Nouvelles interactions numériques et apprentissages scolaires : Entretien avec Mireille Bétrancourt
Bibliographic record
Abstract
Mireille Bétrancourt est Professeure en technologies de l’information et processus d’apprentissage à la faculté de Psychologie et des Sciences de l’Education de l’Université de Genève (Suisse), où elle dirige l’unité de Technologies de formation et d’apprentissage (TECFA). L’objet général de ses travaux porte sur la conception des ressources numériques pédagogiques dans une perspective cognitive et ergonomique, et sur les usages des technologies numériques dans différents contextes de formation et d’enseignement. Dans cet entretien, elle souligne l’importance de la congruence technopédagogique des nouvelles interactions sensori-motrices avec les exigences de la tâche d’apprentissage et du contexte scolaire dans lequel elles sont utilisées, afin de mieux en évaluer les apports réels.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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; both teacher heads agree on what is shown here.
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".