Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning
This addresses student dropout in education by providing a tool for decision-makers, but it is incremental as it applies existing offline RL methods to a specific domain.
The paper tackled the problem of high dropout rates in tertiary education by proposing a decision support method using offline reinforcement learning to select aid actions for students, with experiments showing it could achieve 1.0 to 1.5 times the cumulative reward of the logged policy.
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.