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Reseach Article

A multivariate LMS with �Hybrid Recommender System using Association Rules for Peer Learners

Published on August 2011 by M.Ravichandran, Dr.G.Kulanthaivel
International Conference on Advanced Computer Technology
Foundation of Computer Science USA
ICACT - Number 1
August 2011
Authors: M.Ravichandran, Dr.G.Kulanthaivel
c336bbe1-d4be-40c2-b58d-fa23d4ac6f80

M.Ravichandran, Dr.G.Kulanthaivel . A multivariate LMS with �Hybrid Recommender System using Association Rules for Peer Learners. International Conference on Advanced Computer Technology. ICACT, 1 (August 2011), 17-20.

@article{
author = { M.Ravichandran, Dr.G.Kulanthaivel },
title = { A multivariate LMS with �Hybrid Recommender System using Association Rules for Peer Learners },
journal = { International Conference on Advanced Computer Technology },
issue_date = { August 2011 },
volume = { ICACT },
number = { 1 },
month = { August },
year = { 2011 },
issn = 0975-8887,
pages = { 17-20 },
numpages = 4,
url = { /proceedings/icact/number1/3228-icact069/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advanced Computer Technology
%A M.Ravichandran
%A Dr.G.Kulanthaivel
%T A multivariate LMS with �Hybrid Recommender System using Association Rules for Peer Learners
%J International Conference on Advanced Computer Technology
%@ 0975-8887
%V ICACT
%N 1
%P 17-20
%D 2011
%I International Journal of Computer Applications
Abstract

In eLearning system sharing of event experiencehas attracted the cognitive process of researchers to improve the efficacy of learning . Now a days the higher learning environment losses of face to face interaction with various educators, lectures , facilitators and tutors. Recommender systems are increasingly being used in today’s world. Collaborative filtering, together with association rules mining are probably themost widely used methods to implement recommender systems. In this paper we undertake a review of past research conducted in the area of recommender systems with the focus being the use of association rule mining. We propose a novel methodology that combines the use of association mining with the use of distance metrics such as the Jaccard measure to identify effective e-Learners that belong to the same type to recommend appropriate LE’S to peer learners for the improvement of learning. . Our experimental results on the sample learners profile dataset shows that the use of the Jaccard metric improved the coverage of recommendations over the use of the standard association rule mining method.

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Index Terms

Computer Science
Information Sciences

Keywords

Datamining Clustering Classification Multivariate analysis e-Learning Experience sharing