Innover, collaborer, apprendre : l’initiative École en réseau dans la mise en pratique de l’apprentissage à distance des élèves et des enseignants
Bibliographic record
Abstract
L’initiative École en réseau (ÉER) soutient des enseignants à collaborer autour de projets interclasses pour enrichir et diversifier l’environnement d’apprentissage par le numérique (Allaire et al., 2008). Les pratiques mises en œuvre ont permis de développer dans ÉER une expertise dans le travail en réseau et l’apprentissage connecté (Bruillard et al., soumis). Au printemps 2020, la situation mondiale a forcé le monde éducatif à revoir leurs manières de soutenir les élèves. La transposition de la classe en réseau, à la classe à distance fut aisée pour les enseignants d’ÉER. L’article relate l’innovation, la collaboration et l’apprentissage en réseau de cette forme pédagogique particulière mise en œuvre par ÉER pour la formation à distance des enseignants et l’enseignement à distance de l’ordre préscolaire et primaire.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".