Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 595-601.DOI: 10.11772/j.issn.1001-9081.2019071222

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Student grade prediction method based on knowledge graph and collaborative filtering

Xi CHEN1, Guang MEI1, Jinjin ZHANG2, Weisheng XU1,3()   

  1. 1.College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
    2.Education Technology and Computing Center,Tongji University,Shanghai 200092,China
    3.Informatics Office,Tongji University,Shanghai 200092,China
  • Received:2019-07-15 Revised:2019-09-06 Accepted:2019-09-06 Online:2019-10-25 Published:2020-02-10
  • Contact: Weisheng XU
  • About author:CHEN Xi, born in 1995, M. S. candidate. Her research interests include data mining, natural language processing.
    MEI Guang, born in 1989, Ph. D. candidate. His research interests include education informatization, data mining, artificial intelligence.
    ZHANG Jinjin, born in 1994. Her research interests include design of intelligent teaching environment, education informatization.
  • Supported by:
    the National Natural Science Foundation of China(71540022)


陈曦1, 梅广1, 张金金2, 许维胜1,3()   

  1. 1.同济大学 电子与信息工程学院,上海 201804
    2.同济大学 教育技术与计算中心,上海 200092
    3.同济大学 信息化办公室,上海 200092
  • 通讯作者: 许维胜
  • 作者简介:陈曦(1995—),女,安徽芜湖人,硕士研究生,主要研究方向:数据挖掘、自然语言处理
  • 基金资助:


Focusing on the prediction of student grade in the undergraduate teaching of higher education, a prediction algorithm based on course Knowledge Graph (KG) was proposed. Firstly, a course KG representing course information was constructed. Then, the neighbor-based methods and the KG representation learning-based methods were used to calculate the similarity of the courses on the knowledge level based on the KG, and those knowledge similarities among courses were integrated into the traditional grade prediction framework Collaborative Filtering (CF). Finally, the performance of the algorithm with fusing KG and the common prediction algorithm in different data sparsities were compared in experiments. Experimental results show that in the data sparse scenario, compared with the traditional CF algorithm, the neighbor-based algorithm has the Root Mean Square Error (RMSE) reduced by about 11% and the Mean Absolute Error (MAE) reduced by about 9%; and compared with the traditional CF algorithm, KG representation learning-based algorithm has the RMSE reduced by about 17.55% and the MAE reduced by about 11.40%. Experimental results indicate that the CF algorithm using KG can significantly reduce the prediction error, which proves that the KG can be used as information supplement in the lack of historical data, thus helping CF to obtain better prediction results.

Key words: Collaborative Filtering (CF), Knowledge Graph (KG), grade prediction, Educational Data Mining (EDM), intelligent campus



关键词: 协同过滤, 知识图谱, 成绩预测, 教育数据挖掘, 智慧校园

CLC Number: