Students' Academic Performance and Engagement Prediction in a Virtual Learning Environment Using Random Forest with Data Balancing

Virtual learning environment (VLE) is vital in the current age and is being extensively used around the world for knowledge sharing. VLE is helping the distance-learning process, however, it is a challenge to keep students engaged all the time as compared to face-to-face lectures. Students do not participate actively in academic activities, which affects their learning curves. This study proposes the solution of analyzing students' engagement and predicting their academic performance using a random forest classifier in conjunction with the SMOTE data-balancing technique. The Open University Learning Analytics Dataset (OULAD) was used in the study to simulate the teaching-learning environment. Data from six different time periods was noted to create students' profiles comprised of assessments scores and engagements. This helped to identify early weak points and preempted the students performance for improvement through profiling. The proposed methodology demonstrated 5% enhanced performance with SMOTE data balancing as opposed to without using it. Similarly, the AUC under the ROC curve is 0.96, which shows the significance of the proposed model.