## 4 Results and Discussion
### Expert Evaluation  
To evaluate the quality of the EduTalk dataset generated by the EduDCM framework, a rigorous expert review was conducted. This process involved three domain experts with extensive experience in educational dialogue analysis. The first expert is a professor in educational technology with over 15 years of experience in classroom interaction research, focusing on pedagogical strategies and dialogue analysis. The second expert is a senior researcher specializing in natural language processing for education, with a background in developing automated annotation tools for educational datasets. The third expert is a curriculum designer with a Ph.D. in education, who has contributed to the development of taxonomy-based frameworks for evaluating classroom interactions. A random sample comprising 30% of the dataset, totaling 1560 instances, was selected to ensure a comprehensive assessment. Each dialogue utterance was annotated according to the pedagogical categories outlined in the TalkMoves taxonomy, including categories such as Learning Community, Content Knowledge, and Rigorous Thinking, with subcategories such as Relating to another student, Pressing for accuracy, and Providing evidence or reasoning.  
The results presented in Table 1 demonstrate that the EduTalk dataset achieves high semantic accuracy across most categories, with an average positive annotation rate of 85%. Categories such as _Learning Community: Keeping everyone together_ and _Learning Community: Getting students to relate to another's ideas_ exhibit the highest accuracy rates (94% and 91%, respectively), highlighting the EduDCM framework's ability to capture well-structured teacher talk moves. Similarly, categories like _Learning Community: Restating_ and _Content Knowledge: Pressing for accuracy_ show strong performance, with positive annotation rates exceeding 85%. These findings suggest that the EduDCM framework is particularly adept at identifying and annotating dialogue types with clear pedagogical functions. However, the categories _Content Knowledge: Making a claim_ and _Rigorous Thinking: Providing evidence or reasoning_ demonstrated relatively lower accuracy rates, with positive annotation rates of 77% and 74%, respectively. This indicates challenges in accurately capturing student talk moves involving abstract reasoning or detailed evidence. These discrepancies could be attributed to the variability in how students articulate reasoning and evidence, often requiring contextual interpretation that may not always align with predefined annotation rules.  
Consistency in annotations was generally high for categories with well-defined boundaries, such as _Learning Community: Keeping everyone together_ and _Learning Community: Restating_. In contrast, categories like _Rigorous Thinking: Pressing for reasoning_ and _Rigorous Thinking: Providing evidence or reasoning_ exhibited more variability in expert agreement, highlighting the need for further refinement in the framework's handling of nuanced or context-dependent utterances. The representational coverage of categories across the dataset appears balanced, with no single category disproportionately dominating the annotations. This ensures that the EduTalk dataset provides a diverse and equitable foundation for training and evaluating dialogue classification models. Overall, the expert evaluation underscores the strength of the EduDCM framework in generating high-quality educational datasets while identifying opportunities for improvement in handling complex dialogue types involving reasoning and evidence.