Insights and Strategies to Support Students with Autism Spectrum Disorder in Post-Secondary Contexts: A Canadian Perspective
Bibliographic record
Abstract
The number of students with Autism Spectrum Disorder (ASD) entering post-secondary institutions is increasing, but students with ASD are struggling more than their typically developing peers, with high rates of mental health challenges and a lower graduation rate. This study’s purpose is to understand the learning needs/experiences of students who identify as being an individual with, or having a formal diagnosis of ASD, while highlighting perspectives of f aculty members and students from Sheridan College. Our research finds that the learning experiences of students with ASD at Sheridan are impacted by: (a) Environmental stressors, (b) Gaps in transitioning from high school to college, (c) Lack of access to leadership opportunities, (d) Lack of awareness or understanding of available supports, (e) Inadequate quiet spaces to support sensory and emotional needs, (f) Difficulty navigating inconsistent approaches in academic accommodation implementation, (g) Too much onus being placed on students to prove their need for accommodations, and (h) Difficulty navigating multiple platforms in the online learning context. Faculty participants’ responses revealed factors and strategies that have impacted their experiences teaching students with ASD including: (a) Building rapport and cultivating trust to encourage open communication and discussion regarding accommodation needs, (b) Recognizing challenges with group formats and allowing choice in group selection or foregoing mandatory group work, (c) Understanding stressors and strategies to support students’ social, communication and emotional challenges, (d) Collaboration with student affairs and Accessible Learning (AL) to support students with ASD, (e) Applying strategies learned in previous training and community employment to support students with ASD.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".