2020
DOI: 10.3991/ijet.v15i09.12691
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Predicting Learners' Performance in Virtual Learning Environment (VLE) based on Demographic, Behavioral and Engagement Antecedents

Abstract: 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 exp… Show more

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Cited by 16 publications
(17 citation statements)
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“…The removal of outliers increased the correlation coefficients in all variables related to the VLE, reaching up to 0.618 with total page views, which indicates a student studying style impacting on the correlation when using all students, as some students may engage less with the VLE but perform quite well in examinations ( Figure 4 ). This observation was also reported elsewhere, where demographic and behavioural features impact student VLE engagement style [ 23 , 24 ]. The use of all of these variables in a simple linear regression model generated a correlation coefficient of 0.6 (Equation (1)) and, by removing the outliers, this increased to 0.724 (Equation (3)) ( Table 2 ).…”
Section: Discussionsupporting
confidence: 85%
“…The removal of outliers increased the correlation coefficients in all variables related to the VLE, reaching up to 0.618 with total page views, which indicates a student studying style impacting on the correlation when using all students, as some students may engage less with the VLE but perform quite well in examinations ( Figure 4 ). This observation was also reported elsewhere, where demographic and behavioural features impact student VLE engagement style [ 23 , 24 ]. The use of all of these variables in a simple linear regression model generated a correlation coefficient of 0.6 (Equation (1)) and, by removing the outliers, this increased to 0.724 (Equation (3)) ( Table 2 ).…”
Section: Discussionsupporting
confidence: 85%
“…The majority of students agree that the quality of virtual learning is sufficient to improve their learning outcomes. This has in common with the results of research on an increase in student learning performance when using virtual learning (Al-azawei & Al-masoudy, 2020). Besides that, it is also in line with research on learning simulation models able to make better understanding of the material obtained by students (Pfahl & Laitenberger, 2004), and learning modules presented in electronic media combined with discussion methods are able to improve concept understanding and problem solving (Wong et al, 2017).…”
Section: Figure 4 Results Of the Learning Evaluation Stagesupporting
confidence: 79%
“…Al-Azawei and Al-Masoudy [18] used both demographic and behavioral features to determine students' learning performance in a virtual learning environment (VLE). After building the prediction model based on the M5P regression algorithm, the results showed that some of the demographic features and all online behavioral variables were predictors of students' performance.…”
Section: Related Literaturementioning
confidence: 99%