EPJ Data Science (Jul 2023)
Leveraging WiFi network logs to infer student collocation and its relationship with academic performance
Abstract
Abstract A comprehensive understanding of collocated social interactions can help campuses and organizations better support their community. Universities could determine new ways to conduct classes and design programs by studying how students have collocated in the past. However, this needs data that describe large groups over a long period. Harnessing user devices to infer collocation, while tempting, is challenged by privacy concerns, power consumption, and maintenance issues. Alternatively, embedding new sensors across the entire campus is expensive. Instead, we investigate an easily accessible data source that can retroactively depict multiple users on campus over a semester, a managed WiFi network. Despite the coarse approximations of collocation provided by WiFi network logs, we demonstrate that leveraging such data can express meaningful outcomes of collocated social interaction. Since a known outcome of collocating with peers is improved performance, we inspected if automatically–inferred collocation behaviors can indicate the individual performance of project group members on a campus. We studied 163 students (in 54 project groups) over 14 weeks. After describing how we determine collocation with the WiFi logs, we present a study to analyze how collocation within groups relates to a student’s final score. We found that modeling collocation behaviors showed a significant correlation (Pearson’s r = 0.24 $r =0.24$ ) with performance (better than models of peer feedback or individual behaviors). These findings emphasize that it is feasible and valuable to characterize collocated social interactions with archived WiFi network logs. We conclude the paper with a discussion of applications for repurposing WiFi logs to describe collocation, along with privacy considerations, and directions for future work.
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