How Higher Education Leaders, Faculty Members, and Professional Staff can Enhance Services and Outcomes for Autistic Students
Bibliographic record
Abstract
The purpose of this research was to explore how Canadian higher education leaders, faculty members, and professional staff can enhance services and outcomes for Autistic students. This study was situated within the pragmatic paradigm and employed a multilevel, sequential, mixed method design. The mixed methods approach included a total of 111 responses, namely, online questionnaires (n=74) and synchronous or asynchronous, semi-structured interviews (n=37). The sample included a total of 79 participants across four stakeholder groups: university middle level leaders (n=23), faculty members (including two who were themselves Autistic, n=16), professional staff members working with Autistic students in offices such as accessibility services, equity and inclusion services, human rights, or student advocacy (n=10), and Autistic students or individuals who had experienced university studies (n=30). Six major findings emerged from this study: 1. The necessity of inclusive leadership to create the vision and inspire others to enhance services and educational outcomes for Autistic students; 2. The importance of including Autistic voices in the development of relevant, person-centred, outcomes-based autism policies; 3. The necessity of person-centred university policies to adequately attend to the spectrum nature of the Autistic condition; 4. The inclusion of a key strategy – Universal Design for Learning (UDL) and assessments – to ensure genuine and equitable teaching and learning systems that can meet the needs of the greatest number of students, particularly Autistic students; 5. The benefit of campus-wide education about autism led by and/or informed by Autistic individuals to create environmental changes that strengthens a university community’s understanding and acceptance of differences; and 6. The advantage of having Autistic students integrated within higher education communities. The Waisman Model of Best Practice for Autistic Inclusion and Success in Higher Education and a set of recommendations were developed from the findings. This model was designed to provide a pragmatic strategy for leaders to facilitate greater empowerment to all stakeholders, to inform policy and practices, and to innovate teaching, learning, and assessment strategies to benefit all students, but especially those who have unique learning needs, namely, Autistic students.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".