Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Numerous approaches have been developed to group learners’ behavior in an online/blended learning environment. However, most clustering analyses this particular field only consider numeric features despite the existence of categoric that are found important other studies. In study, we compare K-Means and K-Prototypes algorithms cluster a flipped classroom implementation. From model selection, produced by algorithm — which included is better one. The statistical analysis results selected shows significant differences inter-cluster comparisons, implying good separation data. More importantly, can identify each then be used help learners achieving learning.