Un panorama de la recherche sur l’évaluation formative des apprentissages
Bibliographic record
Abstract
Initialement, la recherche sur l’évaluation formative des apprentissages relevait d’initiatives isolées, sans qu’il n’y ait de significations partagées dans la communauté scientifique, rendant difficile l’élaboration d’un portrait de cette recherche. Quarante ans plus tard, et sous l’impulsion des études qui se sont multipliées avec la vague de réformes éducatives dans les sociétés occidentales depuis une dizaine d’années, il devient possible de se donner une représentation du domaine aujourd’hui constitué. Il se démarque maintenant diverses tendances en recherche, regroupées autour de différents objets spécifiques rattachés à l’évaluation formative. Cette contribution vise à tracer un panorama de cette recherche en prenant en compte ce qui se fait tant dans le monde francophone qu’anglophone. Comme on le verra, aux côtés des démarches instrumentales bien connues, émergent maintenant des démarches plus informelles relevant de stratégies mises en place par l’enseignant dans le flux des interactions en classe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.204 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".