Familiarity, Current Use, and Interest in Universal Design for Learning Among Online University Instructors
Bibliographic record
Abstract
This study investigated online faculty familiarity, course design use, and professional development interest regarding universal design for learning (UDL) guidelines. The researchers surveyed all 2017 to 2018 online faculty at a large university in the southeastern United States. Findings included 71.6% of faculty reporting familiarity with at least one UDL guideline, with most respondents indicating familiarity with guidelines relating to perception, expression, and communication. Faculty reported the highest implementation of UDL guidelines was for those suggesting options for comprehension as well as expression and communication; the lowest implementation was for those suggesting options for physical action as well as language and support. Survey results also indicated high to moderate interest in learning more about all UDL guidelines, with emphasis on comprehension, persistence, and expression. This study suggests that faculty members desire UDL training and offers possibilities for planning and implementing such professional development in areas targeted to best meet the needs of online faculty.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".