Exploring the prospects of big data analytics in colleges of education in Ghana

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Exploring the prospects of big data analytics in colleges
of education in Ghana
DOI: https://doi.org/10.31920/2633-2930/2021/v2n3a4
Augustine Aduko Alu
St John Bosco’s College of Education
Navrongo-Ghana
alu.aduko@gmail.com
Daniel Azerikatoa Ayoung
Bolgatanga Technical University
Bolgatanga -Ghana
daniel.ayoung@bolgatu.edu.gh
&
Najeeb Gambo Abdulhamid
Jigawa State Polytechnic Dutse, Nigeria
aagambo@jigpoly.edu.ng
Abstract
This study explores Big Data Analytics (BDA) prospects as a tool for the
Colleges of Education (CoE) in Ghana to effectively analyse and manage data
for decision-making and smooth operations. The study employed the Diffusion
of Innovation Theory, through a two-step qualitative case study approach, to
explore big data analytics prospects, benefits, and challenges in CoE. First, we
started with focus group discussions (FGD) to get first-hand information of the
participants' knowledge, perspective and understanding of big data. The FGD
allowed us to identify more suitable candidates for in-depth interviews, which
formed the second part of our data collection. The study found that BDA has
Journal of African Education (JAE)
ISSN: 2633-2922 (Print) ISSN: 2633-2930 (Online)
Indexed by SABINET and EBSCO
Volume 2, Number 3, December 2021
Pp 81-106

Exploring the prospects of big data analytics in colleges …
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enormous prospects in CoE in Ghana because of their benefit in reshaping the
teacher educational landscape. Despite these benefits, we find that there is
currently inadequate IT infrastructure (slow internet and inadequate computing
power), financial resources and skills in-house to support big data analytics. The
current study is unique in its research context, and in its exploration of data
analytics adoption for teacher training institutions. It provides CoE with
insights on the adoption and application of BDA for effective delivery of
educational services and the complexities of its implementation. It gives a useful
insight into learners' performance, including teaching and learning, which would
be an essential benefit in planning teaching activities for educators.
Keywords: Big Data, Colleges of Education, Higher Education Institutions, Diffusion of
Innovation, Ghana
1. Introduction
In recent years, higher education institutions in Ghana have been under
increasing pressure to alter operational and governing structures to
accommodate new teaching standards, emphasising professional values
and attitudes, knowledge and practice to deliver new programmes
(Buabeng et al. 2020). This is especially important in teacher training
institutions which have morphed into degree-awarding institutions.
These new standards are relevant to the regional and national demands
of quality teachers in the pre-tertiary sector. This transformation affects
teaching, learning and research activities as well as the statutes and
policies of Colleges of Education (CoE) in Ghana. Again, the shift also
affects the training of quality teachers and certificates awarded to
teachers in the CoE. This has necessitated new ways of improving and
monitoring student success and other institutional policies.
As a result, CoE in Ghana search for insights from data to create
strategies needed to meet these new demands. Analytics Insights (2020)
suggests that Big Data Analytics (BDA) is an avenue to gain these
insights and make informed decisions as sought by CoE. In recent times,
big data analytics has been deployed in institutions to critically examine
their present challenges, identify ways to address them, and predict future
outcomes (Daniel 2015). Few studies in Ghana suggest the usefulness of
big data analytics in their respective contexts and the various challenges
of its application (Afful et al. 2018; Asare-Kyei 2020). However, very
little is known about the use and prospects of big data in the educational
sector, especially its benefits and challenges in CoE in Ghana. In 2018,
CoE were upgraded to university colleges to offer four-year Bachelor of

