从学习者-LLM教育对话中挖掘有效教学策略
Towards Mining Effective Pedagogical Strategies from Learner-LLM Educational Dialogues
AI总结：
针对LLM教育应用评估忽视互动过程的问题，本文提出通过对话数据收集、DA标注、模式挖掘与预测建模，从学习者-LLM对话中识别有效教学策略。
摘要：
对话在教育场景中发挥着关键作用，然而现有针对大语言模型（LLM）教育应用的评估方法主要关注技术性能或学习成果，往往忽视了对学习者与LLM互动过程的考察。为弥补这一差距，这篇AIED Doctoral Consortium论文介绍了一项正在进行的研究，采用对话分析方法从学习者-LLM对话中识别有效的教学策略。所提出的方法包括对话数据收集、对话行为（DA）标注、DA模式挖掘以及预测模型构建。文中概述了早期洞见，作为未来研究的初步步骤。该工作强调，需要通过关注对话动态和教学策略来评估基于LLM的教育应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
