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Intelligent Educational Data Analysis with Gaussian Processes

  • Jiachun Wang
  • Jing Zhao
  • Shiliang Sun
  • Dongyu Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

As machine learning evolves, it is significant to apply machine learning techniques to the intelligent analysis on educational data and the establishment of more intelligent academic early warning system. In this paper, we use Gaussian process (GP)-based models to discover valuable inherent information in the educational data and make intelligent predictions. Specifically, the mixtures of GP regression model is adopted to select personalized key courses and the GP regression model is applied to predict the course scores. We conduct experiments on real-world data which are collected from two grades in a certain university. The experimental results show that our approaches can make reasonable analysis on educational data and provide prediction information about the unknown scores, thus helping to make more precise academic early warning.

Keywords

Academic early warning Key course selection Course score prediction Gaussian process regression Mixtures of Gaussian processes 

Notes

Acknowledgments

The first two authors Jiachun Wang and Jing Zhao are joint first authors. The corresponding author is Jing Zhao. This work is sponsored by Shanghai Sailing Program, NSFC Project 61673179 and Shanghai Knowledge Service Platform Project (No. ZF1213).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jiachun Wang
    • 1
  • Jing Zhao
    • 1
  • Shiliang Sun
    • 1
  • Dongyu Shi
    • 1
  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiPeople’s Republic of China

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