“Change Isn't Exactly Easy”: Autistic University Students' Lived Learning Experiences During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 (coronavirus disease 2019) pandemic-related isolation measures caused significant unexpected changes in learning experiences for all university students, including autistic students. So far, there has been a lack of information on autistic university students' lived learning experiences caused by the changes in the teaching delivery formats from face-to-face to online during this time. Our study addressed this gap by investigating eight autistic students' reported learning experiences during the rapid changes caused by the pandemic and discussing student-advocated learning supports. Methods: The participants in this qualitative study were eight formally or self-diagnosed, English-speaking, autistic undergraduate and graduate university students from a mid-sized Canadian university. Participants took part in semi-structured interviews that focused on their learning experiences and preferences before and during the pandemic, including what supports they found helpful. To analyze and interpret the data, autistic and nonautistic researchers used reflexive thematic analysis and a consultative participatory approach. Results: Our findings suggest that individual (i.e., organizational skills; mental health), interactional (i.e., prior experiences interacting with instructors and teaching assistants), and environmental (i.e., sensory environments, class sizes, virtual learning environments) factors, which were interrelated, determined the nature and quality of these autistic students' learning experiences and their academic preferences during the pandemic. We also found that each autistic student reported unique learning experiences and needed individualized supports for their learning. Conclusions: Several interrelated factors (individual, interactional, and environmental) affected the nature and quality of autistic university students' experiences during the pandemic. Each student had unique experiences and needed individualized supports.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".