Determining Academic Success in Students with Disabilities in Higher Education
Bibliographic record
Abstract
Students with disabilities have not been fully welcomed in higher education in spite of litigation, court cases, and positive shifts in public perceptions. The transition from high school to college is challenging for students without disabilities. Students with disabilities often get overlooked by their institution and overwhelmed during this transition, contributing to an achievement gap for these students. Student success is measured by retention, academic achievement, and on-time graduation. This research study examined how student success was impacted by a student’s registration with the campus disability office, use of accommodations, and use of institutional and social support systems. This study explored a new frontier of research that dispels the myth that students with disabilities are a homogenous group. The results of this study can be used to increase knowledge regarding students with disabilities and their success in higher education. The results will assist college and university administrators as well as staff in disability services offices in tracking the success of accommodations for students with disabilities. This study can help university administration to better understand the benefits of institutional support services as well as encourage faculty involvement in implementing accommodations and helping students see the benefit of student registration with the campus office of disabilities.
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".