A Match-Making System for Learners and Learning Objects

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TypeArticle
ConferenceThe International Journal of Interactive Technology and Smart Education, 2 (3), August 2005., Troubador Publ
SubjectAgentMatcher; CanLOM; e-Business; e-Learning; learning objects; matchmaking; metadata; metadata generator; RuleML
AbstractWe have proposed and implemented AgentMatcher, an architecture for match-making in e-Business applications. It uses arc-labeled and arc-weighted trees to match buyers and sellers via our novel similarity algorithm. This paper adapts the architecture for matchmaking between learners and learning objects (LOs). It uses the Canadian LearningObject Metadata (CanLOM) repository of the eduSource e-Learning project. ThroughAgentMatcher's new indexing component, known as Learning Object MetadataGenerator (LOMGen), metadata is extracted from HTML LOs for use in CanLOM.LOMGen semi-automatically generates the LO metadata by combining a word frequency count and dictionary lookup. A subset of these metadata terms can be selected from a query interface, which permits adjustment of weights that express user preferences. Webbased prefiltering is then performed over the CanLOM metadata kept in a relational database. Using an XSLT (Extensible Stylesheet Language Transformations) translator, the prefiltered result is transformed into an XML representation, called Weighted Object- Oriented (WOO) RuleML (Rule Markup Language). This is compared to the WOORuleML representation obtained from the query interface by AgentMatcher's core Similarity Engine. The final result is presented as a ranked LO list with a user-specified threshold.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48538
NPARC number5763443
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Record identifier8db0bd01-e3ac-4df3-8316-5f2eb719c395
Record created2009-03-29
Record modified2016-05-09
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