Algorithmic Support for Personalized Course Selection and Scheduling
The work presented in this paper demonstrates the use of context-aware recommendation to facilitate personalized education, by assisting students in selecting courses and course content and mapping a trajectory to graduation. The recommendation algorithm considers a student's profile and their program's curricular requirements in generating a schedule of courses, while aiming to reduce attributes such as cost and time-to-degree. The resulting optimization problem is solved using integer linear programming and graph-based heuristics. The course selection algorithm has been developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with assurance that recommended selections are always valid.
T. Morrow et al., "Algorithmic Support for Personalized Course Selection and Scheduling," Proceedings of the IEEE 44th Annual Computers, Software, and Applications Conference (2020, Madrid, Spain), pp. 143-152, Institute of Electrical and Electronics Engineers (IEEE), Sep 2020.
The definitive version is available at https://doi.org/10.1109/COMPSAC48688.2020.00027
IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC (2020: Jul. 13-17, Madrid, Spain)
Electrical and Computer Engineering
Keywords and Phrases
Context-Aware Recommendation; Integer Linear Programming; PERCEPOLIS; Personalized Education
International Standard Book Number (ISBN)
Article - Conference proceedings
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22 Sep 2020