Leveraging WiFi network logs to infer student collocation and its relationship with academic performance
Leveraging WiFi network logs to infer student collocation and its relationship with academic performance
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利用WiFi网络日志推断学生搭配及其与学业成绩的关系
DOI：
10.1140/epjds/s13688-023-00398-2
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发表时间：
2020
期刊：
影响因子：
3.6
通讯作者：
G. Abowd
中科院分区：
文献类型：
--
作者：
V. D. Swain;H. Kwon;S. Sargolzaei;B. Saket;M. B. Morshed;K. Tran;D. Patel;Yexin Tian;J. Philipose;Y. Cui;T. Plotz;M. Choudhury;G. Abowd
全面了解并置社交互动可以帮助校园和组织更好地支持他们的社区。大学可以通过研究学生过去的搭配情况来确定授课和设计课程的新方式。然而，这需要描述长期大型群体的数据。利用用户设备来推断搭配虽然很诱人，但受到隐私问题、电力消耗和维护问题的挑战。或者，在整个校园内嵌入新传感器的成本很高。取而代之的是，我们调查了一个易于访问的数据源，该数据源可以追溯描述一个学期内校园内的多个用户，即托管WiFi网络。尽管WiFi网络日志提供了粗略的搭配近似值，但我们证明利用这些数据可以表达搭配社交的有意义的结果。由于与同伴搭配的一个已知结果是提高成绩，我们检验了自动推断的搭配行为是否能够反映校园中项目小组成员的个体表现。我们对163名学生(54个项目组)进行了14周的研究。在描述了我们如何使用WiFi日志确定搭配后，我们提出了一项研究，以分析小组内的搭配如何与学生的最终分数相关。我们发现，搭配行为模型与绩效(优于同伴反馈模型或个人行为模型)之间存在显着相关(皮尔逊相关系数r=0.24$r=0.24$)。这些发现强调了用存档的WiFi网络日志来表征配置的社交互动是可行的和有价值的。最后，我们讨论了重新调整WiFi日志的用途以描述搭配的应用，以及隐私方面的考虑，以及未来工作的方向。
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.
如何进行黑客马拉松：简短、密集搭配的社会技术权衡
DOI：
10.1145/2818048.2819946
发表时间：
2016
期刊：
Computer Supported Cooperative Work
影响因子：
--
作者：
Trainer, Erik H.;Kalyanasundaram, Arun;Chaihirunkarn, Chalalai;Herbsleb, James D.
通讯作者：
Herbsleb, James D.
DOI：
10.1145/3338498.3358642
发表时间：
2019-11
期刊：
Proceedings of the 18th ACM Workshop on Privacy in the Electronic Society
影响因子：
--
作者：
Eugene Bagdasaryan;Griffin Berlstein;J. Waterman;Eleanor Birrell;Nate Foster;F. Schneider;D. Estrin
通讯作者：
Eugene Bagdasaryan;Griffin Berlstein;J. Waterman;Eleanor Birrell;Nate Foster;F. Schneider;D. Estrin
影响因子：
3.9
作者：
Yan Wang;Li Shao
通讯作者：
Yan Wang;Li Shao