从师生对话中挖掘有效教学策略，提升LLM教育应用的互动质量。
Towards Mining Effective Pedagogical Strategies from Learner-LLM Educational Dialogues
- 通过对话分析识别教学互动中的关键行为模式
- 构建预测模型以评估不同策略的教学效果
- 适合教育AI研究者与交互设计人员参考
对话在教育场景中至关重要，但现有大型语言模型（LLMs）教育应用的评估方法多聚焦技术性能或学习成果，忽视了学习者与LLM之间的互动过程。为弥合这一差距，本文提出一项正在进行的研究，采用对话分析方法，从学习者-LLM教育对话中挖掘有效的教学策略。该方法包括对话数据收集、对话行为（DA）标注、DA模式挖掘及预测模型构建。初步发现为未来研究提供了基础方向。研究强调应关注对话动态与教学策略，以更全面地评估基于LLM的教育应用。
原文摘要 · Abstract (English)
Dialogue plays a crucial role in educational settings, yet existing evaluation methods for educational applications of large language models (LLMs) primarily focus on technical performance or learning outcomes, often neglecting attention to learner-LLM interactions. To narrow this gap, this AIED Doctoral Consortium paper presents an ongoing study employing a dialogue analysis approach to identify effective pedagogical strategies from learner-LLM dialogues. The proposed approach involves dialogue data collection, dialogue act (DA) annotation, DA pattern mining, and predictive model building. Early insights are outlined as an initial step toward future research. The work underscores the need to evaluate LLM-based educational applications by focusing on dialogue dynamics and pedagogical strategies.
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