Predicting Learners' Performance in Virtual Learning Environment (VLE) based on Demographic, Behavioral and Engagement Antecedents

Ahmed Al-Azawei, Miami Abdul Aziz Al-Masoudy


This study aims at predicting undergraduate students' performance in the Virtual Learning Environment (VLE) based on four time periods of the examined online course. This is to provide an early and continuous prediction of students' academic achievement. This research depends on data from one of the scientific courses at the Open University (OU) in Britain, which offers its lectures using VLE. The data investigated consists of 1938 students in which the influence of demographic and behavioral variables was explored first. Then, three features were generated to improve the prediction accuracy as well as examining the effect of learners' engagement on their academic performance. Accordingly, a comparison was made between the prediction accuracy of integrating the proposed features with the behavioral and demographic features and the use of the original features only. The findings suggest that some of the demographic variables and all behavioral features had a significant impact on students' performance. However, the accuracy was highly improved after using the new generated features. It was found that the level of the financial and service instability, level of participation in the course, assessment grades, the total number of clicks, the interaction with different course activities, and students' engagement were significant predictors of academic achievement.


Educational data mining (EDM), prediction techniques, student performance, Virtual Learning Environment (VLE), Open University (OU)

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Copyright (c) 2020 Ahmed Al-Azawei, Miami Abdul Aziz Al-Masoudy

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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