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Remote Learning in Transnational Education: Relationship between Virtual Learning Engagement and Student Academic
Taher Hatahet1,2, Ahmed A Raouf Mohamed3, Maryam Malekigorji1,2
1School of Pharmacy, Queens University Belfast, Belfast BT9 7BL, UK.
Pharmacy (Basel, Switzerland)
|January 25, 2022
Summary
A predictive model using virtual learning environment (VLE) engagement and coursework marks accurately forecasts student exam performance. Personal factors also significantly influence academic success, offering insights for educators and students in remote learning settings.
Area of Science:
- Educational Technology
- Learning Analytics
- Higher Education
Background:
- The 21st century has reshaped education delivery, expanding transnational and remote learning through virtual learning environments (VLEs).
- Effective remote teaching necessitates student engagement and educator tools to monitor and enhance it.
- Predicting student success in remote settings is crucial for timely interventions.
Purpose of the Study:
- To develop a predictive mathematical model for student exam performance.
- To assess the impact of VLE engagement indicators and coursework marks on exam outcomes.
- To investigate the influence of personal factors on predictive model accuracy.
Main Methods:
- Generation of a predictive mathematical model integrating VLE engagement data and coursework marks.
- Analysis of individual variable contributions (VLE engagement, coursework marks, personal factors) to exam performance prediction.
- Examination of model accuracy with the inclusion of a personal variable (X).
Main Results:
- The developed model achieved a correlation coefficient of 0.724 in predicting exam performance.
- Coursework marks and total VLE page views were identified as major predictors of exam success.
- Personal factors were found to significantly impact model accuracy, particularly for outliers (low engagement, high performance).
Conclusions:
- Student academic attainment is influenced by a combination of VLE engagement, coursework, and personal factors like study style and behavior.
- The predictive model can empower students to enhance self-efficacy and aid educators in supporting disengaged learners.
- Findings are applicable to diverse remote learning contexts and transnational education, informing strategies for student success.
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