The Identification and Ranking of the Components for Measuring the Success of E-learning Systems in Universities using the Fuzzy Network Analysis Approach

Abstract

AbstractIntroduction: Nowadays, the Internet has created a virtual world and become an incentive for universities to invest in e-learning. And in order to accomplish this, they attempt to both use and evaluate e-learning systems. We aimed to identify and rank the components for measuring the success of universities using the fuzzy network analysis approach.Material and Methods: This applicable descriptive survey was done on the users of e-learning systems in the University of Sistan and Baluchestan as well as experts in the field of e-learning. Data were collected using related questionnaires. The reliability of questionnaire was evaluated using Cronbach's alpha for each factor separately as well as for the whole questionnaire. The total reliability coefficient of questions was 0.959. The single sample t test and fuzzy network analysis techniques were used to analyze data.Results: Components of information quality, service quality, system quality, supporting factors, teacher characteristics, student characteristics, and environmental factors are effective in the success of e-learning systems. Moreover, information quality with the relative weight of 0.23 and environmental factors with the relative weight of 0.05 have the maximum and minimum effect, respectively. Conclusions: Focus on data quality and educational content, providing suitable and faster services and infrastructure facilities for e-learning systems can improve e-learning systems in universities.