List of Journals    /    Call For Papers    /    Subscriptions    /    Login
By Author By Title
 About CSC Journals
 CSC Journals Objectives
 List of Journals
 Call For Papers CFP
 Special Issue CFP
 Submission Guidelines
 Peer Review Process
 Helpful Hints For Getting Published
 Plagiarism Policies
 Abstracting & Indexing
 Open Access Policy
 Submit Manuscript
 Reviewer Guidelines
 Editor Guidelines
 Join Us As Editor
 Launch Special Issue
 Suggest New Journal
 Browse CSC Library
 Open Access Policy
 Conference Partnership Program (CPP)
 Abstracting & Indexing
 Discounted Packages
 Archival Subscriptions
 How to Subscribe
 Subscriptions Agents
 Order Form
Knowledge Discovery from Students’ Result Repository: Association Rule Mining Approach
Full text
International Journal of Computer Science and Security (IJCSS)
Table of Contents
Download Complete Issue    PDF(4.92MB)
Volume:  4    Issue:  2
Pages:  149-264
Publication Date:   May 2010
ISSN (Online): 1985-1553
199 - 207
Published Date   
CSC Journals, Kuala Lumpur, Malaysia
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
KEYWORDS:   Association rule mining, Academic performance, Educational data mining, Curriculum, Students’ Result Repository 
This Manuscript is indexed in the following databases/websites:-
1. Directory of Open Access Journals (DOAJ)
2. Docstoc
3. Scribd
5. WorldCat
6. Google Scholar
7. ScientificCommons
8. CiteSeerX
9. Academic Index
10. ResearchGATE
11. refSeek
12. Bielefeld Academic Search Engine (BASE)
13. iSEEK
14. Socol@r
15. Academic Journals Database
16. Libsearch
17. slideshare
Over the years, several statistical tools have been used to analyze students’ performance from different points of view. This paper presents data mining in education environment that identifies students’ failure patterns using association rule mining technique. The identified patterns are analysed to offer a helpful and constructive recommendations to the academic planners in higher institutions of learning to enhance their decision making process. This will also aid in the curriculum structure and modification in order to improve students’ academic performance and trim down failure rate. The software for mining student failed courses was developed and the analytical process was described. 
1 B. Dogan, A. Y. Camurcu. “Association Rule Mining from an Intelligent Tutor” Journal of Educational Technology Systems Volume 36, Number 4 / 2007-2008, pp 433 – 447, 2008
2 F. Castro, A. Vellido, A. Nebot, and F. Mugica. “Applying Data Mining Techniques to e-Learning Problems”. Evolution of Teaching and Learning Paradigms in Intelligent Environment ISBN: 10.1007/978-3-540-71974-8_8 Volume 62, pp 183-221. Springer Berlin Heidelberg, 2007.
3 B.Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer and, W. F. Punch."Predicting student performance: an application of data mining methods with the educational web-based system LON-CAPA" In Proceedings of ASEE/IEEE Frontiers in Education Conference, Boulder, CO: IEEE, 2003.
4 Talavera, L., and Gaudioso, E. “Mining student data to characterize similar behavior groups in unstructured collaboration spaces”. In Proceedings of the Arti_cial Intelligence in Computer Supported Collaborative Learning Workshop at the ECAI ,Valencia, Spain, 2004.
5 ?. Z. ERDO?AN, M. T?MOR . “A data mining application in a student database”. Journal of aeronautics and space technologies ,volume 2 number 2 (53-57) 2005.
6 G.J. Hwang. “A Knowledge-Based System as an Intelligent Learning Advisor on Computer Networks” Journal of Systems, Man, and Cybernetics Vol. 2 , pp.153-158, 1999.
7 G.J. Hwang, T.C.K. Huang,and C.R. Tseng. “A Group-Decision Approach for EvaluatingEducational Web Sites”. Computers & Education Vol. 42 pp. 65-86 , 2004.
8 G.J. Hwang, C.R. Judy, C.H. Wu, C.M. Li and G.H. Hwang. “Development of an Intelligent Management System for Monitoring Educational Web Servers”. In proceedings of the 10th Pacific Asia Conference on Information Systems, PACIS . 2334-2340, 2004.
