Miroirs de l’autorégulation de l’apprentissage : les dilemmes des formateurs d’enseignants
Bibliographic record
Abstract
Les formateurs d’enseignants font face à des demandes croissantes concernant la promotion et l’autorégulation des apprentissages de leurs étudiants. De telles demandes peuvent entraîner plusieurs dilemmes professionnels résultant de conflits de croyances et de conditions d’enseignement qui prévalent. La présente étude examine comment les formateurs d’enseignants sont engagés dans la promotion de l’autorégulation de l’apprentissage, dans le contexte néerlandais de formation des enseignants. Cette étude examine le sens que revêt pour les formateurs d’enseignants, une utilisation active des approches de l’autorégulation de l’apprentissage. Elle dégage plusieurs dilemmes professionnels auxquels font face les formateurs d’enseignants. Les difficultés que ces derniers vivent lors de l’implantation d’une telle approche semblent être liées à leurs propres orientations, expériences et réflexions professionnelles.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".