Leveraging Decision Trees to Forecast Academic Outcomes

Machine learning has become an essential component of modern educational research, offering data-driven strategies for assessing and enhancing student achievement. Within this field, decision tree algorithms have emerged as one of the most effective approaches because of their interpretability, computational simplicity, and flexibility in managing both categorical and continuous data. This paper provides an extensive review of decision tree-based models applied to student performance prediction in educational settings. It consolidates recent literature to discuss the importance of machine learning in contemporary education, outlines the theoretical concepts underlying major decision tree variants, and analyzes diverse predictors influencing academic success. The review highlights the ability of decision tree methods to achieve predictive accuracies between 80% and 95% in various educational scenarios. Furthermore, it compares algorithmic approaches such as ID3, C4.5, CART, and ensemble models while drawing insights from real-world applications documented in prior studies. The paper also examines implementation issues such as bias, data reliability, and interpretability, concluding with recommendations for advancing the role of decision tree analytics in educational decision-making and early academic intervention systems.