This paper introduces PeerMathDial, a new dataset of middle school students' collaborative math problem-solving dialogues, enabling research into student interaction and the potential of LLMs for educational applications.

In plain terms

Understanding how students learn together through "Collaborative Problem Solving (CPS)" is important in education, but there aren't many publicly available datasets of *student-to-student* conversations. Most existing data focuses on teacher-student interactions, making it hard to study how students interact and coordinate on their own. To address this, the researchers created a new dataset called PeerMathDial, which collects real conversations from middle school students working together on math problems in classrooms. This dataset contains 55 dialogues and over 6,000 conversational turns. To help analyze these conversations, they developed a "dialogue act taxonomy," which categorizes different types of speech acts (like asking a question or giving an explanation), assisted by Large Language Models (LLMs). They showed the dataset's usefulness by tracking how dialogues evolve, connecting student traits to their behaviors, and evaluating LLMs' potential for simulating student interactions in educational applications.