A Benchmarking Study of K-Means and SOM Approaches Applied to a Set of Features of MOOC Participants

Rosa Cabedo Gallén
Technical University of Madrid, Spain

Edmundo Tovar
Universidad Politécnica de Madrid, Spain


MOOC format is characterized by the great diversity of enrolled people. This heterogeneity of participants represents a challenging opportunity in order to identify underlying relationships in the internal structure of features that make up participants’ profiles. This paper has the aim of identifying and analyzing a feasible set of MOOC participants’ profiles with the use of two unsupervised clustering techniques, K-Means as a partitional clustering algorithm and Kohonen’s Self-Organizing Maps (SOMs), hereinafter SOM, as a representative technique of Artificial Neural Networks (ANNs).

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