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Using Text Mining and Machine Learning to Predict Reasoning Activities from Think-Aloud Transcripts in Computer Assisted Learning
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Shan Li; Xiaoshan Huang; Tingting Wang; Juan Zheng; Susanne P. Lajoie – Journal of Computing in Higher Education, 2025
Coding think-aloud transcripts is time-consuming and labor-intensive. In this study, we examined the feasibility of predicting students' reasoning activities based on their think-aloud transcripts by leveraging the affordances of text mining and machine learning techniques. We collected the think-aloud data of 34 medical students as they diagnosed…
Descriptors: Information Retrieval, Artificial Intelligence, Prediction, Abstract Reasoning
Predicting the Quality of Robotics-Enhanced Lesson Plans Using Motivation, Academic Standing, and Collaboration Status
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Brian R. Belland; Anna Y. Zhang; Eunseo Lee; Emre Dinç; ChanMin Kim – Journal of Computing in Higher Education, 2025
Computer science can be included in Early Childhood Education (ECE) through the use of block-based coding and robots. But this requires adequate preparation of ECE teachers to work with coding and robots, and integrate such into high quality lesson plans. In this paper, we investigate predictors of lesson plan quality among preservice, early…
Descriptors: Robotics, Educational Quality, Lesson Plans, Early Childhood Education