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What is IMS Learning Design (IMS LD)

Handbook of Research on Learning Design and Learning Objects: Issues, Applications, and Technologies
This is a specification allowing the representation of various pedagogical models to describe learning.
Published in Chapter:
Using IMS Learning Design in Educational Situations
Paul Hazlewood (Liverpool Hope University, UK), Amanda Oddie (Liverpool Hope University, UK), and Mark Barrett-Baxendale (Liverpool Hope University, UK)
DOI: 10.4018/978-1-59904-861-1.ch013
Abstract
IMS Learning Design (IMS LD) is a specification for describing a range of pedagogic approaches. It allows the linking of pedagogical structure, content, and services, whilst keeping the three separate, thus providing the potential for reuse as well as forming the basis for interoperability between learning activities and services. As such, this specification promises unprecedented opportunities to build effective tutor support and presence into e-learning systems. The tools that implement the specification have primarily been used for research purposes and have not been targeted at teaching practitioners or learners working in teaching and learning situations. There is a perception amongst practitioners and tool developers that the specification and tools are too technical or difficult for practitioner use. This chapter examines practitioner use of current tools for creating IMS LD and the use of IMS LD units of learning (UoLs) with learners through projects being undertaken at Liverpool Hope University (LHU). It presents some of the experiences and findings gained from these projects. The chapter also examines current technologies and tools for creating and running IMS LD UoLs, and finally discusses the potential and future for IMS LD.
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Opportunities, Achievements, and Prospects for Use of IMS LD
An interoperability specification focused on the exchange and interoperability of e-learning materials and activities, published by IMS Global Learning Inc. It is in itself also a modeling language, and is strongly based on OUNL’s EML.
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