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

Mahmoud Ragab,Nahla F Omran,Ali O Jifri,Ahmed M K Abdel Aal,Rashid A Saeed

doi:10.1155/2021/6241676

Abstract

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.

Highlights

Today, all higher education institutions face difficulties in the admission process
We focus on supporting colleges in smoking admission decisions by applying data mining techniques to best predict applicants’ academic rendering prior to admission
Improved results are shown by applying ensemble methods combined with classical classifiers (DT, NB, and artificial neural network (ANN)), in order to obtain better prediction performance for the student’s model

Summary

Introduction

All higher education institutions face difficulties in the admission process. Every college ought to make a choice in its admission form which is dependent on legitimate and credible admissions procedures that select the student candidates prone to prevail in its programs. It can be used to detect unwanted students’ behaviors and give suggestions to understudies These models can help teachers with collecting students, getting feedback, and developing courses. We focus on supporting colleges in smoking admission decisions by applying data mining techniques to best predict applicants’ academic rendering prior to admission. These students’ traits are from an academic background, family economic traits, social traits, institutional traits, and personal traits. This work applies three traditional techniques from data mining in this field to produce a performance model Those techniques are neural networks (NN) [9], decision trees [10], and naïve Bayes [11]. To more accurately predict the results, two classifiers were added to each ensemble method by using voting

Related Work and Research Gap

Methodology

Evaluation

Aggregation

Evaluation Results

Conclusions