Big Data Analytics in STEM Education

This chapter examines the role of Big Data Analytics in strengthening student competence in STEM education through predictive analytics and data-driven decision-making. Based on bibliometric mapping of Scopus-indexed publications (2016–2025), it identifies rapid research growth alongside structural concentration and thematic fragmentation. To address this gap, the chapter proposes an integrative framework linking the educational data ecosystem, predictive modeling, early warning systems, and intervention strategies. The framework highlights how predictive analytics enables early identification of at-risk students and supports personalized, adaptive learning. It also emphasizes improvements in both cognitive and non-cognitive outcomes, including problem-solving, persistence, and self-regulated learning. Ethical considerations such as fairness, transparency, and accountability are underscored as essential components in analytics design. Overall, the chapter positions predictive analytics as a responsible decision-support system for advancing inclusive and evidence-based STEM education.