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.
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 for this purpose. While these provide coarse approximations of location, these are easily obtainable and depict multiple users on campus over a semester. We explore how these coarse collocations are related to individual performance. Specifically, we inspect the association between individual performance and the collocation behaviors of project group members. We study 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 find collocation behaviors show a significant correlation (Pearson's r = 0.24) with performance -- better than both peer feedback or individual behaviors like attendance. Finally, we discuss how repurposing WiFi logs can facilitate applications for domains like mental wellbeing and physical health.
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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
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
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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
for this purpose. While these provide coarse approximations of location, these are easily obtainable and depict multiple users on campus over a semester. We explore how these coarse collocations are related to individual performance. Specifically, we inspect the association between individual performance and the collocation behaviors of project group members. We study
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, we find that member performance shows a correlation of 0.75 with performance determined from collocation of groups through
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weeks. Additionally, this paper presents preliminary insights for other campus-centric applications of automatically inferred social interactions. Finally, this paper discusses how repurposing archival
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 find collocation behaviors show a significant correlation (Pearson's r = 0.24) with performance -- better than both peer feedback or individual behaviors like attendance. Finally, we discuss how repurposing
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