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…

Engineering students used ClassTranscribe, an accessible video player, in multiple engineering courses to view course videos and search for video content. The tool collected detailed timestamped student behavioral data from 1,894 students across 25 engineering courses that included what individual students searched for and when. A previous…

This research focuses on course-taking patterns of beginning community college students enrolled in one or more non-remedial science, technology, engineering, and mathematics (STEM) courses during their first year of college, and how these patterns are mapped against upward transfer in STEM fields of study. Drawing upon postsecondary transcript…

The use of computer-assisted qualitative data analysis software (CAQDAS) has not been without controversy, with a pervasive sense of skepticism and resistance towards its adoption by many scholars. Language-based researchers in particular, such as conversation and discourse analysts, have been slow to embrace such tools for their work. In this…