Cultivating Computational Thinking and Social Play among Neurodiverse Preschoolers in Inclusive Classrooms

Research Background and Issues

• Identified Problems or Challenges:
  The needs of neurodivergent children in computational thinking (CT) education have been overlooked by mainstream research and technology development. Although CT has become a core skill in 21st-century education, most existing tools and practices are primarily designed for non-disabled children, with little consideration for children with neurodiverse developmental profiles. Furthermore, existing research predominantly focuses on children aged six and above, with limited studies on how younger neurodivergent children learn and engage in social play through technological mediums.

• Significance:
  Providing appropriate CT learning opportunities for neurodivergent children is crucial. This not only helps bridge educational gaps but also lays the foundation for their academic achievements and social skills development. With 95% of school-aged children with disabilities in the U.S. attending mainstream classrooms, designing inclusive teaching strategies and tools becomes increasingly important.

• Research Motivation and Related Work:
  Previous studies have shown that technology can enhance social play among neurodivergent children, but most focus on older children (aged six and above). This study is motivated by the need to explore how tools like coding robots can help neurodivergent preschool children aged 3-5 learn CT concepts, improve their social interaction and collaboration skills, and address gaps in existing research.

Solution

• Proposed Method or Solution:
  The authors deployed a programmable educational robot called KIBO in two preschool classrooms containing neurodivergent children (12 neurodiverse children and 17 neurotypical children). Based on teacher feedback and observations, they designed a series of age-appropriate teaching activities to help children learn CT concepts and engage in social interactions.

• Innovations:

  1. Focus on the under-researched age group of neurodivergent children aged 3-5.
  2. Adapt existing tools (rather than developing new CT tools) to meet the needs of neurodivergent children, offering dynamic and strategic teaching adjustments.
  3. Utilize the KIBO robot to explore how technology can guide children in building causal relationships, improving collaboration, developing problem-solving skills, and debugging.
  4. Employ interaction analysis methods to uncover the rich multimodal communication practices of neurodivergent children.

• Implementation Steps and Key Techniques:

  1. Preliminary Research: Conduct teacher interviews to understand current teaching practices and the needs of neurodivergent children.
  2. Design Teaching Plans: Adapt the existing CAL-KIBO-PreK curriculum to neurodivergent children, including visual aids, task decomposition, and the use of materials favored by children during play.
  3. Classroom Deployment: Implement an 8-week KIBO teaching program in two preschool classrooms, covering tasks such as recognizing robot components, designing simple codes, and understanding causal relationships.
  4. Data Collection and Analysis: Record children’s interactions using multiple cameras and apply multimodal interaction analysis to reveal their performance in CT learning and social play.

Research Outcomes

• Specific Achievements:

  1. Many neurodivergent children demonstrated an understanding of basic CT concepts such as sequencing, causality, and debugging, and were able to use the KIBO robot for simple coding tasks.
  2. Neurodivergent children engaged in and enjoyed collaborative social play, gradually overcoming conflicts arising from competition.
  3. Dynamic interventions by teachers and researchers (e.g., hands-on guidance or simplified choices) significantly enhanced children’s interest and participation in complex tasks.

• Advantages Compared to Existing Solutions:
  Unlike prior research focusing on adolescents or older children, this study delves into the unique learning characteristics and needs of younger children. Moreover, instead of developing new tools, the study adapts existing tools to accommodate a broader range of learning needs, demonstrating practicality and scalability.

• Experimental or Evaluation Results:

  1. Most children, especially when combined with prior teacher preparation, were able to learn to connect abstract coding concepts with concrete behaviors using the KIBO robot.
  2. For neurodivergent children reliant on specific materials for play (e.g., minimally verbal autistic children), dynamic support strategies proved particularly important.
  3. Children exhibited enhanced emotional feedback (e.g., cheering, celebrating) and cooperative behaviors, indicating that technology can serve as a powerful medium for social interaction.

• Limitations and Future Directions:

  1. Some children showed limited interest or engagement with KIBO (e.g., short attention spans), suggesting the need to further simplify assembly and coding tasks.
  2. Future studies could extend deployment duration and introduce long-term intervention designs for younger children, covering more complex coding concepts such as loops and modular design.
  3. The experiment relied heavily on strong teacher support in the classroom. Future robot designs could incorporate more interactive feedback mechanisms, such as visual or voice prompts, to reduce reliance on adult assistance.

Conclusion

By adapting existing coding robot technology for neurodivergent preschool children, the study demonstrates that these children can grasp basic CT concepts and enhance social interaction through programming practice. The research not only proposes educational adaptation strategies but also encourages the development of more inclusive technological tools to provide equitable learning opportunities for young neurodivergent children.