Comparative Analysis of Decision Tree and Random Forest Models for Student Exam Score Classification

Abstract

Advances in Educational Data Mining (EDM) have encouraged the use of machine learning algorithms to predict students’ academic performance. Although Decision Tree and Random Forest have been widely investigated, most previous studies compare them with multiple classifiers rather than conducting a focused evaluation under a unified framework. This study addresses this gap by systematically comparing the performance of Decision Tree and Random Forest in classifying student exam scores using the same preprocessing, cross-validation, and evaluation procedures. The dataset, obtained from Kaggle, consists of six attributes: student_id, hours studied, sleep hours, attendance percent, previous scores, and exam scores. The research methodology includes data collection, preprocessing, K-Fold Cross Validation, model training, and performance evaluation using a confusion matrix with accuracy, precision, and recall metrics. Experimental results show that Random Forest consistently outperforms Decision Tree, achieving a mean accuracy of 86.50%, compared with 81.50% for Decision Tree. In addition, Random Forest demonstrates greater stability and stronger generalization capability across different validation folds. These findings suggest that Random Forest is a more effective and reliable predictive model for supporting educational decision-making, particularly in identifying students at risk of poor academic performance and enabling timely academic interventions.