Predicting Students’ Academic Performance in the University Using Meta Decision Tree Classifiers

Abstract

Student performance prediction is an area of concern for educational institutions. At the University level learning system, the method or rule adopted to identify the candidates who pass or fail differs depending on various factors such as the course, the department of study and so on. Predicting the result of a student in a course is an issue that has recently been addressed using machine learning techniques. The focus of this work is to find a way to predict a student’s academic performance in the University using the machine learning approach. This is done by using the previous records of the student rather than applying course dependent formulae to predict the student’s final grade. In this work, meta decision tree classifier techniques based on four representative learning algorithms, namely Adaboost, Bagging, Dagging and Grading are used to construct different decision trees. REPTree is used as the decision tree method for meta learning. These four meta learning methods have been compared separately with respect to the training and test sets. Adaboost is found to be the best meta decision classifier for predicting the student’s result based on the marks obtained in the semester.