Predicting Student Performance in Distance Higher Education Using Semi-supervised Techniques

Students’ performance prediction in distance higher education has been widely researched over the past decades. Machine learning techniques and especially supervised learning have been used in numerous studies to identify in time students that are possible to fail in final exams. The identification of in case failure as soon as possible, could lead the academic staff to develop learning strategies aiming to improve students’ overall performance. In this paper, we investigate the effectiveness of semi-supervised techniques in predicting students’ performance in distance higher education. Several experiments take place in our research comparing to the accuracy measures of familiar semi-supervised algorithms. As far as, we are aware various researches deal with students’ performance prediction in distance learning by using machine learning techniques and especially supervised methods, but none of them investigate the effectiveness of semi-supervised algorithms. Our results confirm the advantage of semi-supervised methods and especially the satisfactory performance of Tri-Training algorithm.