Abstract
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.
Highlights
Social relationships have important impacts on people’s lives, and as such, studying them can help to understand a person’s life
We propose spatial similarity, spatiotemporal similarity, and behavior features-based similarity operators, through which we can compute the degree of association among students, and take this as the weight of the edge in the association network
We propose the behavior features-based similarity operator based on the features described in Section 5, and formulate this operator as Equation (5), where u and v denote two different students, mu is the feature vector of student u, and mdu is the d-dimensional feature of mu
Summary
Social relationships have important impacts on people’s lives, and as such, studying them can help to understand a person’s life. Previous works based on behavior data has addressed topics such as constructing student social networks [1,2], predicting academic performance [3,4,5,6,7], Appl. To accurately and intuitively analyze associations, we propose a visual analytic method in which an association network is constructed based on the similarity of students’ behavior.
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