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

November 17, 2025 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Jaclyn Ocumpaugh, Luc Paquette, Ryan S. Baker, Amanda Barany, Jeff Ginger, Nathan Casano, Andres F. Zambrano, Xiner Liu, Zhanlan Wei, Yiqui Zhou, Qianhui Liu, Stephen Hutt, Alexandra M. A. Andres, Nidhi Nasiar, Camille Giordano, Martin van Velsen, Micheal Mogessi arXiv ID 2511.13466 Category cs.HC: Human-Computer Interaction Cross-listed cs.AI, cs.ET Citations 1 Venue arXiv.org Last Checked 4 months ago
Abstract
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.
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