Published: Mar 24, 2014
Abstract
Predicting the academic progress of student is an issue faced by many public universities in emerging countries. Although, those institutions stores large amounts of educational data, they fail to recognize the students that are in danger to leave the system. This paper presents a novel architecture that uses EDM techniques to predict and to identify those who are at dropout risk. This approach allows academic managers to monitor the progress of...
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