# A Two-Phase Ensemble-Based Method for Predicting Learners' Grade in MOOCs
## 4 Performance Evaluation
In the case of CNPC, the IRF without SMOTE was successful in overcoming the traditional RF and RF regressions, while the efficiency trend from the HMPC dataset is distinct. The outcome of the RF regression for HMPC is worse than the those for RF and IRF. However, even the proposed model is deployed without the selected distance features. For both of the datasets, its performance is better than those of RF, IRF, RF regression, and deep learning. It was compared with the tasks that have been mentioned before because all of them learn and predict the learners' grade using the multi-class classification, which uses the imbalanced data directly. The results obtained from them cannot reach the same level as the proposed method can for all of the classes, especially for class 'c0', which is the majority class. Focusing on the two-phase ensemble-based method, the proposed model's base outperforms the previous techniques. To improve it, the centroid distance features are added, and then filtered by the lowest silhouette score between the clusters of grades in the training set. In this step, the CNPC performance improves in all of the measures, except for HMPC. In HMPC, only the weighted F1 score increases, which the confusion matrix will analyze in depth. Figure 6 shows the confusion matrix of five-fold cross-validation from the CNPC dataset experiments to support a detailed discussion. Figure 7 displays the enhancement achieved by deploying the optimization and feature selection to the proposed model's core. Figure 6: Confusion matrix of the proposed model on the CNPC dataset. \begin{table}
\begin{tabular}{c c  
Only 'cen_c0', 'cen_c1', and 'cen_c3' are integrated with CNPC dataset features when feature selection is employed. The silhouette scores of all of the clusters within the dataset denote these important features. The model is then enhanced via Bayesian optimization. Adding the selected distance characteristics improves the model's ability to predict the samples from classes 'c1' through to 'c3' compared to that of the originally proposed model. However, the number of accurate predictions from 'c0' to 'c4', which represent the majority class and the second largest in the dataset, decreases marginally. The HMPC datasets receive 'cen_c0', 'cen_c3', and 'cen_c4' as additional features, and optimization is performed. However, the enhanced performance of this modified model is not very significant. Figure 8 demonstrates the confusion matrix of five-fold cross-validation for predicting the learners' grades using the two-phase ensemble-based model that was applied to the HMPC dataset. In addition, Figure 9 displays the outcomes of applying optimization to the model and including the selected centroid distance features in the dataset. From the confusion matrix, the first phase of the proposed model's binary classification efficiently identifies 'C0' and 'Non-C0'. This may be because class 'c0' of the HMPC has 13 times more data than the other classes do. The prediction of class 'c0' influences the quality of the grade prediction in the HMPC dataset. The accuracy values of 'c2' and 'c3'  
Figure 8: Confusion matrix of the proposed model on the HMPC dataset. Figure 7: Confusion matrix of the optimization on the proposed model with selected distance features on the CNPC dataset. Figure 9: Confusion matrix of the optimization on the proposed model with selected distance features on the HMPC dataset. improve Figure 9's prediction findings, whereas all of the others except for 'c0' decrease. This demonstrates that the specified distance features can help the model to clear the 'c3' feature, and they also influence 'c2', which has the same poor silhouette score as that of 'c3'. This adjustment is in response to the decrease in weight precision and weighted recall and the rise in the weighted F1 scores. To improve the classification performance, Bayesian optimization is enabled for binary and multi-class classification.