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

Thompson, Kate and Kennedy-Clark, Shannon and 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. ISSN 0007-1013

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 British Educational Research Association. Permanent restricted access to published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Historic - Australian Digital Futures Institute
Date Deposited: 22 Sep 2014 22:35
Last Modified: 25 Jul 2017 02:46
Uncontrolled Keywords: collaboration; tense; natural language; processing; discourse analysis
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
08 Information and Computing Sciences > 0804 Data Format > 080403 Data Structures
13 Education > 1303 Specialist Studies in Education > 130309 Learning Sciences
Socio-Economic Objective: C Society > 93 Education and Training > 9301 Learner and Learning > 930102 Learner and Learning Processes
Identification Number or DOI: 10.1111/bjet.12151
URI: http://eprints.usq.edu.au/id/eprint/25578

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