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Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning

by Sunita B Aher, Lobo L.m.r.j.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 6
Year of Publication: 2012
Authors: Sunita B Aher, Lobo L.m.r.j.
10.5120/5542-7598

Sunita B Aher, Lobo L.m.r.j. . Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning. International Journal of Computer Applications. 41, 6 ( March 2012), 1-10. DOI=10.5120/5542-7598

@article{ 10.5120/5542-7598,
author = { Sunita B Aher, Lobo L.m.r.j. },
title = { Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 6 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number6/5542-7598/ },
doi = { 10.5120/5542-7598 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:28:52.854419+05:30
%A Sunita B Aher
%A Lobo L.m.r.j.
%T Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 6
%P 1-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Mining is the extraction of hidden predictive information from large database which can be used in various commercial applications like bioinformatics, E-commerce etc. Association Rule, classification and clustering are three different algorithms in data mining. Course Recommender System plays an important role in identifying the behavior of students interested in particular set of courses. We collect the data regarding the course enrollment for specific set of data. For collecting this data, we use the learning management system like Moodle. After collecting the data, we apply the different combination of data mining algorithm like classification & association rule algorithm, clustering & association rule algorithm, association rule mining in classified & clustered data, combining clustering & classification algorithm in association rule algorithms or simply the association rule algorithm. Here in this paper we use ADTree classification algorithm, Simple K-means Algorithm & Apriori Association Rule algorithm as different machine learning algorithm. So we propose the five different methods to find the best combination of algorithm in recommending the courses to students in E-learning. We compare the result of this combined approach as well as only the association rule algorithm & present the best combination of algorithm for recommendation of courses in E-learning according to our simulation.

References
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  2. 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.
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  4. Al¶³pio Jorge:"Hierarchical Clustering for thematic browsing and summarization of large sets of Association Rules" Supported by the POSI/SRI/39630/2001/Class Project
  5. 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 ISSN 0975 – 8887
  6. Han,J. and Kamber, M. , "Data Mining: Concepts and Techniques", 2nd edition.
  7. Alaa el-Halees (2009) :"Mining Students Data to Analyze e-Learning Behavior: A Case Study. " Accessed from http://uqu. edu. sa/files2/tiny_mce/plugins/filemanager/files/30/papers/f158. pdf on 05-03-2012
  8. "Data Mining Introductory and Advanced Topics" by Margaret H. Dunham
  9. Sunita B Aher and Lobo L. M. R. J. :" Data Preparation Strategy in E-Learning System using Association Rule Algorithm" selected in International Journal of Computer Applications. ISSN 0975 – 8887
  10. Sunita B Aher and Lobo L. M. R. J. Article: A Framework for Recommendation of courses in E-learning System. International Journal of Computer Applications 35(4):21-28, December 2011. Published by Foundation of Computer Science, New York, USA ISSN 0975 – 8887
  11. Weka (2007). http://www. cs. waikato. ac. nz/ml/weka/.
  12. B. Ramasubbareddy, A. Govardhan & A. Ramamohanreddy:"Classification Based on Positive and Negative Association Rules" International Journal of Data Engineering, (IJDE), Volume (2): Issue (2) : 2011 84
  13. Jiayun Guo, Vlado Ke?selj, and Qigang Gao:"Integrating Web Content Clustering into Web Log Association Rule Mining?" supported by NSERC
  14. 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.
  15. Sunita B Aher and Lobo L. M. R. J. :" Association Rule Mining of Classified and Clustered Data of E-Learning System" selected in ICWET-2012, paper will published in International Journal of Computer Applications (IJCA) ISSN : 0975 - 8887
  16. Sunita B Aher and Lobo L. M. R. J. Article: Mining Association Rule in Classified Data for Course Recommender System in E-Learning. International Journal of Computer Applications 39(7):1-7, February 2012. Published by Foundation of Computer Science, New York, USA ISSN 0975 – 8887 Digital Library URI: http://www. ijcaonline. org/archives/volume39/number7/4829-7086
  17. Sunita B Aher and Lobo L. M. R. J. Article: Combination of Clustering, Classification & Association Rule based Approach for Course Recommender System in E-learning. International Journal of Computer Applications 39(7):8-15, February 2012. Published by Foundation of Computer Science, New York, USA. ISSN 0975 – 8887 Digital Library URI: http://www. ijcaonline. org/archives/volume39/number7/4830-7087
  18. Sunita B Aher and LOBO L. M. R. J. . Article: Prediction of Course Selection by Student using Combination of Data Mining Algorithms in E-Learning. International Journal of Computer Applications 40(15):1-7, February 2012. Published by Foundation of Computer Science, New York, USA. ISSN 0975 – 8887 Digital Library URI: http://www. ijcaonline. org/archives/volume40/number15/5053-7085
Index Terms

Computer Science
Information Sciences

Keywords

Weka Machine Learning Algorithm Simple K-means Algorithm Adtree Classification Algorithm Apriori Association Rule Algorithm Moodle

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