Understanding Centrality: Investigating Student Outcomes within a Classroom Social Network

Abstract

Collaborative learning environments in undergraduate introductory physics courses, such as those promoted by Modeling Instruction (MI), influence both student performance and student social interactions. Because collaborative learning is inherently a social activity, we applied Network Analysis methods to examine student social interactions within the classroom using a survey administered periodically in class. We then calculated centrality, which is a family of measures that quantify how connected or "central" a particular student is within the classroom social network. In order to understand what centrality means in this context, we investigated the relationships among centrality, student demographics, and student outcomes in a large-scale MI classroom with 70 students and 6 instructors. We addressed two research questions: "Is centrality predicted by sex, ethnicity, incoming GPA, or Force-Motion Concept Evaluation (FMCE) pre-score?" and "Does centrality predict FMCE gain or final grade in course?" A series of linear regressions showed that centrality can be predicted by sex and incoming GPA, and is a predictor of FMCE gain.