Une architecture multi-agents pour des sessions d'apprentissage Collaboratif
Bibliographic record
Abstract
La collaboration entre étudiants est reconnue pour avoir une grande importance dans une session d'apprentissage. Les discussions qu'elle permet, les échanges de bons procédés entre apprenants apparaissent comme autant de moyens efficaces pour faciliter l'apprentissage d'un sujet chez un étudiant. L'un des principaux problèmes de l'apprentissage collaboratif demeure l'établissement de groupes cohérents à partir du pool d'étudiants. Dans cet article, nous présentons une architecture multi-agents permettant un enseignement collaboratif. Nous utiliserons un algorithme de réseau de neurones pour générer des groupes homogènes. Nous proposerons des agents pour déterminer le groupe correspondant le mieux aux critères de notre leçon puis dispenser le cours tout en recherchant de nouvelles données s'y rapportant sur Internet.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".