Learning Analytics and Predictive Modeling: Data-Driven Strategies for Improving Student Performance and Retention

Abstract

Learning analytics and predictive modeling have emerged as transformational tools within modern digital education systems. By collecting, analyzing, and interpreting student-generated data—attendance, LMS activity logs, assignment submissions, participation tracking, and assessment scores—institutions can predict student performance and identify at-risk learners before academic failure occurs. This study investigates how predictive modeling supports strategic interventions that enhance student engagement, academic success, and institutional retention rates. A mixed-method design was implemented using a sample of 350 undergraduate students from three universities utilizing LMS platforms integrated with analytics dashboards. Data revealed that predictive alert systems improved retention rates by 29% and increased average student performance by 22% after targeted academic interventions. Furthermore, educators reported a reduction in manual monitoring workload and improved decision-making capabilities. The study concludes that learning analytics is no longer just an evaluation tool; it is a proactive, data-driven strategy essential for improving student academic outcomes and minimizing dropout rates.