The Use of Technology to Support the Learning of Children with Down Syndrome in Saudi Arabia
Bibliographic record
Abstract
The research employed a survey questionnaire to explore the type of technological tools available in schools forchildren with Down syndrome (DS) in Saudi Arabia, perceptions of teachers toward the benefits of technology-assistedlearning for DS students, the skills that children with DS need to use technology, the challenges of using technology forchildren with DS, and what can be done to improve the use of technology for children with DS. The data from the 20teachers in two schools were analysed using qualitative data analysis procedures, which yielded several themes.The findings show that the sampled schools have different types of technologies but computers, iPads and projectorsare the most commonly used devices, although there are other devices such as DVD players, mobile phones, andloudspeakers among others. Many teachers understand the benefits of using technology to support children with DSand their views are supported by studies conducted in the past on the same area as presented in extant literature. Thekey challenges to using technology as identified by the teachers include lack of resources such as computers, lack ofsoftware designed in Arabic, and lack of training for teachers to enable them to support children with DS.Recommendations are discussed.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".