Inclusive Online Nursing Education: Learner Perceptions of Universal Design for Learning Approaches
Bibliographic record
Abstract
Diversity in age, background, circumstances, and abilities among post-secondary learners has become increasingly common in online nursing education. Thus, there is a need for educators to build an inclusive environment that is responsive to this variety, to optimize learner achievement. Universal Design for Learning (UDL) offers educators a theoretical framework to proactively design an inclusive online course curriculum that is responsive to varied learner populations and minimizes learning barriers encountered. A convergent mixed-methods descriptive case study was conducted of learners enrolled in a large first-year undergraduate nursing course in Canada, which was redesigned using UDL principles. The purpose of this case study was to answer the research question: How do learners’ rate and describe the effectiveness of instructional strategies used in supporting inclusivity of diverse learning preferences and needs in an online environment. Data was collected in 2020, from a cohort of 230 learners using a survey questionnaire (n=40) and focus group (n=7) by a hired research assistant, for the convergent mixed-methods analysis and resulting discussion. Survey respondents rated accessible course material, inclusive lecture strategies, accommodations, inclusive assessment, and classroom constructs of the survey tool, as being most inclusive of diverse needs. Focus group participants described assessment methods, instructor presence, and UDL based course design elements, as the preferred instructional strategies used in the curriculum, with group work and navigation issues being most problematic. A purposeful selection of synchronous and asynchronous UDL based instructional strategies by educators, which integrate multiple means of engagement, representation, action and expression, offered an inclusive online environment for diverse learning 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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".