Optimization of Engineering Educational Curricula through Multivariate Statistical Models
DOI:
https://doi.org/10.63969/099ybg84Keywords:
educational curricula, engineering education, multivariate statistical models, curricular innovation, learning analytics, higher educationAbstract
Technological transformation and emerging labor market demands have driven the need to modernize educational curricula in engineering programs through approaches supported by data analytics and scientific evaluation. In this context, multivariate statistical models have emerged as strategic tools for strengthening curricular innovation processes, optimizing academic planning, and improving professional training in higher education. The objective of this study was to critically analyze the optimization of educational curricula in engineering through multivariate statistical models by means of an analytical-documentary literature review. The methodology was developed under a qualitative approach through the review of scientific literature published between 2019 and 2026 in indexed databases such as Scopus, Web of Science, ERIC, and Google Scholar, considering methodological criteria inspired by the PRISMA protocol. The results showed that techniques such as factor analysis, multiple regression, cluster analysis, and predictive models allow the identification of significant relationships among academic performance, professional competencies, and labor market needs. Likewise, the reviewed studies indicate that the incorporation of analytical tools promotes curriculum updating processes, academic evaluation, and the design of more flexible and interdisciplinary educational programs. However, limitations related to technological infrastructure, data availability, teacher methodological training, and institutional resistance to educational innovation were also identified. It is concluded that multivariate statistical models have strong potential to strengthen educational quality and curricular transformation processes in engineering, provided that their implementation is integrated with comprehensive and contextualized pedagogical approaches.
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Copyright (c) 2026 Blanca Romina Barcia Rivera , José Aníbal Angulo De León , Erick Daniel Rivera Quiñonez , Erika Gissella Vera Benitez (Autor/a)
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