Abstract
The exploratory factor analysis (EFA) method is regarded as one of the most well-known statistical multivariate analysis methods, which is used to discover the underlying structure of a relatively large set of variables. The EFA has a wide range of applications due to its properties such as data reduction. In this paper, the fuzzy EFA (FEFA) method was developed to maintain the uncertain nature of the data related to the variables. The FEFA method is used to construct an e-learning service quality assessment model. Several assignment criteria have been classified to identify the strengths and weaknesses of an e-learning system, to provide an information system for educational institutions, and rank these information systems. The assessment measures of an e-learning service are determined from the students’ and users’ perspectives by exploring previous models and using open questionnaires. In addition, due to the typical uncertainty of assessment indicators, a questionnaire was designed with triangular fuzzy numbers to increase the value of the information collected from evaluating e-learning users. By implementing the developed FEFA, an e-learning assessment model was constructed with 13 latent variables including reliable infrastructure, benefits and financial support, government support, perception and knowledge, educational facilities, quality of holding classes, entrance conditions, meeting the needs of students, process of education, planning, flexibility of courses, professors’ opinion, and information exchange.
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Abbreviations
- AHP:
-
Analytic hierarchy process
- ANP:
-
Analytic network process
- CIT:
-
Critical incident technique
- EFA:
-
Exploratory factor analysis
- EFQUEL:
-
European Foundation for Quality in e-learning
- FA:
-
Factor analysis
- FEFA:
-
Fuzzy exploratory factor analysis
- FMEA:
-
Failure mode and effects analysis
- IPA:
-
Interaction process analysis
- TOPSIS:
-
Technique for order of preference by similarity to ideal solution
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Baradaran, V., Ghorbani, E. Development of Fuzzy Exploratory Factor Analysis for Designing an E-Learning Service Quality Assessment Model. Int. J. Fuzzy Syst. 22, 1772–1785 (2020). https://doi.org/10.1007/s40815-020-00901-1
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DOI: https://doi.org/10.1007/s40815-020-00901-1