Adapting the Use of Digital Content to Improve the Learning of Numeracy Among Children With Autism Spectrum Disorder in Rwanda: Thematic Content Analysis Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».