Abstract:

Academic institutions often struggle to identify and support at-risk students until midterm or final assessments, by which point interventions may be too late to substantially improve outcomes. This paper explores the potential of early predictive analytics to identify students at risk of underperforming or withdrawing during the first four to five weeks of a standard 15-week semester. Drawing on prior literature and existing open-access datasets, we discuss key indicators of student engagement and performance, such as attendance, assignment completion, and Learning Management System (LMS) activity, and propose a framework for developing interpretable predictive models. We examine the strengths and limitations of various machine learning approaches—including Logistic Regression, Decision Trees, and ensemble methods like Random Forest and Gradient Boosting—for use in educational contexts, highlighting trade-offs between accuracy and interpretability. By outlining a scalable, transparent early-warning system, this conceptual study aims to support initiatives such as automated tutoring referrals, timely academic advising, and targeted instructor feedback. Ultimately, this work advocates for a shift from reactive to preventive academic support, improving student outcomes and strengthening institutional retention efforts.

Keywords:

Academic Performance Prediction, At-Risk Students, Data-Driven Intervention, Early Warning Systems, Educational Data Mining, Higher Education Retention, Interpretable AI Models, Learning Management Systems (LMS), Machine Learning in Education, Predictive Learning Analytics, Scalable Education Techno.