Affiliations: School of Public Health Management, Anhui Medical College, Hefei, China
Correspondence:
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Corresponding author: Xiaoqian Wu, School of Public Health Management, Anhui Medical College, Hefei, China. E-mails: [email protected]; [email protected].
Abstract: BACKGROUND: Traditional methods have the limitations of low accuracy and inconvenient operation in analyzing students’ abnormal behavior. Hence, a more intuitive, flexible, and user-friendly visualization tool is needed to help better understand students’ behavior data. OBJECTIVE: In this study a visual analysis and interactive interface of students’ abnormal behavior based on a clustering algorithm were examined and designed. METHODS: Firstly, this paper discusses the development of traditional methods for analyzing students’ abnormal behavior and visualization technology and discusses its limitations. Then, the K-means clustering algorithm is selected as the solution to find potential abnormal patterns and groups from students’ behaviors. By collecting a large number of students’ behavior data and preprocessing them to extract relevant features, a K-means clustering algorithm is applied to cluster the data and obtain the clustering results of students’ abnormal behaviors. To visually display the clustering results and help users analyze students’ abnormal behaviors, a visual analysis method and an interactive interface are designed to present the clustering results to users. The interactive functions are provided, such as screening, zooming in and out, and correlation analysis, to support users’ in-depth exploration and analysis of data. Finally, the experimental evaluation is carried out, and the effectiveness and practicability of the proposed method are verified by using big data to obtain real student behavior data. RESULTS: The experimental results show that this method can accurately detect and visualize students’ abnormal behaviors and provide intuitive analysis results. CONCLUSION: This paper makes full use of the advantages of big data to understand students’ behavior patterns more comprehensively and provides a new solution for students’ management and behavior analysis in the field of education. Future research can further expand and improve this method to adapt to more complex students’ behavior data and needs.
