Article’s

This paper explores the critical role of Predictive Analytics in education, specifically focusing on forecasting student performance to mitigate high attrition rates. By synthesizing findings from Educational Data Mining (EDM) and Learning Analytics (LA), the study examines the efficacy of various Machine Learning (ML) algorithms, ranging from traditional classifiers like Logistic Regression and Random Forests to advanced Deep Learning architectures such as Long Short-Term Memory (LSTM) networks. The analysis highlights the importance of data granularity, contrasting static demographic features with dynamic behavioral logs, and identifies early prediction as a key challenge for effective intervention. Comparative benchmarks reveal that while Deep Learning excels in processing sequential clickstream data, ensemble methods like XGBoost and Random Forest remain dominant for structured data due to their balance of accuracy and interpretability. The paper concludes by advocating for hybrid systems that integrate the predictive power of complex algorithms with Explainable AI (XAI) techniques, ensuring that insights are actionable for educators and stakeholders.