Sequential Three-Way Decisions for Reducing Uncertainty in Dropout Prediction for Online Courses

Massive Open Online Courses (MOOCs) allow accessing qualitative online educational resources for huge amounts of online students. In this context, the dropout phenomenon is known as a nasty problem faced by several existing studies proposing methods and techniques to make predictions on students who are at risk of dropping out. Although the majority of such studies adopt traditional classification algorithms based on supervised methods, the present work proposes a sequential approach based on Three-Way Decisions and Neighborhood Rough Sets. The underlying idea is to exploit weekly data in order to classify, with high levels of precision, students who are likely going towards dropout or not. In cases of uncertainty, the classification decision is deferred to the next week, when new data is available. Such an approach has the advantage to preserve resources and avoiding wasting them with students erroneously classified at risk of dropout. The sequential application of the approach makes the recall increase as new data is gathered.