Abstract

This study investigates how predictive analytics to be used to design early warning systems (EWS) that identify at-risk students and trigger timely, targeted interventions, while also situating these systems within the broader context of climate finance and education resilience. Drawing on recent empirical work in learning analytics and machine learning, the paper reviews fifteen key studies on student success prediction models, early warning architectures, and intervention effectiveness, along with contemporary analyses of global climate finance flows and their limited allocation to education. The methodology adopts a quantitative, secondary-data design, combining student persistence and retention statistics with published model performance metrics and intervention effect sizes. Descriptive statistics, comparative accuracy analysis of algorithms (such as gradient boosting, random forests, and support vector machines), and cross-tabulation of intervention outcomes are used to derive results. The findings show that advanced ensemble models consistently outperform traditional statistical approaches in predicting student risk, and that structured, multi-tiered interventions—especially those involving parents and targeted at at-risk learners—produce substantially larger improvements in participation, behavior, and achievement than universal programs. At the same time, climate finance for education remains marginal relative to overall climate flows, constraining investments in resilient learning analytics infrastructure and climate-adaptive student support systems. The discussion highlights the opportunity to link predictive EWS with climate finance mechanisms to protect learning continuity in climate-vulnerable regions, while addressing equity, ethics, and data governance. The paper concludes with recommendations for policymakers and institutions to integrate predictive analytics into student success strategies, leverage climate-aligned financing for educational data infrastructure, and advance future research on context-aware models and intervention design in a changing climate.

Keywords: – Predictive Analytics, Student Success, Early Warning Systems, Educational Intervention, Machine Learning