Accessible Computer Technologies for Students With Disabilities in Canadian Higher Education
Bibliographic record
Abstract
Two studies explored how well English and French speaking colleges and universities in Canada address availability and access to new computer and information technologies for individuals with disabilities. In Study 1, 156 professionals who provide disability-related supports on campus responded to structured interview questions. In Study 2, 40 professionals who work in Quebec's Francophone junior/community college system (CEGEP) participated. Results showed that most institutions had specialized adaptive computer equipment, though colleges were less likely than universities, and loan programs providing adaptive computer equipment were seen as very effective. Respondents believed they were not very knowledgeable about adaptive computer technologies and those from Francophone institutions scored lower than from Anglophone institutions. The needs of students were seen as moderately well met, with Francophone respondents more favorable than Anglophone. Respondents from Anglophone universities expressed different needs than those from Anglophone colleges or Francophone institutions. Disability service providers wished students were better equipped and prepared for the postsecondary experience, computer based teaching materials used by professors were more accessible, and more extensive support services for adaptive hardware and software available. We provide recommendations based on universal design principles that are targeted at those involved in technology integration in postsecondary education.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".