Enhancement of Predicting Students Performance Model Using Ensemble Approaches and Educational Data Mining Techniques

Student performance prediction is extremely important in today’s educational system. Predicting student achievement in advance can assist students and teachers in keeping track of the student’s progress. Today, several institutes have implemented a manual ongoing evaluation method. Students benefit from such methods since they help them improve their performance. In this study, we can use educational data mining (EDM), which we recommend as an ensemble classifier to anticipate the understudy accomplishment forecast model based on data mining techniques as classification techniques. This model uses distinct datasets which represent the student’s intercommunication with the instructive model. The exhibition of an understudy’s prescient model is evaluated by a kind of classifiers, for instance, logistic regression, naïve Bayes tree, artificial neural network, support vector system, decision tree, random forest, and k -nearest neighbor. Additionally, we used set processes to evolve the presentation of these classifiers. We utilized Boosting, Random Forest, Bagging, and Voting Algorithms, which are the normal group of techniques used in studies. By using ensemble methods, we will have a good result that demonstrates the dependability of the proposed model. For better productivity, the various classifiers are gathered and, afterward, added to the ensemble method using the Vote procedure. The implementation results demonstrate that the bagging method accomplished a cleared enhancement with the DT model, where the DT algorithm accuracy with bagging increased from 90.4% to 91.4%. Recall results improved from 0.904 to 0.914. Precision results also increased from 0.905 to 0.915.