Neurodiversity on Campus: University Instructors' Experiences Working with Neurodivergent Students
Bibliographic record
Abstract
The growing number or neurodivergent [ND] students enrolled in Canadian postsecondary institutions has increased interest in understanding how the capacity of educators and institutional inclusive policies influence the academic experience of ND learners. The primary concern of this investigation revolved around the dissonance between the expanding commitment of Canadian universities in establishing inclusive guidelines in recent years, persistent stigmatized perspectives, and policy gaps that still pose challenges to improving instructors' capacity, and inclusive teaching dynamics. Accordingly, the purpose of this study was to comprehend instructors' perspectives and experiences working with neurodivergent students at a Canadian research-intensive institution. Literature depicts postsecondary instructors and students as the primary components of an academic learning environment, and the commitment to inclusive educational practices through exploring the effectiveness of teaching and learning strategies has a direct impact on ND students’ academic outcomes. For that purpose, I followed a qualitative research methodology, utilizing an interpretive study approach that allowed me to collect the data through semi-structured interviews of six postsecondary instructors teaching at a Canadian research-intensive university. Suitable to interpretive studies, the thematic analysis method guided the data analysis; the generation of codes; the definition of themes; and the production of the analysis report. The findings suggest a need for greater alignment between policy intent and the professional development [PD] initiatives offered by universities. While institutional policies claim inclusive intent and diverse-oriented initiatives, the realities faced by instructors suggest a disconnection between policy documents and lived practice. Moreover, providing academic accessibility based on individual student responsibility and a generic intervention can fail to support effective inclusion. This study highlights the importance of establishing policies that provide more specificity regarding neurodiversity-related initiatives, seeking to increase the efficacy of PD opportunities for educators. Furthermore, I discuss broader contributions to theory, implications to policy and practice, and recommendations for future research in the field.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".