Using UDL in Graduate Programs in Education to Erode Pedagogical Tension and Contradictions
Bibliographic record
Abstract
Faculties of education should be at the forefront of universal design for learning (UDL) implementation since their focus systematically includes effective, student-centered, inclusive pedagogy. This is unfortunately not the case. The chapter reviews some of the tension which is often observed around the lack of accessible and inclusive practices in graduate education within faculties of education. The chapter then explores and analyzes the data collected by the author in relation to the implementation of UDL in graduate courses in a faculty of education on a Canadian campus. The last section of the chapter takes a wider perspective and examines some of the opportunities and challenges, which are encountered in the implementation of UDL in graduate education more generally, and offers hands-on solutions. It is hoped the chapter will act as a road map for wider UDL implementation within graduate and post-graduate courses and debunk some of the myths that are perpetuated in this regard.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".