Towards Real-Time Homogeneity and Heterogeneity in Student's Beliefs

Finding a similarity in the beliefs of students about trending technology is a challenging task. This paper used cluster analysis to groups the homogeneous beliefs of students concerning the technology provided. For this, we applied the Hierarchical Clustering (HC) approach to primary samples. We used the Agglomerative approach to cluster formation with Ward's method and Squared Euclidean Distance (SED). This technique recommended a maximum of three and a minimum of two optimal clusters. Having three clusters 50% and having two clusters, 100% response beliefs are covered. Automatic detection using the HC method discovered the majority of students' beliefs are positive about use, benefits, outlook, and growth. This paper presented an initial cluster approach to detect the Homogeneity and Heterogeneity of students' responses about the technology. It might help to university management to see the grouping of identical beliefs. This technique can deploy online to frame a new web clustering module of the university.