be deemed eligible to take the examination. For more information on these requirements, please visit the  Behavior Analyst Certification Board®’s website .

Mode of study : This course is designed to fit your busy lifestyle and is offered completely online. All you’ll need is reliable high speed internet access from home. Faculty and students use web-based applications like Instructure Canvas to complete coursework. Visit CU Online’s  technical requirements page  for details.

Cost:  All courses are three graduate credit hours. Tuition is available on the  Student Finances website . This program is not eligible for financial aid.

Meet the Faculty Faculty at CU Denver match your passion for education and believe in giving all students equal access to an excellent education. Our published scholars and community-based advocates in special education are engaged at the local, state, national and international levels. These professionals bring with them a wide breadth of experience. Additionally, our faculty network and collaborate with local district leaders to ensure you are prepared for the current challenges that schools and districts face.

Choose CU Denver’s School of Education & Human Development

You can trust CU Denver to provide an education that will prepare you to be the professional schools seek and children deserve. Inclusive classrooms require teachers who are well-prepared and ready to work with children of all abilities in general classrooms including students with serious emotional disability and specific learning disabilities. We take our leadership role seriously and are committed to graduating the most qualified ABA certified professionals. Additionally, CU Denver’s School of Education and Human Development is counted among  U.S. News & World Report ’s “Best Graduate Schools,” making us one of the top education schools in the country.

CU Denver’s ABA courses comprise a seven-course sequence of 3-credit hour, fully-online courses, taught by experienced CU Denver BCBA instructors. ABA certificate students will complete the required class hours distributed among the BACB®’s 6th Edition Task List areas.

The seven courses (SPED 5450, SPED 5470, SPED 5471, SPED 5480, SPED 5481, SPED 5490, and SPED 5491) are completed in sequence.

Cohorts that begin with the first course (SPED 5450) start every/other semester. 

SPED 5450: Introduction to ABA and Terminology 

SPED 5470: ABA Data 

SPED 5471: Ethics and Implementation in ABA

SPED 5481: Introduction to OMB

SPED 5480: ABA Advanced Data and Behavioral Plans and Applications 

SPED 5490: Autism in Early Intervention 

SPED 5491: Theory and Philosophy of Behaviorism

Required Credits for Completion: 21 Credits

Admission Requirements

Eligibility.

BACB Behavior-Analytic Coursework Pathway Eligibility:

• BCBA applicants must hold a graduate degree from a qualifying institution
• ALL CU Denver ABA certificate students are responsible for finding and completing their own BACB supervised fieldwork experience.

How to apply

• Submit a Graduate CPE Non-degree application

         I. Confirm you are applying for a School of Education & Human Development                      certificate

        II. Select “Graduate Certificate in Applied Behavior Analysis” from the list of                          certificates        III. Select your admit term        IV. Upload Resume, Letter of Intent, and Graduate Degree-conferred transcripts                    (can be unofficial) in the “Upload Documents” tab

    2. Faculty will review complete applications, after the application deadline, in the                 order they were received  

    3. Acceptance notifications will be sent to the email listed on the application

    4. Course registration information will be emailed once the student’s account is                    activated and they are eligible to register for courses  

Application Deadlines

Please note, space is limited, and our program works on rolling admission. Applications are reviewed on a first-come, first-served basis for students who meet the eligibility criteria for their respective programs. Please contact Beth Eagen at  [email protected]  or call 303-315-5000 for application assistance or to learn more about this program.

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ECE Educator Scholarships, administered by the Colorado Department of Higher Education in collaboration with Colorado Department of Early Childhood, are designed for early childhood education students living in Colorado who are pursuing dedicated coursework, certificates and degrees up to a master’s degree. Awards vary dependent upon credit hours of enrollment and program. All eligible students receive some funding.

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• v.45(3); 2022 Sep

Applied Behavior Analysis in Children and Youth with Autism Spectrum Disorders: A Scoping Review

Mojgan gitimoghaddam.

1 University of British Columbia Faculty of Medicine, Vancouver, British Columbia Canada

Natalia Chichkine

2 Club Aviva Recreation Ltd., Coquitlam, British Columbia Canada

Laura McArthur

Sarabjit s. sangha.

3 University of Melbourne Faculty of Medicine, Dentistry and Health Sciences, Melbourne, Australia

Vivien Symington

Associated data.

Not applicable.

This manuscript provides a comprehensive overview of the impact of applied behavior analysis (ABA) on children and youth with autism spectrum disorders (ASD). Seven online databases and identified systematic reviews were searched for published, peer-reviewed, English-language studies examining the impact of ABA on health outcomes. Measured outcomes were classified into eight categories: cognitive, language, social/communication, problem behavior, adaptive behavior, emotional, autism symptoms, and quality of life (QoL) outcomes. Improvements were observed across seven of the eight outcome measures. There were no included studies that measured subject QoL. Moreover, of 770 included study records, only 32 (4%) assessed ABA impact, had a comparison to a control or other intervention, and did not rely on mastery of specific skills to mark improvement. Results reinforce the need for large-scale prospective studies that compare ABA with other non-ABA interventions and include measurements of subject QoL to provide policy makers with valuable information on the impacts of ABA and other existing and emerging interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40614-022-00338-x.

Introduction

Neurodevelopmental disorders and disabilities (ndd/d).

NDD/D consist of a range of diagnoses and functional impairments of a neurological origin that can present as functional deficits in developmental milestones such as language, communication, social skills, intellect, executive functioning, and motor development (American Psychiatric Association, 2013 ; Miller et al., 2013 ; World Health Organization [WHO], 2001 , 2020 ). The prevalence of NDD/D across developed countries in children and youth 18 years of age and younger ranges from 8% to 15% (Arim et al., 2017 ; Boyle et al., 2011 ; Olusanya et al., 2018 ). Many different conditions and functional limitations are included within the scope of NDD/D, including autism spectrum disorders (ASD), attention deficit/hyperactivity disorder (ADHD), Down syndrome, and intellectual disabilities (ID). In particular, ASD has garnered much attention worldwide due to its high prevalence and associated socioeconomic and familial costs (Reichow et al., 2018 ).

ASD is a spectrum of diagnosable neurodevelopmental disorders that include pervasive developmental disorders (PDD), Asperger’s syndrome (AS) and autism. ASD typically presents during the developmental period and includes social communication and interaction difficulties, along with restricted and repetitive behaviors, interests, or activities (WHO, 2020 ). The prevalence of these disorders has increased over the past 20 years due to many combining factors. The global estimated prevalence in children and youth 18 years of age or younger is 0.62%–0.70% but could be as high as 1%–2% (Elsabbagh et al., 2012 ; Fombonne, 2009 ; Idring et al., 2012 ; Russell et al., 2014 ). The lifetime cost for families with a member diagnosed with ASD can range from approximately US$1.4 million in the United States and the United Kingdom, when diagnosed without an additional ID, to US$2.4million in the United States and US$2.2million in the United Kingdom if diagnosed concurrently with an ID (Buescher et al., 2014 ). Due to its increasing prevalence, the need for effective, evidence-based interventions for ASD has grown exponentially. Applied behavior analysis (ABA) and the interventions that are developed from its principles are some of the most often cited evidence-based interventions developed for the treatment of those diagnosed with ASD. As such, ASD will be the primary diagnosis of consideration within the current scoping review.

Applied Behavior Analysis

At its core, ABA is the practice of utilizing the psychological principles of learning theory to enact change on the behaviors seen commonly in individuals diagnosed with ASD (Lovaas et al., 1974 ). Ole Ivar Lovaas produced a method based on the principles of B. F. Skinner’s theory of operant conditioning in the 1970s to help treat children diagnosed with ASD (or “autism” at the time) with the goal of altering their behaviors to improve their social interactions (Lovaas et al., 1973 ; Skinner, 1953 ; Smith & Eikeseth, 2011 ). To evaluate this method, the University of California at Los Angeles (UCLA) Young Autism Project model was developed and empirically tested by measuring the effects of the intervention when administered one-to-one to children diagnosed with ASD for 40 hr per week over the span of 2–3 years (Lovaas, 1987 ). The remarkable findings revealed that 47% of the children who participated in this treatment reached normal intellectual and educational functioning compared to only 2% of a control group (Lovaas, 1987 ).

ABA has evolved over the past 60 years from the core principles established in the early Lovaas model and subsequent UCLA Young Autism Project into many comprehensive treatment models and focused intervention practices, methods, and teaching strategies, all of which aim to address deficits for children and youth with ASD across all levels of functioning, including cognition, language, social skills, problem behavior, and daily living skills (Reichow et al., 2018 ). One notable and often cited foundational model is “antecedents, behavior, and consequences,” otherwise known as the ABC model, in which manipulating either or both the antecedents and consequences of behavior is intended to increase, decrease, or modify the behavior, thus resulting in a transferrable tool to target behaviors of interest effectively (Bijou et al., 1968 ; Dyer, 2013 ). There are also a number of techniques commonly associated with ABA that are worth noting, including reinforcement, extinction, prompting, video modeling, as well as the Picture Exchange Communication System (PECS), though many of these are widely used in other intervention and education settings (Granpeesheh et al., 2009 ; Sandbank et al., 2020 ; Stahmer et al., 2005 ).

Some specific comprehensive ABA-based treatment models that are investigated in this review include early intensive behavioral intervention (EIBI), Early Start Denver Model (ESDM), and Learning Experiences: An Alternative Program for Preschoolers and Their Parents (LEAP). EIBI is an intensive, comprehensive ABA-based treatment model for young children diagnosed with ASD. EIBI targets children under the age of 5 and is often administered 20–40 hr per week for multiple consecutive years (Matson & Smith, 2008 ; Reichow et al., 2018 ). It is conducted one-to-one in a structured setting such as in the home or school, and often utilizes the discrete trial training (DTT) method (Cohen et al., 2006 ; Smith, 2001 ) in conjunction with other, less structured teaching methods such as natural environment training (Granpeesheh et al., 2009 ). Because this is a comprehensive treatment model, the target of the intervention is across all aspects of functioning such as independent living skills, social skills, motor skills, pre-academic and academic skills, and language (Granpeesheh et al., 2009 ). Another comprehensive ABA-based treatment model is ESDM. This model was developed for children with ASD that fall within the age range of 12–60 months. This intervention builds upon the naturalistic teaching methods within ABA to provide a comprehensive, developmental, and relationship-based behavioral intervention targeted at children early in development (Dawson et al., 2010 ). More recently, some comprehensive ABA treatment models have further shifted away from intensive, operant conditioning based one-to-one models into more naturalistic and generalizable programming. LEAP is one such model for children with ASD because it takes place in public school settings (Strain & Bovey, 2011 ). LEAP was developed from fundamental principles of ABA and includes a variety of methods commonly used in ABA such as Pivotal Response Training (PRT), time delay and incidental teaching, in addition to utilizing peer-mediated interventions and the PECS (Strain & Bovey, 2011 ). It is significant that a core principle of LEAP is to strongly emphasize parental and peer involvement with respect to teaching behavioral strategies and relies on naturally occurring, incidental teaching arrangements, in contrast to the directional, adult-driven instruction used in most other segregated ABA intervention strategies (Hoyson et al., 1984 ; Strain & Bovey, 2011 ).

Within these comprehensive treatment models, focused intervention practices that are often utilized and independently investigated can include, but are not limited to, DTT and naturalistic teaching strategies such as PRT and functional communication training (FCT). DTT is one of the most fundamental focused intervention practices of ABA and utilizes sequences of instruction and repetition in a distraction free, one-to-one setting (Smith, 2001 ). The primary focus of DTT is to teach children new behaviors and discriminations. These new behaviors encompass any behavior that was not previously performed by the child knowingly or unknowingly (Smith, 2001 ). Naturalistic teaching forms of ABA have sought to improve the ability to generalize and maintain the positive effects of behavioral interventions while upholding many of the fundamental principles and behaviorism of ABA (Schreibman et al., 2015 ). One such method of naturalistic teaching is through the focused intervention practice of PRT, developed by Koegel and Koegel ( 2006 ), which is focused on improving the self-initiative and motivation of a child to communicate effectively in common real-life settings (Mohammadzaheri et al., 2015 ). Of note, most of these treatments can involve a professional, though many of the more recent studies and iterations of these treatments seek to involve peers, siblings and family members to encourage generalization to real-world settings and people in the child’s personal life (Mohammadzaheri et al., 2015 ; Steiner et al., 2012 ). Another focused intervention practice and naturalistic teaching method is FCT, a differential reinforcement-based procedure developed by Carr and Durand ( 1985 ) that reduces problem behaviors by replacing them with more appropriate communicative responses. This training is commonly used in conjunction with other ABA methods.

Given the history and range in interventions, there is a degree of variability and confusion in the definition of ABA as a system. Definitions range from rigid protocols for some ABA-based programs to collections of specific techniques associated with ABA, to ABA as a system to evaluate practices rather than as an intervention itself. Granpeesheh et al. ( 2009 ) define ABA as “the application of principles of learning and motivation to the solution of problems of social significance” (p. 163). This definition of ABA as a research strategy echoes that of Baer et al. ( 1968 ) through the later 20th century, in particular in terms of behavior study being: (1) applied, (2) behavioral, (3) analytic, (4) technological, (5) conceptually systematic, (6) effective, and (7) capable of generalized outcomes. Agency definitions tend to define it as a therapy, likewise noted by Schreibman et al. ( 2015 ), with different approaches listed as types. For instance, the Centers for Disease Control and Prevention (CDC) defines ABA as a treatment approach, with examples such as DTT, EIBI, ESDM, PRT, and verbal behavior intervention (VBI; CDC & National Center on Birth Defects & Developmental Disabilities, 2019 ). The National Institute of Child Health and Human Development (NIH) lists positive behavioral support (PBS), PRT, EIBI, and DTT as types of ABA (Eunice Kennedy Shriver National Institute of Child Health & Human Development, 2021 ). The Autism Society( n.d. ) follows the same definition as Baer et al., whereas other intervention types such as PRT and extinction are described as ABA procedures or as sharing principles of ABA. Many ABA-derived programs define certain expectations of their practices specifically, such as EIBI setting, intensity, duration, and personnel, although their methods list a variety of techniques deemed ABA-based, such as DTT, precision teaching, and incidental teaching. As combined approaches become more common, it is becoming more difficult to differentiate interventions considered to be ABA-derived from other non-ABA labeled interventions (Smith, 2012 ).

All of the research into these methods, programs, and comprehensive models, combined with the continued investigations into the traditional applications of the ABA-based interventions, results in a wealth of research about the impact of ABA on children and youth with ASD, in particular with respect to improvements in cognitive measures, language skills, and adaptive skills (Eldevik et al., 2009 ; Virués-Ortega, 2010 ). The ensuing amount of scientific evidence has resulted in ABA being considered a “best practice” and thus endorsed by the governments of Canada and the United States for the treatment of children and youth with ASD (Government of Canada, 2018 ; U.S. Department of Health & Human Services, 1999 ).

Rationale for Current Scoping Review

As ABA is a broad intervention which includes many different methods and programs, reviews of the entire scope of the current research are uncommon. To our knowledge, a comprehensive review of the current ABA literature that spans all ABA methods and outcomes for children and youth with ASD, and that includes randomized controlled trials (RCT), clinical controlled trials (CCT), and single-case experimental design (SCED) studies, has not been completed. The current literature consists primarily of systematic reviews and meta-analyses that have investigated the quantifiable and qualitative outcomes of ABA on children with ASD, but few of these studies include SCED, and the results across the reviews inconsistently show significant improvement with ABA interventions.

