A semi-supervised self-trained two-level algorithm for forecasting students’ graduation time

During the last decades, educational data mining has become a significant tool for the prediction of students’ progress and performance. In this work, we present a new semi-supervised self-trained two-level classification algorithm for predicting students’ graduation time. The proposed algorithm ha s three major features: Firstly, it identifies with high accuracy the students at-risk of not completing their studies; secondly, it classifies the students based on their expected graduation time; thirdly, it meaningfully relates the explicit classification information of labeled data with the information hidden in the unlabeled data. Our preliminary numerical experiments indicate that the proposed algorithm exhibits reliable predictions based on the students’ performance during the first two years of their studies.