社会网络分析:通过选课、通勤时间和学生在校表现推断出的友谊

Lionel J. Khalil, M. Khair
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引用次数: 2

摘要

我们的社会网络分析(SNA)评估了与一群朋友一起上课的学生与单独上课的学生的表现。我们通过比较学生共享的课程数量与基于课程约束随机分配到同一教室的概率来评估交朋友的概率。共同学习课程的最少数量被用作确定学生是否属于朋友圈的标准。主要的发现是,部落学生的平均成绩比其他学生高出约0.5个点,而且他们退学和重修的课程也越来越少。考虑到没有朋友的学生,我们测量了通勤距离对GPA和辍学率的影响:GPA非常低和辍学率很高的学生大多是通勤时间明显较高的学生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social network analysis: friendship inferred by chosen courses, commuting time and student performance at university
Our social network analysis (SNA) evaluates the performance of students taking courses with a group of friends versus students used to take courses alone. We evaluate the probability to befriend by comparing the number of courses shared by students with the probability to be assigned in the same classroom randomly based on curriculum constraints. A minimum of courses taken in common is used as a criterion to identify students belonging to a tribe of friends. The main findings are that students in tribes over perform other students by about half point of GPA, and are dropping and repeating fewer courses. Considering student without friends, we measured the impact of the commuting distance on GPA and drop off rate: students with very low GPA and high drop off are mostly students with significantly higher commuting time.
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