For example, in a meta-analysis by Virués-Ortega ( 2010 ), the effectiveness of ABA was investigated across 22 included studies with respect to as many outcomes as possible, including language development, social functioning, intellectual functioning, and daily living skills, for those diagnosed with ASD (Virués-Ortega, 2010 ). The results of this meta-analysis suggested that ABA interventions that were implemented in early childhood and were long-term and comprehensive in design did result in a positive medium to large effect in the areas of language development (pooled effect size of 1.48 for receptive language, 1.47 for expressive language), intellectual functioning (pooled effect size 1.19), acquisition of daily living skills (pooled effect size 0.62), and social functioning (pooled effect size 0.95), when compared to a control group that did not receive ABA intervention. This mirrors the meta-analysis of 29 articles conducted by Makrygianni et al. ( 2018 ), where it was found that ABA programs for children with ASD resulted in moderate to very effective improvements in expressive and receptive language skills, communication skills, nonverbal IQ scores, total adaptive behavior, and socialization, but lesser improvements in daily living skills. In a 2018 meta-analysis by Reichow et al. ( 2018 ), the changes in autism severity, functional behaviors and skills, intelligence, and communication skills were investigated across five articles that included one RCT and four CCTs for EIBI. After conducting meta-analyses of these studies, it was found that the evidence for EIBI improving adaptive behavior compared to treatment as usual comparison groups was positive but weak (mean difference [ MD ] = 9.58; 95% confidence interval ( CI ) 5.57–13.60), whereas there was no evidence that EIBI improved autism symptom severity (standardized mean difference [ SMD ] = −0.34; 95% CI −0.79–0.11; Reichow et al., 2018 ). Therefore, the current literature appears to indicate inconsistent results with respect to the magnitude of improvements seen as a result of ABA interventions for children and youth with ASD.

With respect to the wealth of SCEDs included throughout the ABA literature, Wong et al. ( 2013 ) have noted that existing reviews rarely capture these types of studies, with two notable exceptions conducted by the National Autism Center ( 2009 ) and the National Professional Development Center on ASD (NPDC; Odom et al., 2010 ). These studies still had some key exclusions: the National Autism report excluded articles that (1) did not have statistical analyses, (2) did not include linear graphical presentation of the data for SCEDs, or (3) used qualitative methods, whereas the NPDC report searched for studies on behavioral strategies that fulfilled the requirements of being an evidence-based practice, as defined by the authors (National Autism Center, 2009 , 2015 ; Odom et al., 2010 ). Neither of these reports evaluated the entire scope of the available ABA research with respect to children and youth with ASD, potentially missing the value of the studies that were excluded.

The purpose of the current review therefore is to evaluate the available literature on ABA as an intervention approach in the treatment of ASD in children and youth in an effort to help instruct the scientific community on the most beneficial directions for future research. Moreover, as ABA is commonly recognized at a governmental level as evidence-based, a review of the current ABA literature will help inform other existing and emerging therapies and interventions, researchers, policy makers, and the public of the standard to which established, evidence-based interventions are held. This is accomplished by collecting, compiling, and discussing the available data on the most common outcomes and methods. This includes the most common journals of publication, population metrics, and the transferability of this prominent therapy approach to the real world. As such, the objectives of this scoping review are to examine the extent, range, and nature of research activities regarding the impact of ABA on children and youth with ASD and to identify any gaps in the existing literature regarding ABA outcomes and research designs.

A scoping review study design was selected for the current investigation. According to Colquhoun et al. ( 2014 ), “a scoping review is a form of knowledge synthesis that addresses an exploratory research question aimed at mapping key concepts, types of evidence, and gaps in research related to a defined area or field by systematically searching, selecting, and synthesizing existing knowledge” (p. 1293). Scoping reviews differ from systematic reviews in that they provide an overview of existing evidence regardless of the quality (Tricco et al., 2016 ), and may not formally assess study rigor (Arksey & O’Malley, 2005 ).

The current scoping review was conducted to gather an understanding of the scope of available research regarding the use of ABA as an intervention for children and youth living with NDD/D, and in particular ASD. For the purposes of the current review, ABA will be defined as an intervention informed and developed from behavioral analytic approaches for the treatment of children and youth with ASD. The effect of ABA is defined as the measurable changes in a participant's various outcomes as a result of receiving ABA intervention. These outcomes were not predefined to prevent missing any possible impact. The review comprised a database search, as well as a reference search of selected reviews. A second phase of the literature search was conducted to update the sample to reflect more recent literature. A guiding document by Tricco et al. ( 2016 ) was used for direction and as a reference for conducting this review.

Search Strategy

An initial search was conducted across PubMed, MEDLINE (EBSCOHost), Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Educational Resources Information Center (ERIC), Cochrane Central Register of Controlled Trials (CENTRAL), and Cochrane Database of Systematic Reviews (CDSR) utilizing medical subject heading (MeSH) search terms and limitations to describe the relevant population in the initial search (children and youth with NDD/D) and intervention (ABA) (see Appendix 1 for a full list of search terms for each database). Additional limitations of the search were English language publications, subject age range of 0–18 years, and publication date range. The search was conducted in two phases: January 1, 1997 through December 31, 2017, and January 1, 2018 through December 31, 2020.

Several reviews were selected for a further text search. Data were not extracted directly from eligible reviews. Instead, their selected articles were screened and added to the sample if they were not already included in the initial search. This process was repeated for any secondary reviews that occurred as well. These additions were excluded from the publication date limitation, resulting in the inclusion of a number of studies outside of the initial search date range. Review and meta-analysis results were not coded.

Selection Criteria

A PICO (population, intervention, comparison, outcome) framework was used to guide the selection of articles. Population and intervention were used as eligibility criteria. Although the intervention was restricted to ABA, the population was originally defined broadly as NDD/D in an effort to capture as much of the applicable literature as possible, and later revised to focus on ASD and mixed diagnoses (ASD and other). This included populations where some subjects had other non-ASD diagnoses, such as ADHD, Down syndrome, or ID, whether they co-occurred with ASD within subjects or presented across subjects. Non-ASD diagnoses observed in the mixed-diagnoses category of the current review are described in the results (“Results: Description of Included Studies”) and in Appendix 2 . Outcome was not considered because one objective of the current scoping review was to identify the measured outcomes. Comparison was not used so as not to limit the scope of the review. Study design was not limited in the initial search.

Inclusion criteria for article selection during the initial search comprised (1) English language articles that are (2) about the effects of ABA on (3) children and youth (birth to 18 years) with NDD/D, within (4) the timeframe of January 1, 1997 through December 31, 2020. As described above, screened articles included from selected reviews and secondary reviews were exempt from the date range limitations.

Exclusion criteria comprised (1) hospital-based (inpatient) settings and mixed-setting studies (i.e., those including some inpatient subjects); (2) use of qualitative research methods; (3) publications that are not “research-based” (e.g., newsletters, books); (4) populations exceeding 18 years of age; and (5) combined interventions if not looking specifically at the effectiveness of ABA intervention. In cases of mixed age (i.e. including subjects over 18 years of age) or mixed population (i.e., including typically developing subjects), studies were excluded if it was not possible to extract results for the target population separately. Inpatient settings were excluded because the focus of the current scoping review was on community offerings, not hospital services. A small number of studies were excluded when the methods did not align with typical ABA outcome measures, such as those training response hierarchies or attempting to condition new reinforcers. A library search was conducted for studies that could not be accessed in full online, and any that could not be found were subsequently excluded.

When the diagnostic criteria were narrowed to focus primarily on ASD, articles that contained only non-ASD diagnoses were excluded.

Screen Process and Study Selection

Articles from the original search of online databases were exported to Mendeley® Desktop versions 1.19–2.62.0, a reference management software, where most duplicate studies were automatically identified and removed. Any remaining duplicates from both the database and review search were removed manually. Titles and abstracts of all retrieved articles were then independently reviewed by two researchers following the outlined inclusion and exclusion criteria. Studies were included if the independent reviewers reached agreement, or after further discussion with a third reviewer. Retained articles then underwent full text review for inclusion, following the same steps.

Data Extraction

Articles included following the full text review then underwent data extraction. Extracted data comprised first author, title, year of publication, origin of study, funding sources, study aim, study design, duration of intervention, duration of study, population size, population description, setting, measurement outcomes, measurement tools, and key findings. In cases where results were reported individually for each subject, they were extracted as such. In larger scale studies where only group results were reported, group results were extracted, so long as the group included only the target population.

Data Coding and Synthesis

In general, the entire sample of records included for coding and synthesis was subdivided into three sections concerned with: (1) general ABA Impact, (2) Comparisons of ABA Techniques, and (3) Between-Groups Comparisons of ABA to control or other interventions. These divisions are visually summarized in Figure ​ Figure1 1 and are described below. All records underwent general data coding of basic study information, as well as specific outcome coding, also described below. (Details about coding definitions can be found in Appendix 2 .) Simplified extraction tables for these three subdivisions are available in Appendix 3 (Tables S1 , S2 , and S3 ).

Flowchart Describing the Process of the Current Scoping Review Search, Screening, Data Extraction, and Coding. Note. From an initial search comprising 2,948 records, after screening studies and subdividing multipart studies, a total of 770 study records remained. These were coded in three categories: Comparisons of ABA Techniques, ABA Impact, and Between-Groups Comparisons. Designed with reference to Tricco et al. ( 2016 ) and created using diagrams.net ™/draw.io® from JGraph Ltd. Note that three study records were included in both the ABA Impact section and the Comparisons of ABA Techniques section (Mello et al., 2018 ; Rad et al., 2019 ; Vietze & Lax, 2020 ), and three study records were included in all three coding sections (Dugan, 2006 ; Kalgotra et al., 2019 ; Kovshoff et al., 2011 ).

During the process of coding, articles containing multiple concurrent or consecutive studies were separated into discrete rows, and will hereafter be treated as self-contained studies in this review. In all figures and further text, all coded rows are referred to as “study records.” Once separated, researchers identified and excluded (1) functional analyses or studies focused on their use, (2) preference assessments or studies focused on their use, and (3) predictive studies. Study records were coded independently by two researchers and then discussed to obtain agreement, or referred to a third researcher to obtain agreement. During coding, any further study records found to satisfy the exclusion criteria were excluded.

Items selected for general data coding included publication details, population metrics, and several specific study methods. The population metrics were age, sex, and diagnosis of participants. (Detail on the population coding values can be found in Appendix 2 ). Study records were additionally coded and compared by two independent researchers to identify inclusion of the following methods: (1) follow-up or maintenance, (2) mastery or criterion measures, (3) generalization. Studies including comparison groups were further coded by one researcher to identify the presence of (1) a control group (typically consisting of “eclectic” or treatment as usual), (2) comparisons to other non-ABA intervention/s, or (3) a mix of these.

After general data coding, the sample was separated into two groups for outcome coding: ABA Impact and Comparisons of ABA Techniques. The majority of study records fell into the ABA Impact section, in which study records measured the change in outcomes (e.g., amount improved) as a result of exposure to ABA intervention. In contrast, study records that were primarily concerned with comparing multiple techniques or intensities of ABA were reserved for the Comparisons of ABA Techniques section, because general ABA impact could not easily be determined for the entire study population in these studies. Finally, a select number of study records from the ABA Impact section where ABA interventions were also compared to a control or different intervention were coded a second time to describe these comparisons in the Between-Groups Comparisons section. As noted in Fig. ​ Fig.1, 1 , some studies from the ABA Impact section also fell into the Comparisons of ABA Techniques section, or into all three sections.

Although the search was not restricted, the observed outcome measures were classified into eight categories: cognitive, language, social/communication, problem behavior, adaptive behavior, emotional, autism symptoms, and quality of life (QoL) outcomes. At first, QoL was included to help describe the generalizability and real-life utility of ABA interventions, following the example of Reichow et al. ( 2018 ). However, as no instances of subject QoL measures occurred in this search, this outcome is not included in the subsequent synthesis. Within each category, outcomes were generally classified as improvement, regression, mix, or no change, as can be seen in the extraction tables (Tables S1 , S2 , and S3 in Appendix 3 ).

When more than two variables or interventions were compared, which sometimes occurred in the Comparisons of ABA Techniques and Between-Groups Comparison sections, study records were discussed and split into discrete rows by two researchers to represent simplified or single-variable comparisons in each row. These are termed “comparison records” for the purpose of coding and synthesis. As seen in Tables S2 and S3 in Appendix 3 , further detail was extracted regarding the category of techniques or interventions compared and the relative effectiveness of each.

Prior to coding, researchers categorized outcome measures, measurement scales or strategies, and intervention categories observed during the extraction process into tables in an effort to mitigate potential inconsistencies in coding. For example, in the Comparisons of ABA Techniques section, categories were broadly defined as Teaching, Stimulus Characteristics, Reinforcement, Subject/Setting Characteristics, and Comparisons of ABA Interventions. Further descriptions of these and other categories can be found in Appendix 2 .

Further details on general data coding, as well as outcome coding for ABA Impact, Comparisons of ABA Techniques, and Between-Groups Comparisons can be found in Appendix 2 . Extractions for all three sections can be found in Tables S1 , S2 , and S3 , respectively, in Appendix 3 .

All statistical analyses, compilations, and tabulations were completed using Microsoft® Excel® versions 1805-2111. Descriptive analyses (means, medians, etc.) were calculated using native Excel® functions. Pivot tables were utilized to tabulate frequencies. Figures were generated using Microsoft® Excel® version 2016 MSO, Microsoft® Word® versions 2011–2111, and diagrams.net ™/draw.io® by JGraph Ltd.

In addition, some qualitative characteristics were explored as well, such as observations about the types of methods used in the interventions encountered, the degree of mastery and generalization measures, and how targeted the interventions and measurement tools were.

Identified Studies

As shown in Fig. ​ Fig.1, 1 , the record selection process differed slightly between the two searches spanning 1997–2017 and 2018–2020. This is because the diagnostic criteria for the current manuscript were updated to exclude populations that only contained non-ASD diagnoses, and the removal of records satisfying the new criteria took place at different points for each search.

The database searches yielded a total of 2,074 entries after import to Mendeley®, and 874 entries from selected reviews and secondary reviews. Ten systematic reviews were identified and investigated for the literature search (Brunner & Seung, 2009 ; Dawson & Bernier, 2013 ; Makrygianni et al., 2018 ; Mohammadzaheri et al., 2015 ; Reichow et al., 2014 , 2018 ; Rodgers et al., 2020 ; Shabani & Lam, 2013 ; Spreckley & Boyd, 2009 ; Virués-Ortega, 2010 ). After pulling references from the first five (Brunner & Seung, 2009 ; Dawson & Bernier, 2013 ; Makrygianni et al., 2018 ; Rodgers et al., 2020 ; Shabani & Lam, 2013 ), it was found that the references in the remaining five reviews were duplicates of previously identified references. Secondary reviews from Seida et al. ( 2009 ) and Dawson and Burner ( 2011 ), both cited by Dawson and Bernier ( 2013 ), were also investigated for references (Bassett et al., 2000 ; Bellini & Akullian, 2007 ; Delano, 2007 ; Diggle et al., 2002 ; Horner et al., 2002 ; Hwang & Hughes, 2000 ; Lee et al., 2007 ; McConachie & Diggle, 2007 ; Odom et al., 2003 ; Reichow & Volkmar, 2010 ; Smith, 1999 ). Records from Brunner and Seung ( 2009 ) that were categorized into treatment models that did not fulfill the definition of ABA as per the current review were not considered. In addition, the secondary review by Vismara and Rogers ( 2010 ) was not considered because it was a narrative review. After removing duplicates or entries already existing in the database search, 1,577 entries remained from the database search and 525 from reviews, for a total of 2,102 records.

A total of 1,337 records were removed during title, abstract, and full-text screening because they met the exclusion criteria, were duplicate records, were reviews, or contained only non-ASD diagnoses. Multipart studies were separated into discrete records, yielding a total of 849 study records. A further 34 were excluded at this stage as they were preference assessments, functional analyses, or were concerned with training response hierarchies or conditioning reinforcers, leaving 815 study records. When the diagnostic inclusion criteria were revised, any remaining records containing only non-ASD diagnoses were excluded.

