L'évaluation des apprentissages à l'université. Quelques défis contemporains
Bibliographic record
Abstract
Des besoins exprimés par la nouvelle génération d’étudiants aux conséquences de la pandémie, en passant par la place grandissante du numérique ou encore la discussion croissante autour des méthodes de notation pratiquées dans certains programmes d’études, de nombreux sujets affectent la mise en œuvre de l’évaluation des apprentissages à l’université. Il est aujourd’hui important de se pencher sur certains enjeux émergents afin de faire face aux défis présents et à venir. L’exercice dont fait état ce chapitre a été réalisé en tenant compte de quelques thèmes d’intérêt qui se nourrissent mutuellement, dont l’intégrité académique, l’évaluation en contexte numérique et la méthode de notation. Les réflexions qui en émanent se structurent en trois parties distinctes, qui traiteront successivement des perspectives des étudiants, de celles des enseignants et, enfin, de celles des administrateurs universitaires.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".