Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Preference-based decision making for personalised access to Learning Resources

Preference-based decision making for personalised access to Learning Resources This article addresses the selection of Learning Resources (LRs; learning material, learning activities, etc.) through a preference-based decision-making framework. We consider the case where a set of LRs are maintained within a digital repository and are described in a common format (e.g. through learning technologies specifications and standards). We present a preference-based decision-making framework for selecting among these LRs according to the profile of each individual learner, thus facilitating personalised access to LRs. We argue that the proposed framework overcomes some of the problems caused by the rule-based approaches which are usually employed to facilitate adaptation and personalisation, in general. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Autonomous and Adaptive Communications Systems Inderscience Publishers

Loading next page...
 
/lp/inderscience-publishers/preference-based-decision-making-for-personalised-access-to-learning-9lNzXHOZ00

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1754-8632
eISSN
1754-8640
DOI
10.1504/IJAACS.2008.01981
Publisher site
See Article on Publisher Site

Abstract

This article addresses the selection of Learning Resources (LRs; learning material, learning activities, etc.) through a preference-based decision-making framework. We consider the case where a set of LRs are maintained within a digital repository and are described in a common format (e.g. through learning technologies specifications and standards). We present a preference-based decision-making framework for selecting among these LRs according to the profile of each individual learner, thus facilitating personalised access to LRs. We argue that the proposed framework overcomes some of the problems caused by the rule-based approaches which are usually employed to facilitate adaptation and personalisation, in general.

Journal

International Journal of Autonomous and Adaptive Communications SystemsInderscience Publishers

Published: Jan 1, 2008

There are no references for this article.