Applying the POUR Model to Enhance Digital Accessibility in HyFlex Learning Environments
Bibliographic record
Abstract
The purpose of this study is to advance the accessibility of a hybrid-flexible (HyFlex) learning environment by applying the four attributes of the POUR model (WCAG 2.1, 2018), namely, perceivable, operable, understandable, and robust, to make digital learning content more accessible to all learners. The connections between the POUR principles and the principles of four frameworks instrumental to digital accessibility––Universal Design, Universal Design for Learning, Mobile Seamless Learning, and HyFlex––are discussed. The study describes one educator’s journey to learn the core skills of making learning resources more accessible to undergraduate students at a teaching university in Switzerland. Qualitative data was obtained from a focus group involving three students, as well as from an external evaluator who conducted a digital accessibility check based on commonly used accessibility criteria. This revealed that the criteria were implemented with varying effectiveness. Findings from the focus group suggest that the instructor’s efforts to increase digital accessibility were noticeable. Obstacles were mainly related to navigation issues and the different participation modalities integral to HyFlex. The study offers practical advice for instructors who wish to increase digital accessibility and adaptability in their courses.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".