Naïve Bayes for Detecting Student’s Learning Style Using Felder-Silverman Index - Nurul Hidayat; Lasmedi Afuan (Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, 2021)
This paper focuses on detecting student learning styles using the Felder-Silverman Index Learning Style (FSLM). Providing Adaptivity based on learning styles can support students and make the learning process easier for them. However, the student learning styles need to be identified and understood to provide the appropriate adaptability. In this case, we use a questionnaire instrument to detect student’s learning styles. This paper analyses of students from Professional Education Teacher (PPG) at the Ministry of Research, Technology, and Higher Education (Kemenristek DIKTI). The results show that 1998 students who filled out the questionnaire obtained the following conclusions for each zone with a balanced learning style about 29.9% for dimension processing, 34.78% for input dimension, and 36.98% for understanding dimension. However, most students have a moderate sensing learning style with 31.13% for each zone for the dimension of perception. This research contributes to some areas, such as providing FSLSM learning style with a large dataset and capturing students' learning styles based on four dimensions.