The Quick Red Fox gets the best Data Driven Classroom Interviews: A manual for an interview app and its associated methodology
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909908645969920 |
|---|---|
| author | Ocumpaugh, Jaclyn Paquette, Luc Baker, Ryan S. Barany, Amanda Ginger, Jeff Casano, Nathan Zambrano, Andres F. Liu, Xiner Wei, Zhanlan Zhou, Yiqui Liu, Qianhui Hutt, Stephen Andres, Alexandra M. A. Nasiar, Nidhi Giordano, Camille van Velsen, Martin Mogessi, Micheal |
| author_facet | Ocumpaugh, Jaclyn Paquette, Luc Baker, Ryan S. Barany, Amanda Ginger, Jeff Casano, Nathan Zambrano, Andres F. Liu, Xiner Wei, Zhanlan Zhou, Yiqui Liu, Qianhui Hutt, Stephen Andres, Alexandra M. A. Nasiar, Nidhi Giordano, Camille van Velsen, Martin Mogessi, Micheal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13466 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Quick Red Fox gets the best Data Driven Classroom Interviews: A manual for an interview app and its associated methodology Ocumpaugh, Jaclyn Paquette, Luc Baker, Ryan S. Barany, Amanda Ginger, Jeff Casano, Nathan Zambrano, Andres F. Liu, Xiner Wei, Zhanlan Zhou, Yiqui Liu, Qianhui Hutt, Stephen Andres, Alexandra M. A. Nasiar, Nidhi Giordano, Camille van Velsen, Martin Mogessi, Micheal Human-Computer Interaction Artificial Intelligence Emerging Technologies 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. |
| title | The Quick Red Fox gets the best Data Driven Classroom Interviews: A manual for an interview app and its associated methodology |
| topic | Human-Computer Interaction Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2511.13466 |