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Cited by 142Teaching and Learning ProgrammingInnovative Teaching and Learning MethodsEducational Strategies and Epistemologies

Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of Use

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Loksa, Dastyni, Margulieux, Lauren, Becker, Brett A., Craig, Michelle, Denny, Paul, Pettit, Raymond, Prather, James·2022·ACM Transactions on Computing Education
DOI 10.1145/3487050
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Abstract

Metacognition and self-regulation are important skills for successful learning and have been discussed and researched extensively in the general education literature for several decades. More recently, there has been growing interest in understanding how metacognitive and self-regulatory skills contribute to student success in the context of computing education. This article presents a thorough systematic review of metacognition and self-regulation work in the context of computer programming and an in-depth discussion of the theories that have been leveraged in some way. We also discuss several prominent metacognitive and self-regulation theories from the literature outside of computing education—for example, from psychology and education—that have yet to be applied in the context of programming education. In our investigation, we built a comprehensive corpus of papers on metacognition and self-regulation in programming education, and then employed backward snowballing to provide a deeper examination of foundational theories from outside computing education, some of which have been explored in programming education, and others that have yet to be but hold much promise. In addition, we make new observations about the way these theories are used by the computing education community, and present recommendations on how metacognition and self-regulation can help inform programming education in the future. In particular, we discuss exemplars of studies that have used existing theories to support their design and discussion of results as well as studies that have proposed their own metacognitive theories in the context of programming education. Readers will also find the article a useful resource for helping students in programming courses develop effective strategies for metacognition and self-regulation.

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  • Improved metacognition helps individuals with management of time, focus, and effort, problem-solving, academic performance, emotional well-being, and overall decision-making.

    “Studies have shown that improved metacognition helps individuals with management of time, focus, and effort [212], problem-solving [67], academic performance [45,52,106,126,180,214], emotional well-being [200], and overall decision-making [206].”

    From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [106]

    Section 5 (Domain-Specific Theories Related to Programming Instruction; page unavailable) reports that an intervention promoting metacognitive awareness improved productivity and independence in a programming camp. Section 4.3.1 also describes a relationship between metacognitive strategies and academic outcomes. These passages support parts of the claim in educational settings, but the paper does not establish the broader benefits claimed for emotional well-being or overall decision-making. (LLM, conf=0.86)

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Research works printed but not linked: 5

  1. Susan Bergin and R. Reilly. 2005. The influence of motivation and comfort-level on learning to program. In Proceedings of the PPIG. Psychology of Programming Interest Group, 293-304. Retrieved from https://ppig.org/papers/2005-ppig- 17th-bergin/.
  2. Allyson Fiona Hadwin, Sanna Järvelä, and Mariel Miller. 2018. Self-regulated, co-regulated, and socially shared reg- ulation of learning. In Proceedings of the Handbook of Self-regulation of Learning and Performance, D. H. Schunk and J. A. Greene Greene (Eds.), Vol. 30. Routledge/Taylor & Francis Group, 83-106.
  3. F. Pajares. 1997. Current directions in self-efficacy research. In Proceedings of the Advances in Motivation and Achieve- ment, M. Maehr and P. R. Pintrich (Eds.). JAI Press, Greenwich, CT, 1-49.
  4. S. G. Paris and P. Winograd. 1990. How metacognition can promote academic learning and instruction. In Proceedings of the Dimensions of Thinking and Cognitive Instruction, B. F. Jones and L. Idol (Eds.). Lawrence Erlbaum Associates, Inc., 15-51.
  5. Philip H. Winne and Allyson F. Hadwin. 1998. Studying as self-regulated engagement in learning. Metacognition in Educational Theory and Practice. Routledge.

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Authors
Loksa, D., Margulieux, L., Becker, B. A., Craig, M., Denny, P., Pettit, R., & Prather, J.
Affiliations
  • Towson University (US)
  • Georgia State University (US)
  • University College Dublin (IE)
  • University of Toronto (CA)
  • University of Auckland (NZ)
  • University of Virginia (US)
  • Abilene Christian University (US)
ORCID
Venue
ACM Transactions on Computing Education
Publisher
Association for Computing Machinery (ACM)
Published
2022-12-31
Volume
22
Issue
4
Pages
1-31
ISSN
1946-6226 (ISSN-L)
DOI
10.1145/3487050
OpenAlex
W4213447331
Cited by
140
References
99
Citations by year
2026: 40 · 2025: 35 · 2024: 31 · 2023: 29 · 2022: 5
FWCI
19.32
Type
journal article
Language
en
Open access
Licence
Locations
ACM Transactions on Computing Education (publishedVersion, cc-by-nd)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
Keywords
metacognition, self-regulation, programming education, programming, metacognitive theory, self-regulation theory, student success, learning strategies, computing education

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