Faculty Perspectives on UDL: Exploring Bridges and Barriers for Broader Adoption in Higher Education
Bibliographic record
Abstract
Universal Design for Learning (UDL) strategies aim to reduce learning barriers in the classroom for all students and remove the need for students with disabilities to advocate on their own behalf. Leadership in Scholarship of Teaching and Learning has a role to play in advancing inclusive learning cultures in higher education. At the frontline of higher education delivery, faculty are best positioned to implement UDL practices. Initiatives to encourage broader implementation of UDL require an understanding of the barriers and opportunities in higher education. Published studies that investigate faculty understanding and implementation of UDL have been almost exclusively conducted in US institutions. Our study enriches the existing literature through a mixed methods approach with interviews and a faculty survey in a Canadian context. Themes revealed in our interviews were reinforced by survey findings. Many of the issues raised by faculty, including time and resource constraints, a lack of institutional support, and a lack of understanding are consistent with previous research done in the US, highlighting the systemic challenges for UDL implementation in higher education. To conclude, we explore the limits of a strictly bottom-up approach and contend, in line with recent studies, that top-down initiatives are also vital to encouraging broader implementation of UDL practices.
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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 | 0.068 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".