Volume 4 Number 6 (Dec. 2014)
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IJEEEE 2014 Vol.4(6): 423-434 ISSN: 2010-3654
DOI: 10.17706/IJEEEE.2014.V4.356

Rough Set Theory Based Reasoning of Learning Style in e-Learning

Hemant Rana, Manohar Lal
Abstract— The growth of e-learning is expanding tremendously. In this context, LMS is software for handling various management related activities in respect of learning and its delivery in online mode. The proposed system provides the learning content according to learner's learning style using the extracted rule. Rough sets may be seen as an emerging tool & technique for extracting knowledge from a large set of data. Rough set theory is particularly useful for discovering relationships and used to deal with imprecise or incomplete data. This is a case study in which we suggest an effective way to extract rule which can decide learner's learning style in e-learning environments through RSES software. In this study, we used concept of reducts to extract appropriate knowledge from large datasets and calculate confidence factor for conflicting rules. Rough Set Theory in e-learning environment can bring immense potential and will make E-learning procedure more interesting, decision friendly, and user friendly. The proposed system will be able to increase efficiency of learning as providing learning contents based on learner’s style.

Index Terms— Decision support system, DSS, e-learning, learning style, LMS, rough set theory.

Hemant Rana is with UCMS, Delhi, India. Tel.: 9891679866; email: hemant.mca08@yahoo.in
Manohar Lal is with SOCIS, IGNOU, New Delhi, India.

Cite: Hemant Rana, Manohar Lal, " Rough Set Theory Based Reasoning of Learning Style in e-Learning," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 4, no. 6, pp. 423-434, 2014.

General Information

ISSN: 2010-3654 (Online)
Abbreviated Title: Int. J. e-Educ. e-Bus. e-Manag. e-Learn.
Frequency: Quarterly
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Ms. Nancy Lau
Abstracting/ Indexing: EBSCO, Google Scholar, Electronic Journals Library, QUALIS, ProQuest, INSPEC (IET)
E-mail: ijeeee@iap.org
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