Technology Integration for Students with Disabilities: Empirically Based Recommendations for Faculty
Bibliographic record
Abstract
In 3 empirical studies we examined the computer technology needs and concerns of close to 800 college and university students with various disabilities. Findings indicate that the overwhelming majority of these students used computers, but that almost half needed some type of adaptation to use computers effectively. Data provided by the students and by a small sample of professors underscore the importance of universal design in a variety of areas: courseware development, electronic teaching and learning materials, and campus information technology infrastructure. Sex and age of students were only minimally related to attitudes toward computers or their use in our samples. Key findings summarize the problems faced by students with different disabilities as well as the computer related adaptations that are seen as helpful. These are used to formulate concrete, practical recommendations for faculty to help them ensure full access to their courses.
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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 | 0.075 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".