pp 1–18 | Cite as

Learning performance prediction via convolutional GRU and explainable neural networks in e-learning environments

  • Xizhe Wang
  • Pengze WuEmail author
  • Guang Liu
  • Qionghao Huang
  • Xiaoling Hu
  • Haijiao Xu


Students learning performance prediction is a challenging task due to the dynamic, virtual environments and the personalized needs for different individuals. To ensure that learners’ potential problems can be identified as early as possible, this paper aim to develop a predictive model for effective learning feature extracting, learning performance predicting and result reasoning. We first proposed a general learning feature quantification method to convert the raw data from e-learning systems into sets of independent learning features. Then, a weighted avg-pooling is chosen instead of typical max-pooling in a novel convolutional GRU network for learning performance prediction. Finally, an improved parallel xNN is provided to explain the prediction results. The relevance of positive/negative between features and result could help students find out which part should be improved. Experiments have been carried out over two real online courses data. Results show that our proposed approach performs favorably compared with several other state-of-the-art methods.


Learning performance prediction Learning feature quantification Deep neural network E-learning environments 

Mathematics Subject Classification

68U35 68T01 



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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Xizhe Wang
    • 1
  • Pengze Wu
    • 1
    Email author
  • Guang Liu
    • 1
  • Qionghao Huang
    • 1
  • Xiaoling Hu
    • 1
  • Haijiao Xu
    • 1
  1. 1.School of Information Technology in EducationSouth China Normal UniversityGuangzhouChina

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