A Scoping Review on Open Educational Resources to Support Interactions of Learners with Disabilities
Bibliographic record
Abstract
This scoping review explored the trends in open educational resources (OER) that support the interactions of learners with disabilities and the challenges of supporting these interactions in such environments. Emerging OER and open educational practices allow learners to interact with digital learning resources in self-regulated learning. Since OER assume learners’ self-regulation, research has explored how to promote learner interactions to facilitate better engagement and motivation. Emerging research on OER-enabled pedagogy corroborate this trend. However, despite increasing interest in OER and open educational practices, few studies have demonstrated how OER support various types of interactions for learners with disabilities. Learners with disabilities are likely to experience challenges in interacting with OER due to their modality constraints. A comprehensive literature synthesis is essential to investigate the needs of learners with disabilities in their interactions in OER. In this study, we reviewed and synthesized existing research on how OER and open educational practices support the interactions of learners with disabilities across different OER platforms. Our findings suggest both research and design implications for future OER designs suited for learners with disabilities.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".