Developing a Framework for the Deployment of Predictive Analytics to Improve Postgraduate Student Throughputs at One Comprehensive South African University

Abstract

There is limited understanding of the opportunities available to universities through efficient deployment of predictive analytics. This study sought to develop a framework for the successful deployment of predictive analytics at one university to ensure high-quality postgraduate throughput rates. The study adopted a systematic literature review to elicit the opportunities presented by utilising predictive analytics in decision-making to promote postgraduate student throughput rates. It emerged that literature abounds on the manner big data analytics can be used to benefit universities and students. The study argued that the traditional, non-statistical approach which has long been used to address the unsatisfactory postgraduate throughput rates has failed to yield the required outcomes. It also noted the existing effort and support mechanisms to address postgraduate student retention and throughput rates which are necessary but not sufficient. A critical recommendation is that the proffered model should not be construed as a ‘perfect and single solution’ to capsize the poor postgraduate throughput rates at the university as different limitations exist. The study concluded that there is a clear call for the need to turn the current approach to the management and promotion of postgraduate student success. As such, the opportunities available are for those institutions that are committed to improving and magnifying their future practice by making meaning of the existing large data resources at their disposal.

Keywords

• framework
• higher education institutions
• predictive analytics
• throughput rates