Characterizing Faculty Online Learning Community Interactions Using Social Network Analysis

Abstract

Expanding on other work in the Physics Education Community, we apply Social Network Analysis to a Faculty Online Learning Community focused on facilitating the integration of computation into physics courses. The Partnership for Integration of Computation into Undergraduate Physics (PICUP) uses Slack as a mechanism for continued communication beyond face-to-face meetings. Through our analysis, we use networks to represent the Slack channels, identify lurkers, and use metrics to quantify pariticipation. Additionally, we use randomization techniques to understand how similar our metric values are to networks of similar size and distribution. We highlight the need for using the appropriate randomization model and assumption checking. We characterize participation amongst users in the network and provide potential reasonings for their actions.