Before revision: On university campuses, social interactions among students can explain their academic experiences. However, assessing these interactions with surveys fails to capture their dynamic nature. While these behaviors can be captured with client-based passive sensing, these techniques are limited in scalability. By contrast, infrastructure-based approaches can scale to a large cohort and infer social interactions based on collocation of students. This paper investigates one such approach by leveraging WiFi association logs archived by a managed campus network.
After revision: A comprehensive understanding of collocation can help understand performance outcomes. For university cohorts, this needs data that describes large groups over a long period. Harnessing user devices to infer this, while tempting, is challenged by privacy concerns, power consumption, and maintenance issues. Alternatively, embedding new sensors in the environment is limited by the expense of covering the entire campus. We investigate the feasibility of leveraging WiFi association logs archived by a managed campus network.