Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (13): 251-257.DOI: 10.3778/j.issn.1002-8331.2001-0036

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Student Grade Prediction Based on Graph Auto-Encoder Model

ZHANG Yang, LU Mingming, ZHENG Yiji, LI Haifeng   

  1. 1.School of Computer Science, Central South University, Changsha 410083, China
    2.School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
  • Online:2021-07-01 Published:2021-06-29



  1. 1.中南大学 计算机学院,长沙 410083
    2.中南大学 地球科学与信息物理学院,长沙 410083


The traditional methods of predicting students’grades often require manual screening of characteristics or a large amount of prior knowledge and expert knowledge, the Graph-AE model based on deep learning is proposed to predict students’performance, which can automatically extract features without manual intervention and does not require a lot of prior knowledge. Comparing the Graph-AE model with 13 classical recommendation algorithms, the experimental results show that the Graph-AE model is more accurate on the students’ performance data set than the traditional solutions and can better characterize the relevance and difference between students and courses.

Key words: grade prediction, matrix completion, auto-encoder



关键词: 成绩预测, 矩阵填充, 自编码器