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Meghan M. Parkinson; Seppe Hermans; David Gijbels; Daniel L. Dinsmore – Computer Science Education, 2024
Background and Context: More data are needed about how young learners identify and fix errors while programming in pairs. Objective: The study will identify discernible patterns in the intersection between debugging processes and the type of regulation used during debugging while children engage in coding to drive further theory and model…
Descriptors: Computer Science Education, Troubleshooting, Cooperative Learning, Coding
ChanMin Kim; Brian R. Belland; Lucas Vasconcelos; Roger B. Hill – SAGE Open, 2024
In this qualitative study, preservice early childhood education teachers created block-based code to control robots and used the robots in field experience at local preschools. The study is grounded in a conceptual framework that weaves together playful programing and resilience, interlocking concepts that can explain sustained engagement during…
Descriptors: Play, Resilience (Psychology), Preservice Teachers, Computer Science
Zhanxia Yang; Marina Bers – Computer Science Education, 2024
Background and Context: Historically, women have been underrepresented in computer science. To address this gender gap, researchers advocate for high-quality computer science programs for early childhood. Objectives: This study examines gender differences in coding performance before and after implementing a 24-lesson visual programming curriculum…
Descriptors: Gender Differences, Grade 1, Elementary School Students, Programming