Journal of East China Normal University(Natural Sc ›› 2019, Vol. 2019 ›› Issue (5): 66-73,84.doi: 10.3969/j.issn.1000-5641.2019.05.005

• Data-driven Computational Education • Previous Articles     Next Articles

Performance prediction based on fuzzy clustering and support vector regression

SHEN Hang-jie, JU Sheng-gen, SUN Jie-ping   

  1. College of Computer Science, Sichuan University, Chengdu 610065, China
  • Received:2019-07-28 Online:2019-09-25 Published:2019-10-11

Abstract: Existing performance prediction models tend to overuse different types of attributes, leading to either overly complex prediction methods or models that require manual participation. To improve the accuracy and interpretation of student performance prediction, a method based on fuzzy clustering and support vector regression is proposed. Firstly, fuzzy logic is introduced to calculate the membership matrix, and students are clustered according to their past performance. Then, we use Support Vector Regression (SVR) theory to fit and model performance trajectory for each cluster. Lastly, the final prediction results are adjusted in combination with the students' learning behavior and other related attributes. Experimental results on several baseline datasets demonstrate the validity of the proposed approach.

Key words: fuzzy clustering, support vector regression, prediction, educational data mining

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