# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 4 Experiments and Evaluation
### Comparison of Applied Approach with Existing Approaches  
Numerous research studies in EDM have been conducted using multiple classifiers to predict the performance of students. [PERSON] et al. have recently proposed an ensemble model to identify at-risk students and advise them to regulate their learning. They hybridized four single classifiers with four ensemble algorithms including bagging, random subspace, multilayer perceptron, and random forest. The evaluation results showed that the ensemble model achieved 91.70% accuracy, 86.1%, and the F-score was 87.3% [26]. Another study identified at-risk students, predicting their learning performance through their learning behavior based on their logging data history. They used Logistic Regression along with Random Forest, Multilayer Perceptron, and Gaussian Naive Bayes. The results showed that Random Forest surpasses the baseline Logistic Regression and other models with 89% accuracy, 89% precision, 88% recall, and 88% F1 score [56]. [PERSON] et al. introduced a model to predict students' success based on their daily activities. They hybridized different single classifiers with bagging, boosting, and random forest. The experiment results showed that the model achieved 96.9% accuracy [57]. Another recent study predicted students' intermediate results based on their academic characteristics. The evaluation results indicated that the model attained 96.64% accuracy [58]. A model has been suggested to estimate the institutional performance based on key performance indicators using data mining techniques. The results showed that the artificial neural networks performed better in achieving accuracy (i.e., 82.9%) in comparison to other machine learning models employed in the study [59]. The student performance in a learning management system based on behavioral features was predicted by applying ensemble methods including bagging, boosting, and random forest to augment the performance of classifiers. An accuracy of 91.5% was achieved through the application of ensemble methods to the classifiers to enhance academic performance [60]. [PERSON] et al. introduced a data mining-based forecast model to determine students' accomplishments. The data mining techniques used to evaluate students' performance include a decision tree, logistic regression, a naive Bayes tree, an artificial neural network, a support vector machine, and a k-nearest neighbor. To improve the productivity of these classifiers, they used ensemble methods such as bagging, boosting, random forest, and voting. The results showed that the decision tree algorithm accuracy increased with bagging from 90.4% to 91.4%. Similarly, recall results were increased from 0.904 to 0.914, and precision results were also improved from 0.905 to 0.914 [61]. Another study undertook the task of student performance prediction by extracting features from an e-learning system. The proposed model comprises five traditional machine learning algorithms which were complemented by four well-established ensemble techniques including bagging, boosting, stacking, and voting. The F1 score measured by the NB model by the integration of boosting and GBT with AdaBoost were 0.71% and 0.75%, respectively [62]. [PERSON] et al. 's research focused on the prediction of students' performance using data mining techniques along with the support of ensemble methods. They also proposed novel hybrid classifiers to gain accurate predictions of student performance. The results showed that the hybrid model outperformed the other classifiers in terms of accuracy (i.e., 81.67) precision (i.e., 79.62), recall (i.e., 75.86), and F-score (i.e., 77.69) in comparison to base classifiers and ensemble techniques applied in the same research [54]. The fusion ensemble-based approach introduced in this research study to improve academic performance attained the highest accuracy (i.e.,98.7%) precision (i.e., 98.6%) recall (i.e., 98.6%) and F-score (i.e., 98.6) in comparison to state-of-the-art ensemble approaches proposed in EDM.