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Record W4416028117 · doi:10.1353/tt.2025.a974461

Toward Strengths-Based Accessibility in Higher Education: An ADHD-Centric Approach to Teaching Theatre and Dance Creation Online

2025· article· en· W4416028117 on OpenAlexaboutno aff
Pil Hansen, Emma A. Climie, SJ Cannon, Isabel Martins, Rebecca Weber, J. Scott Long

Bibliographic record

VenueTheatre topics · 2025
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsnot available
Fundersnot available
KeywordsDanceSituatedContext (archaeology)Thematic analysisFocus groupThe artsSituated learningProcess (computing)Online teaching

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.372
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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