Comparisons on Clustering Methods: Use of LMS Log Variables on Academic Courses

Academic analytics guides university decision-makers to assign limited resources more effectively. Especially, diverse academic courses clustered by the usage patterns and levels on LMS (Learning Management System) help understanding instructors’ pedagogical approach and the integration level of ICT (Information and Communication Technology). Further, the clustering results can contribute deciding proper range and levels of financial and technical supports. However, in spite of diverse analytic methodologies, clustering analysis methods often provide different results. This study argues that clustering analysis should be carefully reviewed and consequently required of insights from the researchers and educational practitioners. The purpose of this study is to present implications by using three different clustering analysis including Gaussian Mixture Model, K-Means clustering, and Hierarchical clustering. For this, we have clustered academic courses based on the usage levels and patterns of LMS utilized in higher education using clustering techniques. In this study, 2,639 courses opened during 2013 fall semester in a large private university located in South Korea were analyzed with 13 observation variables that represent the characteristics of academic courses. The results of analysis show that the strengths and weakness of each clustering analysis and provide different implications and approaches for academic leaders and university staff to make strategic decision on development of LMS.