Algorithmic Decision Support for Personalized Education

Abstract

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.

Meeting Name

17th IEEE International Conference on Information Reuse and Integration, IRI 2016 (2016: Jul. 28-30, Pittsburgh, PA)

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Comments

Invited Paper

International Standard Book Number (ISBN)

978-1-5090-3207-5

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

30 Jul 2016

Link to Full Text

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