Motivation helps drive students to success in general chemistry, and active learning environments with social interactions has consistently shown to improve motivation. However, analyzing student outcomes in an interactive environment is best done by considering students not as isolated units but as working together and influencing each other. Therefore, we used social network analysis with self-determination theory as a framework for understanding motivation and social comparison theory as a framework for understanding how students influence each other. When analyzing an undergraduate general chemistry course that has incorporated peer-led team learning using data from the Learning Climate Questionnaire and Intrinsic Motivation Inventory, a series of progressively sophisticated statistical models with data gathered from 270 students shows that perceived competence and relatedness predict student interest in the activities with their peer-led sessions. However, we also found evidence that students tend to become polarized in their interest toward peer-led team learning activities, which is one possible outcome of social comparisons with their peers. In addition to these findings, this project demonstrates how social network analysis can expand how chemistry education researchers consider relational data and the effects of non-independent data on statistical analysis.