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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Internet
Connect to this object online.3rd Floor Main Library
| Call Number: |
A1234.567 |
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| Copy 1 | Available |