Toward Strengths-Based Accessibility in Higher Education: An ADHD-Centric Approach to Teaching Theatre and Dance Creation Online
Bibliographic record
Abstract
Abstract: This article presents an ADHD-centric approach to teaching performance creation online in postsecondary contexts that is transferable to both blended and in-person courses. The approach features a dynamic and engaging relay process of partnered and peer-facilitated creation work within a thematic container; beneficial strategies for instruction; and a strengths-based assessment method. The approach was developed collaboratively and through qualitative focus groups with performing arts students and instructors at a Canadian university, working across theatre, dance, and educational psychology. This transdisciplinary team asked how the strengths and protective factors of students with ADHD can be placed at the center of teaching to provide enabling learning opportunities. The authors situated this work in the context of online teaching because it intensifies and reveals barriers. Like their process of development, this article first matches protective factors and known ADHD strengths, such as creativity, empathy, and problem-solving, with valued skills and ways of working in dance and theatre. Resulting insights are then related to post-pandemic literature on the online teaching of performing arts practice, which reveals challenges and potential solutions. The teaching strategies the authors arrived at by applying these insights are first presented as conceived then further advanced through focus group results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".