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Robin Clausen – Grantee Submission, 2024
Early warning systems (EWS) using analytical tools that have been trained against prior years' data, can reliably predict dropout risk in individual students so that educators may intervene early to help avert this from happening. Risk profiles for dropouts aren't always useful since students often do not conform to the profiles. Researchers with…
Descriptors: Early Intervention, Predictor Variables, Potential Dropouts, At Risk Students
A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction
Nicoletti, Maria do Carmo; de Oliveira, Osvaldo Luiz – Higher Education Studies, 2020
In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characterized as the act of a student engaged in a course…
Descriptors: Artificial Intelligence, Man Machine Systems, Computation, Prediction
Alejo, Anna; Naguib, Karimah; Yao, Haogen – UNICEF, 2023
On March 11, 2020, the World Health Organization declared COVID-19 a global pandemic, resulting in disruptions to education at an unprecedented scale. In response to the urgent need to recover learning losses, countries worldwide have taken RAPID actions to: Reach every child and keep them in school; Assess learning levels regularly; Prioritize…
Descriptors: Educational Change, COVID-19, Pandemics, Educational Policy