Exploiter le potentiel du numérique pour apprendre : enjeux pour le développement professionnel des personnes enseignantes. Quoi, pourquoi et comment
Bibliographic record
Abstract
Au Québec comme ailleurs, les centres de pédagogie offrent des programmes de formation et d’accompagnement au personnel enseignant, mais ceux-ci demeurent largement transmissifs et centrés sur les outils technologiques. Or, le personnel enseignant connait mal le potentiel pédagogique des environnements numériques d’apprentissage comme Moodle et des technologies d’apprentissage innovantes que sont la réalité virtuelle, le jeu vidéo et la ludification, et l’intelligence artificielle et l’espace de fabrication collaboratif. Après avoir exposé ce potentiel, nous proposerons que des dispositifs de développement professionnel à visée transformatrice soient élaborés, et nous en décrirons les caractéristiques principales par l’entremise de deux exemples concrets et de quelques mises en garde.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".