Educational Data Mining and Predictive Modeling in the Age of Artificial Intelligence: An In-Depth Analysis of Research Dynamics DOI Creative Commons
Eloy López Menéses, Pedro C. Mellado-Moreno,

Celia Gallardo Herrerías

и другие.

Computers, Год журнала: 2025, Номер 14(2), С. 68 - 68

Опубликована: Фев. 14, 2025

This article provides a comprehensive analysis of the research dynamics on use Educational Data Mining (EDM) and predictive modeling (PM) in era Artificial Intelligence (AI) based review 793 articles published between 2000 2024 Scopus database. The study employs bibliometric systematic literature to identify emerging trends, methodologies, applications these fields. main objective is examine primary methodologies innovations within AI, especially context EDM PM. It highlights how technologies can optimize prediction student performance, support personalized learning, enable timely interventions through data. also examines role AI improving teaching practices, ensuring that educators maintain control over system minimize potential biases. Furthermore, addresses ethical implications implementation education, such as privacy protection, algorithm transparency, equity access learning. findings suggest has significantly improve educational outcomes tracking, resource allocation, overall effectiveness institutions. responsible education emphasized ensure inclusive fair environments for all students.

Язык: Английский

Educational Data Mining and Predictive Modeling in the Age of Artificial Intelligence: An In-Depth Analysis of Research Dynamics DOI Creative Commons
Eloy López Menéses, Pedro C. Mellado-Moreno,

Celia Gallardo Herrerías

и другие.

Computers, Год журнала: 2025, Номер 14(2), С. 68 - 68

Опубликована: Фев. 14, 2025

This article provides a comprehensive analysis of the research dynamics on use Educational Data Mining (EDM) and predictive modeling (PM) in era Artificial Intelligence (AI) based review 793 articles published between 2000 2024 Scopus database. The study employs bibliometric systematic literature to identify emerging trends, methodologies, applications these fields. main objective is examine primary methodologies innovations within AI, especially context EDM PM. It highlights how technologies can optimize prediction student performance, support personalized learning, enable timely interventions through data. also examines role AI improving teaching practices, ensuring that educators maintain control over system minimize potential biases. Furthermore, addresses ethical implications implementation education, such as privacy protection, algorithm transparency, equity access learning. findings suggest has significantly improve educational outcomes tracking, resource allocation, overall effectiveness institutions. responsible education emphasized ensure inclusive fair environments for all students.

Язык: Английский

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