Classification of Elective Courses According to Kolb Learning Style Inventory by Using Machine Learning Methods

Education and training systems provide students with the knowledge, skills and gains that they will use throughout their lives through the courses they contain. These courses have specific rules, content, learning outcomes and target audience. Students should choose the courses most appropriate for their academic and professional careers. Since there are many parameters in the course selection process, tools such as recommendation systems and decision support systems have been developed to assist students in the course selection process. While developing these systems, artificial intelligence methods are also used. In this study, students were classified by machine learning methods according to Kolb Learning Style Inventory. The course information of the classified students of the relevant class was compared with the results of the questionnaires which is made to the students. With the applied method based on Kolb’s learning style classes, the correct course selection process of the students was 92.07%. The results show that the proposed system can be used successfully in the course selection process.