KMi Seminars
Supporting Context-Awareness and Standards Interoperability in e-Learning
This event took place on Wednesday 14 March 2007 at 11:30

 
Alessio Gugliotta KMi, The Open University

Current technologies aimed at supporting learning goals primarily follow a data and metadata-centric paradigm aimed at providing 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 specific standard specification? like ADL SCORM or the IMS Learning Design standard ? the used learning data is specific to specific learning contexts. The allocation of learning resources ? data or services - usually is done manually at design-time of a content package. Therefore, a content package cannot consider the actual learning context since this is only known at runtime of a package respectively the learning process. These facts limit the reusability of a specific content package across different standards and contexts. To overcome these issues, this paper describes an innovative semantic web service-based approach aimed at changing this data- and metadata-based paradigm to a context-adaptive service-oriented approach following the idea of a dynamic allocation of data and services at runtime of a specific learning process. This approach enables a dynamic adaptation to specific learner needs and objectives and supports the development of abstract semantic process models which are re-usable across different contexts and metadata standards. To illustrate the application of our approach and to prove its feasibility, a prototypical application based on an initial use case scenario is provided.

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KMi Seminars
 

Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.

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