Twenty Years Into the 21st Century – Tech-related Accommodations for College Students with Mental Health and Other Disabilities
Bibliographic record
Abstract
Virtually all North American two- and four-year colleges provide accommodations to their increasing numbers of students with disabilities. To explore technology and non-technology related accommodations for these students we surveyed 118 Canadian two- and four-year college students who self-reported at least one disability, including a mental health related disability, and indicated that they had registered for access services from their college. Seventy-four students without disabilities were included in some analyses. Our findings reveal emerging issues such as non-binary gender and multiple comorbidities, in addition to more targeted recommendations concerning technology use. For example, over half of our sample self-reported multiple disabilities; there is a large number of students with mental health related disabilities (e.g., anxiety disorders, mood disorders), many of whom have comorbid disabilities; binary (male, female) gender designations are outdated; and exam and classroom accommodations without technologies are still the most popular. Grades of students with and without disabilities did not differ. Similarly, the number of different types of accommodations in two- and four-year colleges did not differ. Students generally had high technology related self-efficacy and they saw the substantial benefit of technologies, especially of writing tools. Students with mental health related disability used somewhat fewer technologies for reading, writing and time management. Self-efficacy and perceived benefit were highest for writing technologies. General use technologies such as Microsoft Office and Google Docs that were reported by most students in this study are increasingly used as adaptive aids. In future, use of technology related accommodations is likely to include showing students how to use general use software.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".