Analyzing Students Collaborative Problem-Solving Behaviors in Synergistic STEM+C Learning

Abstract

This study introduces a methodology to investigate students' collaborative behaviors as they work in pairs to build computational models of scientific processes. We expand the Self-Regulated Learning (SRL) framework - specifically, Planning, Enacting, and Reflection - proposed in the literature, applying it to examine students' collaborative problem-solving (CPS) behaviors in a computational modeling task. We analyze these behaviors by employing a Markov Chain (MC) modeling approach that scrutinizes students' model construction and model debugging behaviors during CPS. This involves interpreting their actions in the system collected through computer logs and analyzing their conversations using a Large Language Model (LLM) as they progress through their modeling task in segments. Our analytical framework assesses the behaviors of high- and low-performing students by evaluating their proficiency in completing the specified computational model for a kinematics problem. We employ a mixed-methods approach, combining Markov Chain analysis of student problem-solving transitions with qualitative interpretations of their conversation segments. The results highlight distinct differences in behaviors between high- and low-performing groups, suggesting potential for developing adaptive scaffolds in future work to enhance support for students in collaborative problem-solving.