A comparative study of machine learning algorithms for virtual learning environment performance prediction

Abstract

Virtual learning environment is becoming an increasingly popular study
option for students from diverse cultural and socioeconomic backgrounds
around the world. Although this learning environment is quite adaptable,
improving student performance is difficult due to the online-only learning
method. Therefore, it is essential to investigate students' participation and
performance in virtual learning in order to improve their performance. Using
a publicly available Open University learning analytics dataset, this study
examines a variety of machine learning-based prediction algorithms to
determine the best method for predicting students' academic success, hence
providing additional alternatives for enhancing their academic achievement.
Support vector machine, random forest, Nave Bayes, logical regression, and
decision trees are employed for the purpose of prediction using machine
learning methods. It is noticed that the random forest and logistic regression
approach predict student performance with the highest average accuracy
values compared to the alternatives. In a number of instances, the support
vector machine has been seen to outperform the other methods.