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Abstract Group work is often a critical component of how we ask students to interact while learning in active and interactive environments. A common-sense extension of this feature is the inclusion of group assessments. Moreover, one of the key scientific practices is the development of collaborative working relationships. As instructors, we should be cognizant of our classes’ development in the social crucible of our classroom, along with their development of cognitive and/or problem solving skills. We analyze group exam network data from a two-class introductory physics sequence. In each class, on each of four exams, students took an individual version of the exam and then reworked the exam with classmates. Students recorded their collaborators, and these reports are used to build directed networks. We compare global network measures and node centrality distributions between exams in each semester and contrast these trends between semesters. The networks are partitioned using positional analysis, which blocks nodes by similarities in linking behavior, and by edge betweenness community detection, which groups densely connected nodes. By calculating the block structure for each exam and mapping over time, it is possible to see a stabilizing social structure in the two-class sequence. Comparing global and node-level measures suggests that the period from the first to second exam disrupts network structure, even when the blocks are relatively stable. 

Active learning is broadly shown to improve student outcomes as compared with traditional lecture, but more work must be done to distinguish outcomes between different types of active learning. We collected self-reported student social network data at early and late-semester times in a Peer Instruction classroom. The subsequent networks are modeled using exponential random graph models (ERGMs), which are a family of statistical models used with relational data, like social networks. We discuss preliminary findings using this method for a Peer Instruction class. The best-fit ERGM predicts long "chains" of student edges, such as might arise from students talking along rows in the lecture hall. ERGMs appear to be a promising method for quantifying network topology in active learning classrooms.