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Issue No.04 - October-December (2008 vol.7)
pp: 62-70
Yuichi Nakamura , Kyoto University
Daqing Zhang , Institut TELECOM, France
Shoji Kajita , Nagoya University
Zhiwen Yu , Kyoto University
ABSTRACT
In this article, the authors present an approach for context-aware and QoS-enabled learning content provisioning, one of the essential elements in ubiquitous learning. The essence of the system is recommending the right content, in the right form, to the right learner, based on a wide range of user context information and QoS requirements. To facilitate knowledge interoperability and sharing, they modeled the learner context, content knowledge, and domain knowledge using ontologies. They first propose a knowledge-based semantic recommendation method to acquire the content the user really wants and needs to learn. Then, a fuzzy logic-based decision-making strategy and an adaptive QoS mapping mechanism determine the appropriate presentation according to user's QoS requirements and device/network capability.
INDEX TERMS
ubiquitous learning, context-aware, QoS, content provisioning, adaptability
CITATION
Yuichi Nakamura, Daqing Zhang, Shoji Kajita, Zhiwen Yu, "Content Provisioning for Ubiquitous Learning", IEEE Pervasive Computing, vol.7, no. 4, pp. 62-70, October-December 2008, doi:10.1109/MPRV.2008.69
REFERENCES
1. Y. Chen et al., "A Mobile Scaffolding-Aid-Based Bird-Watching Learning System," IEEE Int'l. Workshop Wireless and Mobile Technologies in Education (WMTE02), IEEE CS Press, 2002, pp. 15–22.
2. Z. Yu et al., "Ontology-Based Semantic Recommendation for Context-Aware E-Learning," 4th Int'l. Conf. Ubiquitous Intelligence and Computing, LNCS 4611, Springer, 2007, pp. 898–907.
3. J.F. Sowa, Conceptual Structures, Addison-Wesley, 1984.
4. Z. Yu et al., "Fuzzy Recommendation towards QoS-Aware Pervasive Learning," IEEE 21st Int'l. Conf. Advanced Information Networking and Applications, IEEE CS Press, 2007, pp. 604–610.
5. Z. Yu et al., "TV Program Recommendation for Multiple Viewers Based on User Profile Merging," User Modeling and User-Adapted Interaction, vol. 16, no. 1, 2006, pp. 63–82.
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