ArticlePDF Available

Abstract and Figures

Business leaders around the world are using emerging technologies to capitalize on data, to create business value and to compete effectively in a digitally driven world. They rely on data analytics to accelerate time to insight and to gain a better understanding of their customers’ needs and wants. However, big data and data analytics solutions in higher education are new topics. There has been limited progress in accumulating the extremely rich data that flow through higher education systems for the purpose of acquiring usable information for students, instructors, administrators and the public. The key objective of this article is to propose a conceptual model for the successful implementation of analytics in higher education. The article also examines some of the potential benefits of big data and analytics as applied to the world of higher education and explores implementation challenges that can be expected. Furthermore, the study reviews key attributes of successful analytics platforms and illustrates some of the routes that might be taken to implement these technologies in education. Finally, it highlights the successful implementation of analytics solutions in several universities.
Content may be subject to copyright.
Opportunities and challenges for big data
analytics in US higher education:
A conceptual model for implementation
Mohsen Attaran, John Stark and Derek Stotler
California State University, USA
Business leaders around the world are using emerging technologies to capitalize on data, to create business value and to
compete effectively in a digitally driven world. They rely on data analytics to accelerate time to insight and to gain a better
understanding of their customers’ needs and wants. However, big data and data analytics solutions in higher education are
new topics. There has been limited progress in accumulating the extremely rich data that flow through higher education
systems for the purpose of acquiring usable information for students, instructors, administrators and the public. The key
objective of this article is to propose a conceptual model for the successful implementation of analytics in higher
education. The article also examines some of the potential benefits of big data and analytics as applied to the world of
higher education and explores implementation challenges that can be expected. Furthermore, the study reviews key
attributes of successful analytics platforms and illustrates some of the routes that might be taken to implement these
technologies in education. Finally, it highlights the successful implementation of analytics solutions in several universities.
adaptive learning, big data, enrolment management, predictive analytics, student performance management, targeted
student advising
Corporations around the world are slowly beginning to
incorporate big data analytics into their business models
and are using it for more educated decision-making. It is
expected that there will be a rapid proliferation of enter-
prises using business intelligence (BI) and analytics to
predict the future with an acceptable level of reliability.
It is also expected that data analytics will become a critical
core competency for professionals of all types (Eiloart,
2017). Three major factors that have allowed for the rise
of big data and big analytics have been increased comput-
ing power, large volumes of data and hardware and soft-
ware innovation (Minelli et al., 2013). Although the current
users of big data and big analytics are primarily large cor-
porations, there are numerous additional industries and
organizations in which a complex data system could advan-
tageously assist decision makers (Bayrak, 2015).
One sector that big data and big analytics have yet to
penetrate is that of higher education. Although there are an
estimated 20.2 million students currently attending some
form of higher education, there has been limited progress in
accumulating and analysing data that flow through the
education system (Hussar and Bailey, 2013). That said, big
data can be integrated into several parts of the education
system and can lead to greater student success (as measured
by retention rates and knowledge acquisition) and donor
giving (as measured by total annual donations) (Burroughs,
2016; Ekowo and Palmer, 2016; Hardee, 2016; Yanosky
and Arroway, 2015). Therefore, it is important to explore
the opportunities and challenges associated with imple-
menting big data and big analytics in the higher education
system and presenting the findings for educators, adminis-
trators and policymakers to consider.
The evolution of big data and analytics
Data are growing faster than ever before. According to
Gartner Research, the volume of data will grow by 800%
over the next 5 years (Groenfeldt, 2012). Every second, we
Corresponding author:
Mohsen Attaran, School of Business and Public Administration, California
State University, Bakersfield, 9001 Stockdale Highway, Bakersfield,
CA 93311-1099, USA.
Industry and Higher Education
ªThe Author(s) 2018
Reprints and permission:
DOI: 10.1177/0950422218770937
create new data. There has been massive growth in video
and photo data, triggered by staggering usage of smart-
phones – all packed with sensors capable of collecting a
wide variety of data. There are roughly 50 billion smart
connected devices in the world, and messages, updates and
images posted on social networks have all contributed to
the data explosion (Marr, 2015). Similarly, growing num-
bers of companies are collecting data from their customers.
Walmart collects more than 2.5 quadrillion bytes of data
every hour from its customer transactions (DeZyre, 2017).
