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

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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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South African Journal of Informaon Management
ISSN: (Online) 1560-683X, (Print) 2078-1865
Page 1 of 8 Original Research
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Authors:
Kleinbooi T. Selowa1
Appolonia I. Ilorah2
Sello N. Mokwena3
Aliaons:
1Department of Informacs,
Faculty of Informaon and
Communicaon Technology,
Tshwane University of
Technology, Polokwane,
South Africa
2Department of Computer
Science, Faculty of
Informaon and
Communicaon Technology,
Tshwane University of
Technology, Polokwane,
South Africa
3Department of Computer
Science, University of
Limpopo, Polokwane,
South Africa
Corresponding author:
Kleinbooi Selowa,
thapediselowa@gmail.com
Dates:
Received: 13 Nov. 2021
Accepted: 08 Feb. 2022
Published: 30 Aug. 2022
How to cite this arcle:
Selowa, K.T., Ilorah, A.I. &
Mokwena, S.N., 2022, ‘Using
Big Data analycs tool to
inuence decision-making in
higher educaon: A case of
South African Technical
and Vocaonal Educaon
and Training colleges’,
South African Journal
ofInformaon
Management 24(1), a1489.
hps://doi.org/10.4102/
sajim.v24i1.1489
Introducon
Organisations need data to inform their strategic and operational decisions. The use of data in
organisational decision-making is old as mankind. Organisations have been storing and analysing
large volumes of data since the advent of data warehouse systems (Daniel & Butson 2013). With
petabytes of data that are generated daily, it would be imprudent to make decisions without
attempting to draw some meaningful inferences from the data (Nanneti 2012). The decision
makers in higher learning institutions need to be able to use analytics to enhance their decision-
making. The data available for decision-making today come in huge volumes, at consistently high
speed and in a variety of formats. This is what is termed as Big Data.
Big Data refers to a framework that allows the analysis and management of a larger amount of
data (Moreno et al. 2016). Furthermore, Big Data is less about data that is big, but more of a
capacity to search, aggregate, and cross-reference large data sets (Boyd & Crawford 2012).
Actor network theory (ANT) is used as a lens to assess and propose how the use of Big Data
Analytics (BDA) in Technical and Vocational Education and Training (TVET) environment can be
used to improve decision-making. The rest of the article is organised as follows: we start with the
background followed by brief review of the literature of BDA, then the discussion of the four
translations of ANT, research methods and then the results and conclusions.
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.
Keywords: decision-making; Big Data; Big Data analytics; TVET colleges; HiPPO; Hadoop;
higher education.
Using Big Data analycs tool to inuence decision-making
in higher educaon: A case of South African Technical
and Vocaonal Educaon and Training colleges
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Copyright: © 2022. The Authors. Licensee: AOSIS. This work is licensed under the Creave Commons Aribuon License.
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Background
Higher learning institutions collect information about
students, pedagogy and related data about staff (Daniel &
Butson 2013). In post-apartheid South Africa, the former
Department of Education was split into two different
departments: Department of Basic Education and Department
of Higher Education and Training (DHET) was then established
in 2018. Higher Institutions of Learning in South Africa are
classified into four divisions as illustrated in Figure 1.
These institutions also include TVET colleges. Technical and
Vocational Education and Training colleges collect data from
students, staff and stakeholders for different reasons and
occasions. The information collected are usually from various
sources and in different formats, such as from social media
(Facebook, Twitter, and Instagram), CCTV cameras, fleet
management sensors in college vehicles and emails as well as
other forms including students’ registration data. The collected
data are not being used in decision-making. The current
decision-making in management is still based on the traditional
way of using past experience and that of the highest paid
person’s opinion (HiPPO) (Appelgren & Nygren 2019).
An example of not using available data is the stampede
which took place when the college opened their gates during
application and registration in one of the colleges. The
stampede could have been avoided had the college analysed
their social media data because the issue was raised there
before the unfortunate incident happened. People were
hospitalised due to the stampede after sleeping outside the
college gate overnight in order to be in front of the queue
when the e college open their gates for registration in the
morning (Tshungu 2018). All this happened because the head
of college instructed the registration staff to wait for him
before registration could begin.
Another example is that students who intended to study at
the college sometimes make inquiries through social media
platforms used by the college. During their enquiry, they
indicate courses of their interest. In most cases, the students
are not placed in the courses they initially wanted to register
for and this leads to the students dropping out or failing the
course several times. The presence of student data should
help college in deciding where to correctly place students.
