Leveraging WiFi Network Logs to Infer Social Interactions: A Case Study of Academic Performance and Student Behavior

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. In their raw form, access point logs can approximate a student's location but with low spatio-temporal resolution. This paper first demonstrates that processing these logs can infer the collocation of 46 students in 34 lectures over 3 months, with a precision of 0.89 and a recall of 0.75. Next, we investigate how this WiFi-based coarse collocation reflects signals of social interaction. With 163 students in 54 project groups, we find that member performance shows a correlation of 0.75 with performance determined from collocation of groups through 14 weeks. Additionally, this paper presents preliminary insights for other campus-centric applications of automatically inferred social interactions. Finally, this paper discusses how repurposing archival WiFi logs can facilitate applications for other domains like mental wellbeing and physical health.