IJIET 2017 Vol.7(11): 809-813 ISSN: 2010-3689
doi: 10.18178/ijiet.2017.7.11.977

Adoption of Feature Selection and Classification Techniques in a Decision Support System

Benilda Eleonor V. Comendador, Ariel M. Sison, and Ruji P. Medina

Abstract—This paper presents a decision support system prototype called eCourse Learning Analytics Decision Support System (eCLADSS) using J48 tree classifier and multiple linear regression models. The system identifies students who are falling behind in a course, notifies those at risk of not completing it, then informs the users the predicted grade a student is likely to obtain without intervention. The developed eCLADSS predicts the performance of the Learning Management System (LMS) users which may help the Distance Education (DE) students succeed in the blended learning approach being provided by the DE educators. It is a model-driven decision support system which provides a good platform for prediction model generation.

Index Terms—Learning analytics, decision support system, classification techniques.

The authors are with Technological Institute of the Philippines, Graduate Programs, Quezon City, Philippines (e-mail: bevcomendador@pup.edu.ph, ariel.sison@eac.edu.ph, ruji.medina@tip.edu.ph).

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Cite: Benilda Eleonor V. Comendador, Ariel M. Sison, and Ruji P. Medina, "Adoption of Feature Selection and Classification Techniques in a Decision Support System," International Journal of Information and Education Technology vol. 7, no. 11, pp. 809-813, 2017.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (Since 2019), EI(INSPEC, IET), EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net