Adaptive Learning on the World Wide Web - Silva Muñoz, Lydia
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Nowadays, the popularity of the Web encourages the development of Hypermedia Systems dedicated to e-learning. However, most of the available Web teaching systems apply the traditional paper-based learning resources presented as html pages, making no use of the new capabilities provided by the Web. There is a challenge to develop educative systems that adapt their instructional content to the style of learning, context, and background of each student. Another research issue concerns the ability to effectively interoperate on the Web reusing learning objects. This work addresses these two issues…mehr

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Produktbeschreibung
Nowadays, the popularity of the Web encourages the development of Hypermedia Systems dedicated to e-learning. However, most of the available Web teaching systems apply the traditional paper-based learning resources presented as html pages, making no use of the new capabilities provided by the Web. There is a challenge to develop educative systems that adapt their instructional content to the style of learning, context, and background of each student. Another research issue concerns the ability to effectively interoperate on the Web reusing learning objects. This work addresses these two issues by using the technologies of the Semantic Web. Our approach models knowledge regarding educative content and learner s profile as Web ontologies whose lexicons are refinements of standard vocabularies Dublin Core and LOM. Those ontologies contain the necessary knowledge for the adaptive on-the-fly configuration of learning objects tailored to the profile of each individual learner. Our declarative approach facilitates maintenance by avoiding hardcoding of configuration strategies and facilitates the widespread reuse of learning objects by publishing their configuration knowledge.
Autorenporträt
Lydia Silva Muñoz has more than 10 years of experience in Information Systems Design, and has authored several articles in Adaptability and Web Standard Metadata. She is a Computer Engineer with a Master¿s degree in Computer Science, and a Ph.D. Candidate at the University of Toronto working on Knowledge Engineering.