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Personalized Adaptive Content System for Context-Aware Mobile Learning


Xinyou Zhao, Fumihiko Anma, Toshie Ninomiya, Toshio Okamoto


Vol. 8  No. 8  pp. 153-161


Mobile and ubiquitous computing devices are transforming the way that learners study. But most of learning contents, designed for desktop platforms, are not suitable for handheld devices. Also, some materials, irrelevant to learner’s preferences or contextual environment, may affect the learning efficiency, and also increase the communication costs. In order to provide adaptive contents based on device capabilities and learner’s experience, this paper presents a functional architecture for personalized adaptation contents. Also, it proposes some algorithms to create the adaptive and intelligent contents for learners. The learning contents created are adaptive to learner’s preference, also adaptive to contextual environment. After the evaluation on a personalized adaptive content system developed, we find that the context-aware mobile learning system can increase the learning efficiency and interest, also resolve the new-item question during adaptation.


Adaptive Content, Mobile Learning, Adaptive Learning, Pervasive Computing, Content Transcoding, e-learning