The 21st century has seen dramatic changes to education delivery which have widened the scope of transnational education and remote learning via various virtual learning environments (VLEs). Efficient remote teaching activities require students to be engaged with taught materials and academic staff, and for educators to be able to track and improve student engagement. This article describes the generation of a predictive mathematical model for students' exam performance using VLE engagement indicators and coursework marks together to enable the creation of a model with a correlation coefficient of 0.724. This article examines the relationship of each of these variables with final exam marks, as well as the addition of personal related variable X on the generated model's accuracy. The generated models show that each variable had a different impact on the prediction of the final exam mark. The results' analysis suggests that coursework marks and total VLE page views were the major attributes, while personal factors were also found to greatly impact model accuracy. Considering the case of outliers, who were students with low VLE engagement achieving high exam marks, it is proposed that personal factors, such as behavioural factors and study style, also have a significant effect on student academic attainment. The generated model can be used by students to improve self-efficacy by adjusting their study style and by educators to provide early interventions to support disengaged students. This model can be replicated in different remote learning settings and transnational education, and the findings might be insightful for courses with remote learning strategies to investigate the key educational, personal and engagement parameters for students' overall success.
Keywords: academic performance; linear regression; modelling; optimization; student engagement; transnational education; virtual learning environment.