Social Reference Model for Adaptive Web Learning

  • Authors:
  • Fawaz Ghali;Alexandra I. Cristea

  • Affiliations:
  • Department of Computer Science, University of Warwick, Coventry, United Kingdom CV4 7AL;Department of Computer Science, University of Warwick, Coventry, United Kingdom CV4 7AL

  • Venue:
  • ICWL '009 Proceedings of the 8th International Conference on Advances in Web Based Learning
  • Year:
  • 2009

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Abstract

In this paper, we describe the design steps of extending LAOS, a five-layer framework for generic adaptive web learning authoring, by adding a social layer to capture (and adapt) information from 1) collaborative authoring (i.e. editing the content of other learners, describing the content using tags, rating the content, and commenting on the content, etc); and 2) authoring for collaboration (i.e., adding authors' activities, such as defining groups of authors, subscribing to other authors, etc). Moreover, the paper presents MOT 2.0, an adaptive E-learning 2.0 system, which is built on the proposed reference model, and finally, we report on our evaluations to validate the new Social Layer by comparing MOT 2.0 with its predecessor, MOT 1.0.