PeerMathDial：面向中学生协作数学问题解决的对话数据集
PeerMathDial: A Middle School Dialogue Dataset for Student Collaborative Math Problem Solving
AI总结：
为解决现有教育对话数据集缺乏同伴互动的问题，构建了首个来自真实中学数学课堂的同伴协作问题解决对话数据集PeerMathDial，包含55段对话、6406轮次，并基于LLM构建对话行为分类体系，用于分析对话演化、学生特质与行为关联及LLM模拟能力。
摘要：
协作问题解决（CPS）是教育中的核心技能，其中同伴互动过程至关重要。然而，现有的教育对话数据集大多聚焦于课堂教学或辅导（即教师/导师-学生互动），而围绕小组内学生-学生互动的数据集有限。这导致研究缺乏资源来探讨学生在真实教育环境中如何互动、协调和共同解决问题。为此，我们引入了PeerMathDial，这是首个从真实中学数学课堂收集的同伴CPS对话数据集。它包含来自27名学生的55段对话，共计6406轮次。为促进CPS话语分析研究，我们进一步构建了一个基于语料库、借助LLM辅助的话语行为分类体系。利用该数据集和话语行为分类体系，我们展示了PeerMathDial在三个用例中的实际应用。首先，我们追踪对话随时间的变化，并衡量教师干预的影响。其次，我们将对话行为与学生调查问卷对齐，揭示学生特质（如自信、领导力）与实际行为之间的联系。第三，通过评估LLM在话语行为预测上的表现，我们初步探索了LLM在教育应用中模拟学生的潜力。我们的数据集和源代码将向社区发布。
英文摘要：
Collaborative Problem Solving (CPS) is a core skill in education, where the process of peer interaction is highly important. However, existing educational dialogue datasets mostly focus on classroom instruction or tutoring (i.e., teacher/tutor-student interaction), yet datasets centering small-group, student-student interaction are limited. This thus leaves research with limited resources for studying how students interact, coordinate, and solve problems together in real educational settings. To address this, we introduce PeerMathDial, the first dataset of peer CPS dialogues collected from authentic middle school math classrooms. It contains 55 dialogues from 27 students, totaling 6,406 turns. To facilitate research on CPS discourse analysis, we further build a corpus-grounded dialogue act taxonomy assisted by LLMs. Using the dataset and the dialogue act taxonomy, we demonstrate the practical applications of PeerMathDial across three use cases. First, we track how dialogues evolve over time and measure the impact of teacher interventions. Second, we align dialogue actions with student surveys to reveal the connection between students' traits (e.g., confidence, leadership) and their actual behaviors. Third, by evaluating LLMs on dialogue act prediction, we glimpse at the potential of LLMs for student simulation in educational applications. Our dataset and source code will be released to the community.
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。
