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.
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.
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.
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.
Tseng, M., Lin, R. and Chen, H. (2011), "Evaluating the effectiveness of e‐learning system in uncertainty", Industrial Management & Data Systems, Vol. 111 No. 6, pp. 869-889. https://doi.org/10.1108/02635571111144955Download as .RIS
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