AMUQquiz et Kaïros : regards croisés sur la création et le développement de deux plateformes d’apprentissage adaptatives
Bibliographic record
Abstract
Cet article examine les principes pédagogiques sous-jacents à deux plateformes d'apprentissage (AMUQuiz et Kaïros), développées de manière indépendante pendant la pandémie de COVID-19.Les différences dans l'implémentation de ces principes lors du développement sont examinées, notamment l'utilisation d'un algorithme adaptatif basé sur le système de classement Elo dans AMUQuiz et la mise en place de situations-problèmes pour encourager l'apprentissage actif dans Kaïros.Enfin, l'article discute du rôle des plateformes d'apprentissage dans un contexte postpandémique, soulignant l'importance de préserver certains principes pédagogiques fondamentaux tels que la confiance en chaque étudiant et étudiante, la lutte contre l'illusion de savoir et la promotion d'une plus grande personnalisation de l'apprentissage.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".