The excessive use of e-learning technology today
has resulted in a massive growth in educational data.
Students' interactions with the e-learning environment,
particularly learning management systems, create huge
amount of data within the shortest period of time. The data
contains hidden information about students' engagement in
various e-learning activities, which can be linked to their
performance. Predicting student performance based on the
usage of e-learning systems in educational institutions is a
big concern, and it has become critical for educational
administrators to better understand why so many students
do badly or fail their courses. However, due to the
numerous features that influence their performance,
making a prognosis is challenging. This study aims to
compare various ensemble techniques against their nonensemble counterpart in predicting students’ performance
on data generated from learning management system. Five
popular algorithms were used: Decision Tree (DT), KNearest Neighbor (KNN), Discriminant Analysis (DA),
Nave Bayes (NB) and Support Vector Machine (SVM). To
improve the performance of the classifiers, ensemble
techniques such as RUSBoost, Bag and AdaBoost were
employed to increase the accuracy of the students'
performance prediction models. The findings revealed that
after applying ensemble methods, it achieved a higher
accuracy was obtained.
E-Learning, Ensemble, Performance, Algorithm, Data.