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Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21–22, 2019, Proceedings, Part II

Research Article

Analysis and Prediction Method of Student Behavior Mining Based on Campus Big Data

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  • @INPROCEEDINGS{10.1007/978-3-030-36405-2_36,
        author={Liyan Tu},
        title={Analysis and Prediction Method of Student Behavior Mining Based on Campus Big Data},
        proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part II},
        proceedings_a={ADHIP PART 2},
        year={2019},
        month={11},
        keywords={Big data Student behavior Prediction model Data mining},
        doi={10.1007/978-3-030-36405-2_36}
    }
    
  • Liyan Tu
    Year: 2019
    Analysis and Prediction Method of Student Behavior Mining Based on Campus Big Data
    ADHIP PART 2
    Springer
    DOI: 10.1007/978-3-030-36405-2_36
Liyan Tu1,*
  • 1: Inner Mongolia University for the Nationalities
*Contact email: tlyimun@163.com

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.

Keywords
Big data Student behavior Prediction model Data mining
Published
2019-11-29
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-36405-2_36
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