  High dropout rates in tertiary education expose a lack of efficiency that
causes frustration of expectations and financial waste. Predicting students at
risk is not enough to avoid student dropout. Usually, an appropriate aid action
must be discovered and applied in the proper time for each student. To tackle
this sequential decision-making problem, we propose a decision support method
to the selection of aid actions for students using offline reinforcement
learning to support decision-makers effectively avoid student dropout.
Additionally, a discretization of student's state space applying two different
clustering methods is evaluated. Our experiments using logged data of real
students shows, through off-policy evaluation, that the method should achieve
roughly 1.0 to 1.5 times as much cumulative reward as the logged policy. So, it
is feasible to help decision-makers apply appropriate aid actions and,
possibly, reduce student dropout.