Based on the background above and the literature, BDA can
help in tracing the students before, during and after the study
(Oracle 2015). Making decisions using the traditional method
of relying on the HiPPO leads or just the results of the
administered tests to misplacement of students in courses,
requesting of staff data every 6 months, long queues during
registrations, and other things going wrong that could be
solved through data-centric decision-making. This study
therefore investigates the use of BDA in decision-making in
the TVET college sector in South Africa.
In order to achieve this goal, the following research questions
were addressed:
1. What role does BDA play in improving organisational
decision-making?
2. What are the decision-making processes used in TVET
colleges presently?
3. In what way can BDA support and enhance decision-
making in TVET colleges?
Literature review
The definition of Big Data, according to Lewis (2014) is data
sources whose very size and complexity creates problems for
standard data management and analysis tools. Big Data is data
sets characterised by their volume, velocity of change, variety
of type that can add value to organisations (see Figure 2).
Furthermore, Kakhani et al. (2013) have added veracity in
their definition and explain the V’s of Big Data as follows:
1. Volume: The amount of data is at very large scale. The
amount of information being collected is so huge that
DHET, Department of Higher Educaon and Training.
FIGURE 1: Higher educaon landscape in South Africa.
DHET
Universies
Technical and Vocaonal
Educaon and Training
(TVET)
Community Educaon
and Training (CET)
Skills development
Source: Selowa, K.T., 2022, ‘Using big data analycs to enhance decision-making in higher
educaon: A case of South African TVET colleges’, Master of compung dissertaon Tshwane
University of Technology, Pretoria
FIGURE 2: Vs of Big Data based on the denion above.
Big Data
Variety
Velocity Volume
Value
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modern database management tools are unable to handle
it and therefore become obsolete.
2. Velocity: Data are produced at an exponential rate. It is
growing continuously in terabytes and petabytes.
3. Variety: We are creating data in all forms — unstructured,
semi-structured and structured data. This data is
heterogeneous in nature. Most of our existing tools work
over homogenous data. Thus, now we require new tools
and techniques which can handle such a large-scale
heterogeneous data.
4. Veracity: The data we are generating is uncertain in
nature. It is hard to know which information is accurate
and which is out of date.
5. Value: The data we are working with is valuable for
society or not.
Analytics is viewed as ‘the use of data, statistical analysis and
explanatory and predictive models to gain insights and act
on complex issues’ (Bichsel 2012). According to Ong (2015),
analytics is the use of data, statistical analysis, explanatory
and predictive models to gain insights and to present data
through various forms of visualisation. These visualisations
can be in the form of graphic presentations like pie charts,
tree maps and others. The Figure 3, as adapted from Ong
clearly shows the taxonomy of BDA.
The picture in Figure 4 shows different kinds of BDA and
their applications.
According to Riahi and Riahi (2018), Big Data is referred to as
the evolution and use of technologies that offer the right user
at the right time with the right information from a mass of
data that has been rising rapidly for a long time in our society.
Similarly, the kind of visualisation Big Data can provide to
enhance teaching and learning and provide a decision
support system for educational establishments to attain
excellence in education is unprecedented (Bhat & Ahmed
2016). In comparison to education, Nannetti (2012) states that
‘firms that emphasize decision-making based on data and
analytics have performed 5% – 6% better – as measured by
output and performance – than firms that rely on intuition
and experience for decision-making’.
Tulasi and Suchithra (2016) indicated that BDA can be used in
higher education to enhance e-learning. They mentioned that
Big Data paradigms are needed in current world to add value
to the processes of educational institutions. There are different
databases that support Big Data, namely:
NoSQL databases – These databases support parallel
processing of the large amount of data.
MapReduce – provides platform to access data in
distributed file systems with intermediate data being
stored on local disks.
MongoDB and Apache Hadoop – are few platforms
which have emerged to store large chunks of data.
Tulasi and Suchithra further state that data-driven decisions
would help the teaching and learning process to evolve and
also indulge in creation of new pedagogy. There are a few
identified analytical methods that can be used to deal with
educational data and they recommend Association Rule
Mining (ARM). Association Rule Mining consists of two parts:
the antecedent which is an element available in the data and
the consequent which is the element obtained along with the
antecedent. The analytics recommended in this study are
however not for decision making but for teaching and
learning.
Daniel (2014) in his writing stated three stages required to
unlock the value of Big Data in any organisation as collection,
analysis and visualisation which he represented as shown in
Figure 5.
The Daniel and Butson (2014), is composed of the following
constructs:
Institutional analytics: These are a variety of operational
data that can be analysed to help with effective decisions
about making improvements at the institutional level.
