Association Rule Mining (ARM) includes one of the most popular algorithms called Apriori Algorithm (AA) in it. AA has some limitations and areas for improvement related to the execution time consumption. The extended AA has some improvements based on the existing algorithm AA. The main findings indicate that not much has been done in an educational data mining considering the volume of data that is observed. Therefore, it is intended to evaluate to what extent an ARM can be utilized in an educational data mining context. Specifically, this paper develops an extended AA mining algorithm and applies it to the higher education system, focusing on the student's course planning system. This project focuses on introducing AA and ARM, implementing the enhanced features of the AA in educational data, develop the course recommender model. Finally, it evaluates the enhanced algorithm compared to the existing AA to help build a course suggestion system for students.