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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/117680...
Part of book or chapter of book . 2006 . Peer-reviewed
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DBLP
Conference object . 2018
Data sources: DBLP
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Authoring Adaptive Learning Designs Using IMS LD

Authors: Adriana J. Berlanga; Francisco J. García-Peñalvo; Jorge Carabias;

Authoring Adaptive Learning Designs Using IMS LD

Abstract

Adaptive Educational Hypermedia Systems have the potential to deliver instruction tailored to the characteristics of each student. However, despite many years of research in the area, these systems have been used only in a few real learning situations. Reasons for this include their use of proprietary semantics in the definition of adaptivity and educational elements, and their lack of interoperation among courses and applications. We claim that an option to overcome these issues is to annotate adaptive rules, techniques, and learning elements using a common notational method, the IMS Learning Design (IMS LD). This paper presents a novel approach to define adaptive learning designs and, particularly, how adaptive hypermedia techniques and adaptive rules can be modelled by means of IMS LD.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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