‘The field was finally kind of level’: nonspeaking autistic students’ perspectives on foundational elements of inclusive virtual learning
Bibliographic record
Abstract
The COVID-19 pandemic led to a swift global shift to virtual learning, raising concerns about ensuring inclusive education for students with disabilities. Augmentative and alternative communication (AAC) users have unique access needs in virtual settings. This study examines lessons learned from pandemic virtual learning experiences of eleven nonspeaking autistic students ages twelve to twenty-two who type, spell, and point to communicate. Students’ educational contexts ranged from middle school through college within the United States, Australia, and Canada. Using an optimistic qualitative approach, we explored how virtual learning can inclusively meet the needs and preferences of nonspeaking autistic students. Our analysis underscored that foundational elements of inclusive virtual learning involve: Connections between competence, communication, and context; Rethinking structures that privilege speech and; Centring and affirming neurodiversity. These findings, informed by nonspeaking autistic students’ perspectives, can enhance inclusive virtual learning by design, and have broader implications for in-person, hybrid, and yet-to-be-imagined contexts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".