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William Villegas-Ch

Biographic Data

ID9817610
NAMEWilliam Villegas-Ch
GIVEN NAMESWilliam
FAMILY NAMEVillegas-Ch
SIGNATUREVILLEGAS-CH W
AFFILIATIONSUniversidad de Las Américas
ORCID0000-0002-5421-7710
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
AUTHOR COUNT2
EDITOR COUNT0
FIRST PUBLICATION YEAR2025
LATEST PUBLICATION YEAR2026
H-INDEX0
  • Modeling informal learning as a dynamic interaction process in digital learning environments

    Open Access•William Villegas-Ch, Pablo Palacios et al.•ARTICLE•Frontiers in Education•2026

    Digital learning environments in higher education generate large volumes of interaction data that reflect a substantial portion of the learning processes occurring outside of formally assessed activities. However, these informal learning dynamics remain difficult to observe and analyze systematically due to their non-evaluative, irregular, and self-organized nature. Most current approaches to educational analytics focus on aggregate indicators or…

  • Machine learning models for academic performance prediction

    Open Access•Rodrigo Guevara-Reyes, Iván Ortiz-Garcés et al.•ARTICLE•Frontiers in Education•2025

    The integration of artificial intelligence in education has enabled the development of predictive models for academic performance. However, most existing approaches lack interpretability and do not provide actionable insights for decision-making. This study addresses these limitations by deploying optimized machine learning models, specifically XGBoost and Random Forest, to predict student performance considering geographically, institutional, so…

No prominent works on this page.

  • Machine learning models for academic performance prediction

    Open Access•Rodrigo Guevara-Reyes, Iván Ortiz-Garcés et al.•ARTICLE•Frontiers in Education•2025

    The integration of artificial intelligence in education has enabled the development of predictive models for academic performance. However, most existing approaches lack interpretability and do not provide actionable insights for decision-making. This study addresses these limitations by deploying optimized machine learning models, specifically XGBoost and Random Forest, to predict student performance considering geographically, institutional, so…

  • Modeling informal learning as a dynamic interaction process in digital learning environments

    Open Access•William Villegas-Ch, Pablo Palacios et al.•ARTICLE•Frontiers in Education•2026

    Digital learning environments in higher education generate large volumes of interaction data that reflect a substantial portion of the learning processes occurring outside of formally assessed activities. However, these informal learning dynamics remain difficult to observe and analyze systematically due to their non-evaluative, irregular, and self-organized nature. Most current approaches to educational analytics focus on aggregate indicators or…

Online Learning and Analytics (2 works) · Artificial Intelligence (1 works) · Computer Science (1 works) · Data science (1 works) · Database (1 works) · Decision support system (1 works) · Decision tree (1 works) · Educational Data Mining (1 works) · Educational Environments and Student Outcomes (1 works) · Explainable Artificial Intelligence (XAI (1 works)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae