Analyse des effets de l’enseignement hybride à l’université : détermination de critères et d’indicateurs de valeurs ajoutées
Bibliographic record
Abstract
Nous visons à contribuer à l’analyse des effets des plateformes d’apprentissage en ligne sur les établissements d’enseignement supérieur en proposant trois catégories et treize critères de valeurs ajoutées. Ainsi, les usages de ces plateformes par les enseignants apportent une valeur ajoutée dans la mesure où ils 1) font évoluer les dispositifs pédagogiques vers davantage de centration sur l’apprentissage, 2) exploitent les potentialités de flexibilité pour mieux répondre à des besoins spécifiques d’étudiants, et 3) stimulent le développement professionnel des enseignants. Nous discutons ensuite d’indicateurs permettant de monitorer ces valeurs ajoutées, dans une perspective de pilotage de l’innovation technopédagogique dans les établissements d’enseignement supérieur.
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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 | 0.009 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".