Theory-led design of instruments and representations in learning analytics: developing a novel tool for orchestration of online collaborative learning

Article

Kelly, Nick, Thompson, Kate and Yeoman, Pippa. 2015. "Theory-led design of instruments and representations in learning analytics: developing a novel tool for orchestration of online collaborative learning." Journal of Learning Analytics. 2 (2), pp. 14-42. https://doi.org/10.18608/jla.2015.22.3
Article Title

Theory-led design of instruments and representations in learning analytics: developing a novel tool for orchestration of online collaborative learning

ERA Journal ID210923
Article CategoryArticle
AuthorsKelly, Nick (Author), Thompson, Kate (Author) and Yeoman, Pippa (Author)
Journal TitleJournal of Learning Analytics
Journal Citation2 (2), pp. 14-42
Number of Pages29
Year2015
ISSN1929-7750
Digital Object Identifier (DOI)https://doi.org/10.18608/jla.2015.22.3
Web Address (URL)https://epress.lib.uts.edu.au/journals/index.php/JLA/article/view/4265
Abstract

This paper describes theory-led design as a way of developing novel tools for learning analytics (LA). It focuses upon the domain of automated discourse analysis (ADA) of group learning activities to help an instructor to orchestrate online groups in real-time. The paper outlines the literature on the development of LA tools within the domain of ADA, and poses an argument for conducting tool development based upon first-principles. It describes first principles as being drawn from theory and that these principles can subsequently inform the structure and behaviour of tools. It presents a framework for this process of theory-led design. The framework is substantiated through the example of developing a new tool for assisting instructors with the orchestration of online groups. A description of the tool is given and examples of results from use with real-world data are presented. The paper concludes with a call for intent on the part of designers to connect the design process explicitly to theory on the basis that this has the potential to yield innovation when developing tools as well as the prospect of outcomes from tools connecting back to theory.

Keywordsautomated discourse analysis; collaborative learning; orchestration; learning analytics; topic detection
ANZSRC Field of Research 2020390409. Learning sciences
Byline AffiliationsAustralian Digital Futures Institute
University of Sydney
Institution of OriginUniversity of Southern Queensland
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