Shaping Inclusive Learning: A Comparative Study Of UDL Engagement Pre- And Post-Pandemic In One Ontario College
Bibliographic record
Abstract
The universal design for learning framework aims to remove barriers from the learning environment so that as many students as possible can fully participate in it. The COVID-19 pandemic has brought about additional challenges in higher education, but in many cases, it has also provided a unique opportunity to examine change. This study investigated students’ and faculty’s perceptions of how frequently various elements of universal design for learning were used in the classroom as well as how useful these elements were perceived to be for student learning. Different groups of students and faculty responded to an online survey pre-pandemic and then again approximately one year into the pandemic. The findings indicated consistently robust correlations between the pre-pandemic and pandemic periods. However, the pandemic initiated certain shifts, notably an uptick in faculty incorporating specific UDL elements, such as recording lectures. Additionally, students perceive a greater number of UDL elements as advantageous for their learning compared to the faculty perspective.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".