The pragmatic maxim as learning analytics research method

Gibson, A; Lang, C

The pragmatic maxim as learning analytics research method

Gibson, A

Lang, C

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Publisher:: ACM Press
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK '18, 2018, pp. 461 - 465
Issue Date:: 2018

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Field	Value	Language
dc.contributor.author	Gibson, A https://orcid.org/0000-0003-4619-6515	en_US
dc.contributor.author	Lang, C	en_US
dc.date	2018-03-07	en_US
dc.date.issued	2018	en_US
dc.identifier.citation	Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK '18, 2018, pp. 461 - 465	en_US
dc.identifier.isbn	9781450364003	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132999
dc.description.abstract	It is arguable that the chief aim of Learning Analytics is to use analytics for meaningful purposes in learning and teaching contexts, and that research in the field should advance this cause. However the field does not present a single clear understanding of what constitutes quality in Learning Analytics research. In this paper we present the Pragmatic Inquiry for Learning Analytics Research (PILAR) method as one approach to conducting Learning Analytics research. Rather than creating a new method, we reintroduce an old method to a new field, drawing on the Pragmatic Maxim, proposed by Charles Sanders Peirce as a principle for making ideas clear. Our instantiation of the Pragmatic Maxim requires the researcher to situate Learning Analytics research within a clearly defined learning context and to consider the analytics in terms of the practical effects on learning. We propose three essential elements and a five step process for addressing them in research. After presenting PILAR we address two potential limitations of the approach, and conclude with some implications for its future use in Learning Analytics research.	en_US
dc.publisher	ACM Press	en_US
dc.relation.ispartof	Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK '18	en_US
dc.relation.ispartof	International Conference on Learning Analytics and Knowledge	en_US
dc.relation.isbasedon	10.1145/3170358.3170384	en_US
dc.rights	© ACM 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK '18, 2018, pp. 461 - 465 http://doi.acm.org/10.1145/3170358.3170384	en_US
dc.title	The pragmatic maxim as learning analytics research method	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Sydney, New South Wales, Australia	en_US
utslib.for	130306 Educational Technology and Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	open_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2018-03-09	en_US
pubs.place-of-publication	USA	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2018-03-07	en_US

Abstract:

It is arguable that the chief aim of Learning Analytics is to use analytics for meaningful purposes in learning and teaching contexts, and that research in the field should advance this cause. However the field does not present a single clear understanding of what constitutes quality in Learning Analytics research. In this paper we present the Pragmatic Inquiry for Learning Analytics Research (PILAR) method as one approach to conducting Learning Analytics research. Rather than creating a new method, we reintroduce an old method to a new field, drawing on the Pragmatic Maxim, proposed by Charles Sanders Peirce as a principle for making ideas clear. Our instantiation of the Pragmatic Maxim requires the researcher to situate Learning Analytics research within a clearly defined learning context and to consider the analytics in terms of the practical effects on learning. We propose three essential elements and a five step process for addressing them in research. After presenting PILAR we address two potential limitations of the approach, and conclude with some implications for its future use in Learning Analytics research.

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http://hdl.handle.net/10453/132999