Study of Education Patterns in Rural and Urban India using Association Rule Mining : Implementation

Authors

  • Sk Althaf Hussain Basha  Professor and Head, Department of MCA, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana , India

Keywords:

Academic Student Performance, Data Mining, Higher Education, Association Mining Apriori algorithm

Abstract

During past two decades, several statistical techniques/methods student analysis were used. Academic accuracy from different perspectives. Over a time period availability of education within the rustic (rural) areas has improved. Further, the developments in urban areas in different sectors have resulted in educational environment changes. Our pursuit in this paper has been to find the strong rules from the available data by applying association rule mining, and there by find the relevance to the student performance associated with the educational environment in which they study. We have identified the association between different attributes of educational environment i.e., the location of the college, type of the college, different social groups, different courses etc., and thereby extract strong association rules. The processed the available data has found the unknown rules and analysis of these rules offering a suitable and build testimonial academic planning’s within higher institutions learnings to heighten their deciding process .They are also helpful for a proper understanding of the educational environment aids to the course of study construction and other enhancements for readily rising students theoretical performance. Through this document we use data mining technique of association rule mining to extract strong rules in education environment that identifies students’ success patterns in different colleges in different social groups and also presents, implementation is done using Java Programing and Oracle Software, Further we have processed the available data to find the pattern of support for these rules from time to time.

References

  1. 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.
  2. 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.
  3. S. Z. ERDOĞAN, M. TİMOR . "A data mining application in a student database". Journal of aeronautics and space technologies ,volume 2 number 2 (53-57) 2005.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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
  16. 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
  17. 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.
  18. Nebot, A., Castro, F., Vellido, A., Mugica, F. (2006). Identification of fuzzy models to predict students performance in an e-learning environment. In International Conference on Web-based  Education, Puerto Vallarta, 74-79.
  19. Chen, c., Chen, M., Li, Y. (2007). Mining key formative assessment rules based on learner portfiles for web-based learning systems. In IEEE International Conference on Advanced Learning Technologies, Japan, 1-5.
  20. Ogor, E.N. (2007). Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques. In Electronics, Robotics and Automotive Mechanics Conference, Washington, DC, 354-359.
  21. Shangping, D., Ping, Z. (2008). A data mining algorithm in distance learning. In International Conference on Computer Supported Cooperative Work in Design, Xian, 1014-1017.
  22. Zafra, A., Ventura, S. (2009), Predicting student grades in learning management systems with multiple instance programming. In International Conference on Educational Data  Mining, Cordoba, Spain, 307-314.
  23. Chan, C.C. (2007). A Framework for Assessing Usage of Web-Based e- Learning Systems. In International Conference on innovative Computing, Information and Control, Washington, DC, 147- 151.
  24. Etchells, T.A., Nebot, A., Vellido, A., Lisboa, P.J.G., Mugica, F. (2006). Learning what is important: feature selection and rule extraction in a virtual course. In European Symposium on Artificial Neural Networks, Bruseles, Belgium, 401-406.
  25. Rakesh Aggarwal, Tomasz Imielinski, Arun Swami, " Mining Association Rules between Sets of Items in Large Databases" ACM Sigmod Conference Washington DC, May 1993.
  26. Rakesh Aggarwal , Ramakrishanan Srikant, "Fast Algorithm for mining Association Rules", IBM Almaden Research Centre, Proceedings of 20th VLDB Conference, Santiago, Chile, 1994.
  27. Sotiris Kotsiantis, Dimitris Kanellopoulos, "Association Rules Mining: A Recent Overview", GESTS International conference on computer science and engineering, Vol. 32(1) pp- 71-82, 2006.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sk Althaf Hussain Basha, " Study of Education Patterns in Rural and Urban India using Association Rule Mining : Implementation , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.578-588, March-April-2018.