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Abstract
Using smart devices for context aware M-learning is becoming increasingly popular. This has led to M-learning technology becoming an indispensable part of today’s learning environment platforms. However, some fundamental issues remain - namely, M-learning still lacks the ability to truly understand the human reaction and user beavertail is due to the fact that current M-learning systems are passive and not aware of learners’ changing contextual situations. They rely on static information about mobile learners. In addition, current M-learning platforms lack the capability to incorporate dynamic contextual situations into learners .
Conclusion
Conclusion
The recent explosion and the proliferation of mobile and smart devices have provided unprecedented opportunities for ubiquitous learning where learners can access learning content anywhere, any time. Thus, this development has enabled adaptive learning where personalized learning content adapted to the individual learner according to her contextual situations, device characteristics, preferences and environment situations can be delivered. This kind of infrastructure allows mobile learners to perform educational activities that traditionally were confined to physical locations. However, in order to provide this kind of infrastructure, existing solutions have not adequately addressed various problems associated with learning in mobile environments. One of these problems relates to the lack of context awareness framework with the capability to provide dynamic contextual information about mobile learners and their environments. Another challenge is the problem related to the development of learner’s preference model capable of utilizing runtime context information to learn and predict learners’ preferences. This thesis proposes to address these issues by designing and developing a context-awareness framework.
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