Tutorat à distance et développement des compétences professionnelles des futurs enseignants d’anglais langue seconde
Bibliographic record
Abstract
S’insérant au cœur du processus d’enseignement et définissant l’intervention éducative, l’enseignant joue un rôle crucial dans tout système d’éducation. La qualité de la formation des maîtres (FM) prend ici toute son importance en ce qu’elle vise à former des enseignants professionnels compétents. Pourtant, malgré la révision des programmes de FM, avance-t-on encore aujourd’hui que davantage pourrait être fait pour rapprocher théorie et pratique en FM. La présente étude vise à documenter cette question en s’intéressant aux effets d’un projet expérimental de tutorat inter-ordre2 à distance comme outil de formation pour les futurs enseignants de langues secondes. Les apprentissages rapportés sont classifiés puis comparés aux 12 compétences professionnelles (CP) qui orientent la FM au Québec (Canada). Les résultats suggèrent que le tutorat à distance permet le développement intégré de plusieurs composantes des CPs visées en FM et la conscientisation des futurs enseignants vis-à-vis de celles-ci.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".