9 G.D. Stathacopoulou, M. Grigoriadou. “Neural Network-Based Fuzzy Modeling of the Student in Intelligent Tutoring Systems”. In proceedings of the International Joint Conference on Neural Networks. Washington ,3517-3521,1999.
10 C.J. Tsai, S.S. Tseng, and C.Y. Lin. “A Two-Phase Fuzzy Mining and Learning Algorithm for Adaptive Learning Environment”. In proceedings of the Alexandrov, V.N., et al. (eds.): International Conference on Computational Science, ICCS 2001. LNCS Vol. 2074. Springer-Verlag, Berlin Heidelberg New York, 429-438. 2001.
11 S. Encheva , S. Tumin. “ Application of Association Rules for Efficient Learning Work-Flow” Intelligent Information Processing III , ISBN 978-0-387-44639-4, pp 499-504 published Springer Boston, 2007.
12 H.H. Hsu, C.H. Chen, W.P. Tai. “Towards Error-Free and Personalized Web-Based Courses”. In proceedings of the 17th International Conference on Advanced Information Networking and Applications, AINA’03. March 27-29, Xian, China, 99-104, 2003.
13 P. L. Hsu, R. Lai, C. C. Chiu, C. I. Hsu (2003) “The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance” [Expert Systems with Applications 25 (2003) 51–62.
14 A.Y.K. Chan, K.O. Chow, and K.S. Cheung. “Online Course Refinement through Association Rule Mining” Journal of Educational Technology Systems Volume 36, Number 4 / 2007-2008, pp 433 – 44, 2008.
15 S. Saxena, A. S.Pandya, R. Stone, S. R. and S. Hsu (2009) “Knowledge Discovery through Data Visualization of Drive Test Data” International Journal of Computer Science and Security (IJCSS), Volume (3): Issue (6) pp. 559-568.
16 S. Das and B. Saha (2009) “Data Quality Mining using Genetic Algorithm” International Journal of Computer Science and Security, (IJCSS) Volume (3) : Issue (2) pp. 105-112
17 M.Anandhavalli , M.K.Ghose and K.Gauthaman(2009) “Mining Spatial Gene Expression Data Using Association Rules”. International Journal of Computer Science and Security, (IJCSS) Volume (3) : Issue (5) pp. 351-357
18 R. Damaševicius. “Analysis of Academic Results for Informatics Course Improvement Using Association Rule Mining”. Information Systems Development Towards a Service Provision Society. ISBN 978-0-387-84809-9 (Print) 978-0-387-84810-5 (Online) pp 357-363, published by Springer US, 2009.
19 Ceglar, J.F Roddick. “Association mining”. ACM Computing Surveys, 38:2, pp. 1-42, 2006
20 S. Kotsiantis, , D. Kanellopoulos. “Association Rules Mining” A Recent Overview.GESTS Int. Transactions on Computer Science and Engineering, Vol. 32 (1), pp. 71-82, 2006.
21 H. Jochen, G. Ulrich and N. Gholamreza . “Algorithms for Association Rule Mining – A General Survey and Comparison”. SIGKDD Exploration, Vol.2, Issue 1, pp 58-64. ACM, 2000.
1 N. Saleeb and G. Dafoulas, “Analogy between Student Perception of Educational Space Dimensions and Size Perspective in 3D Virtual Worlds versus Physical World”, International Journal of Engineering (IJE), 4(3), pp. 210 – 218, 2010.
Olanrewaju Jelili Oyelade : Colleagues
Oladipupo, Olufunke Oyejoke : Colleagues  
  Untitled Document
Copyrights (c) 2012 Computer Science Journals. All rights reserved.
Best viewed at 1152 x 864 resolution. Microsoft Internet Explorer.
Copyrights & Usage: Articles published by CSC Journals are Open Access. Permission to copy and distribute any other content, images, animation and other parts of this website is prohibited. CSC Journals has the rights to take action against individual/group if they are found victim of copying these parts of the website.