冲浪还是睡觉？了解就寝时间对校园的影响
Surf or sleep? Understanding the influence of bedtime patterns on campus
摘要：
不良的睡眠习惯可能会导致严重的身心问题，由于学习负担以及同伴和社会影响，这是大学生常见的问题。了解其影响并识别睡眠习惯不良的学生在教育管理中非常重要。目前的研究大多是基于自我报告和问卷调查，样本量小，社会期望偏差，或所使用的方法不适合教育系统。在本文中，我们开发了一个通用的数据驱动的方法来识别学生的睡眠模式，根据他们的互联网访问模式存储在教育管理系统，并从多个方面探讨其影响。首先，我们设计了一个基于Possion的概率混合模型，根据就寝时间的分布对学生进行聚类，并识别出习惯熬夜的学生。其次，基于校园行为数据，从五个方面（包括八个维度）对大学生进行行为特征分析，并构建贝叶斯网络，探讨行为特征与睡眠习惯之间的关系。最后，我们测试了睡眠习惯的可预测性。本文不仅有助于从认知和行为的角度来理解学生的睡眠，而且还提出了一种新的方法，为各种教育机构提供了一个有效的框架来检测学生的睡眠模式。
英文摘要：
Poor sleep habits may cause serious problems of mind and body, and it is a commonly observed issue for college students due to study workload as well as peer and social influence. Understanding its impact and identifying students with poor sleep habits matters a lot in educational management. Most of the current research is either based on self-reports and questionnaires, suffering from a small sample size and social desirability bias, or the methods used are not suitable for the education system. In this paper, we develop a general data-driven method for identifying students' sleep patterns according to their Internet access pattern stored in the education management system and explore its influence from various aspects. First, we design a Possion-based probabilistic mixture model to cluster students according to the distribution of bedtime and identify students who are used to staying up late. Second, we profile students from five aspects (including eight dimensions) based on campus-behavior data and build Bayesian networks to explore the relationship between behavioral characteristics and sleeping habits. Finally, we test the predictability of sleeping habits. This paper not only contributes to the understanding of student sleep from a cognitive and behavioral perspective but also presents a new approach that provides an effective framework for various educational institutions to detect the sleeping patterns of students.
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。
