Application of machine learning techniques to analyse student interactions and improve the collaboration process

  • Authors:

  • Antonio R. Anaya;Jesús G. Boticario

  • Affiliations:

  • Dpto. Inteligencia Artificial, E.T.S.I.I., UNED, c/Juán del Rosal, S/N, Ciudad Universitaria, 28040 Madrid, Spain;Dpto. Inteligencia Artificial, E.T.S.I.I., UNED, c/Juán del Rosal, S/N, Ciudad Universitaria, 28040 Madrid, Spain

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

In e-learning environments that use the collaboration strategy, providing participants with a set of communication services may not be enough to ensure collaborative learning. It is thus necessary to analyse collaboration regularly and frequently. Using machine learning techniques is recommended when analysing environments where there are a large number of participants or where they control the collaboration process. This research studied two approaches that use machine learning techniques to analyse student collaboration in a long-term collaborative learning experience during the academic years 2006-2007, 2007-2008 and 2008-2009. The aims were to analyse collaboration during the collaboration process and that it should be domain independent. Accordingly, the intention was to be able to carry out the analysis regularly and frequently in different collaborative environments. One of the two approaches classifies students according to their collaboration using unsupervised machine learning techniques, clustering, while the other approach constructs metrics that provide information on collaboration using supervised learning techniques, decision trees. The research results suggest that collaboration can be analysed in this way, thus achieving the aims set out with two different machine learning techniques.