Traction of Students' Curriculum Information Based on Association Rule Optimization

DOI

10.2991/cnci-19.2019.96How to use a DOI?

Keywords

Apriori algorithm, credit weighting, correlation analysis.

Abstract

In colleges and universities, the data on student’s performance are numerous. However, these data are often not well utilized and only served as a query. In order to make better use of these data, this paper will improve the traditional association rules and dig into the course performance information. Through the analysis of the association rules of students' different grades, the correlation between the courses and their traction are sought. The test results show that the optimized data mining algorithm has a good mining effect on the data, which can highlight the importance of different courses and make the data better reflect the links between curriculum and other courses.In this way,it is convenient for the teaching management department to better arrange the order of courses for students,so as to provide support and help for the teaching activities of students and teachers.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhe Zhou
AU  - Yong Ouyang
AU  - Yawen Zeng
PY  - 2019/05
DA  - 2019/05
TI  - Traction of Students' Curriculum Information Based on Association Rule Optimization
BT  - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
PB  - Atlantis Press
SP  - 693
EP  - 698
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnci-19.2019.96
DO  - 10.2991/cnci-19.2019.96
ID  - Zhou2019/05
ER  -