A self-training framework for automatic identification of exploratory dialogue
The dramatic increase in online learning materials over the last decade has made it difficult for individuals to locate information they need. Until now, researchers in the field of Learning Analytics have had to rely on the use of manual approaches to identify exploratory dialogue. This type of dia...
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sg-smu-ink.sis_research-55902019-12-26T07:56:15Z A self-training framework for automatic identification of exploratory dialogue WEI, Zhongyu HE, Yulan SHUM, Simon FERGUSON, Rebecca GAO, Wei WONG, Kam-Fai The dramatic increase in online learning materials over the last decade has made it difficult for individuals to locate information they need. Until now, researchers in the field of Learning Analytics have had to rely on the use of manual approaches to identify exploratory dialogue. This type of dialogue is desirable in online learning environments, since training learners to use it has been shown to improve learning outcomes. In this paper, we frame the problem of exploratory dialogue detection as a binary classification task, classifying a given contribution to an online dialogue as exploratory or non-exploratory. We propose a self-training framework to identify exploratory dialogue. This framework combines cue-phrase matching and K-nearest neighbour (KNN) based instance selection, employing both discourse and topical features for classification. To do this, we first built a corpus from transcripts of synchronous online chat recorded at The Open University annual Learning and Technology Conference in June 2010. Experimental results from this corpus show that our proposed framework outperforms several competitive baselines. 2013-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4587 https://ink.library.smu.edu.sg/context/sis_research/article/5590/viewcontent/2013_CICLing_ExploratoryDialogue.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems |
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Databases and Information Systems WEI, Zhongyu HE, Yulan SHUM, Simon FERGUSON, Rebecca GAO, Wei WONG, Kam-Fai A self-training framework for automatic identification of exploratory dialogue |
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The dramatic increase in online learning materials over the last decade has made it difficult for individuals to locate information they need. Until now, researchers in the field of Learning Analytics have had to rely on the use of manual approaches to identify exploratory dialogue. This type of dialogue is desirable in online learning environments, since training learners to use it has been shown to improve learning outcomes. In this paper, we frame the problem of exploratory dialogue detection as a binary classification task, classifying a given contribution to an online dialogue as exploratory or non-exploratory. We propose a self-training framework to identify exploratory dialogue. This framework combines cue-phrase matching and K-nearest neighbour (KNN) based instance selection, employing both discourse and topical features for classification. To do this, we first built a corpus from transcripts of synchronous online chat recorded at The Open University annual Learning and Technology Conference in June 2010. Experimental results from this corpus show that our proposed framework outperforms several competitive baselines. |
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text |
author |
WEI, Zhongyu HE, Yulan SHUM, Simon FERGUSON, Rebecca GAO, Wei WONG, Kam-Fai |
author_facet |
WEI, Zhongyu HE, Yulan SHUM, Simon FERGUSON, Rebecca GAO, Wei WONG, Kam-Fai |
author_sort |
WEI, Zhongyu |
title |
A self-training framework for automatic identification of exploratory dialogue |
title_short |
A self-training framework for automatic identification of exploratory dialogue |
title_full |
A self-training framework for automatic identification of exploratory dialogue |
title_fullStr |
A self-training framework for automatic identification of exploratory dialogue |
title_full_unstemmed |
A self-training framework for automatic identification of exploratory dialogue |
title_sort |
self-training framework for automatic identification of exploratory dialogue |
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Institutional Knowledge at Singapore Management University |
publishDate |
2013 |
url |
https://ink.library.smu.edu.sg/sis_research/4587 https://ink.library.smu.edu.sg/context/sis_research/article/5590/viewcontent/2013_CICLing_ExploratoryDialogue.pdf |
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