Adapting the Use of Digital Content to Improve the Learning of Numeracy Among Children With Autism Spectrum Disorder in Rwanda: Thematic Content Analysis Study
Bibliographic record
Abstract
BACKGROUND: Many teachers consider it challenging to teach children with autism spectrum disorder (ASD) in an inclusive classroom due to their unique needs and challenges. The integration of information communication technology (ICT) in the education system allows children with ASD to improve their learning. However, these ICT tools should meet their needs to lead a productive life. OBJECTIVE: This study aimed to examine the possibilities of re-creating and adapting digital content to improve the learning of numeracy among children with ASD in inclusive school settings. METHODS: We conducted 7 focus group discussions (FGDs) with 56 teachers from 7 schools and 14 parents from April to November 2019. Each of the FGDs took around 1 hour. Two clustered sets of questions were used: (1) general knowledge about teaching children with ASD and (2) analysis of selected online educational video content of early math (specifically, counting numbers). The researchers used video to understand current methodologies used in teaching children with ASD, possibilities of adaptation of the content in the current teaching environment, future challenges when the content is adapted, and possible solutions to overcome those challenges. All data, including audio recordings, field notes, and participants' comments, were transcribed, recorded, and analyzed following the steps recommended in qualitative data analysis. RESULTS: The researchers identified ten themes from the analysis of the data: (1) awareness of the existence of ASD among children in schools and the community, (2) acceptance of children with ASD in an inclusive classroom and the community, (3) methods and models used when teaching children with ASD, (4)realia used to improve the learning of children with ASD, (5) the design of educational digital content, (6) the accessibility of online educational content, (7) quality of the content of the educational multimedia, (8) the opportunity of using the translated and re-created content inside and outside the classroom, (9) the relevance of the digital content in the Rwandan educational system, and (10) enhancement of the accessibility and quality of the digital content. We found that participants assumed that the content translation, gamification, and re-creation would help teach children with ASD. Moreover, they recommended contextualizing the content, increasing access to digital devices, and further research in the education of different subjects. CONCLUSIONS: Although many studies have identified the possibilities of using ICT to support children with ASD, few studies have documented the possibilities of integrating the existing technologies tested in the international community. This study is charting new territory to investigate online content to suit the context of schools. This study recommends further exploration of possible methodologies, such as applied behavior analysis or verbal behavior therapy, and the development of contextualized technologies that respond to the educational needs of children with ASD.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".