Ethical Considerations in the Practical Application of the Unisa Socio-Critical Model of Student Success
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.
Copyright (c) 2016 Angelo Fynn
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International Licence. The copyright of all content published in IRRODL is retained by the authors.
This copyright agreement and use license ensures, among other things, that an article will be as widely distributed as possible and that the article can be included in any scientific and/or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.