Implementation of the Ensemble Machine Learning Algorithm for Student Dropout Prediction Analysis

Educational Data Mining provides an effective approach to tackle numerous issues within the education sector, including the capacity to perform predictive analyses regarding student attrition based on academic information. In this research, data from the Open University Learning Analytics dataset (OULAD), which is publicly accessible, has been employed, which encompasses student information collected during online learning. We apply various Machine Learning models, including Decision Trees, Naïve Bayes, Logistic Regression, and ensemble approaches like Random Forest and AdaBoost. Among the models tested, Random Forest (RF) achieved the highest accuracy of 89.37%, along with a precision of 89.57% and a recall of 93.86%, using the data splitting approach. When employing an alternative evaluation model, specifically K-Fold Cross Validation, the maximum F1 score achieved was 9.45%. In summary, the ensemble machine learning algorithm, specifically Random Forest (RF), exhibited strong performance in predicting student academic achievement quality.