论文标题
利用WiFi网络日志推断学生搭配及其与学业表现的关系
Leveraging WiFi Network Logs to Infer Student Collocation and its Relationship with Academic Performance
论文作者
论文摘要
对搭配的全面了解可以帮助理解绩效成果。对于大学队列，这需要长期描述大型群体的数据。利用用户设备来推断这一点，虽然诱人，但受到隐私问题，功耗和维护问题的挑战。另外，将新传感器嵌入环境中受到覆盖整个校园的费用的限制。我们研究为此目的利用WiFi关联日志的可行性。尽管这些提供了粗略的位置近似值，但它们很容易获得，并在一个学期中描绘了校园中的多个用户。我们探讨了这些粗搭配与个人表现如何相关的。具体而言，我们检查个人绩效与项目组成员的搭配行为之间的关联。我们在14周内研究了163名学生（54个项目小组）。在描述了如何确定与WiFi日志搭配的方式之后，我们提出了一项研究，以分析组中的搭配如何与学生的最终分数相关。我们发现搭配行为表现出显着的相关性（Pearson的R = 0.24）与性能相关 - 比同伴反馈或出勤率（例如出勤率）都更好。最后，我们讨论重新利用WiFi日志如何促进精神健康和身体健康等领域的应用。
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.
