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ERIC Number: EJ1447953
Record Type: Journal
Publication Date: 2024-Oct
Pages: 23
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1042-1629
EISSN: EISSN-1556-6501
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
Educational Technology Research and Development, v72 n5 p2841-2863 2024
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.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://bibliotheek.ehb.be:2123/
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
Authoring Institution: N/A
Grant or Contract Numbers: 1561567