Formation initiale des enseignants : Validation d’un référentiel de compétences pour la formation des maitres de stages
Bibliographic record
Abstract
Cet article est la version française de : Baco, C., Derobertmasure, A., Bocquillon, M., & Demeuse, M. (2023). Initial teacher training: Validation of a competence reference framework for the training of mentor teachers / cooperating teachers, Frontiers in Education, 7, 1-16. https://doi.org/10.3389/feduc.2022.1010831 Résumé Les maîtres de stage (ou enseignants associés) sont des acteurs incontournables de la formation À travers le monde, les maitres de stage (enseignants associés au Québec) sont cruciaux pour la formation initiale des enseignants. Cet article propose une double validation d’un référentiel de compétences pour la formation des maitres de stage. Cette validation est basée, d’une part, sur la littérature internationale et, d’autre part, sur l’analyse des réponses de 854 maitres de stage belges francophones à un questionnaire sur le niveau de maitrise idéal des tâches inhérentes à leur fonction. Les résultats indiquent une forte convergence entre le référentiel proposé, baptisé RECOMS, la revue de la littérature et l’avis des 854 maitres de stage. Cette forte convergence montre la pertinence du référentiel de compétences proposé pour la formation des maitres de stage en Belgique francophone comme ailleurs.
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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 | 0.044 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".