Information technology (IT) analytics: The purpose of
IT analytics according to Daniel is to integrate data
from different systems such as student information,
learning management and alumni systems, and also
systems that manage learning experiences outside the
classroom.
Academic/programme analytics: These provide data that
administrators can use in order to support strategic
decision-making processes and provide ways for
benchmarking for comparison with other institutions.
The final construct of the UO-TEA by Daniel is Learning
analytics: Its main concern is measuring, collecting,
analysing and reporting of data about learners and
their contexts, for purposes of understanding and
optimising learning and the environments in which it
occurs.
FIGURE 4: Types of Big Data Analycs.
Descripve
analycs
Prescripve
analycs
Predicve
analycs
Diagnosc
analycs
Source: Adapted from Ong, V.K., 2015, ‘Big data and its research implicaons for higher
educaon: Cases from UK higher educon instuons’, 2015 IIAI 4th Internaonal
Congress on AdvancedApplied Informaons 487–491. hps://doi.org/10.1109/IIAI-
AAI.2015.178
FIGURE 3: Taxonomy of Big Data Analycs.
Decision me
Real Time (RT)
Close to RT
Hourly
Weekly
Monthly
Yearly
Analycs
Visualisaon
Exploraon
Explanaon
Predicve
Techniques
Stascs
Econometrics
Machine learning
Computaonal
Simulaon
Opmisaon
Big Data Analycs
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Theorecal foundaon
Underpinned by, and using ANT, this study embraces both
human and non-human actors in the network and considers
them equally valuable and having the potential to be equally
influential in the network, during decision-making processes.
Through the review of the literature, the researchers
(Alexander & Silvis 2014; Cresswell et al. 2010; Paledi &
Alexander 2017; Rivera 2013; Tatnall 2005) noted that ANT
has been successfully used to carry out interpretivist studies
making it relevant to this study.
Latour (1996) indicated that ANT has been developed by
students of science and technology, and its claim is that in
order to understand what holds society together, we have
to reinject the facts made by natural and social sciences and
the artefact designed by engineers. Actor network theory
does not accept any form of separation between the
participants; it emphasises that human and non-human
actors should be analysed in the same way (Paledi &
Alexander 2017).
The four moments of translation in the theoretical framework
are problematisation, interessement, enrolment and mobilisation.
Problemasaon
During this moment, the primary actor/s identifies, defines
and proposes a solution to a situation. The primary actors also:
[D]etermine a set of actors and defined their identities in such a
way as to establish themselves an obligatory passage point in the
network of relationships they were building, making them
indispensable to the network. (Callon 1986)
In this moment of translation, we look at what decisions can
be problematised. Is there any issue that the organisation is
not deciding on and which might be causing or solving a
problem that the college is or might be facing?
Interessement
During this moment, the primary actors use different
processes and negotiations to get the other actors interested
and buy into their proposed solution. The result is that the
other actors either affirms to the proposed solution or become
the primary actor’s allies or not. Callon and Latour (2002)
explain that:
[T]he model of interessement sets out all of the actors who seize
the object or turn away from it and it highlights the points of
articulation between the object and the more or less organised
interests which it gives rise to. (pp. 196–233)
This is the stage where the college might look at who are the
custodians of information or data that may help in the
reaching of the decisions that are to be made. We further see
how these custodians of information will provide the right
solution or better decision on the problems that the college is
facing, thus putting a recruitment of members of a network
(actants) that will be involved in solution.
Enrolment
Enrolment is described as various types of negotiations
and hurdles that go with interessement that will help them
to succeed which results if interessement is successful
(Callon 1986) ‘and a network of aligned interest starts when
actors accepts the roles defined for them’ (Iyamu 2011) but
it does not necessary mean interessement always leads to
enrolment.
Now that we have had a successful building of interest,
members of the network or actants are now moving towards
the solution using whatever tool is available in the network.
Mobilisaon
During mobilisation, all the actors have reached consensus
and working towards the same goal to implement the
targeted solution.
The actants will now be able to reach a common
understanding of who is to do what and when given the
availability of all members. These members (actants) are
both human and non-human. The human actants in this
stage have accepted the usefulness of their no-human
counterparts and are using them because they know the
important role each actant plays in the decision-making
process through BDA.
Only two actor network concepts were adopted for this study,
namely, translation and inscription. These concepts illustrate
the research model for this study. In order to have the moments
of translation, actors or actants will need to work together.
They both recruit each other by recognising the problems in
the organisation or college (problematisation); provide how
each one will play a role in the solution of the identified
problem (interessement and mobilisation) and finally start
using all resources they have and acknowledgement if any
recommendations made. It is only after a balanced network
that inscription can occur. All of that will lead to a harmonious
BDA. Figure 6 is a representation for the research model in
this study.
