3. Producción

Browse

Settings

Author: search.filters.author.Einstein Sánchez BardalesSubject: search.filters.subject.Academic performance

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Data Mining to Identify University Student Dropout Factors
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-11-01)
    University dropout poses academic, social, and economic challenges that call for effective prevention strategies. The objective was to identify determining factors of student dropout through educational data mining and machine learning models. A survey was administered to 527 undergraduate students, and the data were processed with classification algorithms (Adaboost, Gradient Boosting, Extra Trees, Random Forest, Decision Tree, and XGBoost), complemented with interpretation techniques such as SHAP and sensitivity analysis. The results revealed that, in addition to prior academic performance (GPA), psychological support emerged as the most influential predictor across all models, followed by institutional and socioeconomic variables, including academic program, age, and parental job stability. Integrating psychological, institutional, and family factors into predictive systems enhances model accuracy and provides practical evidence to inform educational policies, strengthen student support programs, and design early interventions to promote retention in higher education.
      2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Analysis of factors affecting the academic performance of university students using machine learning
    (Nature Portfolio, 2025-12-01)
    Understanding the determinants of university students' academic performance has become a strategic priority for higher education institutions, especially in contexts marked by social, economic, and academic diversity. However, performance assessment remains a challenge due to the complexity of the educational process and the nonlinear nature of learning behaviors. A machine learning-based prediction model was developed using primary data from 386 university students. The performance of nine educational data mining (EDM) algorithms, including XGBoost, Random Forest, artificial neural networks (ANNs), Support Vector Machines, Decision Trees, Naive Bayes, Logistic Regression, AdaBoost, and K-Nearest Neighbors (KNN), was evaluated using demographic, socioeconomic, academic, social and family, health and wellness, infrastructure and services, and time management and extracurricular activity factors. The results reveal that machine learning is an effective tool for representing nonlinear relationships between academic performance and its determinants, allowing for accurate prediction of academic outcomes and explaining the individual contribution of each variable through sensitivity and interpretability analyses. Despite differences in their predictive accuracy, all algorithms effectively modeled educational dynamics. In particular, those models that integrate multiple student dimensions demonstrated better generalization capabilities. It is concluded that, to achieve an accurate assessment of academic performance in diverse university environments, it is essential to consider influencing factors in machine learning-based predictive models.
      5