Cyberapprentissage en pédagogie médicale : l’internet va-t-il un jour remplacer les professeurs?
Bibliographic record
Abstract
Le cyberapprentissage facilite l’accès en ligne à des ressources pédagogiques, partout et en tout temps. Il peut être utilisé à divers niveaux, comme dans le cadre de l’enseignement de nouveaux concepts, de la simulation, de l’évaluation et du travail collaboratif. Les outils de cyberapprentissage sont aussi excellents pour susciter la participation des apprenants et favoriser l’apprentissage actif. Dans cet article, les auteurs discuteront des différents outils du cyberapprentissage et des cinq étapes de la conception pédagogique en cyberapprentissage, à savoir la définition, la conception, la création, la distribution et la démonstration, puis ils articuleront les meilleures méthodes d’évaluation de l’efficacité de ces outils.
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".