Étude du transfert des apprentissages pour les programmes de formation professionnelle
Bibliographic record
Abstract
En raison des changements technologiques rapides, plusieurs chercheurs soulignent l'importance de concevoir des programmes orientés pour accroître le transfert des apprentissages. Notre étude explore les résultats de recherches sur le transfert pour les programmes de formation professionnelle, dans la perspective d'une approche psychologique puis d'une approche éducationnelle. Elle vise à définir le transfert, à en déterminer les différentes catégories et à identifier les facteurs qui peuvent l'influencer afin d'en évaluer le potentiel de mise en opération dans un programme de formation professionnelle de niveau secondaire. Les résultats permettent d'établir des lignes directrices qui favorisent l'insertion du transfert dans les programmes, et ce, en allant des moyens qui favorisent la promotion du transfert en classe jusqu'à l'élaboration de modules de transfert liés à des méthodes d'enseignement.
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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 | 0.016 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".