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On-demand mathemagenic content for learners

Maycock, Keith and Keating, John (2008) On-demand mathemagenic content for learners. In: Proceedings of the 5th WSEAS/IASME international conference on Engineering education. World Scientific and Engineering Academy and Society, Stevens Point, Wisconsin, United States of America, pp. 155-163. ISBN 978-960-6766-86-2 (Submitted)

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Adaptive learning systems attempt to adapt learning content to suit the needs of the learners using the system. Most adaptive techniques however are constrained by the pedagogical preference of the author of the system and are always constrained to the system they were developed for and the domain content. Additionally there exist many referencing standards to describe instructional content for reuse. The SCORM model produces instructional content that has vast amounts of metadata however this model like all other referencing models acts like a black box, the metadata describes what the content should be like without analysing the actual content. Furthermore the paper analyses some digital repositories to determine the potential of creating an automated learning component that is able to automatically construct learning content suited to the cognitive ability and pedagogical preference of any learner. The paper details the functionality of a Content Analyser (CA) that was created to bridge the gap between the inconsistencies found in digital repositories. Finally the paper concludes with an example learning component that utilizes the CA for building course content to an expected predetermined minimum learning experience suited to each learner's cognitive ability and pedagogical preference delivered through Moodle.

Item Type: Book Section
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
L Education > LC Special aspects / Types of education > E-Learning
Divisions: School of Computing > Staff Research and Publications
Depositing User: Timothy Lawless
Date Deposited: 11 Jun 2014 16:48
Last Modified: 11 Jun 2014 16:56

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