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Autism Spectrum Disorder (ASD) is a neurodevelopmental condition often associated with delayed motor skills. The Motor Assessment Battery for Children – Second Edition (MABC-2) is a standardized motor assessment for identifying motor delays pertaining to ASD. It evaluates fine and gross motor tasks across three domains: Manual Dexterity, Aiming & Catching, and Balance. These tasks are categorized into three age bands: 3-6, 7-10, and 11-16. Virtual Reality (VR) has emerged as a promising intervention in the ASD realm. This study aimed to investigate the potential of VR to assist children with ASD in performing the gross motor skills (i.e., ball skills and balance) in the MABC-2. The children who participated in the study were attendees of a local Autism Summer Camp. Our research focused on adapting motor tasks for ages 7-10 (i.e., Age Band 2) to VR, as most campers fell in this age range. Within the VR environment, children could observe avatar demonstrations and practice motor skills in a highly immersive setting. The VR environment featured avatars demonstrating ball skills and balancing tasks. Developed with the Unity game engine, 3D software Blender, C# scripting, and mixed reality toolkits, this environment was tested on the Meta Quest 2 Oculus. The children's gross motor skill performance was scored before and after VR interactions. The test standard scores were categorized through a traffic-light scoring system comprising red, amber, and green zones. A standard score ≤4 is classified in the red zone, indicating a significant movement difficulty; a standard score >4 and ≤7 is classified in the amber zone, indicating a risk for movement difficulty; and a standard score >7 is classified in the green zone, indicating no movement difficulty detected. Following the VR intervention, we observed a notable improvement in the balance score (p < 0.05). Furthermore, using the Random Forest machine learning model, we analyzed a combined dataset of MABC-2 scores from 250 children across all age bands from the Autism Summer Camp in previous years and the MABC-2 scores from the 18 children in the present study. Our analysis revealed that Balance was crucial in classifying children with ASD with motor delays, with an importance score of 0.195, nearly double that of Manual Dexterity and Aiming & Catching. When the model was exclusively applied to the Balance component score, it achieved an impressive accuracy rate of 91% in identifying children with ASD. In summary, our findings underscore the promise of VR in enhancing balance among children with ASD. The Random Forest analysis reaffirmed the significant role of balance in identifying children with ASD. Given its precision in detecting children with ASD based on their balance performance, we anticipate the potential of future machine learning advancements in this field. Our research validates the effectiveness of a VR-based approach and emphasizes the significance of collaborative research in providing valuable support to the underserved ASD population.
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Automatic assessment of cognitive and emotional states in virtual reality‐based flexibility training for four adolescents with autism
Abstract Tracking students’ learning states to provide tailored learner support is a critical element of an adaptive learning system. This study explores how an automatic assessment is capable of tracking learners’ cognitive and emotional states during virtual reality (VR)‐based representational‐flexibility training. This VR‐based training program aims to promote the flexibility of adolescents with autism spectrum disorder (ASD) in interpreting, selecting and creating multimodal representations during STEM‐related design problem solving. For the automatic assessment, we used both natural language processing (NLP) and machine‐learning techniques to develop a multi‐label classification model. We then trained the model with the data from a total of audio‐ and video‐recorded 66 training sessions of four adolescents with ASD. To validate the model, we implemented both k‐fold cross‐validations and the manual evaluations by expert reviewers. The study finding suggests the feasibility of implementing the NLP and machine‐learning driven automatic assessment to track and assess the cognitive and emotional states of individuals with ASD during VR‐based flexibility training. The study finding also denotes the importance and viability of providing adaptive supports to maintain learners’ cognitive and affective engagement in a highly interactive digital learning environment.
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- Award ID(s):
- 1837917
- PAR ID:
- 10456147
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- British Journal of Educational Technology
- Volume:
- 51
- Issue:
- 5
- ISSN:
- 0007-1013
- Format(s):
- Medium: X Size: p. 1766-1784
- Size(s):
- p. 1766-1784
- Sponsoring Org:
- National Science Foundation
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