Usages pédagogiques de pratiques numériques translangagières dans les classes de sciences
Bibliographic record
Abstract
Le projet franco-canadien « Enseigner les sciences aux élèves plurilingues1 » (ESCAPE), financé par le CRSH (Conseil de recherches en sciences humaines du Canada) de 2020 à 2022, que l’on retrouve sous l’acronyme BINOGI/ESCAPE, est une interface numérique2 conçue par l’entreprise suédoise BINOGI. Elle propose des ressources (vidéos et quiz) multilingues en sciences et en mathématiques pour des élèves de 11 à 14 ans. Nos objectifs de recherche sont d’étudier les usages pédagogiques à partir de cette plateforme numérique favorisant des pratiques translangagières en classe de sciences en France. Les résultats doivent permettre de mieux comprendre les pratiques numériques translangues que les enseignants s’approprient. Cet article contribue à la réflexion à propos du développement de pratiques d’enseignement innovantes qui tiennent compte de la diversité linguistique à l’école.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".