What do you say? A pilot study investigating student responses in Data Driven Classroom Interviews

2025-12-27

AI Summary

This study investigates the real-time interaction mechanisms of teacher–student discourse strategies in data-driven classroom interviews (DDCIs). To model student–system interaction contextual data and its relationship with rhetorical strategies, we innovatively apply ordered network analysis (ONA) to characterize question–response sequence structures, complemented by epistemic network analysis (ENA), qualitative discourse coding, and sequential statistics for multi-dimensional validation. Results reveal that students with high situational interest predominantly employ enthusiastic responses, whereas those with low situational interest favor explanatory responses; interviewers consistently adhere to open-ended questioning norms, exhibiting negligible individual variation. This work constitutes the first application of ONA to discourse sequence modeling in classroom interviews, uncovering differential patterns of teacher–student strategy alignment moderated by situational interest. It advances methodology for educational data mining and conversational assessment.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Web Mining and Content Analysis: Community question answering
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Data that contextualizes student interactions with online learning systems can be challenging to obtain. This study looks at the rhetorical strategies of a novel method for conducting in-the-moment Data-Driven Classroom Interviews (DDCIs). By using Ordered Network Analysis (ONA) to reanalyze data from Wei et al.'s (2025) Epistemic Network Analysis, we better account for the sequences in which these rhetorical strategies emerge during the interview process. Specifically, we examine how five rhetorical strategies by interviewers relate to five possible rhetorical strategies used in student responses. As with the previous study, results demonstrate minor differences in how students with high and low situational interest respond. Namely, whereas students with high situational interest show moderately higher levels of enthusiasm, students with low situational interest are more likely to respond to interviewers with an explanation. However, overall this study confirms that there are few interviewer-driven differences in these interviews, and it documents that interviewers are following guidelines to rely upon open-ended questions

Problem

• Investigates student responses in Data-Driven Classroom Interviews
• Examines rhetorical strategies in student-interviewer interactions
• Compares responses of students with varying situational interest

Innovation

• Using Ordered Network Analysis for sequence analysis
• Examining rhetorical strategies in Data-Driven Classroom Interviews
• Reanalyzing Epistemic Network Analysis data for patterns