Keywords and Phrases
Academic advising; Course recommender; Course selection; Graduation path; Recommender system
"This work proposes an intelligent recommender approach to facilitate personalized education and help students in planning their path to graduation. The original research contribution of this work is to develop a recommender approach that pervasively personalizes and optimizes a student’s path to graduation by accounting for the student’s career interests and academic background. The approach is a multi-objective optimization problem, subject to institutional constraints, with the goal of optimizing the graduation path with respect to one or more criteria, such as time-to-graduation, credit hours taken, and alignment with student’s career interests. The efficacy of the approach is illustrated and verified through its application to undergraduate curriculum in Computer Science, Computer Engineering, and Electrical Engineering at Missouri University of Science and Technology. The proposed approach differs from others in that it combines personalized, content-based course recommendation and graduation path optimization into one multi-objective optimization problem while also maintaining a design that can easily be applied to various disciplines.
The proposed recommender approach is developed as a part of the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which is a system designed to offer several tools for assisting both students and advisors in the advising process. With this approach, PERCEPOLIS is able to generate a full path to graduation for a student satisfying both degree and institutional requirements, while reducing time-to-degree"--Abstract, page iii.
Hurson, A. R.
Stanley, R. Joe
Electrical and Computer Engineering
M.S. in Computer Engineering
Missouri University of Science and Technology
ix, 53 pages
© 2022 Nicolas Charles Dobbins, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Dobbins, Nicolas Charles, "Personalizing student graduation paths using expressed student interests" (2022). Masters Theses. 8106.