Big(ger) data as better data in open distance learning

In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues,...

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Autores principales:	Paul Prinsloo, Elizabeth Archer, Glen Barnes, Yuraisha Chetty, Dion Van Zyl
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2015
Materias:	Big Data learning analytics student success Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/30c112c0e8c24899931241d23726833e
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spelling	oai:doaj.org-article:30c112c0e8c24899931241d23726833e2021-12-02T19:26:04ZBig(ger) data as better data in open distance learning10.19173/irrodl.v16i1.19481492-3831https://doaj.org/article/30c112c0e8c24899931241d23726833e2015-02-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/1948https://doaj.org/toc/1492-3831 In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously considered in realising this potential. The University of South Africa (Unisa) is one of the mega ODL institutions in the world with more than 360,000 students and a range of courses and programmes. Unisa already has access to a staggering amount of student data, hosted in disparate sources, and governed by different processes. As the university moves to mainstreaming online learning, the amount of and need for analyses of data are increasing, raising important questions regarding our assumptions, understanding, data sources, systems and processes. This article presents a descriptive case study of the current state of student data at Unisa, as well as explores the impact of existing data sources and analytic approaches. From the analysis it is clear that in order for big(ger) data to be better data, a number of issues need to be addressed. The article concludes by presenting a number of theses that should form the basis for the imperative to optimise the harvesting, analysis and use of student data. Paul PrinslooElizabeth ArcherGlen BarnesYuraisha ChettyDion Van ZylAthabasca University PressarticleBig Datalearning analyticsstudent successSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 16, Iss 1 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Big Data learning analytics student success Special aspects of education LC8-6691
spellingShingle	Big Data learning analytics student success Special aspects of education LC8-6691 Paul Prinsloo Elizabeth Archer Glen Barnes Yuraisha Chetty Dion Van Zyl Big(ger) data as better data in open distance learning
description	In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously considered in realising this potential. The University of South Africa (Unisa) is one of the mega ODL institutions in the world with more than 360,000 students and a range of courses and programmes. Unisa already has access to a staggering amount of student data, hosted in disparate sources, and governed by different processes. As the university moves to mainstreaming online learning, the amount of and need for analyses of data are increasing, raising important questions regarding our assumptions, understanding, data sources, systems and processes. This article presents a descriptive case study of the current state of student data at Unisa, as well as explores the impact of existing data sources and analytic approaches. From the analysis it is clear that in order for big(ger) data to be better data, a number of issues need to be addressed. The article concludes by presenting a number of theses that should form the basis for the imperative to optimise the harvesting, analysis and use of student data.
format	article
author	Paul Prinsloo Elizabeth Archer Glen Barnes Yuraisha Chetty Dion Van Zyl
author_facet	Paul Prinsloo Elizabeth Archer Glen Barnes Yuraisha Chetty Dion Van Zyl
author_sort	Paul Prinsloo
title	Big(ger) data as better data in open distance learning
title_short	Big(ger) data as better data in open distance learning
title_full	Big(ger) data as better data in open distance learning
title_fullStr	Big(ger) data as better data in open distance learning
title_full_unstemmed	Big(ger) data as better data in open distance learning
title_sort	big(ger) data as better data in open distance learning
publisher	Athabasca University Press
publishDate	2015
url	https://doaj.org/article/30c112c0e8c24899931241d23726833e
work_keys_str_mv	AT paulprinsloo biggerdataasbetterdatainopendistancelearning AT elizabetharcher biggerdataasbetterdatainopendistancelearning AT glenbarnes biggerdataasbetterdatainopendistancelearning AT yuraishachetty biggerdataasbetterdatainopendistancelearning AT dionvanzyl biggerdataasbetterdatainopendistancelearning
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Big(ger) data as better data in open distance learning

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