What are effective classroom dialogue patterns? A study on classroom dialogue patterns of pre-service and expert teachers based on large language models

Bibliographic Data

ID21632185
AuthorsJianxia Ling (0009-0009-9892-1708, Zhejiang Normal University), Jia Zhu (0000-0002-2871-4369, Zhejiang Normal University, corresponding author), Jie Sun (0000-0003-1213-6696, Zhejiang Normal University), Jiewen Sun (Zhejiang Normal University), Jianyang Shi (0009-0006-6580-2161, Zhejiang Normal University), Congcong Ke (0009-0008-2756-4398, Zhejiang Normal University, corresponding author), Zilong Li (0000-0002-2853-531X, Zhejiang Normal University), Mike Timms (EdTech Evaluation), Gwo-Jen Hwang (0000-0001-5155-276X, National Taichung University of Education)
Year2025
Pages1-25
Publication date2025-12-16
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInteractive Learning Environments (JOURNAL)
Journal identifiersISSN: 1049-4820 • E-ISSN: 1744-5191
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10494820.2025.2592125
OpenAlexW4417413019
LanguageEN
References cited46

Classrooms are pivotal arenas in which teachers transmit knowledge and learners cultivate understanding. Drawing on social-constructivist and dialogic-teaching theories, a substantial body of research positions classroom dialogue as the principal mechanism for the coconstruction and transformation of knowledge, as well as the critical nexus linking instruction and learning. Capitalising on authentic dialogue data from junior-secondary mathematics classrooms, we leveraged a fine-tuned large language model (LLM) to automate the encoding of classroom discourse. Descriptive statistics and lag-sequential analyses were then employed to juxtapose the interactional profiles of pre-service and expert teachers. Based on this, we derived an integrated dialogue pattern that amplifies the strengths of both cohorts and, through follow-up teacher interviews, obtained preliminary evidence of its capacity to enhance the professional growth of teachers in the pre-service and the quality of dialogue. Our findings indicate that the fine-tuned LLM achieves robust classification accuracy. Regardless of the level of expertise, prior knowledge activation dominates classroom exchanges; nevertheless, expert teachers orchestrate dialogue more strategically, eliciting higher-order thinking, and construct discourse trajectories that are demonstrably more intricate and adaptive

Computer-Assisted Instruction · Computer-mediated communication · Discourse analysis · Educational technology · Language acquisition · Language model · Teaching method · Technology integration · EFL/ESL Teaching and Learning · Intelligent Tutoring Systems and Adaptive Learning · Second Language Acquisition and Learning

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