Ethical Considerations in the Practical Application of the Unisa Socio-Critical Model of Student Success
Angelo Fynn, University of South Africa
IRRODL Volume 17, Number 6, ISSN 1492-3831 Publisher: Athabasca University Press
The prediction and classification of student performance has always been a central concern within higher education institutions. It is therefore natural for higher education institutions to harvest and analyse student data to inform decisions on education provision in resource constrained South African environments. One of the drivers for the use of learning and academic analytics is the pressures for greater accountability in the areas of improved learning outcomes and student success. Added to this is the pressure on local government to produce well-educated populace to participate in the economy. The counter discourse in the field of big data cautions against algocracy where algorithms takeover the human process of democratic decision making. Proponents of this view argue that we run the risk of creating institutions that are beholden to algorithms predicting student success but are unsure of how they work and are too afraid to ignore their guidance. This paper aims to discuss the ethics, values, and moral arguments revolving the use of big data using a practical demonstration of learning analytics applied at Unisa.
Fynn, A. (2016). Ethical Considerations in the Practical Application of the Unisa Socio-Critical Model of Student Success. The International Review of Research in Open and Distributed Learning, 17(6),. Athabasca University Press.
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Bart Rienties, Open University UK; Christothea Herodotou & Tom Olney, Open University UK, Institute of Educational Technology, Milton Keynes, United Kingdom; Mat Schencks & Avi Boroowa, Open University UK, LTI, Milton Keynes, United Kingdom
The International Review of Research in Open and Distributed Learning Vol. 19, No. 5 (Nov 27, 2018)
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