## 5 Conclusions  
This study presents EduDCM, a novel framework designed to address the critical challenge of generating high-quality educational dialogue classification datasets in low-resource contexts. By integrating distant supervision with the advanced capabilities of LLMs, EduDCM offers a scalable and efficient solution for automating dataset annotation. The framework leverages context-aware label generation, heuristic alignment, and optimized semantic disambiguation to enhance annotation quality while mitigating noise associated with traditional distant supervision methods. The experimental results confirm the effectiveness of EduDCM across several dimensions. The EduTalk dataset, constructed using this framework, shows performance on par with manually annotated datasets, as demonstrated by the robust results achieved by classification models trained on EduTalk. Furthermore, the comparison with Template-Based Generation and Few-Shot GPT methods underscores EduDCM's superior annotation accuracy, category coverage, and consistency. These findings validate EduDCM's ability to address the challenges of low-resource educational NLP tasks effectively. The ablation study further highlights the importance of the LLM-Driven Context-Aware Label Generation component, which significantly improves dataset quality by providing nuanced and context-sensitive annotations. The experiments with various LLMs, including GPT-4.0, also demonstrate the framework's potential for further enhancement through state-of-the-art models, confirming its scalability and adaptability. This paper makes the following key contributions: (1) The innovative proposition of the EduDCM framework, which integrates LLMs and distant supervision for automatic dataset construction, addressing noise and sparsity issues commonly encountered in low-resource educational contexts through dynamic label generation and heuristic alignment. (2) The development of the EduTalk dataset, which encompasses diverse classroom dialogues annotated with pedagogical categories, serving as a valuable resource for advancing educational dialogue classification. (3) The advancement of low-resource educational NLP, as EduDCM demonstrates significant improvements in dataset quality and classification model performance, making it well suited for a range of low-resource educational NLP tasks. Looking forward, future research will focus on expanding the applicability of EduDCM to multilingual and multimodal educational datasets, addressing a broader range of pedagogical interactions. Additionally, refining the alignment and disambiguation processes through advanced machine learning techniques will be a priority to further improve annotation quality and adaptability. Overall, EduDCM establishes a robust foundation for advancing educational dialogue analysis, paving the way for more effective teaching strategies and classroom interaction studies. Conceptualization, C.Q., X.G. and L.Z. ; data curation, H.X. and Y.W. ; funding acquisition, L.Z. ; methodology, C.Q., X.G. and L.Z. ; project administration, X.G. and L.Z. ; resources, H.X. ; software, P.C. and Y.W. ; supervision, X.G. and L.Z. ; visualization, H.X. and Y.W. ; writing--original draft, C.Q. ; writing--review and editing, C.Q., H.X. and L.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Opening Foundation of the State Key Laboratory of Cognitive Intelligence (iED2023-008) and the National Natural Science Foundation of China (62477013). Not applicable. The datasets can be found in the following repositories: TalkMoves [[https://github.com/SummerLab/TalkMoves](https://github.com/SummerLab/TalkMoves)]([https://github.com/SummerLab/TalkMoves](https://github.com/SummerLab/TalkMoves)) (accessed on 15 October 2024); the EduTalk dataset used in this study is not publicly available due to privacy concerns. Access to the dataset can be requested from the first author. The authors declare no conflicts of interest.