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Education (BEd) programmes. As a result, it has altered the curriculum,
teaching, learning, and governance policies. These changes put pressure
on CoEs to build resilience in the face of a demanding educational
training landscape. In furtherance of this quest, the research question of
our study is ―what are the prospects and challenges in adopting big data
analytics in CoE in Ghana‖. We argue that part of building a resilient
ecosystem lies in the data accumulated by these CoE over the years. We
see the light at the end of the tunnel through the adoption of big data
analytics. To the best of our knowledge, this study represents the first
attempt at investigating this phenomenon in higher education in Ghana.
Through a case study, we explore the prospects and challenges in using
big data in CoE in Ghana and determining whether CoE have the
capacity and resources to support big data analytics.
The other sections that follow are the literature review, followed by
the methodology, findings and discussion of the interview results in the
research objectives.
The implications and contributions of big data analytics in the CoE
followed by conclusion sections complete this study.
2. Literature
Big data is described as data that is fundamentally too big and moves too
fast, thus exceeding the processing capacity of conventional database
systems (Manyika et al. 2011). It also covers innovative techniques and
technologies to capture, store, distribute, manage and analyse large size
datasets with diverse structures (Daniel 2015), which are beyond the
capacity of the conventional processing system (Deng and Di 2013).
Deng and Di (2013) further stated that it requires more sophisticated
analyses of data to bring out relationships and insights to influence
decision-making and growth in any learning institution.
Three main characteristics define the phenomenon: Volume,
Velocity, and Variety, often referred to as the 3 Vs (Gantz and Reinsel
2011). Other authors identify two more: Veracity and Value (Wamba et
al. 2015; Höchtl et al. 2015). In recent discourse, Mikalef et al. (2017)
identified two additional characteristics: Variability and Visualization.
2.1 Planning and decision-making
Big data is a trend that has opened the doors to a new approach to
understanding and decision-making in higher education (Guerrero et al.

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2019). BDA in higher education can be transformative, altering the
current administration, teaching, learning and academic work (Baer and
Campbell 2012), contributing to policy and practice outcomes and
helping address contemporary challenges facing higher education (Daniel
2015). It has the potential to transform management decision-making
theory (Boyd and Crawford 2012). Dyché (2014) suggests that big data
discovery efforts can reveal previously unknown findings that can reveal
insights helpful for managerial decision-making. As Watters (2011)
indicated, big data is a tool for informed change in education and
provides evidence to form understanding and make informed (rather
than instinctive) decisions. Daniel (2015) suggests that big data can also
address the challenges associated with finding information at the right
time when data are dispersed across several unlinked data systems in
institutions. He further intimated that by identifying ways of aggregating
data across systems, big data can help improve decision-making
capability. Similarly, Hilbert (2013) reported that big data analytics
delivers a cost-effective prospect to improve decision making. It is
evident that big data analytics when implemented, would help higher
education institutions improve in terms of decision-making regarding
their operations.
The emergence of big data in higher institutions of learning offers
decision-makers the opportunity to extract valuable information about
their operations and systems, predict future trends, and detect possible
challenges (Ahmed et al. 2017). Similarly, Siemen and Long (2011)
indicated that big data presents the most dramatic framework efficiently
utilising the vast array of data and ultimately shaping the future of higher
education.
2.2 Improves operational efficiency
In CoE, big data analytics brings about improved efficiency in the
performance of tasks, resulting in improved productivity (Daniel 2015).
As relationships and insight are gained from data analyses, it reduces the
time spent in executing tasks and decision-making issues. Big data also
interprets various administrative and operational data to evaluate
institutional performance (Hrabowski et al. 2011; Picciano 2012). Big
data can generate significant value by making information transparent
and usable to institutions, thereby helping them expose variability and
boost their performance (Ahmed et al. 2017). As Jones (2012) posits,

