L’entretien d’évaluation : un retour aux sources pédagogiques
Bibliographic record
Abstract
"Comment évaluer chaque étudiante et chaque étudiant de manière authentique dans un monde transformé par l’IA, où les travaux d’équipe complexes sont essentiels, mais où les contraintes de temps limitent la qualité des évaluations? Cette présentation aborde l’entretien d’évaluation, une approche qui place les étudiantes et les étudiants au cœur de la relation pédagogique. Elle démontre comment cet outil favorise une évaluation juste des apprentissages tout en répondant aux défis quotidiens des membres du corps professoral. Deux professeurs de génie mécanique du Cégep du Vieux Montréal témoigneront de leurs expériences enrichissantes. Ils présenteront les outils utilisés, compareront leurs méthodes et détailleront les résultats obtenus. Leur démarche met en lumière les bénéfices de l’entretien d’évaluation pour améliorer les pratiques éducatives et renforcer le lien entre apprentissage et évaluation." -- AQPC
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 | 0.066 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".