Predictive Analytics to Enhance Learning Outcomes: Cases from UK Schools - David Green; Emily Thompson; Isaac Ochieng (Yayasan Adra Karima Hubbi, 2025)
Background. The increasing integration of data-driven technologies in education has positioned predictive analytics as a promising tool for enhancing student learning outcomes. In the UK, schools are beginning to leverage predictive models to identify at-risk learners, personalize instruction, and inform pedagogical decisions. Purpose. This study investigates the practical application and impact of predictive analytics in secondary education settings across selected schools in England and Scotland. The primary objective is to assess how predictive tools are used to improve academic performance, engagement, and targeted interventions. Method. A qualitative case study approach was employed, involving interviews with school leaders, data analysts, and teachers in six institutions, alongside document analysis and system usage observations. Results. The findings reveal that predictive analytics, when implemented with pedagogical alignment and ethical oversight, significantly supports early identification of student needs and enables timely academic interventions. However, challenges persist in terms of data literacy among staff, algorithmic transparency, and balancing predictive insights with professional judgment. Conclusion. The study concludes that predictive analytics can enhance learning outcomes when embedded within a holistic educational framework that prioritizes equity, accountability, and human-centered decision-making.