Ensemble learning for mining educational data

ASMAE BENTALEB; JAAFAR ABOUCHABAKA

No Detail Found!

Ensemble learning for mining educational data

Author(s)

ASMAE BENTALEB Department of Computer Science, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

JAAFAR ABOUCHABAKA Department of Computer Science, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

Abstract

Predicting students' academic performance allows giving feedback that helps in making the right decisions. Previous studies proposed using traditional statistical methods; however, they are tedious and not practical especially with the large volume of data. As for the solution, this article proposes a classification model to classify students according to their performance. It combines some of the well-known classification machine learning algorithms with Xgboost. The combination is done thanks to the voting classifier. This model was applied on two datasets and gave good results for the two: 85% and 92%.

Publication Details

Page(s) 2715-2722

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 9, Year: 2022

Request the full text from PASTIC

Keywords

E-learning, model, XGBoost, Recommendation, Student Academic Performance, Voting Classifier

Citations

Downloads

128

Views