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BDA improves processes and workflows, which results in the general
improvement of institutional effectiveness.
2.3 Support for Teaching and Learning
Big data analytics is also about the measurement, collection, analysis, and
reporting of data about learners and their contexts to understand and
optimize learning and the environments in which it occurs (Watters
2011). Similarly, Martin and Thawabieh (2017) posit that big data
analytics represents customized learning environments to the learners,
obtain information about the learning habits of learners, help develop
long-term learning plans, improves existing instructional practices, and
measures and monitors teaching progress. Teachers can also be
evaluated, and recommendations given for improvement. All of these are
possible through the effective development and use of big data analytics
in educational institutions (Seke 2015; Shacklock 2016; Bienkowski et al.
2012). In essence, big data analytics enables comprehensive evaluative
reports about teaching, learning and research in higher education.
Big data connects applications and data across different
environments to generate actionable insights for improved student
experience on campus (Dell Technologies 2020). In recent times, higher
education institutions now require a fast, flexible platform to improve
student success and drive students, alumni and faculty (Kellen et al.
2013). They suggest that these platforms connect people, processes and
technology to build a connected campus where the volume of datasets is
consolidated and shared across multiple systems. It eliminates disparate
storage silos by merging data into a single shared volume with access
across multiple systems protocols (Dell Technologies 2020). It is scale-
out storage for data consolidation (Daniel and Butson 2013).
2.4 Competitive advantage
Big data provides many opportunities and competitive advantages (Jeble
et al. 2016). Higher education institutions have been reshaped using data
to create a competitive advantage to meet annual enrolment, retention
and revenue goals. Student data is gathered to craft models to predict
their choices, actions and outcomes (Murrell 2019). Decisions about
student support services, programming and resources are made based on
this data. Any institution that can harness its data adequately has a
competitive advantage over the other. BDA derives important insights

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that can ultimately provide a competitive edge (Constantiou and
Kallinikos 2015).
2.5 Challenges in the Use of Big Data in the Colleges of Education
in Ghana
Despite the enormous benefits derived from Big Data, it poses some
unique challenges for higher education institutions. Privacy and security
are considered the most important big data analytics issues (Shrivastva et
al 2014). As higher education institutions look to utilize data to provide
personalized information for the strategy of their operations, there arise
legal ramifications (Taylor-Sakyi 2016). Some of these challenges related
to data security and privacy include data breaches, data integrity, data
availability, and data backup (Terzi et al. 2015; Joshi and Kadhiwala 2017;
Riaz et al. 2020; Agrawal and Nyamful 2016).
The lack of interoperability of institutional data systems means that
mass administrative, classroom and online data can cause additional
difficulties for Big data implementation in higher education institutions
(Mahroeian and Daniel 2016). Integration then becomes a significant
challenge, especially when data comes in structured and unstructured
formats and needs to be merged from different data sources (Mahroeian
and Daniel 2016). The fusing of various data, for example, social media,
images and videos, into a single system may bring distortion in the final
results if it is not done well with the right data. This brings into focus the
quality and availability of data. Big data is dependent on the quality of
data collected and the robustness of the measures or indicators used.
This brings the additional concern of a lack of standardized measures
and indicators that make comparisons difficult to handle.
There is a skills shortage in data analytics worldwide, making the lack
of expertise in big data analytics a major concern (Domingue et al. 2014;
Al-Sakran 2015). Mikalef et al. (2017) assert that the utilisation of big data
technologies and tools is highly dependent on the skills and knowledge
of the human resources (staff). Staff technical knowledge on database
management, data retrieval, programming knowledge such as
MapReduce, and cloud service management is paramount for effective
data analytics and maximum value extraction from big data. Thus,
thinking analytically about data is an essential skill for staff throughout
institutions (Prescott 2014). Persaud (2019) suggests that employers seek
workers with strong functional and cognitive competencies in data

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analytics, computing, and business combined with a range of social
competencies and specific personality traits.
Information Technology infrastructure is another major challenge
(Mukthar and Sultan 2017). The authors further echoed that big data
creates new challenges for IT infrastructure, both hardware and software,
necessary to generate, collect, store and analyze data in higher education
institutions. Furthermore, their analysis proves that BDA deals with
thousands of clusters and nodes of data which require high storage
capacity, high-performance computing systems (HPC), high internet
speeds and computing power necessary to manage the data, analysis and
user queries. This may increase the spending of higher education
institutions on further IT investment to be compatible with BDA
requirements (Mukthar and Sultan 2017).
3.Theoretical Perspective
The Diffusion of Innovation Theory (DOI) is the explanatory tool used
to guide the investigation of BDA prospects in higher education in
Ghana. The DOI was spearheaded by Everett M. Rogers and became
famous through his book ―Diffusion of Innovations‖ (Rogers 2003).
DOI has proposed significant and comprehensive attributes that help
explain how new technology (BDA) change occurs within university and
college settings (Miller et al. 2005). The theory is one of the most popular
theories for studying the adoption of information technologies (IT) and
understanding how IT innovations spread within and between higher
institutions (Venkatesh et al. 2003; Rogers 2003). According to this
theory, innovation is an idea, process, or technology perceived as new or
unfamiliar to individuals within a particular area or social system.
Diffusion is how the information about the innovation flows from one
person to another over time within the social environment (Zhang et al.
2015).
Diffusion of innovation theory serves as a conceptual framework for
identifying conditions that advance innovation adoption and related
adoption methods (Schmidt and Brown 2017; Emani et al. 2012). The
theory enables the examination of how certain educational technologies
are adopted. It allows the focus to be toward perceived innovation
attributes that progressively drive adoption (Sanson-Fisher 2004). Rogers
considered the attributes of innovation to be influential factors for
adoption. He stated that the five attributes of innovation- namely,
relative advantage, compatibility, complexity/simplicity, observability,

