A Multi-Stage Approach to Personalized Course Selection and Scheduling
Abstract
Recommender systems that utilize pertinent and available contextual information are applicable to and useful in a broad range of domains. This paper utilizes context-aware recommendation to facilitate personalized education and assist students in selecting courses (or in non-traditional curricula, learning artifacts) that meet curricular requirements, leverage their skills and background, and are relevant to their interests. The research contribution described in this paper is a methodology that generates a schedule of courses (and associated course content) that takes into consideration a student's profile, while meeting curricular and prerequisite requirements and aiming to reduce attributes such as cost and time-to-degree. The optimization problem - multiple integer linear programming problems and a single scheduling problem - is solved in stages using a known linear solver as well as graph-based heuristics. The efficacy of the algorithm is demonstrated through a case study.
Recommended Citation
T. Morrow et al., "A Multi-Stage Approach to Personalized Course Selection and Scheduling," Proceedings of the IEEE International Conference on Information Reuse and Integration (2017, San Diego, CA), pp. 253 - 262, Institute of Electrical and Electronics Engineers (IEEE), Aug 2017.
The definitive version is available at https://doi.org/10.1109/IRI.2017.58
Meeting Name
IEEE International Conference on Information Reuse and Integration (2017: Aug. 4-6, San Diego, CA)
Department(s)
Computer Science
Second Department
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Curricula; Graphic Methods; Information Use; Optimization; Scheduling; Students; Context-Aware Recommendations; Contextual Information; Integer Linear Programming; Multistage Approach; Optimization Problems; PERCEPOLIS; Personalized Course; Personalized Learning; Integer Programming; Context-Aware Recommendation; Ontologies; Recommender Systems; Computational Modeling; Context Modeling; Object Oriented Modeling; Education Administrative Data Processing; Educational Courses; Graph Theory; Linear Programming; Ubiquitous Computing
International Standard Book Number (ISBN)
978-1538615621; 978-1538615638
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Aug 2017