## 2 Related Work
### Ensemble Learning Approaches with Student Success Prediction  
Ensemble learning has been widely adopted to improve classification accuracy by combining multiple models, thereby reducing variance and increasing robustness [23]. Techniques like bagging [24], boosting [25], and stacking [26] create ensemble models that generalize better to unseen data, addressing the limitations of individual models [27]. In particular, deep learning ensembles have shown promise in tasks requiring both high-level feature extraction and fine-grained pattern recognition [28]. Ensemble approaches have been used extensively in educational research to integrate predictive models and increase the reliability of student success predictions.  
[PERSON] et al. [29] introduced an approach that integrates CNN feature extraction with machine learning models to estimate students' academic outcomes. By utilizing an ensemble model of Random Forest and SVM, the approach achieved a classification accuracy of 98.99%, outperforming existing models. [PERSON] et al. [30] developed a method for estimating student performance in video-based learning environments, utilizing ensemble learning techniques including stacking, boosting, and bagging. The experimental findings indicate that boosting reached the best achievement, with an accuracy of 90.9%, outperforming stacking and bagging. [PERSON] et al. [31] employed an ensemble model that integrates multiple machine learning classifiers as base learners and Logistic Regression as the final learner to forecast student outcomes on the OULAD dataset. The model achieved 98% accuracy, a 4% improvement over traditional methods. [PERSON] et al. [32] used features extracted by a CNN in combination with an ensemble learning model that integrates SVM and RF classifiers to forecast student academic success. The proposed method achieved an accuracy of 97.88%, outperforming traditional models.  
Ensemble learning can also be used to identify the most relevant features of the dataset associated with students' performance. For instance, a hybrid machine learning framework that combines several classification models and ensemble architecture to identify the most effective predictive model has been proposed [33]. The results show that the ensemble methods achieve greater predictive accuracy compared to individual classifiers, with the model accuracy enhanced by the application of feature selection techniques. Furthermore, ensemble learning can be utilized to forecast students' performance in e-learning environments. For example, a study has introduced an ensemble meta-model that aggregates predictions from optimal models to improve student classification accuracy, resulting in a final predictive model [34], and the findings indicate that the ensemble meta-model achieves superior performance compared to single models, with an accuracy of 93%.  
In summary, ensemble learning is a powerful technique for student academic performance prediction, since it is capable of enhancing the performance of individual models and providing more robust predictions. Through the combination of the strengths of diverse models and feature selection techniques, ensemble learning can provide accurate predictions of students' performance and help educators recognize students who need extra assistance and interventions.