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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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Career paths and university education: factors that determine the employment status of university graduates
(Frontiers Media, 2025-01-01)
Employment outcomes are more strongly associated with specific career paths than with academic performance. Despite expanding university access, significant gaps persist between the training received and labor market conditions. The objective was to identify and analyze the factors that influence the employment situation of university graduates. A quantitative explanatory approach was used, with a sample of 3,009 graduates. A structured survey was administered, and the data were analyzed using Logistic regression, Lasso regression, and Random Forest models. The results show that the variables with the greatest predictive power are the type of contract, time spent working, and income level. In contrast, academic variables such as GPA and theoretical or practical training showed little relevance. In comparison, employability is more associated with specific career paths than academic merits. The study reveals important findings for universities to strengthen applied training, encourage early entry into the workforce, and develop monitoring systems that allow them to adapt their educational offerings to the real demands of the professional environment. Understanding the factors that influence graduate employability is crucial to enhancing the significance of education and improving professional opportunities.
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Artificial intelligence skills and their impact on the employability of university graduates
(Frontiers Media, 2025-01-01)
Artificial intelligence (AI) has emerged as a transformative technology in multiple areas, including the labor market. Its incorporation into organizations redefines professional profiles, required skills, and employability conditions. In this context, it is essential to understand how university graduates are preparing to face these changes and what role their AI skills play in their integration into the workforce. The study aimed to analyze the level of AI skills and their impact on the employability of university graduates through a quantitative and descriptive design. A survey was conducted with a sample of 148 undergraduate and graduate graduates. The data were analyzed using descriptive statistics and visualized using graphs. The results indicated that graduates who report greater knowledge and more frequent use of AI tools, especially generative ones such as ChatGPT, are more likely to be employed in areas related to their majors and to perceive higher productivity and better professional alignment. However, a generational gap in digital skills was also identified, as well as a widespread feeling of insufficient preparation for the challenges of the current labor market. The conclusion is that AI skills are consolidating as a key differentiating factor in employability and that their formal incorporation into university curricula is urgently needed. The implications of the study point to the need for an educational transformation that integrates AI as a transversal skill, promotes ongoing teacher training, and fosters policies that guarantee inclusive education aligned with the challenges of the digital age.
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