Applied Network Science (May 2022)
Social network development in classrooms
Abstract
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.
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