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and trialability as determinants of the adoption and diffusion of the
innovation in a targeted higher education institution (Sanson-Fisher
2004). Each component is highlighted as a backdrop for considering the
diffusion of BDA in higher education institutions.
Relative advantage is the degree to which the user perceives benefits or
improvements upon the existing technology by adopting an innovation
(Rogers 2003). This means if staff and managers of CoEs perceive that
BDA will support a person's performance to produce better outcomes,
they are likely to adopt the new technology. Compatibility captures how
well the innovation fits into the existing technical and social environment
(Rogers 2003). The new technology (BDA) should be in
consonance with staff preference and other existing systems. This might
help to increase the probability of staff and managers adopting BDA if
the system can be integrated into other devices. The more an innovation
is perceived as consistent with the values, experience and needs of
potential adopters, the greater its prospects for diffusion and adoption
(Jwaifell and Gasaymeh 2013). Complexity/simplicity measures the degree
to which an innovation is perceived to be challenging to understand,
implemented or use (Rogers, 2003). In this current research, the CoE
perception about the degree of effort needed to learn and use big data
analytics in their operations influences the system's adoption. More
importantly, new ideas that are simpler to understand for the potential
adopter are adopted more quickly than innovations that require the
adopter to develop new skills and understandings (Jwaifell and
Gasaymeh 2013). Less complex innovation is more likely to be rapidly
accepted by end-users. Trialability is the ability of an innovation to be put
on trial without total commitment and with minimal investment (Rogers
2003). The opportunity for staff to easily access the new technology to
familiarize with influences their acceptance of the innovation. An
innovation that can be experimented with on a limited basis is more
likely to be adopted by individuals (Jwaifell and Gasaymeh 2013). Finally,
Observability is the extent to which the benefits of an innovation are
visible to potential adopters (Rogers 2003). As the staff of CoE witness
proactive performance and concrete outcomes, they will build trust for
the innovation. Therefore, only when the results are perceived as
beneficial will innovation be adopted (Moore and Benbasat 1991).
Adopting an innovation is possible if staff have knowledge and
awareness about the innovation and its mechanisms and functions
(Mohammadi et al. 2018). This persuades staff to view the innovation
based on its perceived attributes (relative advantage, complexity, etc).