Source: Adapted from Daniel, B.K. & Butson, R., 2013, ‘ Technology enhanced analycs (tea)
in higher educaon’, in Internaonalconferenceoneducaonaltechnologies, Oblinger 2012,
pp. 89–96
FIGURE 5: Stages of Big Data.
Data visualisaon
Data analysis
Data collecon
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Methodology
Given the fact that this study aimed to investigate how
BDA can influence decision-making in TVET colleges in
South Africa, it, therefore, calls for data to be collected
within the participants’ natural setting and from the
participants who are involved in the organisational
decision-making in the TVET colleges. Semi-structured
interviews were employed for this study. The researcher
interviewed decision-makers (management teams) in the
TVET colleges of Limpopo. The teams interviewed are
either the campus management team or college management
team depending on their level of participation in the
decision-making in the college. This study employed
interviews as the data collection method because interviews
are a more personalised form of data collection method
than questionnaires (Bhattacherjee et al. 2017).
In this study, data collected during interviews were
arranged in themes and patterns according to the constructs
of the framework and relation to the research questions. A
thematic approach to analyse these themes was used. The
coding resulted in several themes that were revisited and
refined. QDA Miner Lite was used as an analysis tool in
this study. The programme was designed to assist
researchers in managing, coding, and analysing qualitative
data. The emerging themes from the interviews were
generated by QDA Miner Lite. They are presented in
Figure 7 and Figure 8.
After identifying the themes, we reviewed the categories
and themes that were identified in order to find if there are
any new insight that may emerge from the data. We
repeated this action until we reached what is called
saturation. Saturation is achieved when no new themes can
be revealed after any further observations and analysis
(Lowe et al. 2018).
Ethical consideraons
A presentation for full ethical approval was made to the
ethics committee and ethics consent was received on
December, 13, 2017. The ethics approval number is FCRE/
ICT/2017/11/015(2).
Results
It was illustrated through literature that organisations that
use BDA in their decision-making gain competitive advantage
and can solve problems proactively. Decision-making is a
process that involves problem-solving, ‘it is assumed that the
decision is the basic activity of management staff and the
information, which is the enabler of the decision’ (Kościelniak
& Puto 2015). In an environment where Big Data is processed,
the staff in the organisation are using data to guide their
decisions to a much higher extent than previously (Björkman
2017). Furthermore, by applying analytics to Big Data,
organisations are able to extract and exploit valuable
information to enhance decision-making and support
informed decisions (Elgendy & Elragal 2016). Insight-Driven
Organisations, an increasing number of organisations are
utilising BDA across their enterprise for improved decision-
making and not limiting them to a single function or process
(Tembhekar 2016).
In the interviews of this study, respondents mentioned that
they become aware of the problems through different
personnel (staff, students). IT and marketing become aware
of problems through newsfeeds from college website, social
media accounts. Education Management Information
Systems (EMIS) become aware of issues through Coltech SQL
queries. (Coltech is the most common information system
that is used to store student data). Admin Managers
mentioned that it is the duty of marketing and IT to ensure
that the colleges are marketed or advertised well as they
communicate with students through notice board, suggestion
box but the staff through emails. The EMIS Managers
confirmed that they use online application only and the
information will be captured by admin of the college using
Coltech. IT Managers mentioned that they use college
website, Facebook, Instagram and WhatsApp for marketing.
The respondents also indicated that the college principal and
senior management as well as council are the ultimate
decision makers when it comes to the college strategic plan
and their job is to implement the strategic plan.
In the interview results, the participants emphasised that
even though they do value team efforts in their planning,
reporting and decision-making, the colleges’ decisions are
made by the highest person in authority. These results also
show that the decision-making is also influenced by the
traditional way of deciding. These colleges adhere to the
HiPPO framework in their decision-making.
Furthermore, there is a clear indication that there is a huge
volume of data from different sources and in different formats
that are being processed in their environment. However, the
data is not analysed for decision-making. Big Data tools are
not available in colleges and therefore that skill, investment
and knowledge in BDA is not there.
Recommendaons
In this network, decisions have to be made by management,
guided by data (information) from college websites, social
FIGURE 6: Moments of translaon of actor-network theory in this study.
Translation
Moments of translation
Problematisation
Interessement
Enrollment
Inscription
Big data analytics
(actor-network)
Actants
humans and
non-human
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FIGURE 8: QDA Miner Lite – screenshot of themes from literature.
FIGURE 7: QDA Miner Lite – screenshot of the themes from interviews.
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media or email as well as policy documents from DHET.
