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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
Zachary M. Savelson; Kasia Muldner – Computer Science Education, 2024
Background and Context: Productive failure (PF) is a learning paradigm that flips the order of instruction: students work on a problem, then receive a lesson. PF increases learning, but less is known about student emotions and collaboration during PF, particularly in a computer science context. Objective: To provide insight on students' emotions…
Descriptors: Student Attitudes, Psychological Patterns, Fear, Failure
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