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5 July 2024 Research on student behavior portrait and early warning based on campus big data
Shaofen Fan, Shuanghuan Lv, Zhefu Li, Chensheng Yan
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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318425 (2024) https://doi.org/10.1117/12.3032908
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
With the comprehensive construction of smart campuses in universities, various departments have built a large number of information systems, generating a large amount of data. The complexity and variety of student data make it an urgent issue to analyze student behavior data from a large and diverse campus student population, explore potential abnormal student behavior, and provide early warning for abnormal behavior. This article uses K-means clustering algorithm to analyze student behavior data, and uses Pearson coefficient to analyze correlation. The experimental results show that students who spend more than 4 hours online for entertainment have a higher risk of failing their grades. By analyzing data such as online entertainment duration, book borrowing volume, consumption, and access control of students, we can profile their behavior, identify abnormal behaviors through data analysis, and provide early warning for campus management.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shaofen Fan, Shuanghuan Lv, Zhefu Li, and Chensheng Yan "Research on student behavior portrait and early warning based on campus big data", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318425 (5 July 2024); https://doi.org/10.1117/12.3032908
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KEYWORDS
Internet
Data modeling
Data analysis
Data centers
Data mining
Network security
Statistical analysis
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