Authors:
Xiwei Wang
1
;
Longyin Cui
2
;
Muhammad Bangash
1
;
Mohammad Bilal
1
;
Luis Rosales
1
and
Wali Chaudhry
1
Affiliations:
1
Department of Computer Science, Northeastern Illinois University, Chicago IL, U.S.A.
;
2
Department of Computer Science, University of Kentucky, Lexington KY, U.S.A.
Keyword(s):
Course Enrollment, Recommender System, Matrix Factorization, Contextual Information.
Abstract:
As an integral component of human society, higher education has been undergoing a transformation in multiple aspects, such as administrative reorganization, pedagogical reform, and technological innovation. To line up with the latest trends, many institutions constantly update their curriculum, which poses challenges to students and their advisors. This paper proposes a machine learning-based course enrollment recommender system that aims to make personalized suggestions to students who expect to take classes in the upcoming semester. Using matrix factorization as the core algorithm, the model exploits several available types of information, including student course enrollment history and other contextual features, such as prerequisite restrictions, course meeting times, instructional methods, and course instructors. The system not only helps students but also facilitates their advisors’ work. Our experimental results show that the recommended courses were highly relevant while provi
ding plenty of options to students.
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