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Thus, enabling staff to decide between using or dismissing the
innovation. The innovation is adopted when staff decide that innovation
is the best available option for moving forward (Miranda et al. 2016).
Evidence shows that the diffusion of innovation theory provides all the
steps necessary to promote a new idea and that individuals‘ perception of
an innovations‘ features can predict adoption (Emani et al. 2012). We
adopt this theoretical orientation as an interpretive framework to explain
the prospects of big data analytics in the new dispensation of CoE in
Ghana.
Methodology
The study adopted a single case study (Yin 2012; Hyett et al. 2014) to
explore big data analytics prospects, benefits, and challenges. Case
studies are used to collect descriptive data through the intensive
examination of an event in a particular group, organisation or situation.
Siggelkow (2007) argues that a single case analysis can be a compelling
example and fill existing theory gaps. The main advantage of using case
studies is that they show how things occur in practice and can be useful
for institutions (Yin 2009).
We are very much aware that ethics in research is essential if results
are to be accepted. We took steps to ensure that data reporting was as far
as possible anonymous, confidential and informed consent was secured
for this study.
Sampling
In our quest to gather our data, we employed a two-step approach in this
study. First, we started with focus group discussion (FGD) to get
firsthand information of the participants' knowledge, perspective and
understanding of big data (Wong 2008). In addition, the FGD allowed us
to identify more suitable candidates for in-depth interviews, which
formed the second part of our data collection.
As for our FGD, we invited 15 participants based on their strategic
role in the school regarding the use and access of various data. All in all,
13 people showed up, with seven during the first session and the
remaining six during the section session. The first session lasted for 1
hour 12 minutes, while the second session lasted for 1 hour 35 minutes,
in which the core questions revolved around prospects, benefits, and
challenges of big data. Our first author facilitated the sessions, while our

Exploring the prospects of big data analytics in colleges …
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second author took the assistant's role by observing interactions and the
effects of group dynamics (Kitzinger 1994, 1995). During the two
sessions, we used voice memos (digital recorder), note-taking and
participants observation as the primary method of our data collection
(Stewart et al. 2007).
Following our initial analysis of the FGD data and identifying more
suitable candidates among the 13 candidates who participated in our
interviews, we then narrowed our selection to 7 participants for a more
in-depth interview. Our rationale for that is to strengthen the quality of
work and mitigate the limitation of FGD since information gathered
through FGD may likely be influenced by groupthink rather than
individual participants' views (Boateng 2012).
Ritchie et al. (2003) assert a diminishing return to a qualitative
sample. As the study goes on, more data does not necessarily lead to new
evidence. This is grounded in practice when Glaser and Strauss (1967)
opined that the sample size in the majority of the qualitative studies
should generally follow the concept of saturation. Charmaz (2006)
suggests that the study's aims are the ultimate driver of the project
design, and therefore, the sample size. For instance, Guest, Bunce and
Johnson (2006) carried out a systematic analysis of their data from the
study of sixty women and concluded that in a study with a high level of
homogeneity among the population, a sample of six interviews might be
sufficient to enable the development of meaningful themes and useful
interpretations. Drawing from expert opinion, Creswell (2007) advises
between three and five interviews per case for case-study strategies, while
Becker and Edwards (2012) note that one participant may be sufficient
for some purposes. Drawing on the works of Hendy and Pearson (2020),
Goldstein (2020) and Dooley et al. (2019), we purposively selected seven
key informants for the in-depth interview because they were deemed to
be knowledgeable (Gaya and Smith 2016) and information-rich (Patton
2002). This comprises four heads of department (HOD), the college
registrar, quality assurance officer and the IT coordinator who are in
supervisory positions, initiate policy and have some level of experience in
analysing data and records management. Table 1 indicates a list of study
participants for in-depth interview.

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Table 1:
Heads of department/Unit and their experiences
Head of Department
Highest Qualification
Experience
Science
MPhil Agronomy
Four years
Technical
MEd Construction Technology
Four years
Social Sciences
MEd Social Studies
Four years
Mathematics/ICT
MPhil Applied Mathematics
Four years
Quality Assurance
MPhil Guidance and Counseling
Four years
ICT unit
MSc Computer Science
Two years
Registry
MBA Administration
11 years
4.2 Data Collection
The seven officers were interviewed face-to-face after the authors had
booked appointments with the participants. The interviews lasted for 1
hour 35 minutes on average in which the core questions revolved around
the prospects, benefits and challenges of big data in CoE in Ghana. The
resources readiness of CoE were also highlighted. During these sessions,
we used voice memos (digital recorder), note taking and participants
observation as the primary method of our data collection (Stewart et al.
2007). The researchers adopted a reflexive iteration process to determine
data saturation (Sargeant 2012). The use of in-depth interviews to collect
data for this study is consistent with the qualitative design, which seeks
to uncover and describe the participants' understandings about a
situation (Burns 1996). Through in-depth interviews, the college staff
shared their knowledge, experiences and skills on issues of big data
analytics. This process presented adequate data that reflected the
perspectives of participants.
4.3 Data Analysis
The qualitative data were coded, categorised, and themes were developed
(Saldaña, 2013) based on the research question. We further examined
individual transcripts and searched for repetitions within and across
narratives and field notes (Ryan and Bernard 2003). The data was visited
and revisited and connecting them with emerging insights, progressively
leading to refined focus and understandings (Srivastava and Hopwood
2009). Member checks were adopted to assess how far the findings of
the analysis and interpretations reflect the issues from their perspective
for confirmation and validation. This produced results that helped
understand big data as a new trend for the CoE in Ghana. Thus,
providing a degree of usefulness for administrators, managers and

