Visual Analytic Method for Students’ Association via Modularity Optimization
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/8/2813 |
| _version_ | 1797570278627213312 |
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| author | XiaoYong Li QinYang Yu Yong Zhang JinWei Dai BaoCai Yin |
| author_facet | XiaoYong Li QinYang Yu Yong Zhang JinWei Dai BaoCai Yin |
| author_sort | XiaoYong Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:22:40Z |
| format | Article |
| id | doaj.art-d5def5c2699b4a31a7ee6c6e65854e71 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:22:40Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d5def5c2699b4a31a7ee6c6e65854e712023-11-19T22:03:18ZengMDPI AGApplied Sciences2076-34172020-04-01108281310.3390/app10082813Visual Analytic Method for Students’ Association via Modularity OptimizationXiaoYong Li0QinYang Yu1Yong Zhang2JinWei Dai3BaoCai Yin4Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing 100124, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaBeijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing 100124, ChinaBeijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing 100124, ChinaBeijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing 100124, ChinaStudents 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.https://www.mdpi.com/2076-3417/10/8/2813behavior dataLouvain algorithmmodularity optimizationstudents associationvisualization |
| spellingShingle | XiaoYong Li QinYang Yu Yong Zhang JinWei Dai BaoCai Yin Visual Analytic Method for Students’ Association via Modularity Optimization Applied Sciences behavior data Louvain algorithm modularity optimization students association visualization |
| title | Visual Analytic Method for Students’ Association via Modularity Optimization |
| title_full | Visual Analytic Method for Students’ Association via Modularity Optimization |
| title_fullStr | Visual Analytic Method for Students’ Association via Modularity Optimization |
| title_full_unstemmed | Visual Analytic Method for Students’ Association via Modularity Optimization |
| title_short | Visual Analytic Method for Students’ Association via Modularity Optimization |
| title_sort | visual analytic method for students association via modularity optimization |
| topic | behavior data Louvain algorithm modularity optimization students association visualization |
| url | https://www.mdpi.com/2076-3417/10/8/2813 |
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