Le processus de validation d’un outil d’autoévaluation portant sur les pratiques d’enseignement en ligne pour un dispositif d’autoformation dédié aux enseignants des cycles supérieurs
Bibliographic record
Abstract
Dans un contexte d’enseignement distant ou hybride, les enseignants des cycles supérieurs sont confrontés à de nombreux défis technopédagogiques. Le dispositif d’autoformation dynamique pour l’innovation (DADI) offre des ressources et un outil d’autoévaluation fondé sur le modèle théorique du savoir technopédagogique disciplinaire (STPD) de Bachy (2014). Cette recherche visait à construire et à valider cet outil d’autoévaluation. La méthodologie de validation a combiné des approches qualitatives et quantitatives, impliquant des experts en sciences de l’éducation et des enseignants des cycles supérieurs issus de disciplines volontairement variées. Cet article se concentre sur les résultats quantitatifs. L’évaluation statistique a été menée auprès de 173 enseignants québécois via un questionnaire en ligne. Les analyses factorielles semi-confirmatoires, à la suite des analyses exploratoires, confirment la validité de l’outil avec 60 items. Elles examinent également la pertinence de cloisonner certains domaines de connaissances dans la pratique effective des enseignants.
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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 | 0.130 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".