Une méthode qualitative–quantitative pour décrire les stratégies d’apprentissage d’élèves en éducation physique et sportive
Bibliographic record
Abstract
Le but de cet article est d‘étudier comment on peut décrire différentes stratégies d’apprentissage utilisées par des élèves en éducation physique et sportive. Vingt-trois sujets âgés de 14 et 15 ans sont filmés alors qu’ils participent à une tâche prescrite par un enseignant. Consécutivement, ils participent à un entretien d’explicitation. Les données comportementales et verbales recueillies sont traitées à l’aide d’une analyse de contenu. Une catégorisation empirique permet de faire émerger six stratégies d’apprentissage : écouter les consignes ; réfléchir et comprendre ; observer-imiter ; visualiser-imager ; focaliser son attention ; répéter. Une analyse discriminante confirme les catégories de stratégies d’apprentissage obtenues. Cette étude montre comment les catégories de variables issues d’une analyse qualitative peuvent être confrontées à l’objectivité d’une analyse statistique.
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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 | 0.079 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".