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

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

Author(s): Rosa Cabedo Gallén, Edmundo Tovar
Subject(s): Social Sciences, Education, Higher Education
Published by: European Distance and E-Learning Network
Keywords: Learning analytics; MOOCs; Non-formal and informal learning

Summary/Abstract: 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).

  • Issue Year: 2016
  • Issue No: 1
  • Page Range: 186-196
  • Page Count: 11
  • Language: English