Developing a Framework for the Deployment of Predictive Analytics to Improve Postgraduate Student Throughputs at One Comprehensive South African University
Israel Kariyana 
, Winter Sinkala & Neliswa Gqoli
Issue: Vol.4 No.12 Special Issue Article 14 pp.175 – 185
DOI: https://doi.org/10.38159/ehass.202341215 | Published online 10th January, 2024
© 2023 The Author(s). This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).
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
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Prof. Israel Kariyana is an Associate Professor in the Department of Continuing Professional Teacher Development, Faculty of Education, Walter Sisulu University, Mthatha Campus, South Africa. His research focuses on Educational Management, Mathematics Education and Sustainable Education.
Prof. Winter Sinkala is an Associate Professor of Mathematics at Walter Sisulu University, Mthatha Campus, South Africa. His research interests include Lie symmetry analysis, fixed point theory, and mathematics education.
Dr. Neliswa Gqoli is a Senior Lecturer at Walter Sisulu University (WSU) in the Department of Adult and Educational Foundations where she teaches Psychology of Learning and Mathematics Teaching. She obtained her Master’s Degree at Walter Sisulu University and PhD (Early Childhood Development) at the University of Free State. Her research focus is on Mathematics Education. As the Senior Researcher in the Faculty, she has published articles and book chapters nationally and internationally. She taught mathematics for 25 years in Eastern Cape Primary Schools. She is a Faculty Board Member and a coordinator for the Post Graduate Certificate in Education at WSU. She is also the chairperson of the Faculty Social Committee. She is a member of the South African Research Association for Early Childhood Education (SARAECE).
Kariyana, Israel, Sinkala, Winter & Gqoli, Neliswa “Developing a Framework for the Deployment of Predictive Analytics to Improve Postgraduate Student Throughputs at One Comprehensive South African University.” E-Journal of Humanities, Arts and Social Sciences 4, no.12 Special Issue (2023): 175 – 185. https://doi.org/10.38159/ehass.202341215
© 2023 The Author(s). Published and Maintained by Noyam Journals. This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).
