New framework simulates student dropout using machine learning

Researchers have developed a new framework for simulating student dropout in higher education using machine learning. The system models dropout risk based on student engagement data from learning management systems and administrative records. It achieves high AUC scores on both training and test data, demonstrating its predictive capabilities, and includes a policy layer to compare different intervention scenarios. AI

IMPACT This framework could enable educational institutions to better predict and intervene in student dropout cases.

RANK_REASON The cluster contains a research paper detailing a new framework for policy simulation. [lever_c_demoted from research: ic=1 ai=1.0]

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• Rafael Silva

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