A Student Advising System Using Association Rule Mining

Raed Shatnawi, Qutaibah Althebyan, Baraq Ghaleb and Mohammed Al-Maolegi
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Raed Shatnawi: Jordan University of Science and Technology, Jordan
Qutaibah Althebyan: Jordan University of Science and Technology, Jordan
Baraq Ghaleb: Edinburgh Napier University, UK
Mohammed Al-Maolegi: Community College, Yemen

International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2021, vol. 16, issue 3, 65-78

Abstract: Academic advising is a time-consuming activity that takes a considerable effort in guiding students to improve student performance. Traditional advising systems depend greatly on the effort of the advisor to find the best selection of courses to improve student performance in the next semester. There is a need to know the associations and patterns among course registration. Finding associations among courses can guide and direct students in selecting the appropriate courses that leads to performance improvement. In this paper, the authors propose to use association rule mining to help both students and advisors in selecting and prioritizing courses. Association rules find dependences among courses that help students in selecting courses based on their performance in previous courses. The association rule mining is conducted on thousands of student records to find associations between courses that have been registered by students in many previous semesters. The system has successfully generated a list of association rules that guide a particular student to select courses. The system was validated on the registration of 100 students, and the precision and recall showed acceptable prediction of courses.

Date: 2021
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