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A Framework for Recommendation of courses in E-learning System

by Sunita B. Aher, Lobo L.M.R.J.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 4
Year of Publication: 2011
Authors: Sunita B. Aher, Lobo L.M.R.J.
10.5120/4387-6091

Sunita B. Aher, Lobo L.M.R.J. . A Framework for Recommendation of courses in E-learning System. International Journal of Computer Applications. 35, 4 ( December 2011), 21-28. DOI=10.5120/4387-6091

@article{ 10.5120/4387-6091,
author = { Sunita B. Aher, Lobo L.M.R.J. },
title = { A Framework for Recommendation of courses in E-learning System },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 4 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 21-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number4/4389-6091/ },
doi = { 10.5120/4387-6091 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:21:08.053574+05:30
%A Sunita B. Aher
%A Lobo L.M.R.J.
%T A Framework for Recommendation of courses in E-learning System
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 4
%P 21-28
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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

References
  1. 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.
  2. 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.
  3. C. Carmona, G. Castillo and E. Millán: Discovering Student Preferences in E-learning, EC-TEL07, pp.33-42 (2007)
  4. 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).
  5. 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).
  6. 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).
  7. “Data Mining Introductory and Advanced Topics” by Margaret H. Dunham
  8. Weka (2007). http://www.cs.waikato.ac.nz/ml/weka/.
  9. 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.
  10. 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)
  11. Cristóbal Romero, Sebastián Ventura, Pedro G. Espejo and César Hervás: Data Mining Algorithms to Classify Students
Index Terms

Computer Science
Information Sciences

Keywords

Moodle Weka Classification Association rule Clustering algorithm