Evaluating Pedagogy and Practice of Universal Design for Learning in Public Schools
Bibliographic record
Abstract
How can education change to meet the demands of effectively educating an increasingly diverse student population with the skills, knowledge, and abilities they need to be productive and successful citizens in the 21st century? One possible solution is to create classrooms, teachers, and schools that embrace the progressive and inclusive practices espoused by Universal Design for Learning (UDL). In addition to being rooted in UDL pedagogy, classrooms designed to meet the challenge of 21st century education need to substantially integrate and utilize advances in technology. The vanguard of literature to date in UDL could be characterized as rhetorical advocacy. That is, UDL literature is in the early stages of introducing and promoting UDL pedagogy, but to date there is not a research base strong enough to establish UDL as a scientifically validated intervention (Edyburn, 2010). UDL might sound like a good idea, but until the research base turns the corner from advocating to assessing and measuring UDL outcomes, the promise of this approach will not be realized. This article describes a study exploring effects and outcomes of a professional development program on the perceptions and practice of UDL principles in K–12 public school inclusive classrooms, and could be one step toward bridging the gap from a good idea to a solidified best practice. Specifically, this study investigated a professional development program’s effect on teachers’ perceptions, conceptualizations, and implementation of UDL principles and practice in their classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.153 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".