On using video lectures data usage to predict university students dropout

Abstract

Technologies have changed many different aspects of people's life and the recent CoVid-19 pandemic proved that education is not an exception. But technologies in education go beyond the simple use of video lectures: technologies might be exploited to improve personal learning. In this paper, we focus on the dropout of studies, a global phenomenon that artificial intelligence techniques are trying to ameliorate. Here, we investigate whether data related to the consumption of video lectures might improve the students' dropout prediction. We consider first-year students enrolled in our Department and we characterize them with personal, scholastic, academic and technological features. Then, we measure the performance of three machine learning algorithms in terms of accuracy and sensitivity. The experimental evaluation shows that Random Forest and KNN perform better that Decision Tree and also shows that data related to the use of video lectures improves the prediction performance for some degree programs (reaching 73% in terms of accuracy and sensitivity). These preliminary results show that the approach is promising and worth exploring in future studies.