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Call for Paper - May 2015 Edition
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A Framework for Recommendation of courses in E-learning System

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 35 - Number 4
Year of Publication: 2011
Authors:
Sunita B. Aher
Lobo L.M.R.J.
10.5120/4387-6091

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. Full text available. BibTeX

@article{key:article,
	author = {Sunita B. Aher and Lobo L.M.R.J.},
	title = {Article: A Framework for Recommendation of courses in E-learning System},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {35},
	number = {4},
	pages = {21-28},
	month = {December},
	note = {Full text available}
}

Abstract

The course recommendation system in e-learning is a system that suggests the best combination of subjects in which the students are interested.

In this paper, we propose a framework for recommendation of courses in the E-learning system. In our approach we collect the data for example student enrollment for a specific set of course. After getting data, we use different combination of algorithm & we analyze the suitability of combination applied for recommendation. In this paper we outline our architecture & we apply the association rule mining at preliminary stage.

References

  • 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.
  • 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.
  • C. Carmona, G. Castillo and E. Millán: Discovering Student Preferences in E-learning, EC-TEL07, pp.33-42 (2007)
  • Hamalainen, W., Suhonen, J., Sutinen, E., & Toivonen, H. (2004). Data mining in personalizing distance education courses. In World conference on open learning and distance education, Hong Kong (pp. 1–11).
  • Zaı¨ane, O. (2002). Building a recommender agent for e-learning systems. In Proceedings of the international conference in education, Auckland, New Zealand (pp. 55–59).
  • Resende, S.D., Pires, V.M.T.: Using Data Warehouse and Data Mining Resources for Ongoing Assessment of Distance Learning. In: IEEE International Conference on Advanced Learning Technologies, ICALT 2002. (2002).
  • “Data Mining Introductory and Advanced Topics” by Margaret H. Dunham
  • Weka (2007). http://www.cs.waikato.ac.nz/ml/weka/.
  • http://www.educationaldatamining.org/ Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.
  • 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 (ISBN: 978-93-80864-71-13)
  • Cristóbal Romero, Sebastián Ventura, Pedro G. Espejo and César Hervás: Data Mining Algorithms to Classify Students