Accommodations and academic performance: First-year university students with disabilities
Bibliographic record
Abstract
Despite growing enrollment of university students with disabilities, they have not achieved academic parity with their non-disabled peers. This study matched 71 first-year university students with disabilities and students without disabilities on three variables: high school average when admitted to university, gender, and program of study. Both groups of students were compared on three measures of academic performance: GPA, failed courses, and dropped courses after first year of university. The relationship between accommodations and academic performance was also analyzed for students with disabilities. Evenwhen matched on admission average, gender, and program of study, students with disabilities had a significantly lower GPA and were more likely to fail courses in their first year than their peers without disabilities. While note-taking in the classroom was associated with being less likely to drop a course, it was also associated with poorer academic performance, as was using a calculator or alternate format during exams. The more accommodations students lost in the transition from high school, the worse they performed academically at university. Students who lost human assistant support in the classroom and theuse of a computer or a memory aid during exams had a significantly lower GPA and were more likely to fail courses in their first year of university compared with students who did not lose these accommodations. These findings have implications for accessibility offices and universities in supporting the access needs and academic success of students with disabilities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".