Multi-objective Optimization of C4.5 Decision Tree for Predicting Student Academic Performance

Applying data mining methods in the educational field has gained a lot of attention among scientists over the last years. Educational Data Mining forms an ever-developing research area aiming to unveil the hidden knowledge in educational data and improve students' learning behavior and outcomes. To this end, a plethora of data mining methods have already been implemented in various educational settings solving a variety of tasks, among which the prediction of students' academic performance as well. Decision trees have proven to be a quite effective method for both classification and regression problems showing a number of considerable advantages, such as efficiency, simplicity, flexibility and interpretability. Moreover, configuration of parameter values has often a material impact on building optimal trees in terms of accuracy and/or size. In this context, the main objective of our study is to yield a highly accurate and interpretable classification tree for the early prognosis of students at risk of failing in a university course. Thereby, effective intervention and support actions could be initiated to motivate students and enhance their performance. The experimental results demonstrate that the induction of the C4.5 decision tree classifier through an evolutionary algorithm, such as the Speed -constrained Multi-objective Particle Swarm Optimization algorithm, yields more accurate and easier to construe trees.