Using Big Data analytics tool to influence decision-making in higher education: A case of South African Technical and Vocational Education and Training colleges

被引:0
|
作者
Selowa, Kleinbooi T. [1 ]
Ilorah, Appolonia I.
Mokwena, Sello N. [2 ,3 ]
机构
[1] Tshwane Univ Technol, Fac Informat & Commun Technol, Dept Informat, Polokwane, South Africa
[2] Tshwane Univ Technol, Fac Informat & Commun Technol, Dept Comp Sci, Polokwane, South Africa
[3] Univ Limpopo, Dept Comp Sci, Polokwane, South Africa
来源
关键词
decision-making; Big Data; Big Data analytics; TVET colleges; HiPPO; Hadoop; higher education;
D O I
10.4102/sajim.v24i1.1489
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
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.
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