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Daiki Matsumoto; Atsushi Shimada; Yuta Taniguchi – International Association for Development of the Information Society, 2025
Predicting learner actions and intentions is crucial for providing personalized real-time support and early intervention in programming education. This approach enables proactive, context-aware assistance that is difficult for human instructors to deliver by foreseeing signs of potential struggles and misconceptions, or by inferring a learner's…
Descriptors: Prediction, Programming, Coding, Models
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Tianxiao Yang; Jongpil Cheon – Computer Science Education, 2025
Background and context: There were few studies indicating if students' computational thinking (CT) self-efficacy and their CT performance were aligned with each other. Objectives: The study was to investigate if there was a discrepancy between students' CT self-efficacy and their CT performance. Method: Involving 104 non-CS undergraduate students…
Descriptors: Self Efficacy, Computer Science Education, Prediction, Teacher Expectations of Students