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 lves : stratgies motivationnelles employes par les personnes enseignantes de la formation professionnelle qubcoise oeuvrant en contexte de formation individualise Formation et profession 31(1) 2023 1 sum Cette tude visait identifier les stratgies motivationnelles employes par les personnes enseignantes de la formation professionnelle recourant un dispositif de formation individualise et vrifier si ces stratgies diffraient de celles employes par celles qui interviennent dans un dispositif de formation collective. Dans l' ensemble, les rsultats ont permis de rendre compte des diffrences entre les stratgies motivationnelles employes selon le dispositif d' enseignement mis en place. Ces rsultats et l' engouement pour la formation individualise soulignent la ncessit de poursuivre les recherches pour mieux comprendre le processus motivationnel des lves qui s'y inscrivent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".