Detector-driven classroom interviewing: focusing qualitative researcher time by selecting cases in situ

Ryan S. Baker, Stephen Hutt, Nigel Bosch, Jaclyn Ocumpaugh, Gautam Biswas, Luc Paquette, J. M.Alexandra Andres, Nidhi Nasiar, Anabil Munshi

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a new method for selecting cases for in situ, immediate interview research: detector-driven classroom interviewing (DDCI). Published work in educational data mining and learning analytics has yielded highly scalable measures that can detect key aspects of student interaction with computer-based learning in close to real-time. These measures detect a variety of constructs and make it possible to increase the precision and time-efficiency of this form of research. We review four examples that show how the method can be used to study why students become frustrated and how they respond, how anxiety influences how students respond to frustration, how metacognition interacts with affect, and how to improve the design of an adaptive learning system. Lastly, we compare DDCI to other mixed-methods approaches and outline opportunities for detector-driven classroom interviewing in research and practice, including research opportunities, design improvement opportunities, and pedagogical opportunities for teachers.

Original languageEnglish (US)
JournalEducational Technology Research and Development
StateAccepted/In press - 2023


  • Computer-based learning
  • Detector-driven classroom interviewing
  • Interview research
  • Mixed methods

ASJC Scopus subject areas

  • Education


Dive into the research topics of 'Detector-driven classroom interviewing: focusing qualitative researcher time by selecting cases in situ'. Together they form a unique fingerprint.

Cite this