Accessibility Challenges in OER and MOOC: MLR Analysis Considering the Pandemic Years
Bibliographic record
Abstract
The review of state of the art on creating and managing learning resources and accessible Open Educational Resources (OER) and Massive Open Online Courses (MOOC) is a topic that cannot only consider formal literature. The evidence and lack of a measurement consensus require the inclusion of contextual information, corroborating scientific results with practical experiences. For this reason, this article presents a review of accessibility models, OER and MOOC, considering the gray literature to capture experiences and trying to establish a shared understanding of the terminology commonly used in research on virtual accessibility and its impact on higher education. The bibliographic review relies on analyzing articles and scientific publications related to the topic following the Multivocal Literature Review (MLR) format. The results of this review establish that it is possible to apply accessibility review methodologies with transversal actions in the creation and management of learning resources and MOOCs. The research is related to one of the seventeen sustainable development goals defined by the United Nations to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".