Model of Providing Assistive Technologies in Special Education Schools
Bibliographic record
Abstract
Most students diagnosed with disabilities in Thai special education schools received assistive technologies, but this did not guarantee the greatest benefits. The purpose of this study was to survey the provision, use and needs of assistive technologies, as well as the perspectives of key informants regarding a model of providing them in special education schools. The participants were selected by the purposive sampling method, and they comprised 120 students with visual, physical, hearing or intellectual disabilities from four special education schools in Chiang Mai, Thailand; and 24 key informants such as parents or caregivers, teachers, school principals and school therapists. The instruments consisted of an assistive technology checklist and a semi-structured interview. Results showed that a category of assistive technologies was provided for students with disabilities, with the highest being "services", followed by "media" and then "facilities". Furthermore, mostly students with physical disabilities were provided with assistive technologies, but those with visual disabilities needed it more. Finally, the model of providing assistive technologies was composed of 5 components: Collaboration; Holistic perspective; Independent management of schools; Learning systems and a production manual for users; and Development of an assistive technology center, driven by 3 major sources such as Government and Private organizations, and Schools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".