Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop-Out Prediction in MOOC

Abstract

Despite being in high demand as a lifelong learner and academic material supplement, the implementation of Massive Open Online Courses (MOOC) has problems, one of which is the dropout rate (DO) of students which reaches 93%. As one of the solutions to this problem, Machine Learning can be utilized as a risk management and early warning system for students who have the potential to drop out. The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. As a form of contribution, this study will compare the performance of models built from stacking and blending techniques. The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-model uses XGBoost. These algorithms are used to build models with stacking and blending techniques. The experimental results using stacking are 82.53% accuracy, 84.48% precision, 94.12% recall, and 89.04% F1-Score. Meanwhile, blending obtained 83.39% accuracy, 85.31% precision, 94.21% recall, and 89.54% F1-Score. These results are supported by model testing using k-fold cross-validation and confusion matrix techniques which show the same results. That is, blending is 0.86% higher than stacking so it can be concluded that blending has better performance than stacking in the MOOC student dropout prediction case study.