Advertisement

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)

Abstract

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.

Keywords

Data mining Apriori algorithm SPADE algorithm Grade analysis Curriculum link 

References

  1. 1.
    Yao, S.L.: Application and research on correlation between colleges courses based on data mining. Bull. Sci. Technol. 28(12), 232–234 (2012).  https://doi.org/10.13774/j.cnki.kjtb.2012.12.018CrossRefGoogle Scholar
  2. 2.
    Liu, Y.L.: Application of data mining to decision of college students’ management. J. Chengdu Univ. Inf. Technol. 21(3), 373–377 (2006).  https://doi.org/10.3969/j.issn.1671-1742.2006.03.013CrossRefGoogle Scholar
  3. 3.
    Zhu, D.F.: Applied research of association rules algorithm on Universities’ Educational Management System. Dissertation for Master Degree, Zhejiang University of Technology (2013)Google Scholar
  4. 4.
    Tao, T.T.: An analysis of students’ consumption and learning behavior based on campus card and cloud classroom data. Dissertation for Master Degree, Central China Normal University (2017)Google Scholar
  5. 5.
    Zhao, H.: The research and application of data mining technology in analysis for students’ performance. Dissertation for Master Degree, Dalian Maritime University (2007)Google Scholar
  6. 6.
    Jiang, H.Y.: The application of Apriori association algorithm in student’s results. J. Anshan Norm. Univ. 9(2), 48–50 (2007).  https://doi.org/10.3969/j.issn.1008-2441.2007.02.015CrossRefGoogle Scholar
  7. 7.
    He, C., Song, J., Zhuo, T.: Curriculum association model and student performance prediction based on spectral clustering of frequent pattern. Appl. Res. Comput. 32(10), 2930–2933 (2015).  https://doi.org/10.3969/j.issn.1001-3695.2015.10.011CrossRefGoogle Scholar
  8. 8.
    Hao, X.F., Tan, Y.S., Wang, J.Y.: Research and implementation of parallel Apriori algorithm on Hadoop platform. Comput. Mod. 2013(3), 1–4 (2013).  https://doi.org/10.3969/j.issn.1006-2475.2013.03.001CrossRefGoogle Scholar
  9. 9.
    Li, Z.L.: Research and application of Apriori algorithm based on cluster and compression matrix. Dissertation for Master Degree, Suzhou University (2010)Google Scholar
  10. 10.
    Yang, C.Y.: Research and the application of Apriori algorithm in the analysis of student grade. Dissertation for Master Degree, Hunan University (2016)Google Scholar
  11. 11.
    Shao, X.K.: The Research on Apriori algorithm and the application in the undergraduate enrollment data mining. Dissertation for Master Degree, Beijing Jiaotong University (2016)Google Scholar
  12. 12.
    Dong, H.: Association rule mining based on the interestingness about vocational college courses. J. Jishou Univ. (Nat. Sci. Ed.) 33(3), 41–46 (2012).  https://doi.org/10.3969/j.issn.1007-2985.2012.03.011
  13. 13.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: International Conference on Very Large Databases, pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  14. 14.
    Cui, Y., Bao, Z.Q.: Survey of association rule mining. Appl. Res. Comput. 33(2), 330–334 (2016).  https://doi.org/10.3969/j.issn.1001-3695.2016.02.002CrossRefGoogle Scholar
  15. 15.
    Liu, J.Y., Jia, X.Y.: Multi-label classification algorithm based on association rule mining. J. Softw. 28(11), 2865–2878 (2017).  https://doi.org/10.13328/j.cnki.jos.005341MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zhao, H.Y., Li, X.J., Cai, L.C.: Overview of association rules Apriori mining algorithm. J. Sichuan Univ. Sci. Eng. (Nat. Sci. Ed.) 24(01), 66–70 (2011).  https://doi.org/10.3969/j.issn.1673-1549.2011.01.019CrossRefGoogle Scholar
  17. 17.
    Srikant, R., Agrawal, R.: Mining sequential patterns: generalization sand performance improvements. In: Proceedings of the 5th International Conference on Extending Data Base Technology, pp. 3–7. Springer, London (1996)Google Scholar
  18. 18.
    Zaki. M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(01), 31–60 (2001).  https://doi.org/10.1023/a:1007652502315CrossRefGoogle Scholar
  19. 19.
    Dang, Y.M.: Research on the sequential pattern mining algorithms. J. Jiangxi Norm. Univ. (Nat. Sci. Ed.) 33(05), 604–607 (2009).  https://doi.org/10.16357/j.cnki.issn1000-5862.2009.05.025

Copyright information

© 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

Personalised recommendations