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: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
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.