Theoretical Research on Early Warning Analysis of Students’ Grades

  • Su-hua ZhengEmail author
  • Xiao-qiang Xi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


Based on the basic theory of data mining, the classical association rule algorithm, Apriori algorithm is used to analyze the grade data of students majoring in computer science and technology and information and computing science of a university, which aims to find out the intrinsic links between the courses and put forward some meaningful early warning rules. Since a lot of rules that obtained by the Apriori algorithm do not conform to logic, effective rules need to be screened artificially according to the prior knowledge of courses sequence, which will waste a lot of time and effort. So SPADE algorithm based on sequential pattern mining is introduced to obtain early warning rules that base on time series. The results show that there is a strong correlation among professional core courses. The obtained rules can provide early warning for students, provide reference for teachers’ teaching plans, and assist in the formulation of professional training programs.


Data mining Apriori algorithm SPADE algorithm Grade analysis Curriculum link 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of ScienceXi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.Institute of Internet of Things and IT-Based IndustrializationXi’an University of Posts and TelecommunicationsXi’anChina

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