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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
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
Yavuz Akbulut; Onur Dönmez; Beril Ceylan; Tayfun Firat – Journal of Computing in Higher Education, 2025
Providing pre-training on new material can simplify complex content for learners who may need guidance to understand basic facts and organize their efforts. However, the effect of pre-training on learning outcomes is controversial because it tends to vary by context. Our aim was to investigate the effectiveness of pre-training in reducing…
Descriptors: Training, Cognitive Processes, Difficulty Level, Academic Achievement

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