Catégorisation de techniques de rétroaction pour l’enseignement universitaire
Bibliographic record
Abstract
Cet article présente une catégorisation de techniques de rétroaction située par rapport au triangle didactique. Peu utilisées ou peu explicites, les techniques de rétroaction sont pourtant des leviers pour favoriser l’apprentissage des étudiants dans le contexte universitaire. En effet, elles soutiennent les régulations dans les interactions entre les apprenants, l’enseignant et le savoir. La catégorisation proposée répond à quatre questions : qui sont-ils, que savent-ils, comment apprennent-ils et qu’ont-ils appris. À partir de ces quatre questions simples, l’enseignant peut construire son dispositif et proposer des situations d’enseignement favorisant l’apprentissage des étudiants en milieu universitaire. Cette grille de lecture originale comme support à l’évaluation formative répond à des préoccupations actuelles des enseignants confrontés à l’effet de massification notamment pour le premier cycle universitaire.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".