Implementing Universal Design for Learning (UDL) in Online Courses: Perspectives of faculty and students at Prince Sattam bin Abdulaziz University
Bibliographic record
Abstract
This study investigates the extent to which universal design for learning (UDL) principles have been used in online courses according to the opinions of professors and students at Prince Sattam bin Abdulaziz University. Using a descriptive approach, the researchers created two questionnaires, one for faculty members and one for students. Each questionnaire contained 36 items on the main principles of UDL: multiple means of representation, multiple means of performance and expression, and multiple means of motivation and participation. The sample consisted of 75 male and femal faculty members and 112 students, who were selected randomly. The results suggested that online courses helped faculty achieve some UDL principles and raise awareness about those principles. There was also high agreement between faculty and students about the positive impact of distance learning, which reportedly increased students’ exposure to many UDL elements, such as offering more means of communication and interaction. In addition, students reported high levels of participation in classes implementing UDL principles.
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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 | 0.008 | 0.010 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| 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".