Web-Based Social Stories and Games for Children with Autism
Bibliographic record
Abstract
Children with (ASD) may respond well to web–based learning because computers can provide features such as repetition, visual stimuli and independent interactions that appeal to them. However, there has been limited testing of web-based learning especially outside of institutional settings. The study reported on in this paper involved the testing of open, web-based games and social stories for children with Autism Spectrum Disorder (ASD). The web-based learning was accessible by parents, teachers, health professionals and children in an institutional and home setting and consisted of four social stories and seven games housed in a website. Pre- and post-testing of the web-based learning took place over a three-month period with 10 children with ASD enrolled in a special-education center in North-Eastern Thailand. Testing was conducted using observation. Analysis involved descriptive statistics, parametric t-tests and Wilcoxon Signed-Rank tests. Results revealed improvement for all behaviors although not for all children. Implications include the need for future studies that rely on more participants and that focus on transferability of learned behaviors to real-life contexts. Future studies might also include longitudinal designs to determine sustainability of newly learned behaviors and the design of web-based environments that adapt to or are more specifically tailored to individual needs of children 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".