The Use of Information Communication Technologies Among Children With Autism Spectrum Disorders: Descriptive Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of Autism Spectrum Disorder (ASD) appears to be increasing globally due to the complex interaction of multiple biopsychosocial and environmental factors. Mobile phones, tablets, and other electronic gadgets have transformed our means of communication, and have also changed both healthcare and how we learn. These technological enhancements may have a positive impact on the lives of children, but there is currently a global scarcity of information on how information technology influences the education of children with ASD. OBJECTIVE: This study was conducted in Rwandan schools and communities, and aimed to understand the perceptions of students with ASD, their parents, and their teachers, on the use of Information and Communication Technology (ICT) in the education of those with ASD. METHODS: This qualitative descriptive study was conducted from December 2017 to July 2018. Researchers conducted four focus group discussions (FGDs) with 54 participants from different backgrounds: teachers, parents, and students with ASD. Each of the FGDs took approximately two and a half hours. A predefined set of open-ended questions were selected to discover people's perceptions regarding assistive technologies used in ASD, their effectiveness, the scope of using them in their context, and upcoming challenges during implementation. The interviews were recorded, transcribed, and analyzed. RESULTS: The findings of the study revealed seven key themes: (1) the use of ICT for the education of children with ASD; (2) existing augmentative facilities for learning; (3) current patterns of use of ICT in education; (4) preferred areas of learning for ASD students; (5) integration of ICT into educational programs; (6) areas of interest outside the classroom; and (7) future opportunities and challenges in Rwanda. We found most of the study participants assumed that appropriate technology and related innovations might solve the challenges faced by learners with ASD in classrooms. Moreover, they thought that children with ASD more so enjoyed watching television, playing digital games, and drawing objects using gadgets than interacting with people or playing with other children. CONCLUSIONS: The use of various low-cost technical devices can aid with teaching and the education of children with autism in Rwanda. However, this area requires further research to discover the impact ICT can have on the education of children with ASD, so this study may become a starting point for further research in the area.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".