Predicting undergraduate academic performance in a leading Peruvian university: A machine learning approach
Despite improved higher education accessibility in low and middle-income countries (LMICs), challenges persist in student drop-out, especially for socio-economically disadvantaged students. While machine learning models have enhanced our understanding of this challenge by predicting academic perform...
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| Main Authors: | , |
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| Format: | Book |
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Pontificia Universidad Católica del Perú,
2024-04-01T00:00:00Z.
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| Summary: | Despite improved higher education accessibility in low and middle-income countries (LMICs), challenges persist in student drop-out, especially for socio-economically disadvantaged students. While machine learning models have enhanced our understanding of this challenge by predicting academic performance, many studies overlook LMIC-specific institutional factors or focus on specific courses, limiting their generalizability and policy uses. To address these issues, the authors compiled a comprehensive database using administrative and census data to predict undergraduate academic performance at the Pontifical Catholic University of Peru (PUCP). The study found that the most effective models were tree-based ensembles, particularly Random Forest, with key predictors including prior secondary school performance and university admission test scores. They present a high-performing model using only ten features that can predict future academic performance and potentially aid in reducing student drop-out at PUCP. |
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| Item Description: | 10.18800/educacion.202401.M003 1019-9403 2304-4322 |