Bibliographic record
Abstract
In Ontario, Canada, universities are obligated under the Accessibility for Ontarians with Disabilities Act (AODA) to ensure that people with disabilities do not face barriers to education, and they are free from barriers in society more broadly. Those who produce online curriculum for postsecondary education in the province need at least a basic understanding of digital accessibility, and for some roles, like software or web developers, a level of expertise is required. However, finding people with the right knowledge, skills, and attitude can be difficult. This problem can be attributed to the fact that until recently digital accessibility skills have received little attention in post-secondary education. To address the issue, in 2015, with support from the Government of Ontario, we began several projects to develop digital accessibility curriculum. These efforts created a series of free Massive Open Online Courses (MOOCs) aimed at teaching digital accessibility skills to audiences ranging from office support workers, to managers, to developers, to digital accessibility specialists. The MOOCs ran between 2016 and 2019 and served more than 5000 participants, with more than 600 successfully completing the requirements for the digital badge(s) awarded. Following the MOOCs project, the content of the courses was converted into Open Educational Resources (OERs) that could be used as textbooks to support the introduction of digital accessibility topics over a range of subject areas, with encouragement for others to reuse the content to add accessibility related topics into their teaching. The OERs were downloaded more than 10,000 times between late 2020 and late 2022 and provided the base content for four open courses developed through OERU. In this article the pedagogy and curriculum for this digital accessibility training are described.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.014 |
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".