Evaluating Splitting Approaches in the Context of Student Dropout
  Prediction
Bruno de M. Barros Hugo A. D. do Nascimento Raphael Guedes Sandro E. Monsueto
The prediction of academic dropout, with the aim of preventing it, is one of
the current challenges of higher education institutions. Machine learning
techniques are a great ally in this task. However, attention is needed in the
way that academic data are used by such methods, so that it reflects the
reality of the prediction problem under study and allows achieving good
results. In this paper, we study strategies for splitting and using academic
data in order to create training and testing sets. Through a conceptual
analysis and experiments with data from a public higher education institution,
we show that a random proportional data splitting, and even a simple temporal
splitting are not suitable for dropout prediction. The study indicates that a
temporal splitting combined with a time-based selection of the students'
incremental academic histories leads to the best strategy for the problem in
question.