Development of an Expert System for Identifying Students' Learning Styles Using the Euclidean Probability Method

Learning styles play an important role in determining the most effective teaching strategies by aligning instructional methods with students’ individual preferences in receiving, processing, and understanding information. However, classroom teaching is often applied uniformly, disregarding the differences in learning styles among students. This can hinder the effectiveness of the learning process. This research aims to develop a web-based expert system using the Euclidean Probability method to identify the dominant learning styles of students at SMK Negeri 3 Lhokseumawe. The system processes input data representing student characteristics and calculates the proximity to each learning style category using the Euclidean distance formula. A total of 110 student data entries were analyzed, revealing that 32 students (29.09%) had a Visual learning style, 26 students (23.64%) were Auditory, 16 students (14.55%) were Read/Write, and 36 students (32.73%) were Kinesthetic learners. The results showed that the Kinesthetic learning style was the most dominant among students. Therefore, this expert system can efficiently assist in determining students' learning styles, allowing for quick and accurate identification of their learning preferences. This supports the development of more personalized and adaptive learning strategies, which are expected to enhance student engagement and learning outcomes.