14th IEEE International Conference on Advanced Learning Technologies (ICALT) - Advanced Technologies for Supporting Open Access to Formal and Informal Learning, Athens, Greece, 7 - 10 July 2014, pp.109-111
The aim of this study is to identify clusters of students who interact with an online learning environment in similar ways. The study included analyzing three-month interaction data from 74 undergraduates in the online learning environment using the Self Organizing Map (SOM) clustering method. The results of analysis revealed the existence of three distinct groups of students, labeled by their interaction (non-active, active, very active) and course success (low learning, medium learning, high learning). These are the preliminary results of the study and the cluster data which was obtained here is intended to be used in further studies for classifying new students or adaptation and personalization purposes.