Applying Social Network Analysis in a course supported by a LMS: Report of a case study

Social interactions, when analyzed, can provide several information to help intuitive understanding of individuals and their interactions. Social Network Analysis (SNA) is a study field that investigates people's interaction patterns. These patterns, when mapped, enable a detailed and in-depth view of individuals' groups. In the context of distance learning, this paper presents an analysis that uses SNA metrics for helping the teacher of a Learning Management System (LMS) to understand the social structure of students' interactions and more quickly identify active and inactive students within the class. The study was conducted in a discipline offered in a higher education institution. Four SNA metrics were used in the results. These metrics include: density, degree centrality, the relative centrality and global centrality. Through these metrics it became possible to measure the relationships between students and find out, for example, which students needed more attention from the teacher. In addition, it was possible to identify students who interact more and students who are relationship bridges on the network, among other information. Thus, the results showed that SNA helped the teacher in the understanding of the interaction structure between students.