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

TL;DR

This paper introduces the Quick Red Fox app and methodology for efficient Data Driven Classroom Interviews, enabling targeted, minimally disruptive research on student interactions in digital learning environments.

Contribution

It presents an open-source Android app and detailed manual for conducting targeted interviews based on real-time behavioral triggers in educational settings.

Findings

QRF effectively identifies key student behaviors for interviews

The methodology reduces researcher time and learning disruption

Supports integration with existing student modeling tools

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.