Leveraging WiFi network logs to infer student collocation and its relationship with academic performance

EPJ Data Science

Regular Article

Munmun De Choudhury¹  Mehrab Bin Morshed¹  Bahador Saket¹  Hyeokhyen Kwon¹  Kathy Tran¹  Joshua Philipose¹  Yulai Cui¹  Yexin Tian¹  Thomas Plötz¹  Sonia Sargolzaei¹  Vedant Das Swain¹  Devashru Patel¹  Gregory D. Abowd² 

[1] Georgia Institute of Technology, North Ave NW, Atlanta, USA;Georgia Institute of Technology, North Ave NW, Atlanta, USA;Northeastern University, 360 Huntington Ave, Boston, USA;

关键词: Wireless sensor networks;    Infrastructure sensing;    Collocation;    Social interactions;    Student behavior;    Academic performance;   

DOI  :  10.1140/epjds/s13688-023-00398-2

 received in 2021-05-14, accepted in 2023-06-09,  发布年份 2023

来源: Springer

【 摘 要 】

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’sr=0.24\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$r =0.24$\end{document}) 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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