La planification flexible des démarches d’évaluation, un levier vers une évaluation pour apprendre ?
Bibliographic record
Abstract
L’article discute l’apport de la planification des démarches d’évaluation comme levier pour soutenir les enseignants dans la mise en oeuvre d’une évaluation pour apprendre (assessment for learning) en classe. Il propose une réflexion théorique sur les apports d’une planification flexible, caractérisée dans l’article comme une planification approfondie et structurée (hiérarchisée) des démarches d’évaluation mais qui laisse aussi une place importante aux ajustements dans l’interaction (dynamique) et qui implique les apprenants (interactive). La contribution de l’article est de faire le lien entre les travaux scientifiques portant sur la planification de l’enseignement-apprentissage et ceux sur l’évaluation des apprentissages. Les objectifs de l’article consistent à 1) expliciter ce qui caractérise une planification flexible des démarches d’évaluation, et 2) discuter des apports d’une telle planification dans la mise en oeuvre d’une évaluation-soutien d’apprentissage dans les classes.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".