According to McAfee and Brynjolfsson, in 2012, about 2.5
billion gigabytes of data were being created each day, and
that number is doubling every 40 months or so. By 2020,
about 1.7 megabytes of new information will be created
every second for every human being on the planet. By then,
our accumulated universe of data will have reached 44
trillion gigabytes (McAfee and Brynjolfsson, 2012).
Big data defined
The term ‘big data’ was coined in mid-1990s and is
defined as collections of data so large, complex and
dynamic that they exceed the processing capacity of the
conventional database architectures of organizations
(Weiss and Indurkhya, 1998). According to Gartner, the
world’s leading information technology research and
advisory company, big data is comprised of high-
volume, high-velocity and high-variety data (the ‘3 Vs’,
as shown in Figure 1 – see, for example, https://www.gart,2016).Thesedata
sets are too large to be handled easily and flow in and out
with excessive speed, making them difficult to analyse
and, finally, the range and type of data sources are too
great to assimilate (Diebold, 2012).
The typical organization is therefore challenged in man-
aging big data effectively, as it simply does not fit into the
strictures of current database architectures. At the same
time, big data draws from multiple sources and transactions
and contains valuable patterns and information.
The act of gathering and storing large amounts of data
for eventual analysis is not new. Since the 1950s, busi-
nesses have been using basic analytics to uncover hidden
patterns and trends, show changes over time and confirm or
challenge theories (Asllani, 2015). As enterprises amass
broader pools of data in big data platforms, they have
increased opportunities to mine those data for predictive
insights. As they cannot, typically, manage the data effec-
tively with their current database architecture, they need to
seek alternative ways to process big data (Bayrak, 2015).
A well-defined data management strategy is essential for
the successful use of big data in corporations (Bughin,
2016). Data and analytics are playing increasingly
important roles in improving competitive advantage
(Taylor, 2012), and corporations see big data and the ability
to analyse it as an important driver of innovation and a
significant source of value creation (Tan et al., 2015).
The rise of analytics
Analytics, in the form of BI, is defined as a set of technol-
ogies, processes and tools that use data to predict likely
behaviour by individuals, machinery or other entities
(Mashingaidze and Backhouse, 2017). If the right type of
analytics is used, big data can deliver richer insights and
uncover hidden patterns and relationships. More data could
translate into more possibilities for a business, but only if
their real meaning can be ascertained (Minelli et al., 2013).
The new benefits that modern data analytics brings to
the table are speed and efficiency. The ability to work
faster – and stay agile – gives organizations a new com-
petitive edge (Bayrak, 2015). Cloud computing technol-
ogy (CCT) has emerged as the preferred technology for
fulfilling the infrastructure and software needs of an enter-
prise via the Internet (Attaran, 2017). A recent study by
the McKinsey Global Institute indicates that the pace of
change is accelerating and the analytics revolution is gain-
computational power. Widespread access to the cloud,
insightful data visualizations, interactive business dash-
boards and the rise of self-service analytics have made
the technology available and affordable for businesses
of all sizes. Suddenly, advanced analytics is not just for
the analysts (Henke et al., 2016).
Today’s analytics landscape
The past few years have seen an explosion in the business
use of analytics. Corporations are using analytical tools,
including BI, dashboards and data mining to gain a better
understanding of their present customers and to identify
Figure 1. The three Vs of big data.
2Industry and Higher Education XX(X)
potential customers and their needs. With the help of new
tools, enterprises can leverage big data analytics to drive a
host of business objectives, from streamlining operations to
improving customer relations (Henke et al., 2016). In fact,
big data analytics is set to transform virtually every busi-
ness activity, bringing opportunities for enhanced customer
service, optimized production levels, superior capacity
planning, reduced repair and maintenance costs and
improved working capital utilization (Bughin, 2016).
According to a 2016 Forester study, the top three tangible
analytics benefits are increased margin, profitability and
increased gross sales (Evelson and Bennett, 2015). Analy-
tics is commonly used in the areas of, for example, finance,
marketing, human resources, healthcare and government
policymaking (Zwilling, 2016). Several research studies
have documented the advantages and widespread applica-
tions of analytics tools in corporations around the world
(Eckerson, 2016; Evelson and Bennett, 2015; Gaitho,
2017; Henke et al., 2016; Lebied, 2017; Minelli et al.,
2013; Roy, 2011).
Traditional reporting-based BI platforms are not
designed to handle the exponential growth of the sources,
volume and complexity of data. The traditional platforms
enforce strict data and report governance, allowing access
only to specialized reporting groups. In contrast, the mod-
ern approach views data governance as an important step in
creating self-service analytics. Modern BI platforms sup-
port organizational needs for greater accessibility, agility
and analytical insight from a diverse range of data sources.
