Estimating student dropout in distance higher education using semi-supervised techniques

Nowadays, distance higher education has rapidly increased due to advance and integration of information and communications' technology. Students who attend online distance courses have often family obligations and job commitments and are usually in 'high risk' of dropout during their attendance. It is of a highly importance to identify such students, through paying extra attention and support to them could possibly minimize the possibility of student failure or even dropout. The present research intends to study whether semi-supervised techniques could be useful in student dropout prediction in distance higher education. Semi-supervised learning aims to generate reliable predictions using few labeled and many unlabeled data. Labeled data are difficult obtainable quite often, as they require many experts, a lot of human effort and time in experiments. As far as, we are aware in several studies propose and compare supervised methods for students' dropout prediction rates in higher education, but none of them investigates the effectiveness of semi-supervised methods. The results of our experiments reveal that a good predictive accuracy can be achieved using few labeled data in comparison to well known supervised learning algorithms. For that purpose we have developed a web-based tool to estimate if an individual student is going to dropout.