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stakeholders of higher institutions. The findings generated information
concerning the effectiveness and management of data in CoE.
5. Findings and discussion
In this section, we present the emerging issues by addressing the purpose
of the study, which sought to explore the prospects of big data Analytics
as a new tool for the CoE in Ghana. The findings are discussed under
four key themes derived from the analytical lens of DOI theory. The first
three themes represent participants‘ views on BDA at the CoE
(definition, benefits and challenges). The last theme focuses on resource
readiness and the capacity of CoE.
5.1 Big Data Analytics
This theme captures participants‘ reflections on BDA. The views of the
participants were fused and presented as follows.
The study participants stated that they were involved in some form
of data analysis. By this, it suggests that some of the participants know
data analysis. During the FGD, some reported that data analysis;
involves the use of differential statistical tools to produce reliable
findings. Whereas others said that data analysis uses differential data
sets to examine each component of data provided. It enables the
discovery of useful information to arrive at informed
conclusions. (FDG1)
The extract from the participants seems to demonstrate their
involvement in analyses of data in their institution. Since staff exhibited
some data analysis experiences, the new technology (BDA) should have
the potential to fit into the existing staff preference, past experiences,
technical and social environment. Therefore, the ease of use and
simplicity perceived by staff and managers that want to adopt BDA
hastens adoption (Sanson-Fisher 2004).
With regards to knowledge of big data, two participants stated the
following;
It is data in large amounts that is not easily processed by a
standard database system. (FGD2)
A large amount of data from operational activities. (FDG2)

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These quotes demonstrate the interviewee‘s knowledge of big data and
feed into their perceived usefulness of this new technology. Their views
reflect the descriptions of Russom (2011), Wu et al. (2013) and Segal
(2021) when they indicate that big data concerns large volumes of
complex growing data sets with multiple, autonomous sources.
5.2 Benefits of Big Data Analytics in Colleges of Education (COE)
This theme solicited views from the participants on how Big Data
Analytics (BDA) could be helpful to higher educational institutions,
especially CoE. In the interviews, comments from the participants
revealed that;
Big data analytics produces fast data analyses and presents comprehensive
outcomes of various indicators and data patterns for decision-
making. (FDG1)
These reflect some benefits of using the system. Attaran et al. (2018)
suggest that higher education institutions can take advantage of big data
analytics to make decisions that would transform many activities,
including enrolment, student support, alumni engagement, financial aid
administration, and other learning and operational functions. Picciano
(2012), Savin-Baden (2015) and Sin and Muthu (2015) established that
BDA allows for evidence-based decision making. It is appropriate then
to claim that adopting this new technology (BDA) depends on the
system‘s perceived usefulness (relative advantages) to the institutions and
staff. If the institutions or staff know that the new technology (BDA) will
improve their work performance and provide positive outcomes, they
will adopt the system. The perception of advantages such as cost-
effectiveness and efficient decision-making influence adoption.
The interviews further solicited from participants how BDA could
increase the effectiveness and operations of CoE. In the following ways:
It can be used to understand students learning behaviour to model
effective systems for improved training of student teachers. It also
enhances teaching and learning. (Key Informant1)
These are supported by Hussein and Mohamed (2015) whose study
claimed that big data analytics enrich the analysis and reporting of data
about learners and their contexts to understand and optimize learning
and the environments in which it occurs. This is further reiterated by

