Algorithmic Decision Support for Personalized Education
This paper describes algorithmic decision support that facilitates recommendation of course schedules personalized to the background and interests of a given student. More specifically, recommendations are made with prioritized consideration of four categories of information: (1) degree requirements, (2) student interests, (3) student performance, and (4) time-to-degree. All four categories of information may not be available for a given student. The algorithm generates personalized recommendations by constructing a graph from degree requirements, identifying critical paths in the graph, and placing such paths within a course schedule. We describe the implementation of the algorithm in the context of PERCEPOLIS, a Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support, a framework constructed in our earlier work on personalized learning.
T. Morrow et al., "Algorithmic Decision Support for Personalized Education," Proceedings of the 17th IEEE International Conference on Information Reuse and Integration (2016, Pittsburgh, PA), Institute of Electrical and Electronics Engineers (IEEE), Jul 2016.
The definitive version is available at https://doi.org/10.1109/IRI.2016.32
17th IEEE International Conference on Information Reuse and Integration, IRI 2016 (2016: Jul. 28-30, Pittsburgh, PA)
Electrical and Computer Engineering
Intelligent Systems Center
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
30 Jul 2016