用大模型预测教学对话中教师策略与学生表现，发现现有模型效果有限。
Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues
- 用Llama 3和GPT-4o分析数学辅导对话中的教师行为模式
- 教师策略与学生结果高度相关，但模型预测未来策略能力不足
- 适合教育AI研究者关注模型对教学行为的理解能力
近年来，随着在线学习兴起和大语言模型（LLMs）赋能AI助教，教学对话受到广泛关注。研究表明，教师采用的教学策略显著影响学生表现，因此亟需能够预测教师行为及其对学生影响的方法。然而，当前针对对话中教师策略预测的研究仍较少。本文探究现代大模型（特别是Llama 3和GPT-4o）在两个数学辅导对话数据集上预测未来教师策略及学生结果的能力。结果显示，即使最先进的模型也难以准确预测教师后续策略，而教师策略本身却能有效预示学生结果，凸显该任务仍需更强大的方法支持。
原文摘要 · Abstract (English)
Tutoring dialogues have gained significant attention in recent years, given the prominence of online learning and the emerging tutoring abilities of artificial intelligence (AI) agents powered by large language models (LLMs). Recent studies have shown that the strategies used by tutors can have significant effects on student outcomes, necessitating methods to predict how tutors will behave and how their actions impact students. However, few works have studied predicting tutor strategy in dialogues. Therefore, in this work we investigate the ability of modern LLMs, particularly Llama 3 and GPT-4o, to predict both future tutor moves and student outcomes in dialogues, using two math tutoring dialogue datasets. We find that even state-of-the-art LLMs struggle to predict future tutor strategy while tutor strategy is highly indicative of student outcomes, outlining a need for more powerful methods to approach this task.
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