A Student Advising System Using Association Rule Mining

Autor/inn/en	Shatnawi, Raed; Althebyan, Qutaibah; Ghaleb, Baraq; Al-Maolegi, Mohammed
Titel	A Student Advising System Using Association Rule Mining
Quelle	In: International Journal of Web-Based Learning and Teaching Technologies, 16 (2021) 3, S.65-78, Artikel 5 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1548-1093
Schlagwörter	Academic Advising; Course Selection (Students); Educational Technology; Information Technology; College Students; Foreign Countries; Data Collection; Information Retrieval; Faculty Advisers; Course Content; Jordan + Suchen Sie Ihr Suchwort? Akademischer Rat; Course selection; Student; Students; Kurswahl; Unterrichtsmedien; Informationstechnologie; Collegestudent; Ausland; Data capture; Datensammlung; Kursprogramm
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01