Clustering Learners in a Learning Management System to Provide Adaptivity

Abstract

Learning Management Systems are a great source of data about the learners and their learning behavior. Educational Data Mining (EDM) together with Learning Analytics (LA) are emerging topics because of the huge amount of educational data coming from these systems. Knowledge gained from LA and EDM can be used for the adaptivity of learning systems to provide learners a personalized learning environment. This paper presents the clustering analysis of Moodle data in terms of learners’ preferences on different assessment methods. Clustering is made by using four different algorithms and different number of clusters to find the most suitable method for a future adaptive learning system.