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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 virtual patients in an intelligent tutoring system. The think-aloud data were transcribed and segmented into 2,792 meaningful units. We used a text mining tool to analyze the linguistic features of think-aloud segments. Meanwhile, we manually coded the think-aloud segments using a medical reasoning coding scheme. We then trained eight types of supervised machine learning algorithms to predict reasoning activities based on the linguistic features of students’ think-aloud transcripts. We further investigated if the performance of prediction models differed between high and …