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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
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Giang Thi Linh Hoang; Neomy Storch – Iranian Journal of Language Teaching Research, 2024
Research has suggested that the type of feedback learners receive can impact on whether learners understand the feedback, the extent to which they engage with it, and whether they incorporate it in their revised drafts. However, to date, only a small number of studies have investigated learner engagement with corrective feedback provided by…
Descriptors: Case Studies, Language Processing, English (Second Language), Second Language Learning