Adaptive and Personalized Content Architecture for Mobile Learning System

X. Zhao, T. Ninomiya, F. Anma, and T. Okamoto (Japan)


Content Adaptation, Mobile Learner, Adaptive Learning, Pervasive Computing, Personalized Content


Most of learning contents existing today have been designed for desktop computers and high-speed network connections, and are not suitable for handheld devices with limited resources and computing capabilities. Also, some materials, irrelevant to learner’s preferences or contextual environment, may be delivered to mobile learners, which affect the learning efficiency, and also increase the learner’s communication costs and channel burdens. In order to provide adaptive contents based on device capabilities and learner’s experience, this paper presents a functional architecture for mobile learning environment. The architecture consists of four engines: learner context engine, detector engine, adaptive contents delivery engine, and transcoding engine. By detecting contextual data and identifying learner’s preferences, adaptive contents delivery engine can dispense adaptive contents to learners. Finally, the architecture has been successfully conducted in a trial PPT document content adaptation system.

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