Apprendre à intégrer le tableau numérique interactif de manière collaborative à l’éducation préscolaire
Bibliographic record
Abstract
Au Quebec, l’implantation massive de tableaux numeriques interactifs (TNI) en education a debute en 2011, dans le but de rendre l’enseignement plus interactif. Mais l’implantation des TNI, en tant qu’innovation technologique, n’a pas mene a des innovations pedagogiques. Cet article rapporte les resultats d’une recherche-action menee aupres d’enseignants (n = 21) a l’education prescolaire, pour les former a une utilisation optimale du TNI et pour etudier le developpement de leur capacite a l’integrer en classe. Les donnees ont ete recueillies a l’aide d’entrevues et de questionnaires en debut et en fin de projet, de meme que par des bilans de pratiques partages et des journaux de reflexion completes lors des rencontres collectives. Les resultats demontrent que les enseignants ont adopte des pratiques innovantes en s’eloignant de leur utilisation traditionnelle habituelle du TNI pour engager leurs eleves dans des activites de coconstruction des idees.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".