利用WiFi网络模型推断学生搭配及其与学业成绩的关系
Leveraging WiFi Network Logs to Infer Student Collocation and its Relationship with Academic Performance
摘要：
对搭配的全面理解有助于理解绩效结果。对于大学队列，这需要描述长期大群体的数据。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。或者，在环境中嵌入新的传感器受到覆盖整个校园的费用的限制。我们调查利用WiFi关联日志用于此目的的可行性。虽然这些提供了粗略的近似位置，但这些很容易获得，并描述了一个学期内校园内的多个用户。我们探讨这些粗糙的搭配是如何与个人的表现。具体而言，我们检查个人绩效和项目组成员的搭配行为之间的关联。我们研究了163名学生（在54个项目组）超过14周。在描述了我们如何确定搭配的WiFi日志，我们提出了一项研究，分析如何搭配组内涉及到学生的最终成绩。我们发现，搭配行为与绩效表现出显著的相关性（Pearson's r = 0.24），比同伴反馈或个人行为（如出勤）更好。最后，我们将讨论如何重新利用WiFi日志可以促进心理健康和身体健康等领域的应用程序。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
