Transition to Higher Education for Students with Autism: Challenges and Support Needs
Bibliographic record
Abstract
Increasing pieces evidence suggest that learners with autism spectrum disorder (ASD) and their families experience limiting challenges in their transition at different developmental and academic levels in life. It is not clear, however, what the specific challenges that limit their successful transition are, despite parents' and teachers' efforts to support them. The current study sought to investigate the factors challenging students with ASD in transitioning to higher education. The study adopted a phenomenological qualitative research design with a total of 10first-year students with ASD in higher education institutions in Nigeria. The one-on-one interview was conducted, guided by a semi-structured interview schedule. Data collected were analyzed using content analysis, through the inductive thematic procedure. The results revealed five main themes, which include: academic functioning difficulties, social difficulties, structural issues, mental health problems, and lack of resources and supports. Each major theme was discussed based on the emerging subthemes. The findings of the study suggest an increased need for academic, social, materials, and environmental supports for students with ASD who transition to higher education institutions. More supports should be put in place to help learners with autism develop personal resources that will encourage their success in higher education institutions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".