Hence, actors are defined in the decision-making process.
In light of the findings that show the lack of analytics and
lack of Big Data tools, the study recommends that colleges
should invest in analytics skills for personnel and invest in
Big Data frameworks/tools.
Invest in personnel skills
Technical and Vocational Education and Training Colleges
need to invest in information architecture and BDA. There
is a skills gap in analytics. Big Data Analytics skills have
the ability to improve operational effectiveness and
efficiencies of generating great revenues in business
(Alsghaier 2017). There are some institutions in South
Africa like Nelson Mandela University, ATTI, and CTU
that are able to train people for Big Data and Analytics
through the Cisco academy. Furthermore, there are online
organisations such as Eudemy, Coursera, Dataquest and
Cisco networking academy that offer the same training for
free or a small fee for certification. Mokwena (2011)
suggested that institutions can improve the usage of
information systems by providing adequate training
offered by experienced trainers, providing enough tools,
improving the support offered to personnel as well as
making sure there are incentives for personnel who take up
such information systems. Furthermore, ‘the onus is then
on the management to ensure that they provide adequate
support needed by implementers and users of the proposed
technological intervention’ (Ilorah et al. 2017).
Invest in Big Data frameworks/tools
In order for TVET colleges to gain more insight and improve
their work in learning, preventive, predictive, prescriptive
and institutional analytics through Big Data, they must invest
in Big Data tools. There are many tools used for BDA such as
MapReduce, MongoDB, Azure, Hadoop, Flink, Storm, and
Samza.
The most recommended tool that is recommended is Hadoop.
The advantage about Hadoop is that it is a scalable open-
source computation framework that works across many host
servers which may not necessarily be high-performance
computers (da Silva et al. 2018). Furthermore, Hadoop is an
open source project of Apache. Some enterprises launched
their own Hadoop distributions with tools to manage and
administer the cluster and also with a free/premium policy
(Lavanya & Murali 2016).
Big Data Analytics that can be used in TVET colleges.
According to Riahi and Riahi (2018), BDA can be categorised
into four types, namely:
Descriptive analytics: It is the initial stage of data processing
wherein a set of historical data is created. Descriptive
analytics produces future probabilities and trends and
provides an idea about what is likely to occur in the future.
Diagnostic analytics: Diagnostic analytics investigates and
focuses on the root cause of a problem. It is used to determine
reasons why things happened. This kind of analytics attempts
to identify and understand the causes of events and
behaviours.
Predictive analytics: These analytics use past data to predict
the future. It is mainly focused on forecasting or predicting
what is likely to happen. Predictive analytics uses many
techniques such as data mining and artificial intelligence (AI)
to analyse current data and make scenarios of what is likely
to occur.
Prescriptive analytics: It tries to answer the question: What
should be done? Its dedication is focused on finding the right
action to be taken. Descriptive analytics produces historical
data while prescriptive analytics uses these parameters to
find the best solution.
Conclusion
This study uses ANT as a lens to investigate how decision-
making in institutions of higher learning, particularly TVET
colleges in South Africa can be integrated with BDA. We
propose that the use of BDA can improve decision-making
and propose different BDA tools available in the market. Big
data analytical tools produce instant alerts and afford
feedbacks to instructors and scholars on academic
performance by analysing fundamental complex data patterns
(Riffai et al. 2016). This approach will help in predicting a
dropout student, student who needs additional help or even a
student who needs more challenging assignments ((Riffai et
al. 2016). TVET colleges already are processing Big Data even
though some may not even be aware of it. Furthermore, this
study will propose/demonstrate how a balanced actor-
network can ensure smooth and enhanced decision-making
through BDA.
Future research
This study intends to promote knowledge exploitation
where the research contributions address problems relevant
to the use of BDA for decision-making in the higher
education sector in South Africa, especially in the TVET
college sector. However, there are fewer studies about Big
Data, BDA, or BI in the South African TVET context. Future
research could investigate BI or Big Data use or readiness in
this sector.
Acknowledgements
Compeng interests
The authors declare that they have no financial or personal
relationships that may have inappropriately influenced them
in writing this article.
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Authors’ contribuons
K.T.S. declared that the work in the article is a study that he
undertook for his Master’s Degree at Tshwane University of
Technology under the supervision of S.N.M. and A.I.I.
Funding informaon
This research received no specific grant from any funding
agency in the public, commercial or not-for-profit sectors.
Data availability
Some of the themes/codes from the interviews are in Figure 8.
Disclaimer
The views and opinions expressed in this article are those of
the authors and do not necessarily reflect the official policy or
position of any affiliated agency of the authors.
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