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Making has historically encompassed electronics and digital fabrication technologies. Increasingly, Making projects have started incorporating computational aspects. For example, a microprocessor board is attached to electronics and 3D-printed components so that they can be programmed to perform certain functions. Adding computation to Making enables a creation to become more dynamic and interactive. However, incorporating computation into Making also increases the difficulty of integrating Making into formal educational settings not only because students need to handle an additional set of skills and concepts, but also in terms of the additional overhead in preparation. This work deployed computationally-based Making activities in authentic 5th and 6th grade (ages 10 to 12) science classrooms with 442 students in a public school in the United States for one year. Our research adopts the Design-Based Research (DBR) methodology, and this paper presents insights from our retrospective process of reflection, especially in terms of the challenges of integrating computation-based Making in the school context. Our findings indicate four key challenges to incorporate computation-based Making projects in the formal public school context on a sustained basis; achieving balance between science, Making, and computational thinking, making connections between these distinct concepts apparent, organizational and operational issues, and the delivery of designed lesson plans. Despite these challenges, we believe the potential for a positive impact is large. We make two primary contributions; a comprehensive description of an at-scale computation-based Making project as well as learnings for future Making work in formal educational settings. 

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.