Pourquoi les récits favorisent-ils l'apprentissage?
Bibliographic record
Abstract
Cet article fut publié initialement en deux parties dans les bulletins Perspectives SSF d’octobre et de décembre 2019. Depuis, il sert de document d’accompagnement à l’atelier « Raconter sa matière : enrichir son enseignement grâce aux récits » offert au personnel enseignant en formation supérieur et aux personnes professionnelles en soutien à l’enseignant. L’atelier a également été adapté pour le personnel enseignant du secondaire. On s’intéresse aux raisons qui rendent l’intégration d’histoires intéressante pour les personnes enseignantes, ainsi qu’à ce que les neurosciences nous apprennent des impacts des récits sur le cerveau humain. On se demande par ailleurs comment intégrer plus concrètement les récits à l’enseignement (avec des exemples) tout en évoquant certains risques à le faire, en plus d’examiner ce qui distingue les discours scientifiques et narratifs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".