Technical elective recommendation system using support vector machines

In certain periods of the university, apart from compulsory courses, it takes part in technical elective courses. Students studying at the undergraduate level are required to take a certain number of technical elective courses to complete their credits. However, since there is no guidance on which technical elective courses students take, they choose courses that are not related to their departments. In this study, a web-based system has been developed that can offer technical elective courses for students studying in the Department of Computer Engineering at the Faculty of Computer and Information Sciences at Sakarya University. With the help of this system, many students who are unstable will be provided to choose technical elective courses suitable for their interests. Many different learning techniques are used today, where artificial learning is popular. These are known as supported, unsupported and reinforced learning. Supported or partially reinforced learning can be used when the outputs of the available data are certain. Support Vector Machines, one of the most effective methods, were used in this learning. The web-based system was implemented in Asp.Net MVC environment and Accord.Net was used as the Support Vector library. After students enter the compulsory courses, the model that runs in the background lists the elective courses that they can succeed. As an expanded and future study, a more dynamic subject oriented system can be developed by considering not only the success grade but also subject-based achievements.