Exposing knowledge in speech: Monitoring conceptual development in spoken conversation

Lagatie, R; Wild, F; De Causmaecker, P; Scott, P

Exposing knowledge in speech: Monitoring conceptual development in spoken conversation

Lagatie, R Wild, F De Causmaecker, P Scott, P

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Publication Type:: Conference Proceeding
Citation:: 2011 IST-Africa Conference Proceedings, IST 2011, 2011
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Lagatie, R	en_US
dc.contributor.author	Wild, F	en_US
dc.contributor.author	De Causmaecker, P	en_US
dc.contributor.author	Scott, P https://orcid.org/0000-0001-5944-9361	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	2011 IST-Africa Conference Proceedings, IST 2011, 2011	en_US
dc.identifier.isbn	9781905824267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120610
dc.description.abstract	Automatic evaluation of verbal activities has always been hard. On the one hand, teachers want to stimulate student engagement and therefore often base grades on the number of participations. On the other hand, if quantity is more important than quality, students might participate, but add nothing new to the conversation. Providing feedback for spoken conversations is another challenge, one that is often solved by doing things manually. Both aspects originate from the same problem, the difficulty of extracting semantic knowledge from speech. In this contribution we present our approach to extracting and visualising this semantic knowledge using an open source speech recognition engine and Latent Semantic Analysis. © 2011 IIMC LTD.	en_US
dc.relation.ispartof	2011 IST-Africa Conference Proceedings, IST 2011	en_US
dc.title	Exposing knowledge in speech: Monitoring conceptual development in spoken conversation	en_US
dc.type	Conference Proceeding
utslib.for	1301 Education Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Automatic evaluation of verbal activities has always been hard. On the one hand, teachers want to stimulate student engagement and therefore often base grades on the number of participations. On the other hand, if quantity is more important than quality, students might participate, but add nothing new to the conversation. Providing feedback for spoken conversations is another challenge, one that is often solved by doing things manually. Both aspects originate from the same problem, the difficulty of extracting semantic knowledge from speech. In this contribution we present our approach to extracting and visualising this semantic knowledge using an open source speech recognition engine and Latent Semantic Analysis. © 2011 IIMC LTD.

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http://hdl.handle.net/10453/120610