Comment des universitaires engagent leurs étudiants avec la pédagogie universelle
Bibliographic record
Abstract
En contexte postsecondaire quebecois, les etudiants en situation de handicap soulevent des defis pour l’enseignement et l’organisation des services (Vagneux & Girard, 2014). L’organisation actuelle des services repond seulement aux etudiants ayant un diagnostic par la mise en place d’interventions individualisees. Il apparait necessaire de trouver des solutions equitables et egalitaires repondant a la diversite des besoins de tous les etudiants (Mace & Landry, 2012). Les ecrits sur la diversite etudiante montrent l’importance de s’eloigner des approches centrees sur l’individualite des besoins pour tendre vers des approches collectives centrees sur la pluralite des besoins (Vienneau & Theriault, 2015). La pedagogie universelle pourrait permettre de soutenir autrement que par un diagnostic la diversite des etudiants (Orr, 2009). Sa mise en œuvre permettrait une planification de l’enseignement tenant compte de cette diversite (Ducharme et Montminy, 2015) tout en diminuant l’attention portee aux handicaps, pour se centrer sur les conditions favorables a l’apprentissage de tous. Cette communication presentera les resultats d’une etude de cas realisee aupres de professeurs universitaires quebecois de differentes disciplines ayant mis en œuvre les principes de la pedagogie universelle, plus precisement le premier principe associe aux moyens d’engagement.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".