Pratique réflexive et études de cas : quelques enjeux à l’utilisation de la méthode des cas en formation des maîtres
Bibliographic record
Abstract
Cet article porte sur la méthode des cas, approche qui suscite beaucoup d'intérêt en formation des maîtres. Elle se présente comme un dispositif de formation particulièrement adapté aux objectifs professionnels poursuivis. Ceux-ci consistent, entre autres, à rendre l'enseignant plus réflexif, c'est-à-dire capable de réfléchir sur sa pratique et de l'analyser. Nous identifions divers modes d'exploitation possible des cas en lien avec des objectifs de formation variés. Prenant appui sur notre propre expérience avec cette méthode, nous essayons également de cerner quelques enjeux importants que soulève l'utilisation de la méthode des cas comme outil pédagogique pour développer, chez les étudiants en formation, des habiletés de pratique reflexive.
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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 | 0.063 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".