Predicting Student Performance in Distance Higher Education Using Active Learning

Students' performance prediction in higher education has been identified as one of the most important research problems in machine learning. Educational data mining constitutes an important branch of machine learning trying to effectively analyze students' academic behavior and predict their performance. Over recent years, several machine learning methods have been effectively used in the educational field with remarkable results, and especially supervised classification methods. The early identification of in case fail students is of utmost importance for the academic staff and the universities. In this paper, we investigate the effectiveness of active learning methodologies in predicting students' performance in distance higher education. As far as we are aware of there exists no study dealing with the implementation of active learning methodologies in the educational field. Several experiments take place in our research comparing the accuracy measures of familiar active learners and demonstrating their efficiency by the exploitation of a small labeled dataset together with a large pool of unlabeled data.