The Challenge of Accommodation in Higher Education: A Survey of Adaptive Technology Use in Ontario Universities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it