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Visualizing the LAK/EDM literature using combined concept and rhetorical sentence extraction

Taibi, Davide; Sandor, Agnes; Simsek, Duygu; Buckingham Shum, Simon; De Liddo, Anna and Ferguson, Rebecca (2013). Visualizing the LAK/EDM literature using combined concept and rhetorical sentence extraction. In: Proceedings of the LAK Data Challenge, 3rd Int. Conf. on Learning Analytics and Knowledge (LAK '13), 8-12 April 2013, Leuven, Belgium.

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URL: http://ceur-ws.org/Vol-974/lakdatachallenge2013_07...
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Abstract

Scientific communication demands more than the mere listing of empirical findings or assertion of beliefs. Arguments must be constructed to motivate problems, expose weaknesses, justify higher-order concepts, and support claims to be advancing the field. Researchers learn to signal clearly in their writing when they are making such moves, and the progress of natural language processing technology has made it possible to combine conventional concept extraction with rhetorical analysis that detects these moves. To demonstrate the potential of this technology, this short paper documents preliminary analyses of the dataset published by the Society for Learning Analytics, comprising the full texts from primary conferences and journals in Learning Analytics and Knowledge (LAK) and Educational Data Mining (EDM). We document the steps taken to analyse the papers thematically using Edge Betweenness Clustering, combined with sentence extraction using the Xerox Incremental Parser's rhetorical analysis, which detects the linguistic forms used by authors to signal argumentative discourse moves. Initial results indicate that the refined subset derived from more complex concept extraction and rhetorically significant sentences, yields additional relevant clusters. Finally, we illustrate how the results of this analysis can be rendered as a visual analytics dashboard.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 for the individual papers by the papers' authors.
Extra Information: Edited by Mathieu d'Aquin, Stefan Dietze, Hendrik Drachsler, Eelco Herder, Davide Taibi
Keywords: learning analytics; corpus analysis; scientific rhetoric; visualization; network analysis; natural language processing
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Learning Teaching and Innovation (LTI) > Institute of Educational Technology (IET)
Learning Teaching and Innovation (LTI)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Centre for Research in Education and Educational Technology (CREET)
Related URLs:
Item ID: 37820
Depositing User: Kay Dave
Date Deposited: 26 Jun 2013 09:08
Last Modified: 08 Feb 2017 06:16
URI: http://oro.open.ac.uk/id/eprint/37820
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