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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Education & Learning — Educational Technology (EdTech), K-12 Education & Child Development, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

PeerMathDial is introduced as the first dataset specifically designed for peer Collaborative Problem Solving (CPS) dialogues, collected from authentic middle school math classrooms. This dataset addresses a significant gap in educational research, which previously lacked resources for studying student-student interactions. It comprises 55 dialogues from 27 students, totaling 6,406 turns. To enhance discourse analysis, PeerMathDial also includes a corpus-grounded dialogue act taxonomy, developed with assistance from Large Language Models. The dataset demonstrates practical applications across three key use cases: tracking dialogue evolution and the impact of teacher interventions, aligning student survey data with dialogue actions to reveal connections between student traits like confidence and leadership with actual behaviors, and evaluating LLMs for dialogue act prediction, suggesting their potential for student simulation in educational contexts. The dataset and its source code will be publicly released.

Key takeaway

For research scientists and NLP engineers developing educational AI, PeerMathDial provides an invaluable resource for understanding authentic student-student collaborative problem-solving. You should utilize this dataset and its LLM-assisted dialogue act taxonomy to train and evaluate models for discourse analysis, student behavior prediction, or even student simulation. This enables more nuanced development of AI tutors or collaborative learning tools, moving beyond teacher-student interaction models.

Key insights

PeerMathDial offers the first dataset and LLM-assisted taxonomy for analyzing authentic middle school student-student collaborative math dialogues.

Principles

• Peer interaction is crucial for Collaborative Problem Solving.
• Student-student dialogue data is vital for educational research.
• LLMs can aid in developing dialogue act taxonomies.

Method

Collect authentic middle school math dialogues, then build a corpus-grounded dialogue act taxonomy assisted by LLMs to facilitate discourse analysis.

In practice

• Track dialogue evolution and teacher intervention impact.
• Connect student traits to actual dialogue behaviors.
• Evaluate LLMs for student simulation capabilities.

Topics

• PeerMathDial
• Collaborative Problem Solving
• Dialogue Datasets
• Math Education
• Dialogue Act Taxonomy
• LLM Applications

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.