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http://dx.doi.org/10.3837/tiis.2022.12.003

Student Group Division Algorithm based on Multi-view Attribute Heterogeneous Information Network  

Jia, Xibin (Faculty of Information Technology, Beijing University of Technology)
Lu, Zijia (Faculty of Information Technology, Beijing University of Technology)
Mi, Qing (Faculty of Information Technology, Beijing University of Technology)
An, Zhefeng (Faculty of Humanities and Social Science, Beijing University of Technology)
Li, Xiaoyong (Information Technology Support Center, Beijing University of Technology)
Hong, Min (Department of Computer Software Engineering, Soonchunhyang University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.12, 2022 , pp. 3836-3854 More about this Journal
Abstract
The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.
Keywords
graph deep clustering; heterogeneous information networks; representation learning; student behavior modeling;
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