Interfaces et intégration des environnements pour le soutien aux activités de téléapprentissage
Bibliographic record
Abstract
RESUME: Nous presentons ici la problematique du design d’environnements qui supportent de facon intelligente le teleapprentissage. Nous aborderons differentes approches qui sont utilisees actuellement pour soutenir les activites d’apprentissage en ligne, d’une part celles qui visent a rendre ces environnements plus utilisables, selon les principes d’ergonomie des interfaces, et, d’autre part, celles qui cherchent a soutenir les activites complexes, enrichissant l’environnement de mecanismes de retroaction et de soutien aux activites. Nous examinerons certains environnements qui visent a soutenir les activites des apprenants (exploration, appropriation des contenus, demande d’aide, et collaboration), de meme que les activites des enseignants (evaluation, gestion, intervention didactique, soutien au plan cognitif, meta-cognitif et motivationnel). Nous presenterons certains principes et des exemples afin de souligner des problemes et des solutions interessantes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 teacher head, 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".