Moreover, while traditional systems could take months to
implement, the modern approach takes as little as a few
hours. Latency is no longer tolerated (Henke et al., 2016).
A 2015 study by Gartner identified a shift of focus from
IT-led reporting to business-led self-service analytics
(Gartner, 2015). According to that study, many corpora-
tions have augmented their traditional BI platforms with
more agile solutions to improve their core operations or
launch entirely new business models. The modern BI plat-
form adopted by innovative companies aims to democratize
analytics through self-service capabilities such as ease of
use, agility and flexibility (Table 1) (Gartner, 2015).
Categories of analytics
Analytics is constantly evolving: It has changed dramati-
cally over the years and is advancing rapidly today.
According to Davenport and Dyche (2013), the most pop-
ular categories of analytics are descriptive, predictive and
prescriptive, as shown in Figure 2. These categories build
on each other and enable enterprise to make faster and
smarter decisions. As organizations evolve, they move
from their historical focus on ‘what’ and ‘why’ to more
predictive and prescriptive analysis (Bayrak, 2015).
Descriptive analytics is the simplest of the three cate-
gories. It allows big data to be condensed into smaller,
more useful nuggets of information. Its purpose is to sum-
marize what happened in the past and to uncover patterns
that may offer insights into business performance, so
enabling users to monitor and manage their business pro-
cesses more effectively (Lustig et al., 2010). In descriptive
analytics, data modelling, reporting, visualization and
regression are used to collect and store data efficiently, to
create reports and present information and to identify
trends in the data.
Predictive analytics analyses current and historical data
to provide insights into what will happen and why it will
happen, with an acceptable level of reliability (Abbott,
2014). It involves the use of a variety of models and tech-
niques to project future conditions and situations (Gandomi
and Heidar, 2015). It does not predict one possible future,
but rather multiple futures based on the decision-maker’s
actions. Statistical analysis, data mining, textual analysis,
media mining, forecasting and predictive modelling are
used to identify the probabilities of potential outcomes
and/or the likely results of specific operations (Siegel,
2016). Predictive analytics can help businesses with a wide
range of problems, and companies are using it to analyse
historical data and facts to improve their understanding of
clients’ needs, market potential, products, suppliers and
Table 1. Traditional versus modern analytics.
Analytic workflow
component Traditional platform Modern platform
Data ingestion and
IT-produced IT-enabled
Content authoring Primarily IT staff-
limited usage
Business users –
Analysis Predefined, ad hoc
reporting, based on
predefined model
Insight delivery Distribution and
notifications via
scheduled reports
or portal
Sharing and
open APIs
Figure 2. Analytics – The key categories.
Attaran et al. 3
partners and to identify potential risks and opportunities
(Lebied, 2017).
Finally, the emerging technology of prescriptive analy-
tics goes beyond the descriptive and predictive models and
shows the likely outcome of each decision. It goes a step
further into the future and attempts to identify what should
be done and why. Prescriptive analytics employs tech-
niques such as decision modelling, simulation and optimi-
zation to ascertain actions the organization could take to
achieve the desired outcome (Lustig et al., 2010). The aim
is to evaluate the effect of future decisions and to present
the best course of action to take in order to adjust decisions
before they are actually made (Basu, 2013). This is the
most valuable category of analytics and usually results in
rules and recommendations for next steps. A 2017 survey
by Intel suggested that by 2020, 40%of new investment in
analytics tools would be in predictive and prescriptive ana-
lytics (Intel, 2017).
Business applications of analytics
BI and analytics are more than methods of gathering and
analysing data. They are about adopting the mindset of an
experimenter – a willingness to let data guide a company’s
decision-making process.
Organizations of all sizes are using analytics to support
business core functions, such as marketing, merchandis-
ing, sales and risk management. From banking to manu-
facturing, from retail to healthcare, data analytics is used
to make breakthrough discoveries, deliver better services
and enrich the customer experience. According to recent
studies, corporations are using analytics to gain various
business benefits, including new revenue opportunities,
improved operational efficiency, better customer service,
more effective marketing and competitive advantages
over rivals (Davenport and Dyche, 2013; Gaitho, 2017;
Henke et al., 2016; Kalakota, 2014; Lebied, 2017; Sted-
man, 2017; Siegel, 2016).
