Best of both worlds: Learner perspectives of inclusivity in a blended course
Bibliographic record
Abstract
Objective: Neurodiversity among learners and related learning needs requires educators to move beyond existing dominant forms of traditional didactic lecturing and rigid assessment methods to inclusive practices. To support neurodiverse learners, a first-year undergraduate nursing course adopted a blended delivery model in the Fall 2022 and 2023 semesters, integrating Universal Design for Learning (UDL) principles through synchronous and asynchronous instructional strategies. Methods: In this convergent mixed methods descriptive case study, researchers explored how students rated and described their experiences with the UDL-based course design, using surveys (n = 39) and focus groups (n = 12). Results: While survey and focus group interview findings generally aligned, some learners found the asynchronous weekly units less effective. Thematic analysis revealed five key themes: instructor accessibility and feedback; flexibility and choice; engagement and collaboration; relevant and relatable content; and the impact of stress and anxiety on learning. Although course modifications scored lowest in surveys, focus group participants appreciated instructor flexibility. Conclusions: Overall, the blended UDL approach supported diverse learning needs. Future recommendations include balancing delivery formats and incorporating ongoing student feedback to enhance inclusivity and better support neurodiverse learner needs.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".