PeerMathDial: A Middle School Dialogue Dataset for Student Collaborative Math Problem Solving

2026-06-19

AI Summary

Existing research lacks authentic dialogue data capturing small-group student collaboration on mathematical problem solving, hindering deeper understanding of collaborative interaction mechanisms. This work introduces PeerMathDial, the first dataset of student collaborative problem-solving dialogues drawn from real middle school mathematics classrooms, comprising 55 conversations (6,406 utterances) from 27 students. Leveraging large language models, the authors develop a corpus-driven dialogue act taxonomy to annotate the dataset. The resulting resource enables fine-grained modeling of collaborative processes and demonstrates its utility in diverse applications, including evaluating teacher interventions, analyzing associations between student traits and dialogue behaviors, and simulating educational interactions with large language models. PeerMathDial thus establishes a new paradigm and foundational resource for research on collaborative learning.

Abstract

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.

Problem

• Collaborative Problem Solving
• educational dialogue dataset
• peer interaction
• middle school math
• student-student dialogue

Innovation

• collaborative problem solving
• peer dialogue dataset
• dialogue act taxonomy
• LLM-assisted annotation
• student-student interaction