Educational Resources About Universal Design for Learning
Bibliographic record
Abstract
BACKGROUND: The mandate to provide inclusive education in Canadian schools means that Speech-Language Pathologists (SLPs) need to be well-versed in frameworks such as Universal Design for Learning (UDL) that support learning among students with diverse backgrounds and abilities. To be responsive, professional graduate programs need resources that support teaching SLP students about UDL. PURPOSE: 1) To use an instructional design model and Knowledge Translation (KT) theory to develop educational resources about UDL for SLP graduate students; and 2) to assess feasibility of the resources and SLP students’ perceived and actual UDL knowledge change after resource implementation. METHODS: First, educational resources about UDL were created for SLP students using a process in which the first three phases of the Analysis, Design, Development, Implementation, Evaluation (ADDIE) instructional design model were combined with the Diffusion of Innovations (DOI) KT theory and supported by engagement of key SLP stakeholders. Stakeholder feedback about their involvement in the resource development process was assessed through a focus group and analyzed using conventional content analysis. Next, the last two phases of the ADDIE model were conducted in which the developed resources were implemented and evaluated with 19 SLP students over a three-hour session; resource feasibility and UDL knowledge were measured before and after the session using anonymous, web-based questionnaires. RESULTS: The novel process for developing resources was deemed suitable for creating high-quality theory-informed resources tailored to SLP students. SLP students perceived the resources to be practical and acceptable. There was a statistically significant improvement in students’ perceived UDL knowledge as well as improvements in actual UDL knowledge. CONCLUSION: Health educators could consider the described methodology when developing content-specific resources for health professional students. This thesis introduces a new set of resources that could be used to address an important gap in SLP training.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".