Thus, the total sample included in the quantitative and qualitative synthesis comprised 770 study records. This entire sample was analyzed for general data metrics (see Fig. ​ Fig.1). 1 ). References for the 709 included articles can be found in Appendix 4 .

Description of Included Studies

Overall, agreement between raters was approximately 80% across all coding categories. The range of included outcome categories was selected in order not to limit the scope of the literature search and synthesis for this review so that a comprehensive review of the application of ABA for ASD and mixed-diagnosis populations across the entire time span and age range of the search could be conducted. Frequently occurring other diagnoses in the mixed-diagnoses category included ADHD; ID; global developmental delay (GDD) or other developmental delays; oppositional defiant disorder (ODD); Down syndrome; cerebral palsy (CP); fetal alcohol spectrum disorders (FASD); Angelman syndrome; Fragile X; obsessive-compulsive disorder (OCD); Tourette syndrome; traumatic brain injury (TBI); epilepsy or seizure disorders; sensory integration or processing disorders; speech/language delays; learning disabilities; and behavior, emotional, or mood disorders.

The most frequently occurring publication year was 2020. The earliest publication reviewed was from 1977 and the most recent from 2020. Thirty percent were from 2000–2009 and 61% were from 2010–2020. The remaining years comprised 9% of the journals reviewed.

The 5-year impact factor (IF) characteristics were determined by removing duplicate journals prior to calculation. IFs were accessed from Journal Citation Reports, via Clarivate™. The unique median IF was 2.56. The lowest impact journal had an IF of 0.71 and the highest had an IF of 9.92. Most of the reviewed study records were from the Journal of Applied Behavior Analysis (55%). The next most frequent journal was the Journal of Autism and Developmental Disorders , representing 4% of the journal cohort. Dissertations accounted for 4% of the cohort. Analysis of Verbal Behavior and Behavioral Interventions each made up 3% of our journal cohort, and the remaining journals contributed 1%–2% each. Journals contributing less than 1% were grouped as “Other,” making up 16% of the total cohort. Within the cohort of study records, 48% of records had participants that were solely male, 45% were of mixed sex, and 4% of the publications had solely female participants. Seventy-six percent of study records had participants with only ASD, and 24% had participants in the mixed-diagnoses category.

In the study records reviewed, 33% had one or two participants, whereas 31% of the publications had three participants, and 13% had four. Study records with 5 to 9 participants accounted for 11% of the total and 13% had more than 10 participants. The median number of participants was 3, whereas the mean number of participants was 8.12.

Overall, it was found that study records that included a smaller sample size (e.g., N ≤ 4) often investigated specific skills, tasks, or responses that varied based on each specific child (Gongola, 2009 ; Plavnick & Ferreri, 2011 ; Sullivan et al., 2020 ). Many studies modified the intervention or the definition of mastery dependent on the child or task given (Charlop-Christy & Daneshvar, 2003 ; Charlop et al., 1985 ; Ezzeddine et al., 2020 ; Lyons et al., 2007 ; Romaniuk et al., 2002 ).

Within the cohort of study records, 41% had some follow-up measure, 40% had some criterion or mastery measure, and 31% of publications had some generalization measure.

Study Outcomes and Findings

After the general data coding stage, any study records from the total sample ( N = 770) looking only at ABA Impact were coded for outcomes ( N = 551), i.e., improvement, regression, mix, or no change in the eight outlined outcome categories. Any study records comparing different ABA techniques ( N = 225) were designated for the next section (see “Comparisons of ABA Techniques,” below). The eight outcomes considered were cognitive, language, social/communication, problem behavior, adaptive behavior, emotional, autism symptoms, and QoL outcomes. Subject QoL is not reported in any tables, as there were no instances of this outcome being measured in the current cohort of study records.

The majority of study records reported improvement across all outcome categories, with 63%–88% of study records reporting improvement across the various outcome measures. In contrast, 0%–2% reported regression, 13%–36% reported mixed results, and 0%–13% reported no change (Fig. ​ (Fig.2 2 ).

Distribution of Improved, Regressed, Mixed, and Unchanged Results in the ABA Impact Section across the Measured Outcomes ( N = 551 study records)

When observing outcome measures by age group (see Appendix 5 , Table S4 ), among study records conducted with participants between ages 0–5 years, cognitive, language, and social/communication were the most commonly studied outcomes, at 22%, 23%, and 23% respectively. Of these, 66%, 68%, and 57% reported an improvement, respectively. Meanwhile, for ages 6–12, problem behavior and language were the most commonly studied outcomes at 25% each. Among these respective outcomes, 86% and 71% reported improvement. For ages 13–18, the most commonly studied outcome was cognitive (26%), followed by adaptive behavior (20%). Of these, 83% and 86% reported improvement, respectively. Finally, in the mixed-age groups, the most commonly studied outcome was language (28%), followed by social/communication (20%) and cognitive (20%). Of these three most studied outcomes, improvement was reported at 61%, 65%, and 62%, respectively. Detailed findings are available in Table S4 of Appendix 5 .

Outcome measures were also divided by sex. Among the study records that only observed females, the most commonly studied outcome was problem behavior at 33%, with social/communication following at 23%. Improvement was recorded 85% and 67% of the time, respectively, for these outcomes. Among records looking at only males, language was the most studied outcome at 26%, followed by cognitive and social/communication at 21% each. These improved at 62%, 66%, and 59%, respectively. Among publications with mixed sexes, the most studied outcome measures were language (25%), cognitive (22%), and social/communication (21%). Of these, 65%, 71%, and 67% showed improvement, respectively.

Outcome measures were then divided by diagnosis (Tables S5 and S6 ). Among study records solely studying ASD, the most commonly studied outcomes were language, cognitive, and social/communication, making up 25%, 22%, and 22% respectively. Among these respective outcome measures, 68%, 68%, and 63% reported improvement. In the mixed-diagnoses category, the most studied outcomes were problem behavior (31%) and language (22%), with 70% and 58% reporting improvements, respectively. Detailed findings are available in Tables S5 and S6 in Appendix 5 .

Next, secondary measures were classified. These included the presence of follow-up, whether interventions assessed mastery or criterion, and whether interventions assessed generalization. Out of the ABA Impact cohort, 41% had some follow-up, 40% had some measure of mastery/criterion, and 31% had some measure of generalization. Among study records that showed improvement within the various outcome measures, use of follow-up measures varied. Records that recorded improvements in cognitive, language, social/communication, and problem behavior outcomes had follow-up measures 47%–59% of the time. Records recording improvement in adaptive behavior and emotional outcomes had follow-up measures 67% and 64% of the time, respectively. Studies reporting improvement in autism symptoms had follow-up measures 100% of the time (see Appendix 5 , Table S7 ). Within the current cohort, out of the study records that signified some improvement, the frequency of mastery/criterion measures varied. Measures of mastery/criterion ranged from 0% and 14%, respectively, for autism symptoms and problem behavior improved outcomes, to 25% and 29%, respectively, for adaptive behavior and social/communication, and 43%–49% for cognitive, language, and emotional improved outcomes (Table S7 ). With regard to generalization, no study records showing improvements in autism symptoms assessed any measure of generalization. Among other outcomes, generalization measures ranged from 14% for emotional improved outcomes, 24%–29% for problem behavior, adaptive behavior, and cognitive improved outcomes, and 39% and 46%, respectively, for language and social/communication improved outcomes (Table S7 ).

Comparisons of ABA Techniques

Many records from the current search investigated the effectiveness of different ABA methods or variables in delivery. This section of study records was further divided into discrete records wherever more than two variables were compared, for a total of 307 comparison records, which were then coded for outcomes. In this case, coding included which category of comparison was studied, and indicated whether one ABA method performed better, or if the results were mixed or had no change.

Five categories of variables were defined: Teaching, Stimulus Characteristics, Reinforcement, Subject/Setting Characteristics, and Comparing ABA Interventions. These are further described in Appendix 2 . Within these categories, most comparison records were unique in the methods examined and thus could not be easily compared across this selection of records. That said, some trends were identified. First, many different teaching procedures were compared, such as how instructions were provided, tact versus listener training, or serial versus concurrent training (Arntzen & Almås, 2002 ; Delfs et al., 2014 ; Lee & Singer-Dudek, 2012 ). Several comparison records investigated the quality of the teaching procedures, commonly with respect to the integrity of reinforcement or teaching techniques (Carroll et al., 2013 ; Odluyurt et al., 2012 ). Others investigated the differences in personnel delivering the ABA interventions, such as a parent or clinician (Hayward et al., 2009 ; Lindgren et al., 2016 ), or differences in program delivery, such as via specific modeling, reinforcing, or prompting techniques (Campanaro et al., 2020 ; Jessel et al., 2020 ; Quigley et al., 2018 ). A number of comparison records compared time characteristics, such as reinforcement schedules or delays (Majdalany et al., 2016 ; Sy & Vollmer, 2012 ). Factors related to reinforcement in general were commonly compared and diverse in nature, spanning the quality, preference, presentation, and other aspects of reinforcement (Allison et al., 2012 ; Carroll et al., 2016 ; Fisher et al., 2000 ; Groskreutz et al., 2011 ). A few comparison records examined subject characteristics, such as the effectiveness of an ABA intervention based on the age of participant entry into the program or their diagnosis (Luiselli et al., 2000 ; Schreck et al., 2000 ), but slightly more commonly measured was the effectiveness of interventions administered in different settings such as at school, at a clinic, or at home (Hayward et al., 2009 ; Sallows & Graupner, 2005 ; Schreck et al., 2000 ). Some comparison records compared specific ABA intervention techniques, such as PRT, the Lovaas/UCLA model, or response interruption and redirection (RIRD), to one another (Dwiggins, 2009 ; Fernell et al., 2011 ; Lydon et al., 2011 ; Mohammadzaheri et al., 2014 ; Saini et al., 2015 ).

Table S8 (located in Appendix 5 ) displays the Comparisons of ABA Techniques group analysis of various intervention categories compared in the outcome measures. Teaching was the most commonly compared intervention category across six outcome measures, ranging from 38% to 64%, except for emotional (25%), and autism symptoms (10%). Comparing ABA interventions was the most commonly studied comparison in the emotional outcome (50%; 2 out of 4 comparison records), and subject/setting characteristics was the most commonly studied comparison in the autism symptom outcome (70%; 7 out of 10 comparison records). The improvement of one method over another was not always prevalent (Fig. ​ (Fig.3). 3 ). Within the cognitive, language, and social/communication outcomes, 37%–40% of comparison records found that one method exhibited greater improvement than the other, whereas 47%–56% had mixed outcomes. This is similar for adaptive behavior, where 52% found that one method exhibited greater improvement and 39% were mixed. On the other hand, outcome measures for problem behavior and autism symptoms more clearly showed that one method exhibited greater improvement, at 65% and 70% (7 out of 10 records), respectively.

Percentage Distribution of Results Where One Method Improved More, Results were Mixed, Results had No Change, or Results were Unknown (had No Quantifiable Measure) in Comparisons of ABA Techniques Group across the Measured Outcomes ( N = 225 comparison records)

Between-Groups Comparisons

Many records also investigated the effectiveness of ABA against other interventions or control groups. From the ABA Impact section, these study records comparing measures between groups ( N = 49) were coded a second time. These were also divided into discrete records whenever more than two groups were compared, for a total of 58 comparison records, which were then coded for outcomes. In this section, coding indicated whether one intervention performed better, or whether there was a mix, no change, or regression. The main interventions of interest in this section were categorized into ABA, EIBI, and I-ABA. Frequent comparisons were to control, which included eclectic (nonspecified), treatment as usual (TAU), or waitlist groups; nursing; portage; the Developmental, Individual Differences, Relationship-based intervention (DIR); or other interventions such as sensory integration therapy and the modified sequential-oral-sensory approach (M-SOS). These categories are further detailed in Appendix 2 .

Due to the nature of these interventions, most were longitudinal in study duration, as results were measured after 1 or more years. Moreover, validated measurement tools including Vineland Adaptive Behavior Scales (VABS), Reynell Developmental Language Scales (RDLS), and Bayley Scales of Infant Development-Revised (BSID-R), were more often used to measure changes in this section than in the ABA Impact and Comparisons of ABA Techniques sections, as well as validated parent/caregiver surveys such as the Achenbach Child Behavior Checklist or the Nisonger Child Behavior Rating (Eikeseth et al., 2007 ; Kovshoff et al., 2011 ; Smith et al., 2000 ). Few study records in this category included specific and differentiated probes into the generalization of the improvements seen ( n = 3; Dugan, 2006 ; Leaf et al., 2017 ; Peterson et al., 2019 ), and few included measurements of mastery or criterion ( n = 3; Birnbrauer & Leach, 1993 ; Dugan, 2006 ; Hilton & Seal, 2007 ).

Among the Between-Groups Comparisons (see Appendix 5 , Table S9 ), the ABA coding category was the most often improved, showing improvement over the comparison group at least 36% of the time across all outcomes. I-ABA showed improvement over the comparison 18%–30% of the time among cognitive, language, social/communication, adaptive behavior, and autism symptom outcomes. EIBI showed improvement over the comparison 21%–25% of the time among the cognitive, language, social/communication, and adaptive behavior outcomes. TAU and Other interventions occasionally showed greater improvement in some outcome measures (≤ 22% of the time). Nursery, portage, and DIR showed little to no improvement over ABA treatment groups.

Further Observations between Coding Groups

Figure ​ Figure4 4 shows the distribution of the number of participants across the whole sample, ABA Impact, Comparisons of ABA Techniques, and Between-Groups Comparisons cohorts. The highest number of participants in a study record was 332, whereas the lowest was 1. The Between-Groups Comparisons section had the highest median number of participants at 34, and the largest variation in the number of samples with an interquartile range (IQR) of 37. The entire cohort, ABA Impact section and Comparisons of ABA Techniques section each had a median number of 3 and an IQR of 1, respectively.

Distribution of the Number of Participants in the Entire Cohort, ABA Impact, Comparisons of ABA Techniques, and Between-Groups Comparisons sections. Note. The entire cohort, ABA Impact section, and Comparisons of ABA Techniques section each had a median of 3 participants and an IQR of 1, whereas the Between-Groups Comparisons section had a median of 34 participants and an IQR of 37

In addition to having larger sample sizes and more frequent use of validated measurement scales, records in the Between-Groups Comparisons section more often incorporated statistical analyses, approximately 85% of the time compared with approximately 15% of the entire cohort. Although statistical significance was not considered when initially coding the results in order to align with the rest of the sample, an informal review was conducted based on the reported statistical significance of the improvement of one condition over another. Overall, it was found that not all improvements were significant or assessed for statistical significance (Dawson et al., 2010 ; Dugan, 2006 ; Howard et al., 2014 ; Kovshoff et al., 2011 ). Among the outcome measures defined in the current review, some records showed significant improvement in some but not all contributing measures (Eikeseth et al., 2002 ; Reed et al., 2007a ; Zachor et al., 2007 ). Others had statistically significant improvement in all contributing measures of a given outcome (Dixon et al., 2018 ; Howard et al., 2005 ; Lovaas, 1987 ; Novack et al., 2019 ; Smith et al., 2000 ; Zachor et al., 2007 ).

