Home   >   CSC-OpenAccess Library   >    Manuscript Information
Knowledge Discovery from Students’ Result Repository: Association Rule Mining Approach
Olanrewaju Jelili Oyelade, Oladipupo, Olufunke Oyejoke
Pages - 199 - 207     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
Association rule mining, Academic performance, Educational data mining, Curriculum, Students’ Result Repository
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 Google Scholar 
2 Academic Journals Database 
3 ScientificCommons 
4 Academic Index 
5 CiteSeerX 
6 refSeek 
7 iSEEK 
8 Socol@r  
9 ResearchGATE 
10 Libsearch 
11 Bielefeld Academic Search Engine (BASE) 
12 Scribd 
13 WorldCat 
14 SlideShare 
16 PdfSR 
?. 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.
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.
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
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.
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.
Ceglar, J.F Roddick. “Association mining”. ACM Computing Surveys, 38:2, pp. 1-42, 2006
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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
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.
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.
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.
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.
Mr. Olanrewaju Jelili Oyelade
Covenant University - Nigeria
Mr. Oladipupo, Olufunke Oyejoke
Covenant University - Nigeria