FORMER DES ENSEIGNANTS PAR LE BIAIS D’ENVIRONNEMENTS D’APPRENTISSAGE NUMÉRIQUES MULTIMODAUX
Bibliographic record
Abstract
Notre recherche part d’un double constat. D’une part, la didactique de l’écrit en langues s’inscrit plus dans une tradition scolaire, normative et conventionnelle, que dans une relation entre l’écrit et son environnement social. D’autre part, nos enseignants stagiaires sont issus de ce dispositif scolaire et universitaire normatif. La question est donc simple : est-il possible de former des enseignants qui ne se bornent pas à reproduire cette didactique de l’écrit normé ? Notre démarche scientifique s’inspire des théories autour de la multimodalité et de l’enseignant designer. Les données recueillies et analysées sont constituées des dialogues et des productions multimodales des enseignants stagiaires. Cette recherche, bien que limitée, montre que tous les enseignants stagiaires ont acquis des compétences translittéraciques sans formation préalable. Ils ont aussi pu concevoir de nouveaux designs pédagogiques, translittéraciques et multimodaux de grande qualité.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".