Assessing the language of chat for teamwork dialogue

Shibani, A; Koh, E; Lai, V; Shim, KJ

Assessing the language of chat for teamwork dialogue

Shibani, A

Koh, E Lai, V Shim, KJ

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Publication Type:: Journal Article
Citation:: Educational Technology and Society, 2017, 20 (2), pp. 224 - 237
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Shibani, A https://orcid.org/0000-0003-4619-8684	en_US
dc.contributor.author	Koh, E	en_US
dc.contributor.author	Lai, V	en_US
dc.contributor.author	Shim, KJ	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Educational Technology and Society, 2017, 20 (2), pp. 224 - 237	en_US
dc.identifier.issn	1176-3647	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126957
dc.description.abstract	In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study will evaluate and explain preprocessing and classification methods used to analyze teamwork dialogue from a dataset of chat data. Analytics methods evaluated in this study provide a direction for assessing the language of chat for teamwork dialogue and can help extend the work of technology enhanced language learning to n ot only focus on academic competency, but on the communication aspect too.	en_US
dc.relation.ispartof	Educational Technology and Society	en_US
dc.subject.classification	Education	en_US
dc.title	Assessing the language of chat for teamwork dialogue	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	20	en_US
utslib.for	1303 Specialist Studies in Education	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Transdisciplinary Innovation
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study will evaluate and explain preprocessing and classification methods used to analyze teamwork dialogue from a dataset of chat data. Analytics methods evaluated in this study provide a direction for assessing the language of chat for teamwork dialogue and can help extend the work of technology enhanced language learning to n ot only focus on academic competency, but on the communication aspect too.

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http://hdl.handle.net/10453/126957