# High-Quality Classroom Dialogue Automatic Analysis System
## 1 Introduction  
Classroom dialogue serves as the primary medium for instruction and is closely tied to students' academic performance. Research indicates that the quality of this dialogue plays a critical role in determining the overall effectiveness of lessons [1]. Classroom interactions offer students opportunities to articulate their reasoning, while also enabling teachers to gain deeper insights into students' cognitive processes and learning [2, 3, 4]. Consequently, in real classroom settings, it is essential to have objective observations that provide insights into the quality of specific teaching processes [5]. Classroom analysis is crucial for evaluating classroom interactions and delivering timely feedback, which enhances both teachingeffectiveness and student learning outcomes [6]. Research has shown that immediate feedback is especially effective in facilitating specific and corrective adjustments to teaching behaviors [7,8]. Traditionally, classroom analysis relied on professionals with expertise in teacher training who would observe one or two class periods and provide personalized, in-depth feedback on teaching practices [9]. While effective in offering targeted recommendations, this approach has inherent limitations: it is time-consuming, labor-intensive, and struggles to capture subtle teaching moves, such as the nuances of classroom dialogue and conversational dynamics, in real-time. Video recordings introduced a more accurate and rich opportunity for reflection and analysis. Yet, extracting actionable insights from recordings remains a considerable challenge due to the time and effort required for review. In the 1970s, systematic models for classroom interaction analysis began to emerge, with the Flanders [10] Interaction Analysis System (FIAS) being one of the most widely adopted frameworks for dialogue analysis. FIAS utilizes a time-slicing method, recording classroom interactions every 3 s. While groundbreaking at the time, its focus on surface-level teacher language behaviors limits its ability to capture deeper conversational dynamics. As technology progressed, researchers began exploring automated systems that could streamline the analysis process and reduce human effort. The Language Environment Analysis (LENA) system marked an early milestone in automated feedback for educational settings. Designed primarily for early childhood contexts, LENA provides key metrics such as Adult Word Count (AWC), Conversational Turns, and Child Vocalization Frequency by analyzing classroom recordings [11,12]. While its ability to automate quantitative assessments has proven valuable, LENA's focus on surface-level metrics limits its applicability for understanding deeper conversational dynamics or broader instructional contexts. Recent advancements in artificial intelligence (AI) have significantly expanded the potential of automated tools for classroom analysis. AI-based systems now offer scalable solutions that go beyond traditional methods, enabling efficient dialogue classification and deeper insights into teacher-student interactions. Studies utilizing low-cost recording equipment demonstrate the feasibility of capturing classroom dynamics for modeling [13]. Machine learning techniques, such as SVMs [14], Random Forests [15], and Logistic Regression [16], have been employed for this purpose. Additionally, while earlier approaches like in [PERSON] et al. [17] used single-level coding frameworks with limited contextual awareness, more recent work by [PERSON] et al. [5] showcases the advantages of state-of-the-art deep transfer learning models, such as BERT, in providing accurate and nuanced feedback across diverse classroom scenarios. However, these approaches are often limited to small-scale datasets and lack a systematic framework. To address these challenges, automated classroom analysis systems have emerged as a research hotspot, aiming to provide real-time insights into teaching and learning processes.