Self-Regulation, Coregulation, and Socially Shared Regulation: Exploring Perspectives of Social in Self-Regulated Learning Theory
Bibliographic record
Abstract
Background/Context Models of self-regulated learning (SRL) have increasingly acknowledged aspects of social context influence in its process; however, great diversity exists in the theoretical positioning of “social” in these models. Purpose/Objective/Research Question/Focus of Study The purpose of this review article is to introduce and contrast social aspects across three perspectives: self-regulated learning, coregulated learning, and socially shared regulation of learning. Research Design The kind of research design taken in this review paper is an analytic essay. The article contrasts self-regulated, coregulated, and socially shared regulation of learning in terms of theory, operational definition, and research approaches. Data Collection and Analysis Chapters and articles were collected through search engines (e.g., EBSCOhost, PsycINFO, PsycARTICLES, ERIC). Findings/Results Three different perspectives are summarized: self-regulation, coregulation, and socially shared regulation of learning. Conclusions/Recommendations In this article, we contrasted three different perspectives of social in each model, as well as research based on each model. In doing so, the article introduces a language for describing various bodies of work that strive to consider roles of individual and social context in the regulation of learning. We hope to provide a frame for considering multimethodological approaches to study SRL in future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.058 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".