Les facteurs sous-jacents au transfert des compétences informatiques construites par les futurs maîtres du primaire sur le plan de l’intervention éducative
Bibliographic record
Abstract
Cet article présente les résultats de deux enquêtes menées auprès d’enseignants du primaire et d’étudiants du programme de formation initiale à l’enseignement au préscolaire et au primaire de l’Université de Sherbrooke. Les chercheurs font d’abord état de la documentation scientifique relative à l’identification des facteurs qui favorisent ou inhibent l’intégration des TIC en enseignement, tant chez les enseignants chevronnés que chez les novices. Ils examinent ensuite l’impact éventuel des effets de modelage de la formation pratique sur la reproduction des modèles actuels de recours à ces technologies. Enfin, ils s’interrogent sur la probabilité de changer les pratiques en fonction de la stabilité des modèles d’intervention éducative préconisés en formation initiale à l’enseignement et dans les milieux de pratique.
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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 | 0.020 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".