The Transition Online: A Mixed-Methods Study of the Impact of COVID-19 on Students with Disabilities in Higher Education
Bibliographic record
Abstract
Following the World Health Organization’s announcement of the global pandemic because of the Coronavirus Disease 2019, most Canadian universities transitioned to offering their courses exclusively online. One group affected by this transition was students with disabilities. Previous research has shown that the university experience for students with disabilities differs from those of their non-disabled peers. However, their unique needs are often not taken into consideration. As a result, students can become marginalized and alienated from the online classroom. In partnership with Student Accessibility Services, this research revealed the impact of the transition to online learning because of the pandemic for university students with disabilities. Students registered with Student Accessibility Services completed a survey about the effects of online learning during a pandemic on the students’ lives, education, and instructional and accommodation. It was clear from the results that online education during COVID-19 affected all aspects of the students’ lives, particularly to their mental health. This research provided a much-needed opportunity for students with disabilities to share the factors influencing their educational experience and identified recommendations instructors should consider when developing online courses to increase accessibility and improve engagement.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".