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Designing metadata for learning and assessment objects

Designing metadata for learning and assessment objects

Peter, Sophie, Bacon, Elizabeth and Dastbaz, Mohammad (2010) Designing metadata for learning and assessment objects. In: E-Learn 2010: Proceedings of the World Conference on E-learning in Corporate, Government, Healthcare, and Higher Education. Association for the Advancement of Computing in Education (AACE), Chesapeake, VA, USA, pp. 2083-2090. ISBN 978-1-880094-83-9

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Abstract

Learning resources metadata is a large research area. Standards have been developed, including IEEE LOM, SCORM and the Dublin Core Metadata initiative. This paper discusses these standards and development issues. The SCORM model can be extended in a number of ways and this research describes how this is possible. The paper then discusses the metadata design for the learning resources within an e-learning platform (Peter, Bacon and Dastbaz 2008). This e-learning platform is designed to offer personalisation and adaptability for the learner. The system metadata has been designed to enable effective searches of the learning resources which will then enable the system to offer personalised learning resources for the learner. The issues considered when designing this metadata are discussed within this paper.

Item Type: Conference Proceedings
Title of Proceedings: E-Learn 2010: Proceedings of the World Conference on E-learning in Corporate, Government, Healthcare, and Higher Education
Additional Information: [1] Paper presented at E-Learn 2010: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, held 18-22 October 2010, Orlando, FL USA.
Uncontrolled Keywords: development of the SCORM model, designing metadata, personalised e-learning platforms, intelligent tagging
Subjects: L Education > LB Theory and practice of education
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Greenwich Research into Innovative Pedagogies (GRIP)
Related URLs:
Last Modified: 18 Apr 2018 10:34
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/5444

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