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A Proposed Data Mining Framework for Higher Education System

by Ayman E. Khedr, Ahmed I. El Seddawy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 7
Year of Publication: 2015
Authors: Ayman E. Khedr, Ahmed I. El Seddawy
10.5120/19839-1693

Ayman E. Khedr, Ahmed I. El Seddawy . A Proposed Data Mining Framework for Higher Education System. International Journal of Computer Applications. 113, 7 ( March 2015), 24-31. DOI=10.5120/19839-1693

@article{ 10.5120/19839-1693,
author = { Ayman E. Khedr, Ahmed I. El Seddawy },
title = { A Proposed Data Mining Framework for Higher Education System },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 7 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number7/19839-1693/ },
doi = { 10.5120/19839-1693 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:20.631594+05:30
%A Ayman E. Khedr
%A Ahmed I. El Seddawy
%T A Proposed Data Mining Framework for Higher Education System
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 7
%P 24-31
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Educational data mining is concerned with the development methods for exploring the unique types of data that come from the educational context. Furthermore, educational data mining is an emerging discipline that concerned with the developing methods for exploring the unique types of data that come from the educational context. This study focuses on the way of applying data mining techniques for higher education system by using the most common techniques on most common application called Moodle system in education system. There are an increasing numbers of researches that interest in using data mining in education system. The proposed system for Higher Educational Data Mining System (HEDMS) is concerned with the developing methods that discover useful knowledge from data that extracted from educational system. The data collated form historical and usage data reside in the databases of educational institutes. The proposed system helps to get sufficient results which consist of several steps in our case study starting with collected data, pre-processing, applying data mining techniques and visualization results. We collected students' data from Moodle database.

References
  1. Salmon, Gill, 2011. "E-moderating: the key to teaching and learning online", Vol. 45, pp. 49-53.
  2. Karrer, T, 2012, "What is eLearning 2. 0: Elearningtech", www. blogspot. com.
  3. Bates, 2011. "Technology, e-Learning and Distance Education London: Routledge".
  4. Mamcenko, J. and Sileikiene, I. , Fep. 2006, "Analysis of E-exam Data using Data Mining Techniques", Spain.
  5. Ayesha A. , Mustafa T. and Khan M. I. , 2010, "Data Mining Model for Higher Education System". European Journal of Scientific Research, Vol. 43, No. 1, pp. 24-29.
  6. Garcia E. , Romero C. , Ventura S. and Calders T. , 2013, "Drawbacks and solutions of applying association rule mining in learning management systems", Proceedings of the International Workshop on Applying Data Mining in e-Learning, pp. 13-23.
  7. Merceron, A. andYacef, K. , 2013, "Interestingness Measures for Association Rules in Educational Data". Proceedings of the first International Conference on Educational Data Mining, Canada, pp. 35-41.
  8. Merceron, A. and Yacef, K. , 2009, "Educational Data Mining: a Case Study, School of Information Technologies, University of Sydney", Australia, Vol. 11, No. 5.
  9. Han, J. and Kamber, M. , 2001, "Data mining: concepts and techniques", San Francisco, Morgan Kaufman.
  10. Merceron, A. and Yacef, K. , 2004. "Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results", Journal of Interactive Learning Research (JILR), Vol. 15, No. 4, pp. 319-346.
  11. Herin, D. , Sala, M. and Pompidor, P. , 2002, "Evaluating and Revising Courses from Web Resources Educational", In Int. Conf. on Intelligent Tutoring Systems, Spain, pp. 208–218.
  12. Chen, Han, and Yu. 1996. "Knowledge and Data Engineering Data mining: An overview from a database perspective", IEEE Trans, Vol. 30, No. 3, pp. 8:866-883.
  13. Fayyad U. , Piatetsky G. and Frawley W. J, 1991. "Press definition of KDD at KDD96". Knowledge Discovery in Databases, AAAI/MIT, Vol. 30, No. 3, pp. 8:866-883.
  14. Gartner, February 2000, "Evolution of data mining", Gartner Group Advanced Technologies and Applications Research Note.
  15. Turban, E. , Aronson J. , Liang T. and Sharda R, 2007, "Decision Support and Business Intelligence Systems", eighth edition. Prentice Hall.
  16. Hwang, G. , "A Knowledge-Based System as an Intelligent Learning Advisor on Computer Networks", 2000, Vol. 30, No. 3, pp. 153–158.
  17. Matsui, T. , and Okamoto, T. , 2003, "Knowledge Discovery from Learning History Data and its Effective Use for Learning Process Assessment under the e-Learning Environment", In: Crawford, Society for Information Technology and Teacher Education International Conference, pp. 3141–3144.
  18. Mostow, J. , Beck, J. , Cen, H. , Cuneo, A. , Gouvea, E. , and Heiner, C. , 2013, "An Educational Data Mining Tool to Browse Tutor-Student Interactions: Time Will tell",in: Proceedings of the Workshop on Educational Data Mining, pp. 15–22.
  19. Mohammad H. and Jafar H, September, 2011. "Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning", IJDKP.
  20. Gabrijela D. and Dragana P. September, 2011, "The Use of Data Mining Methods For Analyzing And Evaluating Course Quality In The Moodle System", IJDKP, Vol. 47, No. 3, pp. 17-27.
  21. Sonali A. , Pandey G. and Tiwari M. , 2010. "Data Mining In Education: Data Classification and Decision Tree Approach" Vol. 11, No. 5.
  22. Félix C. , Alfredo V. , Àngela N. and Francisco M. , 2010, "Applying Data Mining Techniques to E-Learning Problems" Vol. 2, No. 6.
  23. Mamcenko, J. , Sileikiene, I. , 2013, "Analysis of E-exam Data using Data Mining Techniques", Vol. 10, No. 7.
  24. Khedr, A. Elsayed, "Towards Three Dimensional Analyses for Applying E-Learning Evaluation Model: The Case of E-Learning in Helwan University", International Journal of Computer Science Issues (IJCSI), Vol. 9: Issue: 4: No. 1, July 2012, Pp. 161-166.
  25. Amira M. Idrees, Ayman E. Khedr and Ahmed I. El Seddawy, 2014, "Performance Tuning of K-Mean Clustering Algorithm a Step towards Efficient DSS ", Vol. 2, No. 6.
Index Terms

Computer Science
Information Sciences

Keywords

Moodle VLE LMS MLE EDM DSS DM MIS Clustering Classification Association Rule K-Mean Olapand Visualization

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