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Cited by 1Innovative Teaching and Learning MethodsEducation, Achievement, and GiftednessEducational Strategies and Epistemologies

A Conceptual Framework to Integrate Research Programs on Self-Regulation in Education

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Wortha, Franz, Gerjets, Peter, Azevedo, Roger, Bernacki, Matthew, Brucker, Birgit, Bühler, Babette, Ehlis, Ann-Christine, Kasneci, Enkelejda +6 more·2026·Educational Psychology Review
DOI 10.1007/s10648-026-10196-7PMC PMC13454015
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Abstract

Self-regulation (SR) has emerged as a pivotal construct in educational research, yet its diverse theoretical accounts often lack comprehensive integration. This paper addresses this by proposing an integrative conceptual framework that consolidates four core research traditions on SR in educational settings, each emphasizing distinct underlying mechanisms: (1) Stable Dispositions (e.g., personality traits; Matthews et al., 2009; Roberts et al., 2007; Song et al., 2020), (2) Limited Resources (e.g., working memory capacity and executive functioning; Friedman & Miyake, 2017; Paas et al., 2003; Sweller, 1994), (3) Driving Forces (e.g., motivation, interest, and affect; Eccles & Wigfield, 2002; Hidi & Renninger, 2006; Trautwein et al., 2019), and (4) Learning Activities (e.g., self-regulated learning, cognitive and metacognitive strategies; Winne & Hadwin, 1998; 2008; Zimmerman, 2000). By synthesizing these perspectives, our framework positions SR as a dynamic, interdependent system, emphasizing how interactions and compensatory mechanisms explain individual differences and influence learning outcomes. We further argue for an integrative methodological toolkit, leveraging advanced machine learning and computational models, to capture the multi-level, temporal, and social dynamics inherent in SR. This holistic approach is crucial for advancing a comprehensive and ecologically valid understanding of SR in educational contexts.

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  • The integrative framework by Wortha et al. (2026) distinguishes four key components that interact to shape learning outcomes: personal dispositions, driving forces, limited resources, and learning activities.

    “This model distinguishes four key components that interact to shape learning outcomes: personal dispositions (e.g., personality traits such as conscientiousness) that describe learners' constraints when entering a learning episode, driving forces (e.g., current motivation) that energize learning behaviors, limited resources (e.g., cognitive capacity, executive functions) that determine the”

    From Enhancing School Students' Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial (2026) · cites this paper as Wortha et al., 2026

  • Motivational components-particularly utility value and interest-may be more amenable to scalable GenAI-based support than cognitive learning strategies, at least in interventions designed like the ones in this study.

    “Drawing on the integrative model of self-regulation in education (Wortha et al., 2026), our results suggest that motivational components-particularly utility value and interest-may be more amenable to scalable GenAI-based support than cognitive learning strategies (at least in the form of interventions that are designed like the ones in our study).”

    From Enhancing School Students' Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial (2026) · cites this paper as Wortha et al., 2026

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  1. National Research Council. (2011). Assessing 21st century skills: Summary of a workshop. Washington, DC: The National Academic Press. 22379654
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  5. Butterfield, E. C., Albertson, L. R., & Johnston, J. C. (1995). On making cognitive theory more general and developmentally pertinent. In F. Weinert, & W. Schneider (eds.), Memory performance and competencies: Issues in growth and development (pp. 181–206). Hillsdale, NJ: Erlbaum.
  6. Gutman, L. M., & Schoon, I. (2013). The impact of non-cognitive skills on outcomes for young people. a literature review. Education Endowment Foundation.
  7. Herman, C. F. (1979). The validity issue with respect to event data. In D. Munton (ed.), Measuring international behavior: Public sources, events and validity (pp. 29–40). Halifax, Canada: Dalhousie University.
  8. Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa, FL, US: Psychological Assessment Resources.
  9. Oprescu, M., Syrgkanis, V., & Wu, Z. S. (2019). Orthogonal random forest for causal inference. In International conference on machine learning (pp. 4932–4941)
  10. Rieger, S., Göllner, R., Spengler, M., Trautwein, U., Nagengast, B., & Roberts, B. W. (2017). Social cognitive constructs are just as stable as the big five between grades 5 and 8. AERA Open, 3 (3), 1–9. 10.1177/2332858417717 · DOI
  11. Schunk, D. H., & Greene, J. A. (eds.). (2018a). Handbook of self-regulation of learning and performance (2nd ed.). New York, NY: Routledge.
  12. Winne, P. H. (2001). Self-regulated learning viewed from models of information processing. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 153–189). Erlbaum.
  13. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (eds.), Metacognition in educational theory and practice (pp. 277-304). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.
  14. Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In D. Schunk & B. J. Zimmerman (eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297–314). Mahwah, NJ: Erlbaum.
  15. Wolters, C. A. (2010). Self-regulated learning and the 21st century competencies Self published.

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Authors
Wortha, F., Gerjets, P., Azevedo, R., Bernacki, M., Brucker, B., Bühler, B., Ehlis, A.-C., Kasneci, E., Miyake, A., Murayama, K., Nagengast, B., Roberts, B., Tibus, M., & Trautwein, U.
Affiliations
  • Loughborough University (GB)
  • Leibniz-Institut für Wissensmedien (DE)
  • University of Central Florida (US)
  • University of North Carolina at Chapel Hill (US)
  • Technical University of Munich (DE)
  • Universitätsklinikum Tübingen (DE)
  • University of Colorado Boulder (US)
  • University of Tübingen (DE)
  • University of Illinois Urbana-Champaign (US)
Corresponding author
  • Wortha, Franz
  • Gerjets, Peter
ORCID
Venue
Educational Psychology Review
Publisher
Springer Science and Business Media LLC
Published
2026-08-10
Volume
38
Issue
1
Pages
110-110
ISSN
  • 1040-726X (ISSN-L)
  • 1573-336X
DOI
10.1007/s10648-026-10196-7
PMC
PMC13454015
OpenAlex
W7202050467
Cited by
0
References
212
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Educational Psychology Review (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
Funders
  • Economic and Social Research Council (ES/W002914/1)
  • Exzellenzinitiative des Bundes und der Länder (GSC1028)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Education, Achievement, and Giftedness
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
Keywords
self-regulation, conceptual framework, personality traits, resource constraints, working memory capacity, executive function, motivation, interest, affect, student self-regulation, metacognitive strategies, individual differences, compensatory mechanisms, machine learning, computational modeling

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