Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of Use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it