Potentialities of Learning Analytics to Overcome Students Dropout in Distance Higher Education

Abstract

In the distance higher education context, the understanding of the dropout phenomenon has progressed, moving from the perception that it is a sign of the deficient quality of the education system to the perception that it is an explicit sign of individual choice, which leads to underlining the importance of studying how dropouts learn in online courses. Completion or dropout of students in higher education is a subject that needs deep research. Learning analytics (LA) can be used as a modern alternative to help predict possible risks of failure and prevent them. The aim of this work is to highlight the potential of the learning analytics technique to mitigate or even prevent the phenomenon of student dropout in online higher education. Thus, a state of the art of learning analytics by describing contributions and their applications is established. The study shows that the evolution of learning analytics technology makes it possible to analyse the cumulative database of students summarizing their experiences during the course to predict students at risk of dropping out.