Abstract

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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.

Expanding access to higher education has taken priority in South Africa. There is a focus on improving entry into learning contexts and subsequent economic and social mobility opportunities by developing attributes in graduates that are in line with employment sector expectations. Work-integrated learning (WIL) processes serve to expose students to the real expectations of the workplace with the intention of bridging the gap between study completion and work readiness. The implementation and coordination of WIL placements are therefore an important component of professional degrees such as psychology masters. This article adopts the theory of connectivism as the central lens and emphasizes that knowledge is gathered through a network of connections between entities (nodes) that may consist of individuals, groups, fields, ideas or communities. The article describes the learning experience from the student’s perspective and provides an example of how the connectivist approach can be used to bridge the complex learning process in professional qualifications.

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