The entire cohort of records explored had few occurrences of RCTs, the “gold standard” of research. Of the 12 identified RCTs, 5 were categorized into this review’s Comparisons of ABA Techniques section, whereas the remaining 7 included comparisons to controls or other interventions (Cihon et al., 2020 ; Dawson et al., 2010 ; Koenig et al., 2010 ; Landa et al., 2011 ; Leaf et al., 2017 , 2020 ; Mohammadzaheri et al., 2014 , 2015 ; Peterson et al., 2019 ; Reitzel et al., 2013 ; Scheithauer et al., 2020 ; Smith et al., 2000 ). In the interest of identifying a subset of more rigorous records, a three-step filter was conducted (Fig. ​ (Fig.5). 5 ). This was not a formal assessment of study quality, but rather a way to identify the proportion of investigated studies with several specific characteristics. After removing the section of studies looking at Comparisons of ABA Techniques, as well as any studies assessing mastery or criterion, and following with a filter for any inclusion of a comparison to control or other intervention, 32 study records (4%) remained out of 770. That is, only 4% of the entire sample assessed ABA impact, had a comparison group, and did not rely on mastery of specific skills to mark improvement.

Filter Flow Sheet Representing Study Records after the Subsequent Removal of Various Factors. Note. The first filter removed study records that compared various ABA techniques, where 551 of 770 (72%) of records remained. Next, study records that assessed mastery/criterion were removed, leaving 361 of 770 (47%) of records. Next, study records without any comparison group were removed, leaving 32 of 770 (4% records)

There was an observed increase in the amount of ABA literature between 2018 and 2020 compared to the 20-year search between 1997 and 2017. There was also an observed increase in larger scale studies between 2018 and 2020, as also evidenced by the higher frequency of RCTs ( N = 4; Cihon et al., 2020 ; Leaf et al., 2020 ; Peterson et al., 2019 ; Scheithauer et al., 2020 ) compared to the preceding 20-year period ( N = 8, Dawson et al., 2010 ; Koenig et al., 2010 ; Landa et al., 2011 ; Leaf et al., 2017 ; Mohammadzaheri et al., 2014 , 2015 ; Reitzel et al., 2013 ; Smith et al., 2000 ), but overall no notable change in the demographics, sample size, frequencies of outcomes measured, or teaching procedures.

The increasing prevalence of ASD in children and youth across the world has placed evidence-based interventions that treat these disabilities and disorders in high demand. ABA has been at the forefront of these interventions for decades and is recommended by many governments, including in the United States and Canada, as a well-established, scientifically proven therapy (Government of Canada, 2018 ; U.S. Department of Health & Human Services, 1999 ). Due to these prominent endorsements, existing and emerging interventions should be held to the same standard as established ABA interventions. That said, to our knowledge, a scoping review into all of the pertinent scientific evidence surrounding ABA has not yet been undertaken. This may result in knowledge gaps regarding this long-standing and widely used intervention and was the reasoning behind the current scoping review.

The results of the current scoping review are consistent with previous review articles and meta-analyses into the overall trend of positive effects of ABA. For example, there were overwhelming positive improvements in the majority of study records with respect to cognition, language development, social skills and communication, and adaptive behavior, along with reductions in problem behavior (Dawson & Bernier, 2013 ). In the ABA Impact section of the current review, 63%–88% of study records reported improvement across these same outcome measures, in addition to improvements in emotional and autism symptoms outcome measures (Fig. ​ (Fig.2). 2 ). The results of the current analysis into the demographics of these studies are also consistent with the existing literature, as the majority of the participants were male (48%) or there was a mix of females and males (45%) within multiparticipant studies (Kim et al., 2011 ; Lai et al., 2014 ; Miller et al., 2016 ). Further, the sole diagnosis of ASD was more common than mixed diagnoses, as 76% of study records recorded ASD without other diagnoses or comorbidities, again consistent with previous research into ABA (Dawson & Bernier, 2013 ). With respect to age distribution within the current review, the current results further mirror the previously published literature on EIBI, as children of a younger age tended to be predominately measured on outcomes of cognition, language skills, and social skills (Dawson & Burner, 2011 ; Reichow et al., 2012 ; Virués-Ortega, 2010 ). Children aged 6–12 years were most often measured with respect to changes in problem behavior and language skills, and those 13–18 years of age were most often measured with respect to changes in adaptive behavior and cognitive outcomes, again similar to previous research in older children and youth (Granpeesheh et al., 2009 ). As reported in other research, participants diagnosed solely with ASD were most often measured upon changes in cognition, language, and social skills and communication (Reichow et al., 2012 ). It is interesting that the mixed-diagnoses category was also commonly measured on language outcomes, but the most common outcome measure was problem behavior, at 31% of study records in the ABA Impact section.

Based on the number of study records ( N = 770, Fig. ​ Fig.1), 1 ), the current findings confirm there is a wealth of scientific knowledge regarding the effect of ABA on children and youth with ASD. Many studies have been published in peer-reviewed journals, but the quality of these studies requires further consideration. The lack of non-ABA comparison groups, rigorous study design, follow-up measures or investigation into generalization of reported outcomes, as well as factors such as small sample sizes, assessment of mastery or criterion, and the use of individualized methods to attain a particular skill or behavior for individual participants, could all contribute to and potentially confound the overarching positive findings seen in ABA research studies.

The gold standard of research is typically denoted as a RCT, followed by CCT or prospective studies. As evident through this scoping review, 64% of all the study records included three or fewer participants, and the median number of participants was three, indicating methods more consistent with SCED. SCEDs are exceedingly valuable within the field of ABA as they inform practitioners of the most effective methods and improve the delivery of ABA services (Tincani & Travers, 2019 ), in addition to facilitating innovation and detecting changes upon intervention (Smith, 2012 ). Specific attention can be given to measuring individual changes over time, across differing experimental conditions, in repeated conditions, and with other individuals in order to help establish validity (Perone, 2018 ). However, this type of study design may not measure statistical significance, lacks generalizability (Tincani & Travers, 2019 ), and does not assess long-term global effects (Smith, 2012 ). Although the overall positive results seen across all outcome measures may reflect the individualized impact of ABA, they may not reflect the more global changes or potential impacts on other children or youth with ASD that undergo the same treatment. In addition, many of these study records investigated specific skills, tasks, or responses that varied based on the child (Plavnick & Ferreri, 2011 ; Romaniuk et al., 2002 ), potentially making replication and generalization of the overall positive findings to the general population of children and youth with ASD difficult (Smith, 2012 ).

Few (6%) study records compared ABA interventions to control groups or other non-ABA interventions. Study records that did investigate ABA compared to a control group (typically TAU) or other intervention more often measured statistical significance, had larger sample sizes (Kamio et al., 2015 ; Koenig et al., 2010 ), and/or used validated measurement tools such as RDLS and BSID-R (Cohen et al., 2006 ; Eikeseth et al., 2002 ; Howard et al., 2005 ; Kovshoff et al., 2011 ; Remington et al., 2007 ). It is interesting that more recent meta-analyses have trended towards fewer statistically significant improvements than what has been previously reported (Reichow et al., 2018 ; Rodgers et al., 2020 ). The comparison records in the current review that did have large enough sample sizes to warrant a statistical analysis against a comparison group often did not find significance across all values or measurement tools used (Cohen et al., 2006 ). That said, a number of study records in the current review, some of which were also investigated by Reichow and colleagues (Cohen et al., 2006 ; Howard et al., 2014 ; Magiati et al., 2007 ; Remington et al., 2007 ), had comparison groups that differed to varying degrees from the treatment groups in terms of intensity, duration, location, or qualifications of intervention administrators, potentially raising questions about comparisons made between the groups (Reichow et al., 2018 ).

The current findings are also consistent with other publications with respect to the comparison of ABA techniques, as 225 of the study records investigated the efficacy of various ABA methods compared to one another. Another review found that approximately half of the comparison articles investigated found that one method was better than the other(s), and the other half of the sample indicated that the methods were equally effective (Shabani & Lam, 2013 ). Thus, this result indicated that only half of the comparisons analyzed truly contributed to the best practices of ABA (Shabani & Lam, 2013 ). In the current review, this was showcased through cognitive and language outcome measures, which found that only 38% and 37% of the comparison records, respectively, reported greater improvement with one method over the other. These investigations, often SCED, are undoubtedly important within the ABA field of research and to further analyze the effectiveness of one technique or method over another in order to optimize intervention strategies, particularly if rigorously designed (Lobo et al., 2017 ; Smith, 2012 ), or designed with an effort to assess and understand social validity (Snodgrass et al., 2021 ), but do not provide enough information on the overall effectiveness of ABA as a whole on the larger population of children and youth with ASD (Shabani & Lam, 2013 ).

Approximately 40% of the study records measured success in the given treatment through the assessment or attainment of some level of mastery or criterion for the desired skill or behavior (Grannan & Rehfeldt, 2012 ; Grow et al., 2011 ; Toussaint et al., 2016 ). Because study methods frequently continue until mastery or criterion in order to solidify behaviors and promote better maintenance (Luiselli et al., 2008 ; McDougale et al., 2020 ), positive improvements occur organically as subjects attain these desired measures. However, this may not accurately indicate the ability of a participant to maintain such a skill, particularly if the mastery criterion is low (McDougale et al., 2020 ; Richling et al., 2019 ). In some instances, criterion parameters and/or experimental procedures were altered in order to reach the desired measure (Charlop et al., 1985 ; Valentino et al., 2015 ). Thus, discretion should be taken when evaluating outcomes reliant on the mastery or extinction of skills or behaviors (McDougale et al., 2020 ). In addition, only 41% of the records conducted some form of investigation into follow-up or maintenance of the given outcome measure(s). This may not be reflective of the long-term effects of the overall positive outcomes. Likewise, generalization was only investigated in 31% of the study records, again prompting the question of whether or not these task- or behavior-specific improvements resulted in overall changes in the child’s skills, function, or behaviors. Further research may be required to assess retained changes rather than changes upon intervention (Bishop-Fitzpatrick et al., 2013 ; Smith, 2012 ).

In summary, the above results can be visualized through a filter of the study records (Fig. ​ (Fig.5). 5 ). Out of the 770 (100%) study records that were reviewed in depth, most showed positive results. When study records that used a method with a potential bias for positive results—such as those that compared one ABA treatment to another or assessed the mastery or criterion of a skill or behavior—were excluded, 361 (47%) study records remained. Furthermore, when study records that did not compare to a control or other intervention were excluded, 32 (4%) of the study records remained. These results may indicate gaps in the current ABA research approach, further supporting previous research about the standard of existing ABA literature (Reichow et al., 2018 ; Smith, 2012 ). These findings also support recommendations from Smith ( 2012 ), suggesting that RCTs comparing ABA to other interventions may be instrumental in evaluating both individual and global changes, as well as revising existing intervention models.

Limitations of the Current Review

The limitations of the current scoping review are: (1) the broadness of the outcome measures investigated; (2) the potential confounding measure of generalization independently versus within a standardized scale; (3) the definition of ABA itself versus its many treatment derivatives; and (4) the continual development of the diagnostic tools used to assess ASD. Each of these will be described in turn below.

Many of the study records investigated specific tasks, responses, or skills. Thus, improvements in areas such as cognition may be misleading, because both improvements on specific tasks and improvements on full-scale cognitive assessments were scored as improvements in the cognitive outcome category (Grow et al., 2011 ; Howard et al., 2005 ). In addition, some of the outcome measures had considerable overlap in definitions, such as the cognition, language, social/communication, and adaptive behavior categories, thus potentially resulting in the coding of multiple outcome measures for a similar task. For example, receptive labeling tasks were coded under both cognitive and language outcome measures (Grow et al., 2011 ).

The infrequent use of generalization seen in the Between-Groups Comparison section could be a result of the greater use of validated tools in this section of records (Cohen et al., 2006 ; Remington et al., 2007 ). Measurement tools such as VABS incorporate measures of generalization into the scale, and though not often specified as an independent measure of generalization, multiple environmental locations for the interventions (e.g., home and school) or multiple individuals interacting with the participants may have been measured.

Given the length of time that ABA has been utilized in treating children with ASD, and its having become the basis for many intervention techniques, it can be difficult to discern whether a particular treatment follows all of the principles of ABA and to what extent. This was seen in a recent review investigating all available interventions for children and youth with ASD (Whitehouse et al., 2020 ). It may be difficult for families, governments, and policy makers to evaluate available evidence appropriately (Whitehouse et al., 2020 ). For example, PECS was developed utilizing ABA principles and is commonly used in conjunction with ABA therapy, but it is also used throughout speech and language therapy, education systems that are not solely ABA, and simply as a communication-based intervention (Howlin et al., 2007 ; Lerna et al., 2012 ; Pasco & Tohill, 2011 ). Even within the ABA field there are conflicting definitions of ABA between the research community and public sector (Schreibman et al., 2015 ), adding another layer of complexity for policy makers when it comes to deciding whether to fund specific programs, specific types of professionals, or a combination of both. For the same reason, there may be some treatments, methods or techniques that have not been included within this scoping review. Further, although the use of “applied behavior analysis” as a search term may not have captured the full extent of behavioral research, its inclusion as both a MeSH term and keyword will have returned any records indexed by the reviewed databases as “applied behavior analysis,” satisfying the initial search criteria for the current scoping review.

As the understood spectrum of ASD and the diagnostic tools for ASD have changed drastically over the decades in which the investigated articles were published, the represented population may have also changed throughout the years, potentially influencing the acceptability of study findings (Reichow et al., 2018 ). Furthermore, the initial objective for this scoping review included searching across all NDD/D, not just ASD. Thus, the ASD MeSH term of “autistic disorder and autism spectrum disorder” may have potentially resulted in missed studies that included only AS or PDD-NOS diagnoses. That said, as this review was intended to find the scope of the research surrounding the impact of ABA on children and youth with ASD over a time frame of 23 years and across all available research, the authors believe all of the applicable scope was covered within reason.

Recommendations for Future Research

Recommendations for the further advancement in the field of ABA interventions for children and youth with ASD often include increasing the duration of the study, investigating comparisons to other non-ABA interventions, conducting follow-up studies for adults who participated in ABA interventions as children, and increasing the overall sample sizes. There has been an ongoing recommendation for larger scale studies over the last 20 years with respect to children and youth with ASD (Eldevik et al., 2009 ; Reichow et al., 2018 ; Smith, 2012 ), as well as for long-term outcomes for adults with ASD (Bishop-Fitzpatrick et al., 2013 ; Rodgers et al., 2020 ). With respect to EIBI in particular, there is increasing importance for large-scale studies comparing the effectiveness of EIBI against other non-ABA interventions, including developmental social pragmatic (DSP) interventions (Rodgers et al., 2020 ), which was also evident in the current review, as most comparison records that measured the effectiveness of EIBI compared their results to those of TAU or eclectic treatment approaches (90%; 9 out of 10 comparison records). Overall, although there are merits to both SCEDs and larger-scale group study designs (Lobo et al., 2017 ; Smith, 2012 ) there is a greater need for the latter when evaluating ABA. Our findings are in line with the perspective that ABA literature already has a wealth of SCEDs and is overdue for large scale studies such as RCTs to assess existing practices and, perhaps more importantly, to reevaluate and revise evolving ABA practices in the rapidly developing field of intervention for ASD (Smith, 2012 ).

An important note in terms of finding appropriate and effective interventions in the treatment for ASD, which is not limited to ABA, is the establishment of standards of care (SoC). Unfortunately, even though there is a wealth of knowledge regarding the assessment, diagnosis and treatment of ASD, there is still no clear SoC for the treatment of ASD (Department of Defense, 2019 , 2020 ). In general, outcome measures should indicate a true measure of benefit to the child and their family, in addition to providing relevance within practice and the ability to replicate across research (Rodgers et al., 2020 ). Recent studies have questioned outcome measures such as cognition and adaptive behaviors when evaluating ASD treatments, and a call for standardized outcome measures that are truly reflective of the benefit for the child and family is beginning to grow (Rodgers et al., 2020 ). Our recommendation is for more rigorous large-scale prospective comparison studies between ABA and emerging interventions, such as DSP interventions, to be conducted in order to develop gold standard treatment options with a defined SoC for children and families with ASD.

