MECH: A Cost-Effective Multi-Task Cascade Framework for Classroom Opinion Evolution Recognition

摘要

Classroom discourse analysis is critical for tracing cognitive restructuring, yet existing research predominantly focuses on Dialogue Acts (DA), overlooking the deeper dimension of Opinion Evolution (OE). In this paper, we formally define the task of Classroom Opinion Evolution Recognition and introduce the Classroom Opinion Evolution Dataset (COED). Addressing the "Accuracy-Cost-Data" trilemma in real-world educational scenarios and the "overconfidence" failure mode of traditional confidence-based cascading systems on long-tail samples, we propose the Multi-task Enhanced Cascade Hybrid (MECH) framework. Grounded in the CODA (Continuous Opinions and Discrete Actions) theory, MECH conceptually translates the "Action-Opinion" dualism into a risk-aware routing mechanism. Instead of relying solely on prediction confidence, this mechanism utilizes high-risk argumentative DA signals derived from multi-task learning to construct a "semantic safety net" effectively routing implicit or ambiguous samples to a Large Language Model for reasoning. Experimental results demonstrate that MECH achieves a state-of-the-art accuracy of 78.55% while reducing API costs by 44.4%. Furthermore, the framework exhibits robustness in few-shot scenarios (using only 20% of data), offering a cost-effective and interpretable solution for large-scale educational dialogue analysis. Our code and data are available at https://github.com/ywh24284-code/MECH.