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

  • Liyan TuEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 302)


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.


Big data Student behavior Prediction model Data mining 


  1. 1.
    Lambiotte, R., Kosinski, M.: Tracking the digital footprints of personality. Proc. IEEE 102(12), 1934–1939 (2014)CrossRefGoogle Scholar
  2. 2.
    Sun, A., Ji, T., Wang, J., et al.: Wearable mobile internet devices involved in big data solution for education. Int. J. Embed. Syst. 8(4), 293 (2016)CrossRefGoogle Scholar
  3. 3.
    Hasbun, T., Araya, A., Villalon, J.: Extracurricular activities as dropout prediction factors in higher education using decision trees. In: 2016 IEEE 16 International Conference on Advanced Learning Technologies (ICALT), pp. 242–244 (2016)Google Scholar
  4. 4.
    Hammoud, S.: MapReduce network enabled algorithms for classification based on association rules. Brunel University School of Engineering and Design Ph.D. theses (2011)Google Scholar
  5. 5.
    Maillo, J., Triguero, I., et al.: kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data. Knowl. Based Syst. 117, 3–15 (2017)CrossRefGoogle Scholar
  6. 6.
    Arias, J., Gamez, J.A., Puerta, J.M.: Learning distributed discrete Bayesian network classifiers under Map Reduce with Apache spark. Knowl. Based Syst. 117, 16–26 (2017)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Inner Mongolia University for the NationalitiesTongliaoChina

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