Quelques résultats relatifs aux connaissances disciplinaires de professeurs stagiaires dans des situations simulées d’évaluation de productions d’élèves en mathématiques
Bibliographic record
Abstract
Dans cet article, nous étudions l’activité de dix-huit enseignants stagiaires – professeurs des écoles et professeurs de mathématiques – accomplissant des tâches d’évaluation de productions d’élèves en mathématiques. La recherche s’inscrit dans une double orientation didactique : didactique professionnelle et didactique des mathématiques. Le dispositif de recherche, organisé autour d’un « simulateur », permet de ménager des conditions expérimentales analogues pour tous les professeurs. L’identification et la catégorisation des connaissances mobilisées par les enseignants lors de l’accomplissement des tâches d’évaluation servent d’appui à une analyse comparative de leur activité. Les résultats obtenus montrent que les connaissances disciplinaires jouent un rôle essentiel dans la réalisation des tâches que nous leur proposons.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".