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Evaluating the effectiveness of e‐learning system in uncertainty

Evaluating the effectiveness of e‐learning system in uncertainty Purpose – Electronic learning (e‐learning) has gradually become an important part of university education. There is a trend among universities in Taiwan to offer more and more e‐learning courses. The effectiveness of teaching or learning in an e‐learning system can be quantified by multi‐criteria measures. The purpose of this study was to evaluate the effectiveness of teaching or learning in an e‐learning system measures in linguistic preferences. Design/methodology/approach – A generalized quantitative evaluation model that considers both the interdependence among measures and the fuzziness of subjective perception is currently lacking in the literature. The results indicated that the fuzzy analytical network process is a simple, suitable, and effective method of identifying the primary measures that influence the effectiveness of e‐learning, specifically in the context of interdependent measures and varying linguistic preferences. Findings – The most significant measures of e‐learning effectiveness were the quality of the e‐learning system and learner attractiveness. Enhanced usage of multimedia features can attract learner attention and may eventually increase learner attractiveness. Reducing the waiting time for learning materials to load may improve the quality of the system. Furthermore, the management should actively maintain and improve the responsiveness of instructors to learner inquiries. Originality/value – The main contributions of this study are twofold. First, the evaluation can be considered as a complex‐dependence, hierarchical decision‐making problem. This study contains a review of the literature and identifies 21 criteria and five aspects to measure e‐learning system effectiveness. Second, this study integrates fuzzy set theory and the ANP to develop an evaluation model that prioritizes the relative weights of the proposed measures. The proposed method can be used to handle dependence within a set of measures and to construct a hierarchical structure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Management & Data Systems Emerald Publishing

Evaluating the effectiveness of e‐learning system in uncertainty

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0263-5577
DOI
10.1108/02635571111144955
Publisher site
See Article on Publisher Site

Abstract

Purpose – Electronic learning (e‐learning) has gradually become an important part of university education. There is a trend among universities in Taiwan to offer more and more e‐learning courses. The effectiveness of teaching or learning in an e‐learning system can be quantified by multi‐criteria measures. The purpose of this study was to evaluate the effectiveness of teaching or learning in an e‐learning system measures in linguistic preferences. Design/methodology/approach – A generalized quantitative evaluation model that considers both the interdependence among measures and the fuzziness of subjective perception is currently lacking in the literature. The results indicated that the fuzzy analytical network process is a simple, suitable, and effective method of identifying the primary measures that influence the effectiveness of e‐learning, specifically in the context of interdependent measures and varying linguistic preferences. Findings – The most significant measures of e‐learning effectiveness were the quality of the e‐learning system and learner attractiveness. Enhanced usage of multimedia features can attract learner attention and may eventually increase learner attractiveness. Reducing the waiting time for learning materials to load may improve the quality of the system. Furthermore, the management should actively maintain and improve the responsiveness of instructors to learner inquiries. Originality/value – The main contributions of this study are twofold. First, the evaluation can be considered as a complex‐dependence, hierarchical decision‐making problem. This study contains a review of the literature and identifies 21 criteria and five aspects to measure e‐learning system effectiveness. Second, this study integrates fuzzy set theory and the ANP to develop an evaluation model that prioritizes the relative weights of the proposed measures. The proposed method can be used to handle dependence within a set of measures and to construct a hierarchical structure.

Journal

Industrial Management & Data Systems – Emerald Publishing

Published: Jun 28, 2011

Keywords: E‐learning system effectiveness; Analytical network process; Fuzzy set theory; Computer based learning; Taiwan

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