Title: Improving the unreliability of competence information: an argumentation to apply information fusion in learning networks

Authors: Yongwu Miao, Peter Sloep, Hans Hummel, Rob Koper

Addresses: Centre for learning Sciences and Technologies, Open University of the Netherlands, Valkenburgerweg 177, NL-PO Box 2960, 6401 DL Heerlen, The Netherlands. ' Centre for learning Sciences and Technologies, Open University of the Netherlands, Valkenburgerweg 177, NL-PO Box 2960, 6401 DL Heerlen, The Netherlands. ' Centre for learning Sciences and Technologies, Open University of the Netherlands, Valkenburgerweg 177, NL-PO Box 2960, 6401 DL Heerlen, The Netherlands. ' Centre for learning Sciences and Technologies, Open University of the Netherlands, Valkenburgerweg 177, NL-PO Box 2960, 6401 DL Heerlen, The Netherlands

Abstract: Automated competence tracking and management is crucial for an effective and efficient lifelong competence development in learning networks. In this paper, we systematically analyse the problem of unreliability of competence information in learning networks. In tracking the development of competences in learning networks, a large amount of competence information can be gathered from diverse sources and diverse types of sources. Individual information is more or less credible. This paper investigates information fusion technologies that may be applied to address the problem and that show promise as candidate solutions for achieving an improved estimate of competences by fusing information coming from multiple sources and diverse types of sources.

Keywords: competences; learning networks; information fusion; automated competence tracking; lifelong learning; competence development; self-directed learning; competence profile; competence proficiency level; evidence records; competence records.

DOI: 10.1504/IJCEELL.2009.028833

International Journal of Continuing Engineering Education and Life-Long Learning, 2009 Vol.19 No.4/5/6, pp.366 - 380

Published online: 06 Oct 2009 *

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