thesis
Social personalized e-learning framework
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Abstract
This thesis discusses the topic of how to improve adaptive and personalized e-learning in
order to provide novel learning experiences. A recent literature review revealed that
adaptive and personalized e-learning systems are not widely used. There is a lack of
interoperability between adaptive systems and learning management systems, in addition
to limited collaborative and social features.
First of all, this thesis investigates the interoperability issue via two case studies. The first
case study focuses on how to achieve interoperability between adaptive systems and
learning management systems using e-learning standards and the second case study
focuses on how to augment e-learning standards with adaptive features. Secondly, this
thesis proposes a new social framework for personalized e-learning, in order to provide
adaptive and personalized e-learning platforms with new social features. This is not just
about creating learning content, but also about developing new ways of learning. For
instance, in the presented vision, adaptive learning does not refer to individuals only, but
also to groups. Furthermore, the boundaries between authors and learners become less
distinct in the Web 2.0 context.
Finally, a new social personalized prototype is introduced based on the new social
framework for personalized e-learning in order to test and evaluate this framework. The
implementation and evaluation of the new system were carried out through a number of
case studies