Integrating Learning Object Repositories Using a Mediator Architecture

Kärger, Philipp; Ullrich, Carsten; Melis, Erica

doi:10.1007/11876663_16

Cited by 11 publications

(9 citation statements)

References 6 publications

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“…Content integration and repository searches through peer-to-peer mechanisms are proposed in Edutella [32]. Edutella's architecture is based on mediators which enable the integration of LORs [33]. Different methods are applied to retrieve educational content [34], including the metric proposed for the calculation of similarity between different LOs.…”

Section: State Of the Artmentioning

confidence: 99%

Smart System for the Retrieval of Digital Educational Content

et al. 2019

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The education sector is a major generator, consumer, and depositary of educational content. Thanks to technological advances, today's educators and learners have ubiquitous and on-demand access to information. Technology has made it possible for us to communicate and share information effortlessly from anywhere in the world. However, the availability of vast amounts of heterogeneous educational content will not be useful unless we search, retrieve and integrate it, creating interoperable educational environments. The current challenges to integrating educational content arise from its distribution over several repositories. This research proposes AIREH (architecture for intelligent retrieval of educational content from heterogeneous environments), for the retrieval of digital content through agent-based virtual organizations. This flexible architecture facilitates the search for and integration of heterogeneous content through an information retrieval model that involves both case-based reasoning and federated search. Moreover, AIREH is based on an adaptive organization model for distributed planning, thanks to which, it manages open systems flexibly, dynamically, and effectively. The conducted case study gives very promising results and demonstrates the advantages of using agent-based virtual organizations in the retrieval of labeled digital content. The proposed model is flexible, customizable, comprehensive and efficient.In educational technology, one of the most widespread approaches to making educational content reusable and interoperable is to fragment it into independent, modular units that can be used in different environments and applications [1]. This modular approach makes it possible to easily access and combine educational content items for a single learning objective, this is termed as a learning object (LO) [2,3]. The ability to access simple didactic resources specifically designed for use in different contexts facilitates the process of creating educational content. This reduces the time of creating and compiling educational resources, enabling the efficient development of comprehensive educational courses. The change in the creation process takes place when it is based on the search and aggregation of these small modular pieces, as simple didactic resources specifically designed to be used in different contexts. Normally, LO are stored in digital repositories, called learning object repositories (LORs). These systems must also have the functionalities of importation, exportation, identification, and retrieval of content [4,5]. The user usually interacts with the information contained in the repository via their web browser.To support the reuse process (speed, flexibility, and uniformity), the learning object has external descriptors or labels, which enable the identification, organization, search and retrieval of content items. These descriptors are created with standard-based metadata, ensuring the interoperability of systems and the description of content items using an adequate set of attribu...

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Section: State Of the Artmentioning

confidence: 99%

Smart System for the Retrieval of Digital Educational Content

et al. 2019

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“…The query consists of a partial metadata description with three parts: the classes of the educational resources, property metadata, and relational metadata. Using a mediator architecture, this query is passed to all connected learning object repositories (and translated into their specific terms, if necessary) (Kärger, Ullrich, & Melis, 2006). The set of identifiers that meet the description is then returned to the planner.…”

Section: Accessing Information From External Knowledge Sourcesmentioning

confidence: 99%

Pedagogically founded courseware generation based on HTN-planning

Ullrich

Melis

2009

Expert Systems with Applications

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“…Bouzeghoub and Elbyed (2006) explored ontology mapping for LORs interoperability. Kárger et al (2006) employed a mediator architecture to integrate LORs. Recently, proposed the ProLearn query language to implement interoperability for searching LORs.…”

Section: Learning Objects Searching and Sharingmentioning

confidence: 99%