Application of an Improved Genetic Algorithm Multicriteria Satisfaction Analysis with Use of Matlab Code: A Case Study of MOODLE - Evgenios Avgerinos; Nikolaos Manikaros; Roza Vlachou (Journal of Systemics, Cybernetics and Informatics)

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This paper presents an evaluation of user satisfaction with the MOODLE platform using the Genetic Algorithm Multicriteria Satisfaction Analysis (GA-MUSA) method, and compares the results to the conventional MUSA method. A questionnaire was developed and administered to 100 participants (students and professors), and the data was analyzed using both methods. The results showed that the GA-MUSA method produced a higher overall satisfaction level compared to the Conventional MUSA method. The study also conducted a correlation analysis to determine the relationship between demographic variables and satisfaction levels. The findings suggest that MOODLE experience is the most important demographic variable related to satisfaction levels. The present study contributes to the existing literature by providing valuable insights into the use of GA-MUSA method to evaluate user satisfaction with educative software.