Les effets de l’accompagnement technopédagogique des enseignants sur leurs options pédagogiques, leurs pratiques et leur développement professionnel
Bibliographic record
Abstract
Aujourd’hui, on entend de plus en plus parler d’accompagnement des enseignants, notamment à l’aide du numérique et des technologies de l’information et de la communication (TIC). Mais cet accompagnement est-il efficace ? Quelles sont les formes d’accompagnement les plus pertinentes ? Pour aborder cette délicate et difficile question, nous avons mis en place un ensemble d’instruments permettant de jauger les effets de l’accompagnement technopédagogique des enseignants dans le supérieur. Concrètement, il s’agit (1) d’instrumentaliser quelques modèles de développement professionnel d’enseignants en « univers TIC », (2) de proposer des outils permettant de mesurer des effets de différentes formes d’accompagnement technopédagogique, (3) d’analyser les résultats de ces mesures dans trois contextes différents (Louvain-la-Neuve, Sherbrooke et Lyon) et (4) de comparer ces résultats en leur donnant du sens par rapport aux modes privilégiés d’accompagnement dans ces institutions.
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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 | 0.010 | 0.042 |
| 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 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".