Machine Learning for Corporate Learning in Virtual Environments: A Systematic Review

Bautista C.; Karlo L.J.; Osco M.; Elias F.; Vasquez S.F.A.; Suazo J.M.A. · Proceedings - 2024 14th International Conference on Dependable Systems, Services and Technologies: Trustworthy AI, Internet of Robotics, and Big Data for a Safe and Secure World, DESSERT 2024 (2024) · DOI: 10.1109/dessert65323.2024.11122200

Resumen

Machine Learning for Corporate Learning in Virtual Environments: A Systematic Review. This review aims to understand the applications of machine learning in the methodologies, technologies, and techniques used in the field of corporate learning. Method: A PICO approach was adopted in formulating search strategies, using databases such as Scopus. Subsequently, the PRISMA methodology was applied for the analysis of 150 original articles, of which 10 met the inclusion criteria. Results: The systematic review reveals a fascinating research landscape from the authors and a significant increase in research in the field of machine learning. Different methods used in the studies were identified, as well as a variety of efficiency metrics, providing a global and comprehensive view of the model's impact. Conclusions: The importance of machine learning is highlighted in areas such as improving learning processes, personalized adaptation of educational content, and individual performance assessment, but the need for more rigorous evaluations in this field is also emphasized.

Instituciones: Universidad Tecnológica del Perú