La mise en œuvre des principes de flexibilité de la pédagogie universelle : une étude de cas en contexte universitaire québécois
Bibliographic record
Abstract
The heterogenization of Quebec university students raises several challenges (Vagneux & Girard, 2014), including the exclusivity of access to specialized services for diagnosed students. Universities must find solutions that meet the diverse educational needs of all their students while maintaining their high standards (Mace & Landry, 2012) without forcing students to present diagnostic evidence. Universal Design for Learning, by its flexibility, appears promising since it considers this diversity differently than by a diagnosis (Orr & Bachman Hammig, 2009). This case study aims to understand better the process of implementing Universal Design for Learning for Quebec academics. The results, from interviews and observations, describe the stages of this implementation as well as the educational strategies deployed. This study offers concrete solutions to support universities in responding collectively and flexibly to the varied educational needs of all their students. Keywords: case study, denormalization, disability studies, inclusive practice, universal design for learning
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".