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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
Is Cross-Discipline Better than Same-Discipline for Cognitive Engagement in Computer Supported Collaborative Learning? An Empirical Study Using Epistemic Network Analysis
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Linjing Wu; Shuang Yu; Qingtang Liu; Junmin Ye; Xinxin Zheng; Jianhu Wang – Journal of Computing in Higher Education, 2025
Interdisciplinary collaboration is widely used in research, industry, and education. Understanding the differences in cognitive processes between cross-discipline and same-discipline groups can improve instruction in collaborative learning. In this study, students volunteered to participate in cross-discipline or same-discipline collaborative…
Descriptors: Intellectual Disciplines, Cooperative Learning, Comparative Analysis, Interdisciplinary Approach