Volume 4 Number 5 (Oct. 2014)
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IJEEEE 2014 Vol.4(5): 384-395 ISSN: 2010-3654
DOI: 10.7763/IJEEEE.2014.V4.353

Rough Set Based Decision Support System (RSBDSS) for e-Learning

Rajiv, Hemant Rana, Manohar Lal
Abstract— E-learning is a planned form of teaching and learning experience that uses a wide spectrum of technologies including, Internet and WWW to deliver education at a distance. In this context, LMS is software for handling various management related activities in respect of learning and its delivery in online mode. To facilitate quality education, the identification & selection of various factors that may influence a students’ academic performance is very important. The importance of personalization of learners’ needs has now been realized and is agreed to by most of the e-Learners, authors and the managers of web-based education system. Working positively on these factors may improve the performance of the student. The proposed system may be seen as a helping hand to creators of contents, educators and teachers of the course. In today’s scenario, Personalization is a topic of research and Rough set may contribute a lot. Rough sets may be seen as an emerging tool & technique in this respect. Rough set theory is particularly useful for discovering relationships and used to deal with imprecise or incomplete data. This process is commonly called knowledge discovery or data mining. E-learning systems like LMSs deal with a large number of students at a time, it is difficult to evaluate their level manually, in this context RSBDSS can act as an add-on with existing systems like LMS or LCMS to assist them as decision maker.

Index Terms— AI, DSS, e-learning, RSBDSS, rough set theory.

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

Cite: Rajiv, Hemant Rana, Manohar Lal, " Rough Set Based Decision Support System (RSBDSS) for e-Learning," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 4, no. 5, pp. 384-395, 2014.

General Information

ISSN: 2010-3654
Frequency: Quarterly (Since 2015)
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Mr. Ron C. Wu
Abstracting/ Indexing: Google Scholar, Electronic Journals Library, QUALIS, ProQuest, EI (INSPEC, IET)
E-mail: ijeeee@iap.org
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