Predictive Analytics in Digital Education Systems: A Systematic Review of Student Risk Detection Models

Abstract

The rapid growth of digital education platforms and learning management systems has generated extensive educational data that can be analyzed to improve learning outcomes and institutional decision-making. One of the most critical challenges faced by digital education systems is the identification of students who are at risk of academic failure or course dropout. Predictive analytics has emerged as a powerful analytical approach that utilizes machine learning, data mining, and statistical modeling techniques to analyze student data and forecast academic performance. By examining patterns in learner behavior, engagement levels, and academic progress, predictive models enable educational institutions to detect early warning signals that indicate potential learning difficulties. Early identification of at-risk students allows educators and administrators to implement timely interventions such as personalized learning support, tutoring programs, and adaptive learning pathways that improve student success and retention rates.

This systematic review examines the current state of research on predictive analytics models used in digital education systems for student risk detection. The study analyzes a wide range of predictive approaches applied in e-learning environments, including logistic regression, decision trees, random forests, support vector machines, neural networks, and deep learning models. The review also explores the types of educational data used in predictive modeling, such as learning management system logs, student engagement metrics, assessment scores, forum participation, and behavioral interaction patterns. In addition, the paper evaluates the performance and effectiveness of different predictive models in identifying students who are at risk of failing courses or dropping out of online programs.

The findings of this review indicate that predictive analytics significantly enhances the ability of digital education systems to monitor student progress and detect risk patterns at early stages of the learning process. Predictive models that combine behavioral, academic, and demographic data have demonstrated high levels of accuracy in forecasting student outcomes. However, the implementation of predictive analytics systems also presents several challenges, including issues related to data privacy, algorithmic bias, model interpretability, and integration with educational practices. Furthermore, the effectiveness of predictive analytics depends on the quality and completeness of the educational datasets used for training machine learning models.

The review highlights the importance of integrating predictive analytics with learning analytics dashboards and adaptive learning technologies to support data-driven decision-making in education. By combining predictive models with personalized learning interventions, digital education systems can create more responsive and student-centered learning environments. The study concludes that predictive analytics represents a transformative tool for improving student success in digital education platforms, but further research is needed to develop more transparent, ethical, and scalable predictive learning systems.