PREDICTING STUDENT PERFORMANCE IN A CORE ENGINEERING COURSE USING DECISION TREE METHOD

Abstract

This paper aims at using decision tree method to predict student performance in one of the core engineering courses: Strength of Materials. Three research questions are taken into consideration: 1) Can student performance be predicted by using Decision Tree? 2) Do a student's score in prerequisite course, Current Semester and Cumulative GPA play a significant role in student performance throughout the related course? 3) Does Decision Tree predict more accurate than traditional regression techniques (Artificial Neural Network and Multivariate Linear Regression)? It is believed that this study will be helpful for researchers and lecturers as a gap is found in the literature. Applications and results of the methods have shown that decision tree is a powerful tool to predict the student performance in a core course.