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• W3155376332 abstract "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." @default.
• W3155376332 created "2021-04-26" @default.
• W3155376332 creator A5015026639 @default.
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• W3155376332 date "2021-04-20" @default.
• W3155376332 modified "2023-09-27" @default.
• W3155376332 title "Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning" @default.
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• W3155376332 hasPublicationYear "2021" @default.
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