Improving the evaluation process of students’ performance utilizing a decision support software

During the last decades, educational data mining constitutes a significant tool, offering a first step and a helping hand in the prediction of students’ progress and performance. In this work, we present a user-friendly decision support software, for accurately predicting the students’ performance at the final examinations of the academic year. The proposed software incorporates a classification scheme which has two major features. Firstly, it identifies with high accuracy the students at-risk of failing the final examinations; secondly, it classifies the students based on their predicted grades. Our numerical experiments show that it achieves better performance than any examined single learning algorithm. The proposed software was developed to provide assistance to students’ evaluation and mostly to the early identification of students’ at-risk in order to take proper actions for improving their performance.