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Zhanlan Wei

Publications and source records attributed to Zhanlan Wei.

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What do you say? A pilot study investigating student responses in Data Driven Classroom Interviews

Data that contextualizes student interactions with online learning systems can be challenging to obtain. This study looks at the rhetorical strategies of a novel method for conducting in-the-moment Data-Driven Classroom Interviews (DDCIs). By using Ordered Network Analysis (ONA) to reanalyze data from Wei et al.'s (2025) Epistemic Network Analysis, we better account for the sequences in which these rhetorical strategies emerge during the interview process. Specifically, we examine how five rhetorical strategies by interviewers relate to five possible rhetorical strategies used in student responses. As with the previous study, results demonstrate minor differences in how students with high and low situational interest respond. Namely, whereas students with high situational interest show moderately higher levels of enthusiasm, students with low situational interest are more likely to respond to interviewers with an explanation. However, overall this study confirms that there are few interviewer-driven differences in these interviews, and it documents that interviewers are following guidelines to rely upon open-ended questions

cs.HC

The Quick Red Fox gets the best Data Driven Classroom Interviews: A manual for an interview app and its associated methodology

Data Driven Classroom Interviews (DDCIs) are an interviewing technique that is facilitated by recent technological developments in the learning analytics community. DDCIs are short, targeted interviews that allow researchers to contextualize students' interactions with a digital learning environment (e.g., intelligent tutoring systems or educational games) while minimizing the amount of time that the researcher interrupts that learning experience, and focusing researcher time on the events they most want to focus on DDCIs are facilitated by a research tool called the Quick Red Fox (QRF)--an open-source server-client Android app that optimizes researcher time by directing interviewers to users that have just displayed an interesting behavior (previously defined by the research team). QRF integrates with existing student modeling technologies (e.g., behavior-sensing, affect-sensing, detection of self-regulated learning) to alert researchers to key moments in a learner's experience. This manual documents the tech while providing training on the processes involved in developing triggers and interview techniques; it also suggests methods of analyses.

cs.HC