The Challenge of Accommodation in Higher Education: A Survey of Adaptive Technology Use in Ontario Universities
Bibliographic record
Abstract
This paper provides an overview of adaptive technologies currently being used in Ontario Universities. Results of this study may help disability service providers in Ontario in understanding the current challenges of training students with disabilities in using adaptive technologies as well as improving service delivery methods. Participants were recruited through a listserv and asked to answer an online survey. Data were analyzed using descriptive statistics and anecdotal narratives. Results indicated that students with learning disabilities are not familiar with adaptive technologies that would best suit their academic needs and that training in adaptive technology occurred on an individual basis or in small group settings as opposed to large groups. Participants indicated that they use low-cost equivalents and adaptive technologies housed in open laboratories in order to serve students with financial needs. Challenges faced by Assistive technologists included: consistency in assistive technology use by the students they serve, effective training while semester coursework is in progress, and fitting individuals with very unique needs to the available technology. A series of best practices and accomplishments were identified by the participants.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".