Predictive Analytics in Student Performance and Retention Strategies

Abstract

Artificial intelligence (AI) is reshaping higher education, with predictive analytics playing a pivotal role in improving student outcomes. By leveraging large-scale, real-time, and historical data, institutions can anticipate academic performance, identify at-risk students, and intervene early to prevent attrition. This chapter explores how colleges and universities implement AI-driven predictive models to enhance academic advising, personalize learning, and support strategic decision-making. It critically examines the theoretical foundations, algorithmic methods, and practical applications through real-world case studies. In doing so, it highlights how data-informed approaches enable more responsive, proactive educational environments. The chapter also addresses key ethical and privacy concerns, advocating for transparent, equitable practices that prioritize student agency. Ultimately, it offers actionable insights for educators and policymakers on responsibly integrating predictive analytics to foster student success and institutional resilience.

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