文献类型： 外文期刊
作者： Li, XiaoYong 1 ; Yu, QinYang 3 ; Zhang, Yong 1 ; Dai, JinWei 1 ; Yin, BaoCai 1 ;
作者机构： 1.Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
2.Beijing Univ Technol, Informat Technol Support Ctr, Beijing 100124, Peoples R China
3.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词： behavior data; Louvain algorithm; modularity optimization; students association; visualization
期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.679； 五年影响因子：2.736 ）
ISSN：
年卷期： 2020 年 10 卷 8 期
页码：
收录情况： SCI
摘要： Students spend most of their time living and studying on campus, especially in Asia, and they form various types of associations in addition to those with classmates and roommates. It is necessary for university authorities to master these types of associations, so as to provide appropriate services, such as psychological guidance and academic advice. With the rapid development of the "smart campus," many kinds of student behavior data are recorded, which provides an unprecedented opportunity to deeply analyze students' associations. In this paper, we propose a visual analytic method to construct students' association networks by computing the similarity of their behavior data. We discover student communities using the popular Louvain (or BGLL) algorithm, which can extract community structures based on modularity optimization. Using various visualization charts, we visualized associations among students so as to intuitively express them. We evaluated our method using the real behavior data of undergraduates in a university in Beijing. The experimental results indicate that this method is effective and intuitive for student association analysis.
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