Évaluer : entre innovation et tradition. Résultats préliminaires d’une boussole interactive pour innover en évaluation des apprentissages
Bibliographic record
Abstract
L’Observatoire interuniversitaire sur les pratiques innovantes d’évaluation des apprentissages (OPIEVA) a pour mission principale de répertorier, à tous les ordres d’enseignement, les pratiques innovantes d’évaluation des apprentissages, sur le plan des savoirs et des pratiques. Parmi ses travaux, l’OPIEVA a créé un espace interactif permettant le dialogue sur l’évaluation et l’innovation ayant comme activité phare la mise en place de boussoles pour guider les enseignants2et les chercheurs vers l’amélioration des apprentissages. La première boussole se nomme Évaluer: entre innovation et tradition et sonde les perceptions des enseignants sur la nature innovante ou traditionnelle de leurs pratiques évaluatives. L’objectif de cet article est de partager les résultats des premiers répondants-enseignants à la Boussole 1(première itération)en les juxtaposant à quelques enjeux liés aux pratiques évaluatives avec, comme référents, les théories centrées sur l’apprentissage.
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 | 0.032 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".