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Item type:Publication, 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.1 - Some of the metrics are blocked by yourconsent 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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data Mining to Identify Factors Associated with University Student Retention(Multidisciplinary Digital Publishing Institute (MDPI), 2026-04-01)Student retention has become a major challenge for higher education institutions due to the influence that academic, socioeconomic, family, and motivational factors exert on students' academic continuity. In this context, understanding the determinants that explain university persistence is essential for designing effective retention strategies. Based on the analysis of factors related to motivation, commitment, attitude, academic integration, and social and economic conditions, retention patterns were examined in a population of 532 university students, of whom 57.7% showed high retention, 38.2% medium retention, and 4.1% low retention. To identify the factors with the greatest influence on academic continuity, educational data mining techniques and supervised classification models were applied and evaluated using stratified 10-fold cross-validation. Tree-based ensemble models showed the most consistent predictive performance, with Random Forest achieving the best results (accuracy = 0.729 ± 0.058; F1-macro = 0.636 ± 0.136). Model interpretability was examined through SHAP analysis, which revealed that transportation conditions (0.249), task completion (0.170), absence of work obligations (0.168), and course completion (0.164) were the most influential predictors in the classification of retention levels. In addition, sensitivity analysis indicated that academic commitment accounts for 41.6% of the predictive impact, followed by motivation (23.5%). These findings demonstrate that student retention is shaped by the interaction of academic, motivational, and contextual factors and provide practical implications for the development of **early warning systems, personalized tutoring programs, psychosocial support initiatives, and financial assistance policies aimed at strengthening university retention.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Characterization of the stress level of university students using data mining algorithms(Frontiers Media, 2025-11-21)There is concern about the levels of stress faced by college students and their effects on mental health and academic performance. This study aimed to characterize academic stress levels in college students, using data mining algorithms to classify and predict risk patterns. Data were collected from 287 students using the SISCO Academic Stress Inventory, and classification algorithms and association rules were applied using WEKA software. The results revealed that 75.3% of the students experienced high stress levels, primarily linked to psychological reactions and academic demands. It also compared the predictive performance of 13 algorithms, where J48, LMT, and SimpleLogistic achieved classification accuracies above 89%, surpassing results previously reported in similar educational contexts. Association rule mining further showed that being single and childless was strongly correlated with elevated stress levels, highlighting demographic risk profiles often overlooked in earlier research. By integrating predictive modeling with demographic and behavioral factors, this study extended prior literature by showing how data mining can simultaneously classify and explain academic stress, offering actionable insights for universities to design targeted, evidence-based interventions.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of the digital competencies of regional micro and small enterprises (MSEs)(Springer Nature, 2026-12-01)Digital transformation is crucial for the sustainability and competitiveness of micro and small enterprises (MSEs). This study assessed the level of digital competencies of 119 regional MSEs using the DigComp 2.1 framework. Fifty-four percent of enterprises achieved an advanced level in digital communication and collaboration, and 47.9% in digital literacy. In contrast, only 25.2% achieved this level in content creation, and 24.4% in digital privacy and security. Ordinal regression analysis revealed that formal education (odds ratios OR = 11.6), digital communication and collaboration (OR = 4.7 × 105), and digital privacy and security (OR = 2.3 × 105) significantly increase the likelihood of advanced digital competencies. Although MSEs have made progress in integrating digital technologies, it is essential to strengthen training programs and support policies to close the identified gaps. The main limitation of this study is its non-probabilistic sampling in a single region, which restricts the generalization of the findings. Although the study focuses on a single region, the results provide a valuable and representative view of digital competencies. Future research should expand geographical coverage and use longitudinal designs to strengthen the generalization and analysis of the evolution of digital competencies over time.1 - Some of the metrics are blocked by yourconsent 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