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Author: search.filters.author.Jorge Luis Maicelo GuevaraSubject: search.filters.subject.Academic performance

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    Use of technological resources in the era of artificial intelligence and its effect on academic performance
    (Frontiers Media, 2026-07-08)
    The advancement of digital technologies and generative artificial intelligence (GenAI) has transformed learning environments in higher education; while tools such as ChatGPT offer personalized experiences and immediate feedback, their relationship with academic performance remains insufficiently understood in university contexts. This study analyzed the use of technological resources in the AI era, considering frequency of use, perceived usefulness, familiarity, and type of use, as well as their contribution to university students' academic performance and identifying technological and contextual factors associated with it. A data-mining approach was employed, applying 10 supervised regression algorithms using 10-fold cross-validation, complemented by interpretability analysis using SHAP values to identify relevant predictors. Regularized regression models (Elastic Net; Lasso) showed the best relative predictive performance. The SHAP analysis revealed that the field of study (? 0.41) and study cycle (? 0.34) are the dominant predictors, while the use of AI for academic writing (? 0.10) emerged as the technological variable with the greatest positive contribution to academic GPA within the model. Academic performance was mainly associated with structural factors related to students' educational trajectories, while AI tools showed a complementary role. The predictive contribution of technology appears to depend more on pedagogical integration than on frequency of use alone. These findings suggest the need to integrate digital strategies into higher education, implementing them in a differentiated manner by discipline and academic level, and promoting the guided and purposeful use of AI and technological resources in learning processes.
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    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.
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    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.
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