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.
- 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
Available at: http://works.bepress.com/a-hurson/8/