Early dropout prediction in distance higher education using active learning

Students' dropout prediction in higher education is an important and challenging research topic for universities. The successful implementation of a distance learning course is fundamental for educational institutions, for this reason the reduction of dropout rates is of vital importance. Although the use of machine learning methods in the educational field is relatively new, significant studies have been presented in recent years dealing with the dropout phenomenon. These studies point out several factors influencing the successful course completion, while indicating the complexity and difficulty of accurate early dropout prediction. The main purpose of this research is to investigate the efficiency of active learning methodologies to predict students' dropout rates in a distance web-based course in a timely manner. Active learning is a typical of methods trying to effectively use unlabeled data along with a small amount of labeled ones. A plethora of experiments are conducted using a variety of active learners indicating that an early prediction of high-risk students can be obtained.