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This document presents a systematic literature review on the use of machine learning techniques to predict student dropout in higher education institutions. The review identifies various attributes and approaches used in current research, highlighting that Decision Trees are the most commonly employed method, with accuracy, recall, and precision as key evaluation metrics. A total of 21 studies were analyzed, revealing that personal/social and academic characteristics are the most frequently examined factors

Author

ivan.ramos

Language

EN