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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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.2012.12.018
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.1671-1742.2006.03.013
Zhu, D.F.: Applied research of association rules algorithm on Universities’ Educational Management System. Dissertation for Master Degree, Zhejiang University of Technology (2013)
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)
Zhao, H.: The research and application of data mining technology in analysis for students’ performance. Dissertation for Master Degree, Dalian Maritime University (2007)
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.1008-2441.2007.02.015
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.1001-3695.2015.10.011
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.1006-2475.2013.03.001
Li, Z.L.: Research and application of Apriori algorithm based on cluster and compression matrix. Dissertation for Master Degree, Suzhou University (2010)
Yang, C.Y.: Research and the application of Apriori algorithm in the analysis of student grade. Dissertation for Master Degree, Hunan University (2016)
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)
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
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)
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.1001-3695.2016.02.002
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.005341
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.1673-1549.2011.01.019
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)
Zaki. M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(01), 31–60 (2001). https://doi.org/10.1023/a:1007652502315
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, Sh., Xi, Xq. (2019). Theoretical Research on Early Warning Analysis of Students’ Grades. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-03766-6_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03765-9
Online ISBN: 978-3-030-03766-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)