A Decision Support System for Course Offering in Online Higher Education Institutes

DOI

10.1080/18756891.2013.808428How to use a DOI?

Keywords

Decision Support system, E-learning, Neural networks, Course offering, Student course selection, What-if analysis, Goal seeking

Abstract

Prior to every academic semester, every department's administrator is required to offer the best overall set of courses to meet student requirements, instructor needs and department regulations. The key contributions of this research is firstly, determining the potential factors that influence student behavior on the online courses they choose, secondly, modeling the course offering problem and fitting a function to a training set of data using neural network approach, thirdly, design and implementation of a decision support system to help the department's administrator to simulate student behavior in course selection process and support his/her decisions on the courses to be offered, and lastly, employing the proposed decision support system to perform what-if analysis and goal seeking behavior. The samples of the experiments came from 298 online graduate courses in 14 academic terms from 2005 to 2011. The results revealed high prediction accuracy on the experimental data. The performance of the introduced decision support system was also compared with three well-known regression techniques, “support vector regression”, “K-nearest neighborhood”, “decision tree” and a traditional approach. The finding exposed that the suggested decision support system outperformed the others significantly.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Ahmad A. Kardan
AU  - Hamid Sadeghi
PY  - 2013
DA  - 2013/09/01
TI  - A Decision Support System for Course Offering in Online Higher Education Institutes
JO  - International Journal of Computational Intelligence Systems
SP  - 928
EP  - 942
VL  - 6
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.808428
DO  - 10.1080/18756891.2013.808428
ID  - Kardan2013
ER  -