Engaging Clinicians and Graduate Students in the Design and Evaluation of Educational Resources about Universal Design for Learning
Bibliographic record
Abstract
he mandate to provide inclusive education in Canadian schools means that speech-language pathologists need to be well-versed in frameworks, such as Universal Design for Learning, that support learning among students with diverse abilities. To be responsive, professional graduate programs need resources that support teaching speech-language pathology students about Universal Design for Learning. The purpose of this article was to demonstrate (a) how we applied an instructional design model and knowledge translation theory to develop educational resources about Universal Design for Learning for speech-language pathology graduate students and (b) how we assessed the feasibility of these resources and students’ perceived and actual knowledge change about Universal Design for Learning. We created the educational resources using the first three phases of the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) instructional design model together with a knowledge translation theory, Diffusion of Innovations, and through engagement of experienced school speech-language pathologists. Next, we applied the last two phases of ADDIE by delivering our resources to 19 speech-language pathology students during an educational session. We assessed the feasibility of resources and students’ knowledge of Universal Design for Learning through pre–post web-based questionnaires. Preliminary findings indicated that students perceived the resources to be practical and acceptable and there was improvement in students’ perceived knowledge of Universal Design for Learning. Resources should be implemented in a larger student cohort to reassess feasibility and knowledge change. We believe that this novel resource development methodology could serve useful to educators, researchers, and clinicians to develop high-quality, theory-informed educational resources.
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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 | 0.079 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".