# High-Quality Classroom Dialogue Automatic Analysis System
## 2 Related Work  
### Classroom Dialogue Analysis  
The academic community has long been interested in exploring classroom interactions through classification systems. One foundational paradigm is the three-step Initiation-Response-Feedback (IRF) pattern [18], where teachers initiate, students respond, and teachers provide feedback. A variant of IRF, the Initiation-Response-Evaluation (IRE) framework, emphasizes evaluation as a specific form of feedback [1]. These structures have guided many early studies in classroom dialogue. For instance, [PERSON] et al. [19] introduced a coding scheme categorizing 33 communicative acts into eight clusters, offering a comprehensive view of classroom interactions. Similarly, [PERSON] et al. [20] identified six critical themes for dialogic frameworks, further broadening the field's understanding. Frameworks like the Scheme for Educational Dialogue Analysis (SEDA) [19] and its variant, the Teacher Scheme for Educational Dialogue Analysis (T-SEDA) [21], provide scalable approaches to analyze sequences of dialogue in classrooms. Another influential approach, the Academically Productive Talk (APT) framework [22], aims to enhance discussions by promoting critical thinking. While these traditional models have laid the groundwork for understanding classroom dialogue, they often rely on qualitative analyses of short dialogue episodes selected from transcripts, typically covering a limited number of lessons [23]. This method, although insightful, struggles to capture the progressive and dynamic patterns of classroom interactions at scale. Moreover, traditional frameworks tend to focus on individual utterances without adequately considering speaker identity or the broader context of the dialogue, limiting their ability to offer actionable insights into teacher-student dynamics. Recent advancements in artificial intelligence (AI) have addressed many of these  
Figure 1: Illustration of the system construction process. This process includes the formulation of standards, data collection, model training, output generation, and subsequent revisions of the standards.  
shortcomings, offering scalable and nuanced methods for classroom dialogue analysis. For instance, [PERSON] et al. [15] developed a model to distinguish between classroom activities like lecturing, discussion, and group work in mathematics lessons. Expanding this work, [PERSON] et al. [14] used TF-IDF and Word2 vec features with chi-square feature selection to classify teacher dialogues into categories such as lecture, instruction, and question. The TalkMoves dataset [24], a standardized compilation of K-12 mathematics class dialogues, has further advanced the field by integrating pre-trained language models (PLMs) for dialogue classification. These AI-driven methods surpass traditional approaches by efficiently processing large-scale data, incorporating contextual information, and emphasizing speaker identity. Additionally, recent studies [25] highlight the ability of AI systems to analyze entire sequences of dialogue, providing a richer understanding of classroom interactions. However, despite these advancements, there is still a need for further research into combining AI's scalability with the interpretability of traditional frameworks to fully capture the complexities of classroom communication.