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Ibe-Ariwa and Ariwa (2015) and Jain and Pandey (2013), who indicated
that it improves processes and workflows, measure academic and
institutional data and generally improves institutional effectiveness. All
these are achievable by adopting big data analytics in educational
institutions (Seke 2015).
Staff knowledge and awareness of the perceived benefits (Relative
advantage) of the new technology (BDA) and the perceived ease of use
(simplicity) will result in their attitude towards the innovation based on
its perceived attributes. If the attitude is favourable, staff are persuaded
to adopt and use the system to enhance teaching and learning to
improved training of student teachers or otherwise.
Furthermore, the data from the interviews appear to indicate that big
data analytics provide consistent results and data privacy and security.
One of the interviewees said;
Big Data analytics ensures data security and presents reliable
outcomes. (Key Informant4)
This is in line with Mukthar and Sultan‘s (2017) argument that techniques
such as disaster recovery plans, strong password policy, firewalls,
encryption and anti-virus software are implemented to lower the dangers
of losing data. Ahmed (2016) suggests that traditional security solutions
are not useful in diverse and large datasets, requiring more advanced
security models. The perceived benefits (relative advantages) and
perceived ease of use (simplicity) will influence staff adoption and use of
BDA. The behaviour of staff towards the system is dependent on
perceived beneficial outcomes.
5.3 Challenges of BDA use in COE
This theme presents participants' views on the challenges that COE
encounters in BDA. The interview data revealed that big data analytics
seems to be a new concept in the CoE in Ghana. In this study, it is logical to
claim that participants' considerable knowledge of the meaning of big
data does not mean they comprehend how the system operates or
performs analyses. This is due to the external variables (user training,
system characteristics, user participation in design and the
implementation process nature). Most importantly, the awareness of the
attributes (Relative advantage, compatibility, simplicity, trialability) of
BDA seems to be lacking from participants in this study. Daniel (2014)
and Attaran et al. (2018) found that big data analytics in higher education

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are relatively new topics, and there has not been significant development
at any particular higher institution or college.
Furthermore, the data from the interviews suggest several challenges
in adopting BDA in CoE. A significant concern was the internet. A
previous study by Ivwighreghweta and Igere (2014) found that power
outages, slow internet speed and insufficient computers were some of the
barriers militating against efficient internet access in some tertiary
institutions.
The findings of this study revealed inadequate information
technology infrastructure as a challenge as reflected in the comment of a
participant;
We do not have the technology required to support big data analytics in
our college. (Key informant3)
Like Mukthar and Sultan (2017), this study‘s findings suggest that big
data creates a new challenge in the form of information technology
infrastructure to generate, collect, store and analyze data in higher
educational institutions. Thus, the need for more spending on IT
investment to be compatible with BDA requirements.
A significant challenge was the BDA skills gap of staff. An
informant stated;
We do not have skills in BDA, so it will be challenging to implement
it. (Key Informant6)
These are consistent with Mukthar and Sultan (2017), who advocate that
teaching and administrative staff must be equipped with the necessary
skills to perform their roles in a digital, data-driven world. They added
that staff should be provided with appropriate training and support to
improve their digital capability and data management skills. More
importantly, new ideas that are simpler to understand for the potential
adopter are adopted more quickly than innovations that require the
adopter to develop new skills and understandings (Jwaifell and
Gasaymeh 2013).
5.4 Resource readiness and capacity of CoE
This theme presents the views of the participants on the preparedness of
CoE for BDA. Data from the interviews revealed that CoE seem not to