The results of the between-groups comparisons in this scoping review indicated that 23 comparison records compared intensive ABA (20–40 hr of intervention per week) to control or other interventions. Existing literature indicates that 30–40 intervention hours per week for children under the age of 6 results in greater improvements in cognition, language development, social skills, and more (Kovshoff et al., 2011 ; Reed et al., 2007b ). That said, more recent large-scale analyses on children who received 12 months of ABA services indicated that increased intensity does not necessarily predict better outcomes (Department of Defense, 2020 ). In a meta-analysis completed by Rodgers et al. ( 2020 ), autism symptoms showed no statistically significant improvements with higher intensity EIBI treatments as opposed to lower intensity EIBI treatments. It was also found that no one age group demonstrated improvement when correlated with the number of hours of rendered ABA services (Department of Defense, 2020 ). This evidence suggests there may be insufficient recent research justifying the need for high-intensity interventions, indicating that more research studies need to be conducted in the field of ABA in terms of assessing ABA impact with different or lower intensity interventions.

Most of the current literature surrounding ABA-based interventions lacks investigations into the QoL of children with ASD and instead focuses on aberrant behaviors (Reichow et al., 2018 ; Whitehouse et al., 2020 ). A recent meta-analysis found that, upon analyzing five articles of higher scientific credence, none conducted investigations into the changes with respect to QoL for the children or parents (Reichow et al., 2018 ). The present scoping review likewise found no occurrences of subject QoL measures in the sample analyzed. Overall changes in QoL for children living with ASD is of the utmost importance, as QoL is “individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” (WHO, 1997 , p. 1). The continued lack of research into long-term effectiveness of ABA treatments is an ongoing concern and should be a focus of future research to help measure QoL (Whitehouse et al., 2020 ) and also to investigate any possible adverse effects (Rodgers et al., 2020 ). For example, recent literature investigating adults with ASD who participated in ABA treatments when they were young has shown increases in incidences of posttraumatic stress disorder (PTSD); this is an emerging field of research in adults with ASD and should be further investigated through long-term studies (Kupferstein, 2018 ).

Future research into the cost-effectiveness of ABA-based interventions compared to existing and emerging interventions should be conducted, as only a few articles within the current review discussed the cost effectiveness of the ABA interventions in use (Farrell et al., 2005 ; Kamio et al., 2015 ; Magiati et al., 2007 ; Park et al., 2020 ). In the few incidences where cost-effectiveness was measured, the results varied. For example, one study found that higher ABA program cost was associated with lesser improvements in language development (Kamio et al., 2015 ), one reported higher costs for the Lovaas/ABA model program (Farrell et al., 2005 ), one found little difference in cost between nursery and ABA interventions (Magiati et al., 2007 ), whereas Park et al. ( 2020 ) found lower costs for their specific ABA model (Korean Advancement of Behavior Analysis [KAVBA]) children’s center as compared to other Comprehensive Application of Behavioral Analysis to Schooling (CABAS) centers. In conclusion, these long-term and intensive interventions should be further investigated with respect to their cost-effectiveness and overall improvements in QoL (Rodgers et al., 2020 ; Whitehouse et al., 2020 ).

As ever in the scientific process, interventions and treatments need consistent and replicative investigations under stringent protocols to ensure the continued efficacy and generalizability of a given intervention. According to the U.S. Department of Health and Human Services ( 1999 ), ABA is the gold standard treatment for ASD, and is funded almost exclusively across North America. The current scoping review spanning 770 study records showed positive and beneficial effects of ABA for children with ASD across seven outcome measures. However, only 32 (4%) assessed ABA impact, had a comparison group, and did not rely on mastery of specific skills to mark improvement.

Without ongoing research and the development of a SoC, governments and policy makers will not have the most up-to-date information that reflects ABA-based and other interventions in terms of the ever-changing landscape of diagnoses, modern technological advancements, changes within the intervention implementation, and measurement tools of treatment efficacy. One such example is the measure of subject QoL, which, as made evident by this scoping review, was not measured in any study record included, but is of utmost importance to truly indicate the overall long-term impact of ABA. Moreover, as the children and youth who participated in ABA-based and other interventions become adults, the long-lasting effects of these interventions should be investigated more thoroughly.

Therefore, large longitudinal prospective studies comparing ABA-based and different interventions treating children and youth with ASD are needed. As ABA is historically based on an operant conditioning approach to treatment whereas many emerging interventions typically use a social pragmatic approach (Whitehouse et al., 2020 ), continued research comparing these two differing ideologies is particularly important, as ABA is currently the bar to which other interventions are held at the governmental level. With a holistic view of all of the scientific evidence behind ABA, governments will be able to more accurately compare any existing and emerging interventions to the well-established norm of ABA. Until a SoC is established, all interventions for children and youth with ASD must be held to the existing standard set by ABA to be considered effective.

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Acknowledgements

This scoping review would not be possible without the help of the people who took the time to move this project forward. We thank Jonathan Agyeman for his assistance in the data analysis, synthesis, and creation of tables and figures following the search update and subsequent revisions. For his detailed refinements during the final stage of our submission, we thank our copy editor, Henry Sporn. We also thank Jake Choi, Sam Brimacombe, Ciara McDaniel, Elizabeth Steczko, and Kristyn Jorgenson for their hard work and contributions with the initial search phase, publication screening, and journal extractions. Likewise, thank you to Alesia DiCicco, and Zachary Betts for their contributions to journal extractions. For their contributions in cleaning publication information for referencing, a special thank you to Sophia Shalchy-Tabrizi, Jodiline Lacsamana, Ghazaleh Bazazan Nowghani, and finally Madeleine Teasell, who also assisted with extractions and numerous revisions throughout the project. We would also thank Alison Davidson and Suk Chan Oh with their help in the initial search and screening; we further thank Alison for her keen eye in proofreading, and Kelley Lloyd-Jones for her perspective as a Behavior Consultant. Last but not least, we give a heartfelt thank-you to Dr. Patrick Myers for taking the time to review our work. His expert feedback was invaluable in completing this vast project.

Authors' Contributions

Mojgan Gitimoghaddam, MD, PhD(c), led and designed the project, wrote and reviewed the article; Natalia Chichkine, BSc, collected data, extracted and coded data, and wrote and reviewed the article; Laura McArthur, BSc, collected data, extracted and coded data, and wrote and reviewed the article; Sarabjit S. Sangha, MSc, coded data, contributed data or analysis tools, assisted with analysis, and wrote and reviewed the article; and Vivien Symington, BA/BPHE, conceived and designed the analysis, extracted and coded data, and wrote and reviewed the article.

Research was supported by Club Aviva Recreation Ltd.

Data Availability

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The authors have no conflicts of interest to declare that are relevant to the content of this article.

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Behavior Analysis In Psychology

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Key Takeaways

• Behavior analysis posits that people’s and organisms’ environments can be arranged so that desirable behaviors become more probable and undesirable behaviors become less probable.
• Behavior analysis has three main branches: behavioralism, which encompasses the philosophy of how behavior analysis should be conducted; the experimental analysis of behavior, which describes basic research into behavior; and applied behavior.
• The 20th century saw a shift from describing behavior internally to describing behavior empirically and observable; B. F. Skinner provides the foundation for how psychologists think about behavior analysis today.
• Commonly applied behavior analysis methods for teaching behavior involve chaining, prompting, and shaping.

Behavior Analysis Defined

Behavior analysis is a science based on the philosophical principles of behaviorism. The key point of behaviorism is that what people do can be understood.

“Behavior is a product of its circumstances, particularly the events that immediately follow the behavior. Behavior analysts have used this information to develop numerous techniques and treatment approaches for analyzing and changing behavior, and ultimately, to improve lives. Because this approach applied behavior analysis (ABA) is largely based on behavior and its consequences, techniques generally involve teaching individuals more effective ways of behaving and working to change the social consequences of existing behavior”. About behavior analysis – Behavior Analyst Certification Board . Bacb.com. Published 2017. https://www.bacb.com/about-behavior-analysis/

Scientists, in the view of behaviorism, can look at behavior as a process that can have any number of causes, internal or external.

Behavioral analysts believe that people can arrange their environments in such a way that desirable behaviors become probable and undesirable behaviors become less probable (Heward and Wood, 2003).

Cooper, Heron, and Heward, 2020 consider science to have several key characteristics: description, prediction, and control.

• This means that applied behavior analysis, as a science, intends to undergo systematic observations (creating collections of facts about observed events that can be quantified, classified, and examined for relations with other known facts);
• create predictions as to the probability that a certain event will occur;
• and then develop a set of functional relations that provide an understanding valuable to the development of technologies.

A fundamental relation comes about when a well-controlled experiment reveals that a specific change in one event can be produced reliably by manipulations of another event and that the changes in the original event are unlikely to be the result of other  confounding variables .

Techniques and Strategies

Behavior analysts use a variety of techniques to change behavior. These include chaining, prompting, and shaping.

Chaining involves breaking a task into smaller components and can be used to help people gain proficiency in complex, multi-step directions.

The idea of chaining is based on the behavior chains, which are strings of individual behaviors that, when connected together, create an end-behavior.

For example, the behavior chain of putting on a coat could involve the verbal stimulus, “Put on your coat,” obtaining the coat from the closet, having the coat in one’s hands, putting one arm in a sleeve, putting the other arm in a sleeve, the coat being on, zipping up the coat, and finally instructor praise (Cooper, Heron, and Heward, 2020).

There are three main methods of creating behavior chains: forward chaining, total-task chaining, and backward chaining.

1. Forward Chaining

In forward chaining, the behaviors that make up a task are taught in their naturally occurring order. For example, a child learning how to tie their shoes may receive reinforcement when the very first step, “pinch lace, “ is performed correctly three times.

The instructor may give the child additional reinforcement when they perform the first and second times correctly in sequence, another three times, and so on. As someone masters one skill, they can link it to the next skill, and this series of skills can eventually constitute a completed task.

McWilliams et al. acknowledge two main advantages to forward chaining: linking smaller chains into larger ones and its ease of use (1990).

2. Total Task Chaining

In total task chaining, the instructor gives the learner training on each step in the task during every session and assists with any step the person cannot perform independently.

The instructor trains the learner with needed assistance until the learner can perform all of the behaviors in the sequence independently.

In one study using total task chaining, Werts, Caldwell, and Wolery (1996) taught skills such as operating an audiotape, sharpening a pencil, and using a calculator to elementary-school students with disabilities.

The researchers probed the students on their ability to perform the entire task response chain. A peer who could do the task chain demonstrated the chain in its entirety while describing every step, and the researchers prompted the learning student to perform the chain.

All three students learned how to complete the response chain after peer modeling (Cooper, Heron, and Heward, 2020; Werts, Caldwell, and Wolery, 1996).

3. Backward Chaining

Backward chaining involves an instructor initially performing all of the behaviors involved in a task except for the final behavior, which the learner performs.

Once the learner learns how to perform the last behavior, the instructor then performs all of the behaviors in the chain except for the final and penultimate behaviors and so on, until the learner can perform all of the behaviors in the chain.

Pierrel and Sherman (1963) notably taught a white rat, Barnabus, to climb a spiral staircase, push down and cross a drawbridge, climb a ladder, pull a toy car by a chain, enter the car and pedal through a tunnel, climb a flight of stairs, run through an enclosed tube, enter an elevator, raise a miniature Brown University flag, exit the elevator, and press a bar to receive a pellet of food using backward chaining.

Another notable example of backward chaining was Hagiopian, Farrel, and Amari’s attempts to teach a 12-year-old male to inject food by mouth, which he had previously expelled.

The researchers began by teaching the boy to swallow by placing a syringe in his mouth.

Eventually, they delivered reinforcement to the boy only once all three “drinking” responses occurred (accepting the juice and swallowing) (Hagiopian, Farrel, and Amari, 1996; Cooper, Heron, and Heward, 2020).

Backward chaining also has a modification, backward chaining with leap ahead, where the instructor does not instruct the learner on every step of the chain.

This is particularly effective when the learner has mastered particular steps of the chain ahead of time (Cooper, Heron, and Heward, 2020).

Prompting, meanwhile, involves using a prompt to trigger a desired response.

It can be used in combination with chaining or shaping, and encourages the learner to perform a task until they learn how and when to do it.

Prompts can involve instructions, demonstrations, touches, or other stimuli.

Shaping describes gradually altering a behavior until it becomes the desired behavior.

For example, a language therapist may use shaping when they develop speech with a client by first reinforcing lip movements, then sound production, and finally, words and sentences.

The number of adjustments and approximations needed to create a desirable behavior depends on that behavior’s complexity.

Shaping can be useful in cases where behaviors cannot be easily learned by instructions, incident experience, or prompts (Cooper, Heron, and Heward, 2020).

One technique that instructors can use in shaping is differential reinforcement.

In differential reinforcement, instructors provide reinforcement for responses that share a predetermined dimension of quality with the desired behavior.

For example, a parent may pass food to a child at the dinner table when the child says “please” but not otherwise. Because only the responses that share this similarity with the desired behavior are reinforced, other responses are performed less and less – they become extinct (Cooper, Heron, and Heward, 2020).

In order to shape behavior, the instructor must determine a set of gradually changing criteria for reinforcement for approximations that look closer and closer to the desired behavior.

For example, a teacher may ask a child who normally speaks at the inaudible volume of 45 decibels to speak at 55 and finally 65 decibels, as demonstrated in Fleece et al. (1981).

The researchers found that shaping increased the children’s voice volume and that this increase persisted over four months.

Shaping can take place over several dimensions: topography, frequency, latency, duration, and amplitude/magnitude.

Topography involves the form of the behavior, such as in refining the motor movements of a golf swing.

Frequency involves the number of responses during a period, such as increasing the number of math problems completed per minute on an assignment.

Latency involves changing the time between the stimulus and the behavior, such as decreasing the time between a parent saying “clean your room” and the child actually cleaning their room.

Duration involves changing the total time for the behavior to take place, such as increasing the length of time a student stays on task.

Amplitude/magnitude involves manipulating the response strength, such as increasing the height of a high jump bar for students in a physical education class.

While shaping can enforce behaviors in a positive way that would otherwise be difficult to learn through instructions, shaping also has several important limitations.

Shaping can be time-consuming, and progress toward the desired behavior may not necessarily be linear.

Shaping requires consistent monitoring of the learner, shaping can be misapplied (such as a child learning to scream at a parent when his quiet calls for ice cream are ignored), and shaping can potentially produce harmful behavior (such as in enforcing a sequence of riskier and riskier behaviors) (Cooper, Heron, and Heward, 2020).

Experimental & Applied Behavior Analysis

Behavior analysis consists of three major branches influenced by three major scientists (Cooper, Heron, and Heward, 2020):

• Behaviorism, which is the philosophy of the science of behavior;
• Experimental analysis of behavior or basic research into behavior;
• Applied behavior analysis aims to develop ways to understand and change behavior (Cooper, Heron, and Heward, 2020).

Behaviorism Assumptions and Philosophy

In the early 1900s, studies of states of consciousness, images, and other mental processes dominated psychology (Cooper, Heron, and Heward, 2020).

These studies often used introspection, or the act of carefully observing one’s own thoughts and feelings, as the method of investigation.

Although this does not fully align with modern scientific methods, authors from the first decade of the 20th century nonetheless defined psychology as the science of behavior (Kazdin, 1978; Cooper, Heron, and Heward, 2020).

Scholars generally agree that John B. Watson influenced psychology to be more focused on the objective study of behavior in his article, “Psychology as the behaviorist views it” (Watson, 1913).

Watson argued that psychology must focus on observable behavior over states of mind and mental processes and, furthermore, that the objective study of behavior as a natural science consists of observing the relationships between environmental stimuli and the responses they evoke.

This idea of behaviorism became known as stimulus-response psychology (Cooper, Heron, and Heward, 2020; Watson, 1913).

Although Watson’s stimulus-response psychology has faced criticism, it nonetheless influenced psychology to be seen as a natural science on par with the physical and biological sciences.

Experimental Analysis of Behavior

Scholars generally agree that B. F. Skinner established the foundations of behavior analysis formally in his 1938 book, The Behavior of Organisms. In this book – a summary of his research from 1930 to 1937 – Skinner emphasized two types of behavior: respondent and operant.

Inspired by Ivan Pavlov’s work, Respondent behavior posits that respondents are “brought out” by stimuli that occur immediately before the behavior.

For example, a bright light could be the stimulus for pupil constriction, and this phenomenon could be called a reflex. However, Skinner also acknowledged that the behavior of organisms can appear spontaneous or, as Skinner calls it, “voluntary” (Cooper, Heron, and Heward, 2020).

This so-called voluntary behavior, Skinner argued, could not be explained by Watson’s stimulus-response theory of behavior (Skinner, 1938).

To confront this contradiction, Skinner turned to the environment to seek the determinants of behavior that did not appear to have an antecedent cause (Cooper, Heron, and Heward, 2020).

Although Skinner did not deny the role that physiological variables played in determining behavior, he believed that this was the domain of other disciplines.

Through his research, Skinner found that the consequences that follow a stimulus are more important to behavior change than the stimulus itself.

This evidence formed the three-term contingency theory of behavior, which, unlike the stimulus-response model, accounted for how the environment can determine parts of learned behavior (Cooper, Heron, and Heward, 2020).

Skinner called behavior according to the three-term contingency model operant behavior. Operant behavior , rather than being elicited by a preceding stimulus (such as the smell of food triggering Pavlov’s dogs to salivate), is influenced by stimulus changes that had followed the behavior in the past.

For example, if a rat had learned that an electric shock would follow an attempt to drink water, the rat may avoid or skirmish when drinking water, regardless of whether or not the water was electrified (Glenn, Ellis, and Greenspoon, 1992).

Skinner named the analysis of operant behavior the experimental analysis of behavior and used quantifiable methods, such as recording the rate at which, say, a rat or a pigeon demonstrated a particular behavior in a controlled and standardized environment.

In addition to influencing the development of the experimental analysis of behavior, Skinner created several treatises on how the principles of behavior could be applied to areas such as education, religion, government, law, and psychotherapy.

The two most famous of these texts are Walden Two (1948) and Science and Human Behavior (1953). In them, Skinner established approaches to the study of behavior, such as mentalism.

Mentalism is an approach to the study of behavior that assumes that a mental or inner dimension differing from the behavior itself exists (Moore, 2003).

These unobserved mental processes – what psychologists such as Moore (1995) call hypothetical constructs – cannot be experimentally manipulated.

Hypothetical constructs can include concepts such as free will, readiness, innate releasers, language acquisition devices, storage and retrieval mechanisms for memory , and information processing (Cooper, Heron, and Heward, 2020).

For example, a rat could push a level every time a light comes on and receive food but refuse to do so when there is no light.

Behaviorists would reject the idea that the rat has created an association (knowledge) between the light being on and the level dispensing food, calling it an explanatory fiction, saying that it contributes nothing to an understanding of the variables that develop or maintain the behavior (Heron, Tincani, Peterson, and Miller, 2005; Cooper, Heron, and Heward, 2020).

Different branches of behavioralism hold different views on events that cannot be observed objectively. There is structuralist, methodological, and cognitively-based behavioralism.

While structuralism and methodological behaviorism reject events not operationally defined and objectively observed (Skinner, 1974), methodological behaviorists often acknowledge the existence of mental events but do not consider them to be within the realm of the analysis of behavior (Skinner, 1974).

Skinner himself created radical behaviorism, which viewed thoughts and feelings (what he calls “private events”) as behavior to be analyzed with the same experimental methods used to analyze publicly observable behavior.

Skinner assumed firstly that private events such as thoughts and feelings are behavior; behavior that takes place within an organism is only distinguishable from “public” behavior by its inaccessibility; and private behavior is influenced by the same kinds of variables as publicly accessible behavior (Cooper, Heron, and Heward, 2020).

Applied Behavior Analysis

Applied behavior analysis is a science that seeks to understand and improve human behavior.

In contrast to other branches of psychology, applied behavior analysis focuses on objectively defining socially significant behaviors and intervening to improve the behaviors studied and uses scientific methods such as objective description, quantification, and controlled experimentation (Cooper, Heron, and Heward, 2020).

Fuller (1949) conducted one of the first studies reporting the applications of operant behavior in humans.

Fuller studied an 18-year-old boy with severe developmental disabilities who squirted a small amount of a warm sugar milk solution into his mouth every time he moved his right arm, which he would otherwise move infrequently.

By the fourth session, the boy had learned to move his arm three times per minute. A number of other researchers established through their research that the principles of behavior studied in animals are also applicable to humans (e.g., Bijou, 1955; Baer, 1960; Ferster and DeMyer, 1961; and Lindsley, 1956).

Scholars agree that applied behavior analysis as a branch could be traced to Ayllon and Michael’s 1959 paper, “The Psychiatric Nurse as a Behavioral Engineer,” where the authors described how nurses in psychiatric hospitals used a number of behavioral techniques to “improve” the functioning of patients.

This application of behavior analysis stretched to education in the form of teaching practices such as contingent teacher praise and attention, token reinforcement systems, curriculum design, and programmed instruction (Hall, Lund, and Jackson, 1968; Cooper, Heron, and Heward, 2020).

Applied behavior analysis began formally with the publication of the Journal of Applied Behavior Analysis and the publication of the paper “Some Current Dimensions of Applied Behavior Analysis” (Baer, Wolf, and Risley, 1968).

This paper outlined the criteria for judging research and practice in applied behavior analysis. Baer, Wolf, and Risley (1968) argued that applied behavior analysis should be applied behavioral, analytic, technological, conceptually systematic, effective, and capable of appropriately generalized outcomes (Cooper, Heron, and Heward, 2020).

Applications of Behavior Analysis

Agroforestry.

Goltz, Mayer, and Orr (2020) examined how applied behavior analysis can be used in encouraging development agencies to design agroforestry programs that reduce erosion and climate change through case studies.

The people enacting these changes could be individuals or large groups. In successful cases of using behavior analysis to modify agroforestry behavior, the groups attempting to create change gave reinforcements to agroforestry “adopters,” occasionally making the reinforcements either more personal or more immediate in order to increase their salience.

Goltz, Mayer, and Orr established that the antecedent-behavior-consequence contingency used by applied behavior analysts could be used to guide the behavior of change strategists, change agents, and change adopters involved in preserving forests (2020).

Dance in People With Neurodevelopmental Disorders

Pontone, Vause, and Zonnevelt (2020) reviewed 19 studies of teaching dance to those with neurodevelopmental disorders and found that eight listed at least one behavior-analytic component as part of their dance intervention package.

For example, five studies used positive reinforcement such as praise to increase desired behaviors, and verbal, model, and gestural prompts were also common.

One study, for example, used auditory feedback to reinforce dance, where the instructors played a clicking sound in response to correct movements and no sound in response to incorrect movements (Carrion et al., 2019).

Another study used chaining to teach the steps of a “dancercise” and transferred this stimulus between different trainers in different settings (O’Connor and Cuvo, 1989).

Ayllon, T., & Michael, J. (1959). The psychiatric nurse as a behavioral engineer 1. Journal of the experimental analysis of behavior, 2 (4), 323-334.

Baer, D. M. (1960). Escape and avoidance response of preschool children to two schedules of reinforcement withdrawal. Journal of the Experimental Analysis of Behavior, 3 (2), 155-159.

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of applied behavior analysis, 1 (1), 91.

Bijou, S. W. (1955). A systematic approach to an experimental analysis of young children. Child Development, 161-168.

Carrion, T. J., Miltenberger, R. G., & Quinn, M. (2019). Using auditory feedback to improve dance movements of children with disabilities. Journal of Developmental and Physical Disabilities, 31 (2), 151–160. 

Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied behavior analysis. Pearson UK.

Ferster, C. B., & DeMyer, M. K. (1961). The development of performances in autistic children in an automatically controlled environment. Journal of Chronic Diseases, 13 (4), 312-345.

Fleece, L., Gross, A., O”Brien, T., Kistner, J., Rothblum, E., & Drabman, R. (1981). ELEVATION OF VOICE VOLUME IN YOUNG DEVELOPMENT ALLY DELAYED CHILDREN VIA AN OPERANT SHAPING PROCEDURE. Journal of Applied Behavior Analysis, 14 (3), 351-355.

Fuller, P. R. (1949). Operant conditioning of a vegetative human organism. The American journal of psychology, 62 (4), 587-590.

Glenn, S. S., Ellis, J., & Greenspoon, J. (1992). On the revolutionary nature of the operant as a unit of behavioral selection. American Psychologist, 47 (11), 1329.

Goltz, S. M., Mayer, A. L., & Orr, B. (2020). Applied behavior analysis as a development tool: Examples from agroforestry. Journal of Sustainable Forestry, 39 (8), 785-799.

Hall, R. V., Lund, D., & Jackson, D. (1968). Effects of teacher attention on study behavior 1. Journal of applied behavior analysis, 1 (1), 1-12.

Hagopian, L. P., Farrell, D. A., & Amari, A. (1996). Treating total liquid refusal with backward chaining and fading. Journal of Applied Behavior Analysis, 29(4), 573-575.

Heron,T. E.,Tincani, M. J., Peterson, S. M.,& Miller, A. D. (2005). Plato’s allegory of the cave revisited. Disciples of the light appeal to the pied pipers and prisoners in the darkness. In W. L. Heward, T. E. Heron, N.A. Neef, S.M. Peterson, D. M. Sainato, G.

Cartledge, R. Gardner, III, L. D. Peterson, S. B. Hersh,&J.C. Dardig (Eds.), Focus on behavior analysis in education: Achievements, challenges, and opportunities (pp. 267–282), Upper Saddle River, NJ: Merrill/Prentice Hall.

Heward, W. L., & Wood, C. L. (2003). Thursday afternoons with Don: Selections from three teleconference seminars on applied behavior analysis. A small matter of proof: The legacy of Donald M. Baer, 293-310.

Lindsley, O. R. (1956). Operant conditioning methods applied to research in chronic schizophrenia. Psychiatric research reports.

Moore, J. (1995). Radical behaviorism and the subjective-objective distinction. The Behavior Analyst, 18 (1), 33-49.

Moore, J. (2003). Behavior analysis, mentalism, and the path to social justice. The Behavior Analyst, 26 (2), 181-193.

O”Conner, Y. M., & Cuvo, A. J. (1989). Teaching dancercise to persons who are mentally handicapped: Programming transfer of stimulus control to a community setting. Behavioral Residential Treatment, 4 (4), 289–311.

Pierrel, R., & Sherman, J. G. (1963). Barnabus, the rat with college training. Brown Alumni Monthly, 63, 8-12.

Pontone, M., Vause, T., & Zonneveld, K. L. (2021). Benefits of recreational dance and behavior analysis for individuals with neurodevelopmental disorders: A literature review . Behavioral Interventions, 36 (1), 195-210.

Skinner, B. F. (1938). The behavior of organisms. New York: Appleton-Century-Crofts.

Skinner, B. F. (1948). Walden two. New York: Macmillan.

Skinner, B. F. (1953). Science and human behavior . New York: MacMillan.

Skinner, B. F. (1974). About behaviorism. New York: Knopf.

Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological review, 20 (2), 158.

Werts, M. G., Caldwell, N. K., & Wolery, M. (1996). Peer modeling of response chains: Observational learning by students with disabilities. Journal of Applied Behavior Analysis, 29 (1), 53-66.

Further Reading

• Pilgrim, C. (2018). Some thoughts on shaping future behavior analysts: A call to stay true to our roots. Behavior analysis in practice, 11 (3), 204-205.

Morris, E. K., Altus, D. E., & Smith, N. G. (2013). A study in the founding of applied behavior analysis through its publications. The Behavior Analyst, 3 6(1), 73-107.

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Interview with Thomas Zane, PhD, BCBA-D

“Different jobs teach you different skills, and the more you expose yourself to situations, the more you will learn.”

In Memory and Honor of H.S. Pennypacker

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Interview with Dwight Harshbarger

“This stuff is powerful, I’m gonna keep doing it.”

Interview with Janet S. Twyman, PhD, BCBA, LBA

…my approach is the same: make sure the behavior is doable; ensure there’s motivation and environmental support; reduce or eliminate coercion; always look to the contingencies.

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“I’m proud of my students and their work, and how we developed a line of research related to communication/verbal behavior and RFT/rule governance in organizations. Developing your niche is hard to do and takes courage.”

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“Behavior analysts should work on speaking to a broader audience in ways that the audience is receptive to and finding ways to disseminate and tell our story more effectively.”

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“While Skinner was a very nice guy he was often demonized. Many people only accept behavior analysis after they see the practical applications.”

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“If you want to make advances in your field, don’t stay in the safe and fashionable middle, go for the edges.”

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“We need to find more and better ways to educate the general public about our science.”

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“​Getting a child with Autism to communicate with signs, symbols or words when they previously used ‘meltdowns.’…I am happy that the company I created gives opportunities to these kids (and also lots of jobs to talented ABA professionals).”

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“We began an instructional program in 1990 with three students. Now there are over a hundred students and more than 10 staff in that program, plus a few other programs in Israel.”

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“I’ve worked in a variety of industries including automotive, human services, non-profit, print, retail, and oil and gas across a range of companies, from global Fortune 500s, creative start-ups, to local small businesses.”

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“I tutored 40 kids when I was 9-12 years old. In college, I became passionate about…catering to children who could do better if we taught them better. Behavior analysis was the vehicle for me to make gains in education.”

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“I use the science of behavior every day! The energy industry is very exciting, and now more than ever.”

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MSU announces new bachelor’s degree in applied behavior analysis

Contact: Bethany Shipp

STARKVILLE, Miss.—Mississippi State is launching a new bachelor’s degree to meet the growing demand for applied behavior analysis service providers nationwide.

Beginning fall 2025, MSU’s Bachelor of Science in Applied Behavior Analysis will be among only a few undergraduate ABA programs in the country, preparing students for impactful careers in behavioral health. Housed in the College of Education’s Department of Counseling, Higher Education Leadership, Educational Psychology, and Foundations, the new program builds upon the success of the university’s existing ABA minor. It offers a dedicated pathway to MSU’s graduate ABA program, which provides necessary coursework for credentialing as a Board Certified Behavior Analyst.

Students in the new program will learn how to utilize the concepts and principles of behavior analysis to effectively design, implement, assess and analyze intervention programs for individuals or groups in a variety of settings, such as clinics, homes and schools. ABA practitioners seek to improve quality of life for a variety of individuals across the lifespan, but most frequently provide services to individuals diagnosed with autism or an intellectual or developmental disability.

Offered in Starkville and online, the curriculum meets the coursework requirements for two certifications in the field of ABA: Registered Behavior Technician and Board Certified Assistant Behavior Analyst. It also positions students to pursue licensure, which is required by most states, including Mississippi.

According to U.S. Employment Demand for Behavior Analysts , the demand for behavior analysts is growing, with a 38% increase statewide and 14% increase nationwide from 2022 to 2023 for BCBA or BCBA-D practitioners. Since Mississippi passed legislation in 2015 to create a governing body for the profession, MSU has been on the front line of providing support and delivering a workforce through its graduate degree and minor in ABA.

“We are thrilled to announce the launch of our Bachelor of Science in Applied Behavior Analysis program,” said Rebecca Spencer, assistant professor and ABA undergraduate program coordinator. “Our ABA minor has exponentially grown in popularity since its launch a few years ago, and this new degree will be the perfect option for many of our current minor students who wish to pursue a career in ABA. By equipping students with the expertise and practical experience necessary to excel in this field, we are committed to making a positive and lasting impact on the lives of individuals and communities, and to help address the growing need for skilled behavior analysts in Mississippi and beyond.”

Daniel Gadke, College of Education associate dean of research, professor and Department of Counseling, Higher Education Leadership, Educational Psychology and Foundations head, echoed Spencer’s excitement over the new degree program.

“MSU’s College of Education has been at the forefront of creating ABA practitioners for the state since 2015,” said Gadke. “With this new degree, we will not only be one of the few universities in the country to offer a BS in ABA, but one of the few universities in the world to offer certification pathways in the field at each level, RBT, BCaBA, BCBA and BCBA-D. I am really thankful for all the hard work the faculty, particularly Ms. Beca Spencer, put into making this new degree a reality.”

Learn more about MSU’s Department of Counseling, Higher Education Leadership, Educational Psychology, and Foundations at www.chef.msstate.edu .

Mississippi State University is taking care of what matters. Learn more at  www.msstate.edu .

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Assessment of the ‘students’ perceptions of education using Dundee Ready Environment Educational Measure (DREEM) inventory at Princess Nora bint Abdulrahman University, Saudi Arabia

• Latefa Hamad Al Fryan 1 ,
• Mahasin Ibrahim Shomo 2 &
• Ibrahim A. Bani   ORCID: orcid.org/0000-0003-0566-1740 3 , 4  

BMC Medical Education volume  24 , Article number:  928 ( 2024 ) Cite this article

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Metrics details

Educational settings in professional health education establishments significantly shape students' academic, social, and emotional experiences. These environments encompass physical, psychological, and social infrastructures of programs or institutions, which jointly influence learning and development. This study analyzed the educational environment at Prince Nora University in Saudi Arabia, a renowned institution in health education.

The primary aim of this study was to evaluate the perceptions of the educational environment among students at Prince Nora University using the Dundee Ready Education Environment Measure (DREEM) inventory. The DREEM inventory is a renowned and validated tool designed to gauge students' perceptions across various dimensions of their educational experience.

Employing a cross-sectional survey design, the research gathered data from a sample of 321 students enrolled in the College of Health and Rehabilitation Sciences at Prince Nord University. The DREEM inventory, which measures the academic, social, and emotional aspects of the learning environment from the student's perspective, was utilized to collect the information.

The findings from the study indicated a positive perception of the educational environment among the students, with an overall mean score of 113.84 out of 200 on the DREEM inventory. Analysis of the subscales revealed that the Student Perceptions of Atmosphere (SPoA) received the highest scores, indicating a favourable environment, while Student Social Self-Perceptions (SSSP) scored the lowest, suggesting areas that may require attention and improvement.

The study successfully showed the utility of the DREEM inventory in assessing the educational environment at Prince Nora University, highlighting its effectiveness as a tool for understanding student perceptions. The positive overall score suggests a conducive learning atmosphere, though the disparity in subscale scores points to potential areas for enhancement. Recommendation: The research suggests that Saudi Arabian universities should implement the DREEM inventory to assess and enhance their educational settings, ultimately delivering a comprehensive and nurturing learning experience for students .

Peer Review reports

Introduction

Professional health education classrooms are a hotbed of academic curiosity worldwide [ 1 ]. The programs' or institutions physical, social, psychological, and other infrastructures make up what they collectively refer to as the educational environment [ 2 ]. It also includes the mindset and actions of the teachers and strategies they employ to convey the course content to the students. It also includes the style of the curriculum employed and the instructional methods employed. The educational environment comprises everything that impacts instruction and study [ 3 ].

Many criteria recognize the traits and characteristics of a learning environment; program rules, governance structures, and other features may also be called educational environment elements [ 4 ]. It is agreed that the school climate significantly affects student achievement [ 5 ]. Thus, it has become clear that reviewing school settings is vital to ensuring that all students receive well-rounded education [ 6 ]. The National Commission for Accreditation and Assessment (NCAAA) in Saudi Arabia prioritizes the quality of the learning environment when conducting program evaluations. Thus, the NCAAA ensures the quality and standardization of higher education institutions and programs [ 7 ]. The establishment of the National Commission for Academic Accreditation and Assessment (NCAAA) in Saudi Arabia is aimed at setting standards, accrediting institutions, and improving the quality of higher education programs [ 8 ]. It strives to guarantee the effectiveness and comprehensiveness of educational and training programs while also assessing their impact on the national economy and development [ 8 ]. The NCAAA strives to establish trust in local and global communities regarding the outcomes of these institutions and programs [ 8 ]. While the NCAAA has only recently incorporated postgraduate programs into its plans, it now allows universities to apply for accreditation of these programs [ 9 ]. The NCAAA's efforts to accredit undergraduate programs have shown noteworthy progress, but its role in accrediting postgraduate programs is still in progress and may not yet provide the same level of assurance as it does for undergraduate programs.

Recent changes to health professional education (HPE) curricula, such as incorporating new teaching and learning practices and evaluation methods and the growing diversity of today's student body, have increased the urgency of reviewing the current state of health-related education [ 10 ]. There is a strong correlation between how students feel about their classrooms and their academic performance. Student data should inform decisions regarding the curriculum, instructional methods, and school infrastructure [ 11 ]. Educators believe that students' exposure to both classroom and clinical settings significantly impacts their development of attitudes, knowledge, abilities, and behaviors as they move through the medical school curriculum [ 12 ].

The quality of educational settings has been evaluated using several tools developed and deployed in recent years [ 13 , 14 , 15 , 16 , 17 , 18 ]. In 1997, a team of medical educators from Dundee University created a reliable instrument for assessing the setting and culture of HPE. Its official title is the Dundee Ready Environment Educational Measure (DREEM). It is widely accepted as a culturally neutral, credible, and accurate assessment tool for gauging the climate of undergraduate HPE programs. The quantitative and qualitative methodologies used to design this instrument are the basis for its widespread acceptance [ 1 ]. The current sample size employed in the assessment of students' perceptions of education using the DREEM inventory at the College of Education, Princess Nora University, Saudi Arabia, was deemed adequate and representative, ensuring the generalizability of the findings to the broader student population. This study's robust sampling methodology and consideration of diverse academic levels contribute to its representativeness and reliability.

Despite the potential value of applying DREEM to the analysis of HPE problems, this tool is not commonly used in health-related programs in Saudi Arabia. DREEM's application of DREEM to the investigation of HPE problems could be very beneficial, but it is not widely used in SA health-related activities. Thus, this study investigated how students in the health professions at Princess Noura University (PNU), an all-female institution in SA, felt about their classroom environment.

This cross-sectional study began on September 28, 2022, for 2 months at the PNU, SA. Faculty of Health and Rehabilitation Sciences. When it opened in 2008, they knew it as the "College of Physiotherapy." It offers only one major: physiotherapy. After then, in 2012, a reorganization led to the college's name changing from "College of Physiotherapy" to "College of Health and Rehabilitation Sciences." Department of Health Sciences, Department of Communication Sciences, Department of Rehabilitation Sciences, Department of Radiology. Thirteen different programs are available and spread across four different departments. These are only available for women.

Sample size

Following Cochran [ 19 ], the optimal sample size was calculated using the following formula ( 1 ):

where n is the number of observations, is the fraction of expected levels (in this case, the DREEM response), Z is the standard deviation for a 95% confidence interval, and d is the intended margin of error. Given that the author could not know how numerous students would respond in each category on the DREEM, we had to use a cutoff of 50% of the possible total scores. π = 0.5, d = 0.05, and z = 1.96. With a 10% non-response rate and finite sample correction factor in mind, the minimum number of participants was 400. It then split the computed sample size across schools based on the relative student population.

Data collection

In September 2021, the College of Education at Princess Nora University in Saudi Arabia was considered to be one of the world's largest educational institutions for women. This university is located in Riyadh, the bustling Saudi capital, and houses thousands of female students. It provides ample opportunities for practical learning experiences in real-world classroom settings in a rich academic and cultural environment. Through research and innovation, the institution aimed to prepare its students to make positive contributions to Saudi Arabia's educational system. Verifying the latest statistics and developments is advisable before relying on the provided information. The participants' responses were gathered using the standard, industry-wide-applied questionnaire, the DREEM. The DREEM inventory consists of 50 items divided into the following five scales:

Twelve components (1, 7, 13, 16, 20, 22, 24, 25, 38, 44, 47, and 48) with a maximum score of 48 make up students' perceptions of learning (SPoL), with scores being interpreted as follows: The results ranged from 0 to 48, with 0 being the worst, 13 to 24 being poor, 25 to 36 being negative, and 37 to 48 being very thought-out in the classroom.

There are 11 items on the Students' Perceptions of Teachers (SPoT) survey (No. 2, 6, 8, 9, 18, 29, 32, 37, 39, 40, and 50) and a maximum score of 44, with the results interpreted as follows: From lowest to highest, the lowest was teachers with a score of 0–11, the lowest was those with a score of 12–22, the middle was those with a score of 23–33, and the highest was those with a score of 34–44, who were deemed excellent educators.

There is a maximum score of 32 on students' academic self-perceptions (SASP), which consists of 8 items (Item No. Items: 2, 10, 22, 26, 27, 31, 41, and 45). Scores of 0–8 were regarded as a sense of absolute failure, 9–16 as a sense of many negative characteristics, 17–24 as a sense of learning more toward the positive, and 25–32 as a sense of confidence.

12 elements (items No. Items 11, 12, 17, 23, 30, 33, 34, 35, 36, 42, 43, and 50) make up the SPoA scale, which can be scored from 0 to 48. From 0 to 12, 13 to 24, 25 to 36, 37 to 48, and 48 and above, the environment was rated as poor, with many problems that needed to be fixed, a more positive atmosphere, and a nice feeling overall.

The Student Social Self-Perceptions (SSSP) contains 7 questions with a maximum score of 28 (questions 3, 4, 14, 15, 19, 28, and 46) and can be interpreted as follows : from 0 to 7, it was awful; from 8 to 14, it was not lovely; from 15 to 21, it was not too bad; and from 22 to 28, it was socially perfect.

After reading each item, students were to rate it on a five-point Likert scale from "strongly agree" to "strongly disagree." The items were rated as follows: a score of 4 indicated complete agreement, 3 indicated moderate agreement, 2 indicated uncertainty, 1 indicated disagreement, and 0 indicated significant disagreement. Altogether, the scale adds up to a total score of 200. In this study, the authors assessed 9 items initially rated negatively (4, 8, 9, 17, 25, 35, 39, 48, and 50). A perfect educational environment received a score of 200 on the original DREEM. A higher number reflects a more favorable rating. Authors have rated each item on a five-point Likert scale from "strongly agree" to "strongly disagree" and interpreted a score of 4 as a total score in the following format: A score between 0 and 50 suggests an impoverished educational environment, a score between 51 and 100 shows many problems, a score between 101 and 150 indicates more positive than negative, and a score between 151–200 indicates an excellent educational environment [ 1 ]. In this study, it was determined that areas with individual items with a mean score of 3.5 or higher are vital, areas with individual items with a mean score of 2.0 or lower require attention, and areas with individual items with mean scores between 2 and 3 are areas of the educational environment that have room for improvement [ 20 ]. Google Survey was used to conduct electronic surveys with students through a web-based program. The students received a link to the questionnaire via an airdrop.

Data analysis

We used the Statistical Package for the Social Sciences (SPSS) for data entry, verification, and analysis (SPSS) (version 25; SPSS Inc. Chicago, IL, USA). Descriptive statistics and inferential methods were used to analyze the data. Comparisons of group means were made using frequency analyses and basic tabulation. It also compares the meaning of the ‘subgroup. The scores were compared on a college-specific basis by using an independent Student’s t-test. To determine whether there was a statistically significant difference between the various cohorts of students defined by academic year and academic program, Kruskal–Wallis analysis was performed. Statistically, a significant result was defined as one with a probability level of less than 0.05.

In this study, approximately 321 students successfully completed the online survey, or 80.3% of the estimated sample size. The average age of these respondents was 22.2 ± 2.001. The majority were located in the Health Sciences Division ( n  = 133/321) and Radiology Sciences Division ( n  = 108/321) (41.4% and 33.6%, respectively). Table 1 shows the distribution of students by year in school. Participation rates were relatively consistent across all years except for the first, in which only 3.4% ( n  = 11/321) of the children were present.

The overall DREEM score for this investigation was 113.84 35.187, which suggests that the educational environment is more favorable than unfavorable. Similarly, the SPoL, SPoT, SASP, SPoA, and SSSP are listed in Table  2 . This explains the items in each category in Table  3 .

Kruskal–Wallis was run with background characteristics as independent variables and DREAM domains as dependent variables to determine whether there was a correlation between the two data sets. Table 4 indicate no statistically significant differences between the five DREAM domains and the demographic information of the students who took the survey.

The quality of an educational institution is crucial for achieving its HPE program goals [ 21 ]. Therefore, this research aimed to assess the school climate perceived by PNU and SA women majoring in health sciences. The survey also sought to identify opinion discrepancies between departments and students of varying ages. The researchers employed the DREEM inventory, which is widely considered the best tool for measuring the educational environment of undergraduate HPE institutions [ 22 ].

According to Gruppen et al. [ 23 ], the quality of an educational institution is intimately tied to the success of any HPE programme. Consequently, this study aimed to investigate the perspectives of female students majoring in health Sciences at PNU in South Africa regarding the university environment [ 24 ]. This study aimed to determine several factors, including whether there were substantial disparities in opinions between various departments or among students of varying ages. The researchers used the DREEM inventory, which is generally regarded as the best approach for measuring the educational environment of undergraduate HPE institutions [ 22 ].

Overall scores

The mean DREEM score in this analysis was 113.84 ± 35.187, suggesting that participants were more likely to have a favorable impression of their school's environment than a negative one [ 10 ]. The results of several other Saudi academic institutions corroborate our findings. Global DREEM test results from King Khalid University [ 25 ], Qassim University [ 26 ], King Fahad Medical City [ 27 ], Tabuk University [ 28 ], Jazan University [ 29 ], and King Abdul Aziz University [ 30 ]were (102, 112.9, 112, 111.5, 105, 104.9, 102) respectively.

Comparing these scores, it can be observed that some institutions, including those in the present study (113.84), had DREEM scores higher than 100, indicating a generally positive perception of the educational environment. King Khalid University, King Fahad Medical City, Tabuk University, and Jazan University also had scores above 100, reflecting favorable perceptions among their students. On the other hand, Qassim University and King Abdul Aziz University had scores below 100, suggesting some areas for improvement in their educational environments as perceived by their students.

It is important to note that the DREEM inventory is a valuable tool for assessing various facets of the educational environment, and its application in multiple institutions helps to identify patterns and trends in students' perceptions. These scores can guide administrators and policymakers to understand the strengths and weaknesses of their educational settings and enable them to make informed decisions to enhance their overall learning experience. By benchmarking their scores against other institutions, universities can gain valuable insights and potentially implement best practices from those with higher DREEM scores to improve their educational landscape.

This was almost consistent with the results of a study conducted at a different university in the U.K.. (139) [ 31 ], Sudan (130) [ 32 ], Nepal (130) [ 20 ], Malaysia (125.3) [ 33 ], Nigeria (118) [ 20 ], Turkey (117.6) [ 34 ], India (117) [ 34 ], and Sri Lanka (108) [ 35 ]. The DREEM inventory subscale analysis is a valuable application. This reveals the benefits and drawbacks of the current school system. With scores of 27.2 on the SPoL scale, 23.1 on the SPoT scale, 18.3 on the SASP scale, 27.3 on the SPoA scale, and 15.7 on the SSSP scale, it is clear found that there were more positives than negatives on the educational environment. These results are consistent with those found in research performed at other SA universities such as Jazan University [ 29 ], Qassim University [ 26 ], and King Khalid University [ 25 ].

Among the DREEM scores, those from various universities in different countries, including the current study, had a DREEM score of 139. Moreover, the study provides DREEM scores from other countries ranging from 130 to 108, as well as the value of the subscale analysis of the DREEM inventory. In the U.K. study, students scored 139, indicating a very positive perception of the educational environment. In addition to Sudan and Nepal, both scored 130, indicating favorable impressions of their respective educational settings. There were also positive perceptions among students in Malaysia, Nigeria, Turkey, India, and Sri Lanka, with scores ranging from 108 to 125.3.

The DREEM score for the current study is "almost at the same level" as that in the U.K. study, even though this is not explicitly stated. The exact DREEM score for this study is not provided, but we can assume that it reflects a positive perception of the educational environment, similar to that in the U.K. Further discussion on the DREEM inventory subscale analysis is provided in this study.

The DREEM score is broken down into five specific subscales: SPoL (Perceptions of Learning), SpoT (Perceptions of Teaching), SASP (Academic Self-Perceptions), SpoA (Perceptions of Atmosphere), and SSSP (socials Selp-Perceptions). Based on the subscale scores discussed in this study, most participants positively perceived their educational environment. According to the SPoL, SpoT, SASP, SpoA, and SSSP scores, educational environment was more positive than negative (27.2, 23.1, 18.3, and 15.7, respectively). Clearly, students were optimistic about their learning experiences, teaching quality, academic self-confidence, and atmosphere within the institution. A brief comparison of the study's results with those of other Saudi Arabian universities, including Jazan University, Qassim University, and King Khalid University, is provided. This study does not provide specific DREEM scores for these universities, but suggests that the findings are consistent with those from other Saudi Arabian institutions. Based on this consistency, students generally perceive these universities as positive for their educational environment.

Focusing on the current study's comparison provides valuable insights into the DREEM scores from various universities worldwide. Students in the current study viewed their educational environment positively, highlighting the value of DREEM's subscale analysis in understanding specific aspects of the educational environment. More detailed information is required for comprehensive conclusions and understanding of the full implications of these findings, including the exact DREEM score from the current study.

'Students' Perceptions of Learning (SPoL) & 'Students' Perceptions of Teachers (SPoT)

Only 53% ( n  = 170), with an average mean of 27.2 ± 9.444, showed a positive perception of learning, and 50% of them ( n  = 160), with a mean of 23.01 ± 7.904, described that teachers were moving in the right direction, as shown in Tables 2 and 3 . This is mainly due to the continuous professional development program implemented by the college and university, which aimed to enhance the faculty's capacities in teaching and learning to include preparation and delivery of the teaching materials, development of a blueprint, and student assessment. The college also has a stringent recruitment process for selecting only the most qualified candidates with excellent teaching backgrounds and high GPAs. Peer assessment was used to evaluate the colleges’ teaching and learning methods to ensure that they performed as expected. The strengths and areas for improvement highlighted in the peer evaluation report were used to inform the continuing professional development goals for the following year. Annual faculty evaluation is also a tool to improve a college's educational atmosphere and pedagogy.

In this study, the authors examined how college and university students perceive their learning and teachers. It was found that 53% of the participants reported that education was positive, while 50% indicated that teachers were making progress. The average means of these scores were 27.2 ± 9.444 for SPoL and 23.01 ± 7.904 for SPoT. Indicators of students' experiences with the learning process and their perceptions of teachers' effectiveness were the DREEM scores for SPoL and SPoT. A SPoL score of 27.2 suggests that slightly more than half of the participants were satisfied with their learning experiences. However, a score of 23.01 for SPoT indicates that about half of the students are satisfied with the teaching methods and approaches, which suggests that they feel their teachers are moving in the right direction.

Positive perceptions of learning and teachers can be attributed to the continuous professional development programs in universities and colleges. The faculty's capacity for teaching and learning is enhanced by continuing professional development. To create a more dynamic and effective learning environment for students, institutions should provide faculty members with opportunities to improve their teaching skills and stay current with the latest teaching methodologies.

Through its stringent recruitment process, the college selects only qualified candidates with excellent teaching backgrounds and high GPAs, resulting in a higher quality faculty and a better educational experience for students. Selecting competent teachers is a critical component of ensuring high-quality instruction for students.

A positive aspect of the college approach is the use of peer assessment to evaluate teaching and learning methods. Experienced colleagues provide unbiased feedback in peer evaluations, highlighting strengths and improvement areas. Using the findings from the peer evaluation report, faculty members can set goals for professional development, ensuring that they address areas that need improvement. Annual faculty evaluation is a valuable tool for assessing and improving a college's educational environment and pedagogy. Using faculty evaluations can provide insights into how well instructors interact with their students, create a supportive learning environment, and adapt teaching methods to meet students' needs. As a result, the college can identify areas for improvement and make data-driven decisions that will enhance the educational experience of all students.

Due to the lack of explicit comparisons with results from other colleges, we cannot directly assess how the college's DREEM scores for SPoL and SPoT compare with those of other colleges. However, continuous professional development programs, peer evaluations, and annual faculty evaluations indicate that the college is taking proactive measures to ensure a positive educational environment for students. Such practices reflect this commitment to academic excellence and student success. The study concludes by emphasizing the importance of SPoLs and teachers. The college and university's continuous professional development programs, stringent recruitment processes, peer evaluations, and annual faculty evaluations were responsible for the positive perceptions of SPoL and SPoT. Emphasis on improving the quality of education and creating a conducive learning environment is evident in these practices. The study does not directly compare college's results with those of other institutions, but the practices mentioned suggest a proactive approach to fostering a positive educational environment.

'Students' Academic-self-perception

Approximately 54% of participants ( n  = 173) felt positive with the mean result of 18.29 ± 6.560 as shown in Table a mean result of 18.29 ± 6.560, as shown in Tables 2 and 3 . The mean result of 18.29 ± 6.560, as shown in Tables 2 and 3 closely relates to the ability of the 'students' to cope with the academic workload [ 26 ]. A well-designed and developed course timetable with more self-directed learning sessions allocated is a leading cause of this positive perception, as seen in the Australian DREEM study [ 36 ]. Many extracurricular activities aligned with program learning outcomes implemented within the course schedule gave the students free time to learn some non-technical skills in pressure-free time, supporting positive perception. The study examined students' perceptions of their ability to cope with the academic workload, with approximately 54% of participants reporting feeling positive. The mean result for this aspect was 18.29 ± 6.560.A significant finding was students' positive perception of their ability to cope with academic workloads, with a mean score of 18.29, above the midpoint of the DREEM scale, indicating that many participants felt confident in managing their academic responsibilities. This positive perception can positively impact student well-being and academic performance.When students feel capable of handling their workload, they are likely to experience less stress and anxiety, which can lead to improved learning outcomes.

According to this study, students' perceptions of their ability to cope with academic workloads are influenced by several factors. Course timetables are essential to students' perceptions of their academic workload. Having self-directed learning sessions in the timetable allows students to manage their time effectively and to control their learning pace. Students are empowered to take control of their learning journey using this approach, which is aligned with active learning and student-centered education principles. A positive perception of coping with academic workload is also supported by the implementation of extracurricular activities that align with program-learning outcomes. A well-rounded educational experience can be achieved by participating in extracurricular activities beyond the core academic curriculum. Students benefit from these activities regarding personal growth, skill development, and social interaction, all of which can reduce stress and improve their overall wellbeing.

Consequently, based on the study's lack of explicit comparisons, we cannot directly compare the current colleges’ scores for dealing with academic workload with those of other colleges or institutions. While a mean score of 18.29 is above the midpoint of the DREEM scale, it indicates a positive perception among participants. Students can manage their academic demands effectively, which is an encouraging sign of their commitment to creating a conducive learning environment. As a result, students' perceptions of their ability to cope with academic workloads were positive. This positive perception is partly attributed to well-designed course schedules with self-directed learning sessions and the implementation of extracurricular activities aligned with the learning outcomes. Students' well-being and academic performance can be enhanced if they positively perceive academic workload management. Further research and comparison could provide a better understanding of students' overall academic experiences compared with other colleges.

'Students' Perceptions of Atmosphere (SPoA)

By “ learning resources”, authors mean things like the physical layout of classrooms and clinics and the attitude and demeanor of instructors during class and patient care. It comprises academic regulations and planning of the academic curriculum. Tables 2 and 3 show that 54% of the students ( n  = 174) felt that the environment had improved. The mean score in this group was 27.339.342. The results are encouraging based on the study's findings at Taibah University's College of Medicine [ 37 ]. The positivity of student perception is based on well-designed timetables, a motivating environment, a wide range of extracurricular activities offered to students to enhance and encourage their interpersonal skills, and academic advisory services, such as academic, psychological, behavioral, and career counseling.

The study discusses the results of SPoL resources, which encompass various aspects, such as the physical layout of classrooms and clinics, instructor attitudes and demeanor during class and patient care, academic regulations, and curriculum planning. Based on a study conducted at Taibah University College of Medicine, 54% of students ( n  = 174) felt that the learning environment had improved, with a mean score of 27.339.342. These results were encouraging. It is noteworthy that 54% of the students positively perceived improved learning resources. A mean score of 27.339.342 indicated that most students perceived positive changes. Positive perceptions suggest that students are satisfied with various aspects of their academic experience such as physical facilities, instructor attitudes, and academic structure.

A well-designed timetable plays an essential role in shaping students' academic experiences. This study identified several factors that influenced students' positive perceptions of learning resources. Optimizing class sessions, clinical rotations, and study time can help students to achieve a balanced and effective learning schedule. Students can better manage their academic workload when their timetables are organized, allowing for a smoother flow of learning activities.

Supportive and encouraging environments can inspire students to strive for academic excellence and to actively participate in their educational journey. Motivating environments foster enthusiasm and engagement among students, which can positively affect learning outcomes. It is beneficial for students' interpersonal skills to participate in various extracurricular activities.Essential life skills, such as teamwork, leadership, and communication, can be developed through such activities beyond the traditional academic setting. Student support can be provided in various ways, including academic, psychological, behavioral, and career counseling. AsAsThrough such advisory services, students can navigate academic challenges and make informed career decisions.

The results of a study conducted by Taibah University College of Medicine are described as encouraging. However, the Taibah University study did not provide specific details regarding the DREEM scores or the learning resources evaluated. As a result, direct comparison is difficult. Ultimately, students' positive perceptions of improved learning resources were encouraging.. Students expressed satisfaction with various aspects of their learning environments. In addition to well-designed timetables, a motivating environment, extracurricular activities, and academic advisory services, the college strives to enhance students' overall learning experiences. Despite the comparison with the Taibah University study being mentioned as encouraging, more comprehensive details and further research are needed to draw meaningful conclusions and understand the SPoL resources across colleges and institutions in a broader context.

'Students' Social Self-Perceptions (SSSP)

In the current study, 50% of students with a mean of 15.66 ± 5.813 perceived social life as more positive, as shown in Tables 2 and 3 , as such various institutes in SA as Jazan University [ 29 ] Qassim University [ 26 ], and King Khalid University [ 25 ]. This is similar to studies conducted in Sudan 17/28 [ 38 ], Pakistan 15.4/28 [ 39 ], and Malaysia 16.7/28 [ 33 ]. It partially attributed the finding of a good social life in this study to the extracurricular activities offered by the college and the Deanship of Student Affairs at the university level. The curriculum is type-centered, with many active learning activities that increase student socialization with colleagues and tutors. In addition, academic advisory services play a significant role in determining social life, providing an excellent psychological support and feedback system for relevant students. In conclusion, this study showed that monitoring the educational environment could provide important information that medical educators should use to address the challenges that need attention and implement improvement changes.

According to the study, half of the students perceived social life more positively, as indicated by a mean score of 15.66 ± 5.813. In addition to comparing these findings with those of other Saudi Arabian universities, this study also compares them with those of Sudan, Pakistan, and Malaysia. According to the current study, extracurricular activities, type-centered curricula with active learning activities, and academic advisory services providing psychological support and feedback to students contributed to the positive perception of social life. Students perceived social life positively in 50% of cases, with a mean score of 15.66. The DREEM scale for social life ranges from 0 to 28, with higher scores indicating a more positive view. A mean score of 15.66 indicates that social life within the educational environment has room for improvement. Social life is viewed positively by several factors identified in this study.

Students' socialization is likely to be promoted by extracurricular activities offered by the college and by the university’s Deanship of Student Affairs. Such activities allow students to interact with their peers outside the classroom, thereby fostering social connections and a sense of belonging. Student engagement and tutor–student interaction can be increased through a type-centered curriculum with active learning activities. Students collaborate and build relationships with their instructors and each other through active learning approaches such as group discussions, team-based projects, and hands-on learning experiences.

Students can benefit from the psychological support and feedback offered by academic advisory services, which can enhance their social life. This can contribute to a more positive social experience for students who receive guidance and mentorship from advisors. The study results were compared with those from other institutes in Saudi Arabia, Sudan, Pakistan, and Malaysia regarding social life perception. Although these studies provide specific DREEM scores, it is evident that perceptions of social life across institutions are similar. Based on the comparable scores, social life deserves attention and improvement in various educational environments.

The study concluded that the educational environment and students' perceptions of social life should be monitored. According to 50% of the participants, extracurricular activities, a type-centered curriculum with active learning, and academic advisory services contribute to a positive social experience. This emphasizes the need for medical educators to address challenges and implement changes to enhance students' social experiences, even though there is still room for improvement. Educators can create a more supportive and enriched social environment for students by understanding their perceptions.

According to the DREEM inventory evaluations, the educational environment at Prince Nora University in Saudi Arabia has generally been positively perceived. The students' recognition of academic ambience appears exceptionally high, although there is scope for bolstering SocialSselP-perceptions. Notably, the DREEM inventory has emerged as a powerful and all-encompassing tool to gauge different facets of the educational environment, proving its irreplaceable value in this scenario. Consequently, it is suggested that other Saudi Arabian universities might find it beneficial to adopt this tool to pinpoint possible difficulties and opportunities for the enhancement of their educational landscapes. Other Saudi Arabian universities should consider adopting the DREEM inventory to identify areas for improvement and opportunities to enhance their own educational settings based on the positive perception of the educational environment at Prince Nora University and the effectiveness of the DREEM inventory.Declaration.

Availability of data and materials

All authors have shared their raw data and attached them to the supplementary files.

Abbreviations

Dundee Ready Environment Educational Measure

Health professional education

National Commission for Accreditation and Assessment

Princess Noura University

Students' Academic Self-Perceptions

Student Perceptions of Atmosphere

Students' perceptions of learning

Students' Perceptions of Teachers

Student Social Self-Perceptions

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Acknowledgements

The authors appreciate Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R283), Princess Nourah Bint Abdulrahman University, Riyadh, SA.

The authors thank Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R283), Princess Nourah Bint Abdulrahman University, Riyadh, SA.

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Latefa Hamad Al Fryan

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Mahasin Ibrahim Shomo

Department of Pathological Sciences, College of Medicine, Ajman University, Ajman, UAE

Ibrahim A. Bani

Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, USA

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Ibrahim Bani designed the research, conceived the idea, developed the theory, verified the analytical methods, supervised the findings of this work, and prepared and edited the manuscript. On the other hand, Latefa Hamad Al Fryan and Mahasin Ibrahim Shomo were responsible for data collection and analysis, which are commonly used to present complex information in a concise and understandable format.

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Al Fryan, L.H., Shomo, M.I. & Bani, I.A. Assessment of the ‘students’ perceptions of education using Dundee Ready Environment Educational Measure (DREEM) inventory at Princess Nora bint Abdulrahman University, Saudi Arabia. BMC Med Educ 24 , 928 (2024). https://doi.org/10.1186/s12909-024-05870-9

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