Web-based Clustering Application for Determining and Understanding Student Engagement Levels in Virtual Learning Environments
Eli Nimy 
& Moeketsi Mosia
Issue: Vol.4 No.12 Special Issue Article 1 pp.4 – 19
DOI: https://doi.org/10.38159/ehass.20234122 | Published online 30th November, 2023
© 2023 The Author(s). This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).
The increasing use of virtual learning environments (VLEs) in recent years has transformed teaching and learning methods. Universities now combine VLEs with traditional classrooms to accommodate hybrid teaching and learning approaches. However, student engagement with VLEs varies, and universities lack the tools to effectively determine and analyse VLE engagement. Consequently, data-driven decision-making regarding VLE usage remains a challenge for universities. This study thus proposed a user-friendly web-based application, using a R shiny framework, to determine and understand student engagement levels in VLEs. In this study, two clustering methods, K-means and Gaussian Mixture Model (GMM) were compared, to identify the most effective method for the proposed application. The results indicated that GMM outperforms K-means by generating more accurate and comprehensive groupings of student engagement levels. One key advantage of the GMM method is its ability to capture uncertainty and provide probabilities of student membership in each level of engagement, which enhances its usefulness for decision-making. Furthermore, the GMM method achieves these outcomes efficiently, saving valuable learning time. This research holds significant implications for education by providing valuable guidance for the development of Educational Data Mining (EDM) applications. Universities can leverage these applications to gain deep insights into VLE usage and enhance their understanding of student engagement. By adopting this web-based application, educators and administrators can make informed decisions and tailor interventions to optimize student learning experiences within VLEs.
Keywords: Virtual Learning Environments, Student Engagement, Clustering.
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Eli Nimy is currently pursuing a Master of Science degree in eScience while concurrently holding a part-time position as a Junior Data Scientist at the Centre for Teaching, Learning, and Programme Development at Sol Plaatje University. Eli’s academic and professional interest centers on the intersection of Data Science and Bayesian Statistics, with a particular passion for their applications in the fields of Education and Healthcare.
Dr. Moeketsi Mosia is currently the Director of the Centre for Teaching, Learning, and Programme Development at Sol Plaatje University. In addition to his administrative role, he also serves as a lecturer for postgraduate students in the Department of Computer Science, Information Technology, and Data Science. Dr. Mosia’s research interests primarily revolve around Learning Analytics, Probabilistic Machine Learning, and Student Learning in Higher Education.
Nimy, Eli & Mosia, Moeketsi. “Web-based Clustering Application for Determining and Understanding Student Engagement Levels in Virtual Learning Environments.” E-Journal of Humanities, Arts and Social Sciences 4, no.12 Special Issue (2023): 4 -19. https://doi.org/10.38159/ehass.20234122
© 2023 The Author(s). Published and Maintained by Noyam Journals. This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).
