High-Quality Classroom Dialogue Automatic Analysis System

Classroom dialogue analysis is crucial as it significantly impacts both knowledge transmission and teacher–student interactions. Since the inception of classroom analysis research, traditional methods such as manual transcription and coding have served as foundational tools for understanding these interactions. While precise and insightful, these methods are inherently time-consuming, labor-intensive, and susceptible to human bias. Moreover, they struggle to handle the scale and complexity of modern classroom data effectively. In contrast, many contemporary deep learning approaches focus primarily on dialogue classification, but often lack the capability to provide deeper interpretative insights. To address these challenges, this study introduces an automated dialogue analysis system that combines scalability, efficiency, and objectivity in evaluating teaching quality. We first collected a large dataset of classroom recordings from primary and secondary schools in China and manually annotated the dialogues using multiple encoding frameworks. Based on these data, we developed an automated analysis system featuring a novel dialogue classification algorithm that incorporates speaker role information for more accurate insights. Additionally, we implemented innovative visualization techniques to automatically generate comprehensive classroom analysis reports, effectively bridging the gap between traditional manual methods and modern automated approaches. Experimental results demonstrated the system’s high accuracy in distinguishing various types of classroom dialogue. Large-scale analysis revealed key patterns in classroom dynamics, showcasing the strong potential of our system to enhance teaching evaluation and provide valuable insights for improving education practices.