The Development of Digital Technology to Support Learning in Children with Disabilities
Bibliographic record
Abstract
This study was conducted with the following objectives: 1) to develop digital technology that supports learning in children with disabilities; 2) to test the effectiveness of digital technology used in children’s learning. The sample selected to study the current context of and needs of digital technology consisted of a group of 46 teachers and parents. The sample group of children with disabilities who participated in the digital technology experiment comprised three children. The sample group who took part in the experiment in fieldwork comprised 45 children from nine types of disabilities; these were purposively sampled. The research instruments consisted of 1) ten easy-books with single vowels in Thai; 2) ninety individual implementation plans; 3) computer software to promote the reading of ten stories; 4) an evaluation form for children with disabilities’ reading ability in Thai vocabulary in Prathom level 1; 5) a survey form for the current context of and the needs for digital technology to promote the children’s learning; 6) an interview form for the current context of and the needs for digital technology to promote the children’s learning. The data were analysed with the statistics of mean, standard deviation (SD) and efficiency assessment, using the E1/E2 formula with the 80/80 criteria. The research found that the effectiveness assessment value of digital technology to promote the children’s learning was found at the rate of 86/90, which was higher than the standard rate.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".