Enseigner dans un programme universitaire innovant : de nouveaux rôles à apprivoiser, des actes pédagogiques à diversifier
Bibliographic record
Abstract
Cette recherche vise à décrire, à partir d’observations empiriques, les rôles exercés et les actes pédagogiques effectués par les enseignantes et les enseignants engagés dans quatre programmes universitaires canadiens jugés innovants. L’observation directe en classe a permis de recueillir leurs propos qui ont été analysés à l’aide de deux grilles construites progressivement au cours de l’étude: la liste des dimensions théoriques et la grille des niveaux de centration sur l’apprentissage. Les résultats obtenus démontrent que les rôles et les actes pédagogiques observés apparaissent moins diversifiés et moins centrés sur l’apprentissage que ce qui est attendu dans un contexte novateur de formation. This research, based on empirical observations, describes the roles and teaching acts carried out by professors engaged in four innovative university programs in Canada. Classrooms observations were used to collect data, which were then analyzed using two grids built progressively during the study: a list of theoretical dimensions and a grid showing degrees of focus on learning. In terms of results, the roles and actions of the four university professors were found to be less diversified and less centered on student learning than was expected from their innovative instructional context.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".