Using centrality measures to analyze social networks among engineering students

Social Network Analysis provides a powerful way to assess social structures within communities. Prior research found that centrality, a quantitative measure of how connected someone is in a network, and social interactions can predict student success as determined by their physics conceptual inventory performance and physics course grade. We aimed to create a visual network of an introductory physics course for engineering students and understand centrality within the network. Visual networks allow us to make inferences about communities which then guide quantitative analysis. Our visual analysis of the network allowed us to identify students central to the network (most connected) and students isolated from the network (not connected). Our data supports that students in affinity-based professional groups are most central in the classroom network. Additionally, we found that there were no isolated students in the network.