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96
have the technologies and resources required to implement big data
analytics. This was evident in the following comments
Colleges would need resource readiness (both human and technology)
for Big Data Analytics and I will also need training on BDA. (Key
Informant2)
This is a clear indication that inadequate infrastructure and human
resources will militate against the adoption of BDA in CoE. This finding
resonates with Attaran et al. (2018) findings which indicated that many
academic institutions lack analytics skills and do not have the internal
resources to take advantage of the wealth of data-driven insights. This
study further corroborates the findings of Yanosky and Arroway's (2015)
research which showed that lack of appropriate financial resources, a
shortage of analysts (digital skills) and insufficient computing power were
some of the challenges that hinder the successful implementation of
BDA. Therefore, perceived attributes (relative advantage, compatibility,
simplicity, trialability and observability) will determine the resistance or
acceptance to change (behaviour and attitude) of CoE staff towards the
BDA system. To increase the probability of adoption, the innovation
(BDA) must address an issue that staff or others perceive to be a
problem in their operations.
In retrospect, of the five dimensions captured by the DOI theory,
our work identifies three dimensions based on the response of our
respondents. These dimensions include compatibility, relative advantage
and complexities as revealed in subsection 5.1 to 5.3, respectively. For
example, in 5.1, one of the respondents opined that;
Since staff exhibited some data analysis experiences, the new
technology (BDA) should have the potential to fit into the existing staff
preference, past experiences, technical and social environment. (Key
Informant7)
It becomes apparent that there is a good potential of adopting big data in
CoE based on its perceived compatibility between what CoE are doing
and what BDA stands for. Similarly, looking at the relative advantage as
an attribute of BDA, it is clear to discern from 5.1 the feedback from our
respondents that there are many potentials for embracing BDA from the
CoE. For instance, some of these respondents argue BDA 'produces fast
analyses of data', enables teachers to 'understand students' learning
behaviour' and 'ensures data security' as well as having the potential to

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'present reliable outcomes'. Another important discovery arising from
our study is the complexities associated with the adoption of BDA due
to the capacity and tools/platforms to process massive data.
In summary, our findings revealed potentials and perceived pitfalls
for the adoption of BDA in CoE. For example, our respondents provide
insights about compatibility and relative advantage of the BDA and at
the same time highlight the potential barriers which revolve around
capacity - technical know-how and tools.
6. Implications and contributions
Colleges and universities face a significant shift in educational, teaching
and learning models and student campus experiences through digital
transformation (Dell Technologies 2020). Students are increasingly taking
advantage of technology on-demand to meet their own learning needs.
This change has brought about the driving force behind the growing
concern for big data analytics in higher education in Ghana.
Again, to help prepare students for successful careers, some higher
institutions of learning sought to transform passive classrooms into
active learning spaces through the use of BDA to make teaching,
learning, and research more meaningful (Daniel 2014). BDA in higher
education can drive innovation and digital transformation at every level,
from classrooms to research laboratories and administrative offices. A
useful insight into learners' performance, including teaching and learning,
would be an essential help in planning teaching activities for educators.
This is possible through the use of big data analytics in institutions of
higher learning.
A new set of skills are required for staff on the use of BDA. There is
a need to organise training that will equip staff on big data regarding
quality data, data integration, security, and privacy. These essential skills
are necessary for a successful BDA adoption, but these are absent in the
study context. For it to be possible in CoE, there is a need to build an
information technology infrastructure that supports this venture.
This study contributes to the literature by awakening the
consciousness of managers of higher education institutions on the
concept and prospects of BDA in the Ghanaian context. As the evidence
base in BDA continues to grow and develop, this paper contributes to
the growing evidence base within Ghana. Most significantly, this study
benefits the management and staff of CoE in their adoption and use of
BDA.

Exploring the prospects of big data analytics in colleges …
98
Conclusion
In conclusion, Big Data helps higher institutions of learning make
decisions faster with more knowledge and clarity. It helps with
forecasting and problem-solving as it turns out to be cheaper than the
traditional methods used to collect, store and process the data for
institutions. Therefore, CoE require new technologies and new
approaches to process large amounts of data within tolerable elapsed
times efficiently.
The study is an illustrative single case study and may not be
generalised to cover all higher education institutions, especially CoE in
Ghana. The results suggest that the sample size should be increased, as a
higher sample size would help make the conclusion more general.
Nonetheless, it has revealed insightful realities such as effective data
analytics and management improving decision-making and operations in
the new landscape of CoE in Ghana.
This study was limited to a single case in one of the oldest CoE in
Ghana and adopted a qualitative approach to data collection and analysis.
Future investigations could explore a more expansive scope while
adopting a mixed-method approach.
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