A match‐making system for learners and learning objects

Harold Boley, Virendrakumar C. Bhavsar, David Hirtle, Anurag Singh, Zhongwei Sun, Lu Yang
2005 Interactive Technology and Smart Education  
npsi/ctrl?action=rtdoc&an=5763443⟨=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=rtdoc&an=5763443⟨=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at Abstract We 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
more » ... tween learners and learning objects (LOs). It uses the Canadian Learning Object Metadata (CanLOM) repository of the eduSource e-Learning project. Through AgentMatcher's new indexing component, known as Learning Object Metadata Generator (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 WOO RuleML 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.
doi:10.1108/17415650580000042 fatcat:oqujo6nh5banhldkd7ny7wwnzm