Predictive analytics study to determine undergraduate students at risk of dropout
In this this work, a study is presented with quantitative variables using machine learning tools to detect undergraduate students at risk of dropping out and the factors associated with this behavior. Clustering algorithms and classification methods were tested to determine the predictive power of s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Education |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feduc.2023.1244686/full |