Performance Evaluation of Learning Styles Based on Fuzzy Logic Inference System

Determining best convenient learning style in accordance with the individual's capabilities and personalities is very important for learning rapidly, easily, and in high quality. When it is thought that each individual has different personality and ability, it can be recognized that each individual's best convenient learning style will be different. Because of the importance of lifelong learning, many methods and approaches have been developed to determine learning styles of the individuals. In this study, a rule based fuzzy logic inference system is developed to determine best convenient learning styles of the engineering faculty stuffs and the students. During studies, two different learning style models namely Honey&Mumford and McCarthy are used in implementations. This study is carried out with a total number of 60 and 26 engineering faculty students and stuffs, respectively. The personal information form and Learning Style Preference Survey of Honey&Mumford and McCarthy are used to collect the data which are analyzed using the techniques of frequency, percentage, mean, standard deviation, and t-test. While Honey&Mumford learning style classifies engineering faculty students and stuffs as Activist, Reflector, Theorist, and Pragmatist; McCarthy learning style classifies them as Innovative, Analytic, Common Sense, and Dynamic. Gender, age, and department are selected as the metrics for evaluation of the system performance. (C) 2016 Wiley Periodicals, Inc