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

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.