UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 6
June-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1906E53


Registration ID:
215674

Page Number

690-693

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Title

IMPROVING MULTI-FACTOR PERFORMANCE IN E-LEARNING ENVIRONMENT

Abstract

Improving Multi-Factor Performance in E-Learning Environment ABSTRACT The main objective of e-learning systems is to improve the student learning performance and satisfaction. This can be achieved by providing a personalized learning experience that identifies and satisfies the individual learner's requirements and abilities. The performance of the e-learning systems can be significantly improved by exploiting dynamic self-learning capabilities that rapidly adapts to prior user interactions within the system and the continuous changes in the environment. In this paper, a multi-factor system using particle swarm optimization for the e-learning systems is proposed. The system incorporates five agents that take into consideration the variations in the capabilities among the different users. First, the project clustering agent is used to cluster a set of learning resources/projects into similar groups. Second, the student clustering agent (SCA) groups students according to their preferences and abilities. Third, the student-project matching agent is used to map each learner's group to a suitable project or particular learning resources according to specific design criteria. Fourth, the student-student matching agent is designed to perform the efficient mapping between different students. Finally, the dynamic SCA (DSCA) is employed to continuously track and analyze the student's behavior within the system such as changes in knowledge and skill levels. Consequently, the DSCA adapts the e-learning environments to accommodate these variations. Experimental results demonstrate the effectiveness of the proposed system in providing near-optimal solutions in considerably less computational time.

Key Words

Data Mining, Clustering, Agglomerative Hierarchical Clustering Algorithm, Major Determination in Senior High School

Cite This Article

"IMPROVING MULTI-FACTOR PERFORMANCE IN E-LEARNING ENVIRONMENT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.690-693, June-2019, Available :http://www.jetir.org/papers/JETIR1906E53.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"IMPROVING MULTI-FACTOR PERFORMANCE IN E-LEARNING ENVIRONMENT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp690-693, June-2019, Available at : http://www.jetir.org/papers/JETIR1906E53.pdf

Publication Details

Published Paper ID: JETIR1906E53
Registration ID: 215674
Published In: Volume 6 | Issue 6 | Year June-2019
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.21486
Page No: 690-693
Country: KANNIYAKUMARI, TAMILNADU, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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