One of the most important issues in the context of mobile computer supported collaborative learning is not only to provide information anywhere and anytime, but also to allow the mobile learners to find the most suitable information (and/or activities) and collaborate with the right persons. the systems mentioned above represent a good solution, so, what are the differences and similarities between them, and which one is more advisable to the m-learner group formation problem?
As I believe, the personalization of the learning content can be achieved either using a recommendation system or an adaptive system.
Dear Mohamed, thank you for your answer. I'm OK with what you believe, but I search exactly to know how can I use these systems in order to improve the quality of group formation process in mobile learning environments.
you must know how does each one of them work, its features to get up differences and similarities, :)
Personalized learning systems are self-managed/autonomic agent based systems which gain cognition via conscious/deep sense rather than collaborative/social firms of learning. Learning recommender systems are controllable picky/choosy social engines which demand vivid data and build evolving patterns of preferences that can be reused in other arenas, all recommender systems learning methods are abstracted from game theory/gamefication models. Adaptive learning systems are based on blended "evolving"/"self-configuring" systems that change their innate behavior automatically from realtime data or new design requirements at runtime, its worth to mention that adaptive learning systems do not consume plans, patterns of behavior to favor the learnt objectives as perceived/ignited by receptor, rather they tend to enhance mechanics and strategies of the learning process.
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