Que mesurent exactement les évaluations de l’enseignement par les étudiant.e.s ? Ce que l’on sait sur ce sujet et ce qui est manquant
Bibliographic record
Abstract
L’évaluation de l’enseignement par les étudiant.e.s (ÉEÉ) demeure le principal outil utilisé pour estimer les compétences du corps professoral ; leur embauche, leur agrégation et leur promotion, tant aux États-Unis qu’au Canada, malgré les critiques formulées à l’égard de sa légitimité et sa validité en tant qu’instrument de mesure. Cependant, une littérature abondante et majoritairement en anglais dénonce la présence de biais qui compromettent les résultats des ÉEÉ avec pour corollaire des pratiques discriminatoires envers les femmes, les personnes racisées et les personnes issues d’autres groupes marginalisés. Cet article présente un résumé de cette littérature et fait quelques suggestions pour de futures recherches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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 teacher head, 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".