Optimiser le questionnement didactique en science et technologie pour mieux rétroagir et évaluer les élèves
Bibliographic record
Abstract
Cet article présente les résultats d’une recherche collaborative portant sur les pratiques de rétroactions d’enseignants du primaire durant l’enseignement de la science et technologie. La problématique concerne le défi d’instaurer des pratiques évaluatives qui soutiennent l’apprentissage. L’analyse des données issues d'entrevues semi-dirigées et de rencontres en communauté d’apprentissage présente l'évolution des enseignantes dans leur compréhension des démarches et des critères d’évaluation. L’interprétation des données permet de dégager un lien entre le questionnement et la rétroaction sur les apprentissages et de souligner l’intérêt de la recherche collaborative pour faire évoluer les pratiques et les cadres de références des chercheurs en éducation.
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 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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".