Design Elements During Development of Videogame Programs for Children with Autism Spectrum Disorder: Stakeholders' Viewpoints
Bibliographic record
Abstract
Introduction: Research has demonstrated that videogame programs can be an effective intervention targeting social challenges among children with autism spectrum disorder (ASD). Despite the rapid growth in developing videogame programs, incorporation of stakeholders' views has been limited. Objective: This project aimed to identify the design elements that should be considered during development of videogame programs for children with ASD, from the perspectives of stakeholders. Materials and Methods: We involved 26 stakeholders, including parents of children with ASD, youth with ASD, and clinicians working with individuals with ASD in focus groups and interviews. Results: Thematic analysis yielded three themes: (1) addressing heterogeneity and diverse needs; (2) mirroring real world; and (3) teaching strategies. Conclusion: Incorporating these elements during development of videogame programs can help enhance the outcomes for children with ASD. By including stakeholders' voices, it is assumed that the developed videogame programs may serve as user-friendly and engaging tools to potentially complement traditional interventions when overcoming social difficulties in individuals with ASD.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".