Using Big Data analytics tool to influence decision-making in higher education: A case of South African Technical and Vocational Education and Training colleges
South African Journal of Information Management
| Field | Value | |
| Title | Using Big Data analytics tool to influence decision-making in higher education: A case of South African Technical and Vocational Education and Training colleges | |
| Creator | Selowa, Kleinbooi T. Ilorah, Appolonia I. Mokwena, Sello N. | |
| Description | Background: Big data analytics in education is a new concept that has the potential to change the decision-making landscape in South African Colleges. Higher institutions of learning, including Technical and Vocation Education Training (TVET) colleges like all other organisations, rely on data for their decision-making. These decisions affect the way pedagogy and student management is administered. Colleges collect huge quantities of data in different formats from students, staff and stakeholders for different reasons and occasions.Objectives: The goal of this study was to investigate how Big Data analytics and their tools may improve decision making in TVET colleges in South Africa through the lens of actor-network theory (ANT).Method: A qualitative, interpretive inquiry was undertaken. A case study using focus group was conducted. The data collected through interviews were arranged into themes and a thematic approach was employed to analyse these themes using QDA Miner Lite software.Results: The results from focus group interviews revealed that TVET colleges collect an enormous amount of data. These data are extracted for different reasons, yet there are no Analytics used for decision-making. Decisions are made by the highest-paid individuals (HiPPO) in colleges.Conclusion: This dissertation recommends that the TVET colleges invest in data science skills for their staff, and Big Data infrastructure. Big Data technologies such as Mongo DB and Hadoop are recommended as the most commonly and advanced tools that can be used for Big Data analytics. | |
| Publisher | AOSIS | |
| Date | 2022-08-30 | |
| Identifier | 10.4102/sajim.v24i1.1489 | |
| Source | SA Journal of Information Management; Vol 24, No 1 (2022); 8 pages 1560-683X 2078-1865 | |
| Language | eng | |
| Relation |
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https://sajim.co.za/index.php/sajim/article/view/1489/2216
https://sajim.co.za/index.php/sajim/article/view/1489/2217
https://sajim.co.za/index.php/sajim/article/view/1489/2218
https://sajim.co.za/index.php/sajim/article/view/1489/2219
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