Discovering indicators of successful collaboration using tense: automated extraction of patterns in discourse

Article

Thompson, Kate, Kennedy-Clark, Shannon, Wheeler, Penny and Kelly, Nick. 2014. "Discovering indicators of successful collaboration using tense: automated extraction of patterns in discourse." British Journal of Educational Technology. 45 (3), pp. 461-470. https://doi.org/10.1111/bjet.12151
Article Title

Discovering indicators of successful collaboration using tense: automated extraction of patterns in discourse

ERA Journal ID20263
Article CategoryArticle
AuthorsThompson, Kate (Author), Kennedy-Clark, Shannon (Author), Wheeler, Penny (Author) and Kelly, Nick (Author)
Journal TitleBritish Journal of Educational Technology
Journal Citation45 (3), pp. 461-470
Number of Pages10
Year2014
Place of PublicationChichester, West Sussex. United Kingdom
ISSN0007-1013
1467-8535
Digital Object Identifier (DOI)https://doi.org/10.1111/bjet.12151
Web Address (URL)http://onlinelibrary.wiley.com/doi/10.1111/bjet.12151/pdf
Abstract

This paper describes a technique for locating indicators of success within the data collected from complex learning environments, proposing an application of e-research to access learner processes and measure and track group progress. The technique combines automated extraction of tense and modality via parts-of-speech tagging with a visualisation of the timing and speaker for each utterance developed to code and analyse learner discourse, exploiting the results of previous, non-automated analyses for validation. The work is developed using a dataset of interactions within a multi-user virtual environment and extended to a more complex dataset of synchronous chat texts during a collaborative design task. This methodology extends natural language processing into computer-based collaboration contexts, discovering the linguistic micro-events that construct the larger phases of successful design-based learning.

Keywordscollaboration; tense; natural language; processing; discourse analysis
ANZSRC Field of Research 2020460208. Natural language processing
461305. Data structures and algorithms
390409. Learning sciences
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Byline AffiliationsUniversity of Sydney
Australian Catholic University
Australian Digital Futures Institute
Institution of OriginUniversity of Southern Queensland
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