Disparités d’adoption des technologies en pédagogie universitaire : un aperçu empirique
Bibliographic record
Abstract
Bien que les études traitant de l'intégration des technologies en pédagogie universitaire soient relativement nombreuses, peu d'entre elles se sont penchées sur les disparités d'adoption des technologies parmi les enseignants universitaires.aussi, l'objectif de cet article est de caractériser les profils d'enseignants universitaires adoptant les technologies.Un questionnaire a été rempli par 391 enseignants de deux universités du Québec.Des analyses par clusters permettent d'identifier trois profils d'enseignants universitaires intégrant les technologies relativement à trois volets de leur enseignement : préparation et gestion, pilotage, et développement professionnel.les résultats laissent penser que les disparités d'adoption ne se sont pas résorbées malgré la succession de plus en plus rapide des technologies en pédagogie universitaire.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".