Addressing context-awareness and standards interoperability in E-Learning: A service-oriented framework based on IRS III

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Abstract

Current technologies aimed at supporting learning goals primarily follow a data and metadata-centric paradigm. They provide the learner with appropriate learning content packages containing the learning process description as well as the learning resources. Whereas process metadata is usually based on a certain standard specification - such as ADL SCORM or the IMS Learning Design - the used learning resources - data or services - are specific to pre-defined learning contexts, and they are allocated manually at design-time. Therefore, a content package cannot consider the actual learning context, since this is only known at runtime of a learning process. These facts limit the reusability of a content package across different standards and contexts. To overcome these issues, this paper proposes an innovative Semantic Web Service-based approach that changes this data- and metadata-based paradigm to a context-adaptive service-oriented approach. In this approach, the learning process is semantically described as a standard-independent process model decomposed into several learning goals. These goals are accomplished at runtime, based on the automatic allocation of the most appropriate service. As a result, we address the dynamic adaptation to specific context and - providing the appropriate mappings to established metadata standards - we enable the reuse of the defined semantic learning process model across different standards. To illustrate the application of our approach and to prove its feasibility, a prototypical application based on an initial use case scenario is proposed. © 2008 Springer-Verlag Berlin Heidelberg.

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APA

Dietze, S., Gugliotta, A., & Domingue, J. (2008). Addressing context-awareness and standards interoperability in E-Learning: A service-oriented framework based on IRS III. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4823 LNCS, pp. 174–183). https://doi.org/10.1007/978-3-540-78139-4_16

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