Research Article
A Comparative Study of Association Rule Algorithms for Course Recommender System in E-learning
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 39 - Issue 1 |
| Published: February 2012 |
| Authors: Sunita B Aher, Lobo L.M.R.J |
10.5120/4788-7021
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Sunita B Aher, Lobo L.M.R.J . A Comparative Study of Association Rule Algorithms for Course Recommender System in E-learning. International Journal of Computer Applications. 39, 1 (February 2012), 48-52. DOI=10.5120/4788-7021
@article{ 10.5120/4788-7021,
author = { Sunita B Aher,Lobo L.M.R.J },
title = { A Comparative Study of Association Rule Algorithms for Course Recommender System in E-learning },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 39 },
number = { 1 },
pages = { 48-52 },
doi = { 10.5120/4788-7021 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Sunita B Aher
%A Lobo L.M.R.J
%T A Comparative Study of Association Rule Algorithms for Course Recommender System in E-learning%T
%J International Journal of Computer Applications
%V 39
%N 1
%P 48-52
%R 10.5120/4788-7021
%I Foundation of Computer Science (FCS), NY, USA
Abstract
A course Recommender System plays an important role in predicting the course selection by student. Here we consider the real data from Moodle course of our college & we try to obtain the result using Weka. Association rule algorithms are used to find out the best combination of courses in E-Learning. Here in this paper we consider four association rule algorithms: Apriori Association Rule, PredictiveApriori Association Rule, Tertius Association Rule & Filtered Associator. We compare the result of these four algorithms & present the result. According to our simulation result, we find that Apriori association algorithms perform better than the Predictive Apriori Association Rule, Tertius Association Rule, & Filtered Associator in predicting the course selection based on student choice.
References
- Castro, F., Vellido, A., Nebot, A., & Mugica, F. (in press). Applying data mining techniques to e-learning problems: A survey and state of the art. In L. C. Jain, R. Tedman, & D. Tedman (Eds.), Evolution of Teaching and learning paradigms in intelligent environment. Studies in Computational Intelligence (Vol. 62). Springer-Verlag.
- Nahar J, Tickle KS, Ali AB, Chen YP: “Significant cancer prevention factor extraction: an association rule discovery approach”. Journal of Medical Systems Volume 35, Number 3, 353-367, DOI: 10.1007/s10916-009-9372-8
- C. Romero, S. Ventura and E. Garcia. Data Mining in Course Management Systems: MOODLE Case Study and Tutorial. Computers and Education, 2007. Num. 51. pp. 368-384.
- Mazid, M.M. Shawkat Ali, A.B.M. Tickle, K.S. : “Finding a unique Association Rule Mining algorithm based on data characteristics” Electrical and Computer Engineering, 2008. ICECE 2008. International Conference Issue Date: 20-22 Dec. 2008 On page(s): 902 - 908 Location: Dhaka E-ISBN: 978-1-4244-2015-5 Print ISBN: 978-1-4244-2014-8
- Gurpreet Kaur1, Naveen Aggarwal2:” Association Rule Mining in XML databases: Performance Evaluation and Analysis” IJCST Vol. 1, Issue 2, December 2010 ISSN : 2 229-4333 ( Print ) | ISSN : 0976 - 8491 (Online )
- Aman Kumar Sharma, Suruchi Sahni:” A Comparative Study of Classification Algorithms for Spam Email Data Analysis” in International Journal of computer science & Engineering ISSN: 0975-3397 Vol. 3 No. 5 May 2011.
- “Data Mining Introductory and Advanced Topics” by Margaret H. Dunham
- Weka (2007). http://www.cs.waikato.ac.nz/ml/weka/.
- Sunita B Aher and Lobo L.M.R.J.. Data Mining in Educational System using WEKA. IJCA Proceedings on International Conference on Emerging Technology Trends (ICETT) (3):20-25, 2011. Published by Foundation of Computer Science, New York, USA (ISBN: 978-93-80864-71-13)
- Sunita B Aher and Lobo L.M.R.J. Article: A Framework for Recommendation of courses in E-learning System. Selected in International Journal of Computer Applications Published by Foundation of Computer Science, New York, USA ISSN 0975 – 8887
- http://www.cs.sunysb.edu/~cse634/lecture_notes/07apriori.pdf accessed on date 02-02-2012
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