CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
Reseach Article

A Fuzzy Logic based Recommender System for E-Learning System with Multi-Agent Framework

by Himanshu Pandey, V. K Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 17
Year of Publication: 2015
Authors: Himanshu Pandey, V. K Singh
10.5120/21793-5140

Himanshu Pandey, V. K Singh . A Fuzzy Logic based Recommender System for E-Learning System with Multi-Agent Framework. International Journal of Computer Applications. 122, 17 ( July 2015), 18-21. DOI=10.5120/21793-5140

@article{ 10.5120/21793-5140,
author = { Himanshu Pandey, V. K Singh },
title = { A Fuzzy Logic based Recommender System for E-Learning System with Multi-Agent Framework },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 17 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 18-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number17/21793-5140/ },
doi = { 10.5120/21793-5140 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:10:49.644192+05:30
%A Himanshu Pandey
%A V. K Singh
%T A Fuzzy Logic based Recommender System for E-Learning System with Multi-Agent Framework
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 17
%P 18-21
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a multi agent based e-learning framework is proposed which is able to provide a personalized experience to the learner by recommending him study material according to his requirements, goals and calibre. A fuzzy logic based recommender agent framework is used to give further suggestions to learner to increase his/her satisfaction and provide enhanced and personalized learning experience. We also used the Matlab to simulate our recommender agent.

References
  1. Fuhua Lin, Peter Holt, Steve Leung and Qin Li , 2006. A multiagent and service-oriented architecture for developing adaptive e-learning systems, Systems Int. J. Cont. Engineering Education and Lifelong Learning, Vol. 16, Nos. 1/2, 2006 .
  2. Antonio GARCIA-CABOT, 2013. A proposal of a multi-agent system for adapting learning contents to user competences, context and mobile device, www. mtf. stuba. sk/docs/doc/casopis_Vedecke_prace/32SN/004_Garcia-Cabot. 2013
  3. E. Herrera-Viedma, A. G. L´opez-Herrera, 2010. A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling, International Journal of Computational Intelligence Systems, Spain. Vol. 3, No. 4 (October, 2010), 420-437.
  4. Mehmet Ali Salahlia ,MuzafferOzdemir a, CumaliYasar, 1982. Building a fuzzy knowledge management system for personalized elearning,Procedia - Social and Behavioral Sciences 46 ( 2012 ) 1978 – 1982.
  5. LivuiPanait, Sean Luke, 2005. Cooperative Multi-Agent Learning: The State of the Art, Springer: Autonomous Agents and Multi-Agent Systems, 11, 387–434, 2005.
  6. Shu-Hsien Liao, 2004. Expert system methodologies and applications—a decade review from 1995 to 2004, . Science direct: Expert Systems with Applications, (2004) 1–11.
  7. Félix Castro, Alfredo Vellido, ÀngelaNebot, and Francisco Mugica,, 2007. Applying Data Mining Techniques to e-Learning Problems, Springer: Applying Data Mining Techniques to e-Learning Problems, Studies in Computational Intelligence (SCI) 62, 183–221 (2007).
  8. E. Herrera-Viedmaa, F. Herreraa, L. Mart 0nezb, J. C. Herreraa, A. G. Lopez, 2004. Incorporating FiIltering techniques in a fuzzy linguistic multi-agent model for information gathering on the web, Science direct: Fuzzy Sets and Systems 148 (2004) 61–83.
  9. International journal of artificial intelligence and interactive multimedia. ISSN: 1989-1660 - VOL. II, NUMBER 1.
  10. Pham Quang Dung and Adina Magda Florea, 2013. Adaptation To Learning Styles In A Multi-Agent E-Learning System, Internet Learning, Volume 2, Number 1, Spring 2013.
  11. Elpiniki I. Papageorgiou and Jose L. Salmeron, 2013. A Review of Fuzzy Cognitive Maps ResearchDuring the Last Decade,IEEE Transactions on fuzzy systems, Vol. 21, No. 1, Februaury 2013.
  12. Elpiniki I. Papageorgiou, 2011. Review study on Fuzzy Cognitive Maps and theirapplications during the last decade, IEEE International Conference on Fuzzy Systems, June 27-30, 2011, Taipei, Taiwan.
  13. Marcelo Godoy Simoes, Introduction to Fuzzy Control. http://inside. mines. edu/~msimoes/documents/Intro_Fuzzy_Logic.
Index Terms

Computer Science
Information Sciences

Keywords

Multi-agent e-learning fuzzy logic personalization recommender system.