This study uses positional analysis to describe the student interaction networks in four research-based introductory physics curricula. Positional analysis is a technique for simplifying the structure of a network into blocks of actors whose connections are more similar to each other than to the rest of the network. This method describes social structure in a way that is comparable between networks of different sizes and densities and can show large-scale patterns such as hierarchy among positions. We detail one positional analysis method and apply it to class sections of Peer Instruction, SCALE-UP, ISLE, and Minnesota Model context-rich problems. At the level of detail shown in the blockmodels, most of the curricula are more alike than different, showing a late-term tendency to form coherent subgroups that communicate actively among themselves but have few interposition links. Initial position assignments tend to change from beginning to end of the term, but in cases where the initial assignment is stable, those students appear to become more connected to each other and to the largest network component. These trends in position structure and stability may be network signatures of active learning classes, but wider data collection is needed to investigate.
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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