2020/05/22 by Vedant Das Swain, Swain, V. Das, Hyang Won Kwon +23 · 2 citations
Computer Science · Social Sciences · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Human-Computer Interaction (cs.HC) #J.4 #Wireless Networks and Protocols
paper · pdf · doi:10.48550/arxiv.2005.11228
openalex publication_date 2020/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.