Motiver ses élèves : stratégies motivationnelles employées par les personnes enseignantes de la formation professionnelle québécoise œuvrant en contexte de formation individualisée
Bibliographic record
Abstract
Motiver ses élèves : stratégies motivationnelles employées par les personnes enseignantes de la formation professionnelle québécoise œuvrant en contexte de formation individualisée Formation et profession 31(1) 2023 • 1 ésumé Cette étude visait à identifier les stratégies motivationnelles employées par les personnes enseignantes de la formation professionnelle recourant à un dispositif de formation individualisée et à vérifier si ces stratégies différaient de celles employées par celles qui interviennent dans un dispositif de formation collective.Dans l' ensemble, les résultats ont permis de rendre compte des différences entre les stratégies motivationnelles employées selon le dispositif d' enseignement mis en place.Ces résultats et l' engouement pour la formation individualisée soulignent la nécessité de poursuivre les recherches pour mieux comprendre le processus motivationnel des élèves qui s'y inscrivent.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".