Modelos predictivos multidimensionales del rendimiento académico basados en variables académicas, conductuales y de interacción digital en estudiantes universitarios

Roberto Vinicio Defas Ayala; Juan Andrés Romero Torres; Roberto Fernando Lozada Lozada

doi:10.65598/rps.6164

Authors

Roberto Vinicio Defas Ayala Universidad Central del Ecuador, Ecuador https://orcid.org/0009-0002-3240-3595
Juan Andrés Romero Torres Universidad de las Fuerzas Armadas ESPE, Ecuador https://orcid.org/0009-0001-8570-5098
Roberto Fernando Lozada Lozada Universidad Politécnica Salesiana, Ecuador https://orcid.org/0009-0007-4634-7981

DOI:

https://doi.org/10.65598/rps.6164

Keywords:

Academic performance, Predictive models, Learning analytics, Higher education, Machine learning, Digital interaction

Abstract

Academic performance in higher education is a key indicator of institutional quality and student retention, particularly in technology-mediated learning environments. The aim of this study was to systematically analyze recent scientific evidence on multidimensional predictive models of academic performance based on academic, behavioral, and digital interaction variables among university students. A systematic review was conducted following PRISMA guidelines, examining peer-reviewed studies published between 2020 and 2025 in indexed databases. Twenty-three empirical studies were selected, encompassing data mining techniques, ensemble methods, deep learning architectures, and explainable artificial intelligence approaches. The findings indicate that predictive models integrating academic records, self-regulated learning behaviors, and learning management system interaction data achieve higher predictive accuracy than unidimensional approaches based solely on historical performance. Although ensemble and deep learning models demonstrate strong statistical performance, challenges remain regarding interpretability, external validation, and institutional applicability. Based on the synthesis of evidence, a conceptual structural model is proposed to integrate these three dimensions as a foundation for sustainable early warning systems. The study concludes that future advancements in academic performance prediction should balance multidimensional integration, analytical transparency, and ethical data governance to ensure robust and transferable implementation in higher education contexts.

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