## 5 Discussion  
The proposed methodology yields comprehensive experimental results that demonstrate the superiority of the HoE-CNN approach over traditional methods. True positive performance of each class labels and distribution of prediction are robustness from the ensemble methodology as shown Figure 8. This evaluation uses benchmark datasets commonly used in FER research, showcasing the model's ability to handle various expressions, poses, and lighting conditions. We also discuss the implications of our findings for educational applications. Ensemble methods often sacrifice interpretability for improved accuracy. Understanding the decision-making process of an ensemble method can be challenging due to the complexity introduced by multiple classifiers. Research efforts are needed to develop techniques for providing insights into ensemble decisions and making the models more interpretable. As the size of datasets continues increasing, scalability becomes a major concern for ensemble methods. Training and combining many classifiers can be computationally expensive and time-consuming. Researchers are exploring parallel and distributed computing techniques for addressing the scalability issues associated with ensemble methods. The selection and combination of diverse classifiers are crucial factors in the performances of ensemble methods. Ongoing research focuses on optimizing ensemble diversity through innovative ensemble generation algorithms, ensemble pruning techniques, or ensemble selection strategies. Developing efficient and effective algorithms to automatically determine the optimal diversity in ensembles remains an active research area. Ensemble methods are susceptible to noisy or corrupted data, which can adversely affect the ensemble performance. Research for developing robust ensemble techniques that are resilient to noise and outliers in data is ongoing. Approaches such as robust aggregation techniques and noise detection and removal methods are being explored to enhance the resilience of ensemble methods against data imperfections.  
Figure 7: The best performance of the confusion matrix by the proposed models (HoE 7-CNN).  
Aspect of online learning, this work integrated with Face Expression Recognition (FER) technology and developed the innovative model to offers a transformative approach to education by fostering a more interactive and personalized learning environment. By analyzing facial expressions in real-time, this innovative combination provides educators with invaluable insights into students' engagement levels, emotional responses, and comprehension as shown in Figure 9.  
The applied models enable instructors to adapt their teaching methods dynamically, offering personalized assistance and tailored resources to students based on their emotional cues. Additionally, FER facilitates the creation of a more empathetic and supportive learning atmosphere, allowing educators to provide timely assistance and guidance to students in need. Ultimately, this integration not only enhances the educational experience by promoting active engagement but also enables educators to refine their teaching strategies  
Figure 8: The example of true positive performance of each class labels and distribution of prediction are calculated by Equation (1).  
Figure 9: Model transfer with online learning education using HoE-CNN approach.  
for better learning outcomes. However, the student face has problems with non-face detection and limitation of their camera.