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Context-Aware Mobile Learning on the Semantic Web
Lakehead University, Canada.
Etisalat University College, United Arab Emirates.
Massey University, New Zealand.ORCID-id: 0000-0002-7312-9089
2008 (Engelska)Ingår i: Advances in Ubiquitous Computing: Future Paradigms and Directions / [ed] S. Kouadri Mostefaoui, Z. Maamar and G. M. Giaglis, IGI Global, 2008, s. 23-44Kapitel i bok, del av antologi (Refereegranskat)
Abstract [en]

This chapter focuses on the theoretical and technological aspects of designing mobile learning (m-learning) services that deliver context-aware learning resources from various locations and devices. Context-aware learning is an important requirement for next generation intelligent m-learning systems. The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this research work, context reflects timeliness and mobility to nurture pervasive instruction throughout the learning ecosystem. In this context of ubiquity that is supported by a new generation of mobile wireless networks and smart mobile devices, it is clear that the notion of context plays a fundamental role since it influences the computational capabilities of the used technology. In particular, three types of context awareness are being considered in this work —platform-awareness, learner-awareness, and task-awareness. In this research work, these contextual elements are defined at the semantic level in order to facilitate discoverability of context-compliant learning resources, adaptability of content and services to devices of various capabilities, and adaptability of services to task at hand and interaction history. The work presented in this chapter contributes towards this direction, making use of the progress in Semantic Web theory and mobile computing to enable context-aware learning that satisfies learning timeliness and mobility requirements.

Ort, förlag, år, upplaga, sidor
IGI Global, 2008. s. 23-44
Nyckelord [en]
Ubiquitous learning, semantic web, personalized learning
Nationell ämneskategori
Data- och informationsvetenskap Data- och informationsvetenskap
Forskningsämne
Teknik
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URN: urn:nbn:se:his:diva-12832DOI: 10.4018/978-1-59904-840-6.ch002Scopus ID: 2-s2.0-70349551525ISBN: 978-1-59904-840-6 OAI: oai:DiVA.org:his-12832DiVA, id: diva2:956095
Tillgänglig från: 2016-08-29 Skapad: 2016-08-29 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

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