Predicting academic performance using machine learning models: A systematic review of the literature
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Abstract
Academic performance prediction has become an area of growing interest in higher education, due to its potential to identify and support at-risk students before they face academic difficulties. This study focuses on the application of machine learning models to predict academic performance, exploring different variables and techniques used in recent research. Through a systematic review of the literature, studies that use ML to predict academic success were analyzed, identifying the most effective variables, criteria, techniques and methodologies. The results highlight the impact of variables such as academic history, sociodemographic, economic and cultural factors on student performance, as well as the effectiveness of techniques such as artificial neural networks, decision trees and support vector machines. Finally, the implications of these findings for the development of more efficient and personalized educational interventions are discussed.
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Ronaldo Andre Del Carpio-Mendoza, Universidad Nacional Mayor de San Marcos - Perú
https://orcid.org/0009-0006-6854-038X
Degree in Mathematics, Universidad Nacional de San Agustín, Master's degree student in Systems Engineering and Computer Science with a minor in Software Engineering, Universidad Nacional Mayor de San Marcos. Professor at the Universidad Católica San Pablo.
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