September 10, 2025Ingenieria SolidariaOpen Access

Predictive model based on artificial intelligence and contextual awareness to identify students at risk of dropping out of university in Panama

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The Random Forest model effectively predicts university dropout rates, offering a balance of accuracy and generalization.
The study employed machine learning techniques, including Logistic Regression, Random Forest, and Gradient Boosting.
Using a context-aware approach, the model integrates academic, sociodemographic, and psychological factors to evaluate dropout risk.
Potential impact on reducing dropout rates highlights the relevance of innovative predictive models in educational settings.

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Observational analysis identifies dropout risks in university students, suggesting AI can enhance retention efforts.

Arenales et al. (2025) studied this question.

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