Student dropout prediction (SDP) is a specific problem in the multidisciplinary field of Learning Analytics (LA). It aims to analyze student withdrawal in distance learning environments by modeling the student behavior when interacting with e-learning platforms. Student dropout prediction should be treated with significant importance because, in the last decade, online courses have propelled a new era on education. Although online education systems have started in the mid 1990, little attention has been paid to the difficulties that these students experience during their studies. The recent diffusion of online courses (especially Massive Open Online Courses - MOOCs), with their enormous number of enrolled students - out of which only a fraction completes their studies successfully - has led to an increased interest on this problem. As a consequence, a growing number of online institutions have commenced to consider the adoption of automated strategies to help predicting their students' withdrawal decision.
In this project we propose to develop solutions based on data analytics and machine learning to support specific actions to favor the reduction of dropouts in distance degree courses. The possibility of analyzing data containing the "digital traces" of students enrolled in these courses (such as data on access to videotaped material, interaction on forums, questions to tutors, time spent listening to individual teaching modules, etc.), can support the development of advanced analytical models, which may favor the early identification of students in difficulty, and the development of customized actions.
