The Open UniversitySkip to content

Discourse-centric learning analytics: mapping the terrain

Knight, Simon and Littleton, Karen (2015). Discourse-centric learning analytics: mapping the terrain. Journal of Learning Analytics, 2(1) pp. 185–209.

Full text available as:
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview
Google Scholar: Look up in Google Scholar


There is an increasing interest in developing learning analytic techniques for the analysis, and support of, high quality learning discourse. This paper maps the terrain of discourse-centric learning analytics (DCLA), outlining the distinctive contribution of DCLA and outlining a definition for the field moving forwards. It is our claim that DCLA provide the opportunity to explore the ways in which: discourse of various forms both resources and evidences learning; the ways in which small and large groups, and individuals make and share meaning together through their language use; and the particular types of language – from discipline specific, to argumentative and socio-emotional – associated with positive learning outcomes. DCLA is thus not merely a computational aid to help detect or evidence ‘good’ and ‘bad’ performance (the focus of many kinds of analytic), but a tool to help investigate questions of interest to researchers, practitioners, and ultimately learners. The paper ends with three core issues for DCLA researchers – the challenge of context in relation to DCLA; the various systems required for DCLA to be effective; and the means through which DCLA might be delivered for maximum impact at the micro (e.g. learner), meso (e.g. school), and macro (e.g. governmental) levels.

Item Type: Journal Item
Copyright Holders: 2015 The Authors
ISSN: 1929-7750
Extra Information: Special section: Self-regulated learning and learning analytics
Keywords: discourse centric learning analytics; learning analytics; discourse; dialogue; collaborative learning; writing; writing analytics; educational data mining; social learning; social learning analytics; data mining; computer supported collaborative learning
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Wellbeing, Education and Language Studies (WELS)
Research Group: Centre for Research in Education and Educational Technology (CREET)
Related URLs:
Item ID: 41712
Depositing User: Simon Knight
Date Deposited: 25 Feb 2015 16:17
Last Modified: 08 Dec 2018 06:49
Share this page:

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU