Abstract
How to effectively mine students’ behavior data is an important content to improve the level of student information management. The platform of student behavior analysis and prediction based on campus big data is established, and the value of big data produced by students’ campus behavior is analyzed. The behavior data of students’ consumption laws, living habits and learning conditions are collected, modeled, analyzed and excavated around the large data environment, and the student behavior is predicted and warned by the stratified model of students’ behavior characteristics. The experimental results verify the effectiveness of the methods used, and the behavior characteristics can be analyzed according to the behavior characteristics of the students, and the students’ behavior will be guided to the overall health direction in a timely manner.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Tu, L. (2019). Analysis and Prediction Method of Student Behavior Mining Based on Campus Big Data. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_36
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DOI: https://doi.org/10.1007/978-3-030-36405-2_36
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