The Role of Self-Regulation in Programming Problem Solving Process and Success
Open accessAbstract
While prior work has investigated many aspects of programming problem solving, the role of self-regulation in problem solving success has received little attention. In this paper we contribute a framework for reasoning about self-regulation in programming problem solving. We then use this framework to investigate how 37 novice programmers of varying experience used self-regulation during a sequence of programming problems. We analyzed the extent to which novices engaged in five kinds of self-regulation during their problem solving, how this self-regulation varied between students enrolled in CS1 and CS2, and how self-regulation played a role in structuring problem solving. We then investigated the relationship between self-regulation and programming errors. Our results indicate that while most novices engage in self-regulation to navigate and inform their problem solving efforts, these self-regulation efforts are only effective when accompanied by programming knowledge adequate to succeed at solving a given problem, and only some types of self-regulation appeared related to errors. We discuss the implications of these findings on problem solving pedagogy in computing education.
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Several participants took out a notepad and pencil when stuck to work the problem, which is an example of reflective metacognitive behavior like self-regulation.
“Several participants in our experiment took out a notepad and pencil when they became stuck in an attempt to work the problem, which is a clear example of a reflective metacognitive behavior like self-regulation [52].”
From “It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers (2024) · cites this paper as [52]
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References 32 · Cited by 143
Research works printed but not linked: 4
- [12]12. Falkner, K, Szabo, C, Vivian, R and Falkner, N.J.G. 2015. Evolution of Software Development Strategies. Proceedings of the 37th International Conference on Software Engineering -Volume 2, 243-52.
- [13]13. Greeno, J.G. and Hall, R.P. 1997. Practicing representation. Phi Delta Kappan 78(5), 361.
- [16]16. Koriat, A. 2016. Processes in Self-monitoring and Self- regulation. The Wiley Blackwell Handbook of Judgment and Decision Making, 2 Volume Set.
- [26]26. Pintrich, P. R., Wolters, C. A., and Baxter, G. P. 2000. Assessing metacognition and self-regulated learning. In G. Schraw, & J. C. Impara (Eds.), Issues in the measurement of metacognition. NE: University of Nebraska-Lincoln.
- Authors
- Loksa, D., & Ko, A. J.
- Affiliations
- University of Washington (US)
- Venue
- Proceedings of the 2016 ACM Conference on International Computing Education Research
- Publisher
- ACM
- Published
- 2016-08-25
- Pages
- 83-91
- OpenAlex
- W2517261848
- Cited by
- 143
- References
- 32
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 22 · 2023: 25 · 2022: 18 · 2021: 19 · 2020: 12 · 2019: 14 · 2018: 3 · 2017: 2
- FWCI
- 5.05
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on International Computing Education Research (ICER) (publishedVersion)
- Funders
- National Science Foundation (1314399 1240786 1153625 1240957 and 1314384)
- 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 › Education › Problem and Project Based Learning
- Keywords
- self-regulation, programming problem solving, novice programmers, CS1, carbon disulfide, coding errors, problem-solving instruction, programming knowledge, metacognition
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