Appropriation par un enseignant d’un dispositif d’aide pour l’enseignement des mathématiques
Bibliographic record
Abstract
Nous étudions dans cet article la mise en œuvre par un enseignant ordinaire d’un type de dispositif d’aide particulier, conçu lors d’une recherche collaborative (Desgagné, 1997 ; Bednarz, 2013). Il s’agit de regarder si l’on retrouve dans ces séances, les cinq fonctions que nous avons pu mettre en évidence lors des mises en œuvre par des enseignants ayant participé à l’élaboration de ce dispositif (Theis et al., 2014 ; Theis et al. , 2016 ; Assude et al. , 2016 a.). Cette étude nous montre que même si l’essentiel des fonctions est préservé, certaines différences de taille apparaissent. En cherchant les causes de ces variations, nous nous sommes intéressés aux conceptions de l’enseignante et à sa vision du métier d’enseignant dans son ensemble. Ces réflexions nous amènent à conjecturer que certaines de ses représentations pourraient entraver l’appropriation de notre dispositif.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".