Social network analysis for technology-enhanced learning: review and future directions

  • Authors:

  • Rory L. L. Sie;Thomas Daniel Ullmann;Kamakshi Rajagopal;Karina Cela;Marlies Bitter-Rijpkema;Peter B. Sloep

  • Affiliations:

  • Open Universiteit in the Netherlands, P.O. Box 2960, 6401 DL Heerlen, The Netherlands;Knowledge Media Institute, Open University, Milton Keynes, MK7 6AA, UK;Open Universiteit in the Netherlands, P.O. Box 2960, 6401 DL Heerlen, The Netherlands;University of Alcalá, Pza. San Diego, s/n - 28801, Alcala de Henares, Madrid, Spain;Open Universiteit in the Netherlands, P.O. Box 2960, 6401 DL Heerlen, The Netherlands;Open Universiteit in the Netherlands, P.O. Box 2960, 6401 DL Heerlen, The Netherlands

  • Venue:
  • International Journal of Technology Enhanced Learning
  • Year:
  • 2012

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Abstract

By nature, learning is social. The interactions by which we learn from others inherently form a network of relationships among people, but also between people and resources. This paper gives an overview of the potential social network analysis (SNA) may have for social learning. It starts with an overview of the history of social learning and how SNA may be of value. The core of the paper outlines the state-of-art of SNA for technology-enhanced learning (TEL), by means of four possible types of SNA applications: visualisation, analysis, simulation, and interventions. In an outlook, future directions of SNA research for TEL are provided.