Key Issues in Modeling and Applying Research on Self‐Regulated Learning
Bibliographic record
Abstract
Les études théoriques et empiriques sur l’autorégulation, l’apprentissage autorégulé et les concepts proches telle que la volition constituent désormais un seeteur fécond de la recherche en psychologie appliquée (voir par exemple ; ). Le résumé de , profond et représentatif de ce travail dans le domaine de l’éducation, présente dans l’espace qui leur est alloué des contributions importantes pour la modélisation de l’apprentissage autorégulé, une discussion de quéstions méthodologiques critiques, une vue d’ensemble des travaux empiriques contemporains et parvient enfin à proposer une orientation pertinente pour les travaux à venir. Comme je dispose moi‐même d’un espace limité pour commenter cet article, je me concentre sur quelques questions que j’estime fondamentales. Theoretical and empirical studies of self‐regulation (SR), self‐regulated learning (SRL), and closely related constructs such as volition have become lively areas of research in applied psycholgy (e.g. see ; ). ) very thoughtful and representative summary of this work in education packs into their allotted space important contributions to modeling SRL, a discussion of critical methodological matters, a survey of modern empirical research, and manages as well to give useful direction to future research. I, too, have limited space for comment on their article, so I focus on just a few issues that I judge are key.
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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 | 0.047 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".