Analytics is used to predict:
stock prices, risk, delinquencies, accidents, health problems,
hospital admissions, malfunctions, oil flow, electricity
outages, sales, donations, clicks, cancellations, fraud, tax eva-
sion, crime, approvals for government benefits, thoughts,
intention, answers, opinions, lies, grades, dropouts, friendship,
romance, pregnancy, divorce, jobs, quitting, wins, votes, and
more. (Siegel, 2016: 25)
The banking industry and financial institutions are using
analytics to analyse the probabilities of risk and default and
to prevent fraud. For example, PayPal is using predictive
data analysis in its efforts to shield its users from fraud and
to halt fraudulent transactions before they are processed
(Burns, 2016). The healthcare industry has used analytics
to process large amounts of information quickly and so to
provide lifesaving diagnoses or treatment options more
rapidly. It is also used in efforts to improve patient care
and to identify potential health risks in advance (Stedman,
2017). Logistics companies (UPS, DHL and FedEx) have
used analytics to reduce costs and improve their operational
efficiency (Rosenbush and Stevens, 2015). The retail
industry employs data analytics to predict customer
responses or potential purchases and for upselling and
cross-selling opportunities (Gaitho, 2017). Target used pre-
dictive analytics to predict from shopping behaviour which
customers had the most probability of rapidly becoming
pregnant (Salleh, 2013). Data analytics is also used in
self-driving cars and robots (DeAngelis, 2015). As a final
example, governments are using pattern recognition in
images and videos for enhanced security and threat detec-
tion (Siegel, 2016; Zwilling, 2016). Other applications of
analytics in the government sector include traffic control,
route planning, intelligent transport systems and congestion
management (Gaitho, 2017).
Overall, a recent study by the Business Application
Research Center (BARC) found that organizations using
data analytics reported an 8%increase in revenue and a
10%reduction in costs. Other reported benefits were
better strategic decisions, better understanding of cus-
tomers and improved control of operating processes
(Bange et al., 2015).
The evolution of analytics in higher
Research findings
A national review of student attainment in US higher edu-
cation conducted by Indiana University and supported by a
grant from the Lumina Foundation paints a bleak picture:
The researchers found that only 53%of students were com-
pleting their post-secondary degree within 6 years (Shapiro
et al., 2012). This report focuses on the 6-year outcomes for
students who began post-secondary education in fall 2009
through spring 2012. The report found an accelerating
decline in overall completion rates and a decline in com-
pletion rates across age group and enrolment intensities.
The main reason for such a decline is not necessarily
lack of academic preparation. A recent analysis of 55 US
colleges (see Scott, 2016; Zinshteyn, 2016) showed that
45%of college students never finished their
more than 40%of students who left these institutions
had a grade point average (GPA) of over 3.0;
many students who had grades with a ‘B’ average or
higher were not coming back after their first year;
75%of dropouts left their studies with at least a 2.0
4Industry and Higher Education XX(X)
These statistics were a wake-up call for many universi-
ties because they generally had not considered students at
risk of dropping out unless they had a GPA of 3.0 or lower.
Many students who were in the middle of the performance
range and who needed help were being missed by these
universities. Faced with such bleak statistics, colleges can
tap big data and predictive analytics to forecast students’
success or failure and to help them to stay in school. With
access to more data than ever before, analytics can become
the foundation for taking action. University system offi-
cials believe predictive analytics can help to increase gra-
duation rates by enabling educators to intervene with
struggling students before failure becomes inevitable: They
hope that predictive analytics can help identify pressure
points that are leading students to drop out (Wells, 2016).
According to Ekowo and Palmer (2016), universities are
employing predictive analytics to identify those students
who are most in need of advice, to develop adaptive learn-
ing courseware and to manage enrolment. Predictive ana-
lytics tools are also being used in other ways in higher
education institutions, as summarized in a study by
Yanosky and Arroway (2015).
Current state of big data in higher education
Big data and big analytics in higher education are relatively
new topics, and there has not been significant development
at any particular university or in any particular state
(Daniel, 2014). A recent survey found that only 41%of the
colleges surveyed used data in their decision-making and
less than half of the responding schools said they were
engaged in predictive analytics (Burroughs, 2016). Another
survey, conducted by Educause in 2015, found only 47%of
respondents identified institutional analytics as a major pri-
ority and only half as many again described learning ana-
lytics as a priority (Yanosky and Arroway, 2015). That
said, one group, western interstate commission for higher
education (WICHE) cooperative for educational technolo-
gies, is currently collecting unidentifiable student data from
a total of 17 partnering schools to be used in analysis in the
near future (Wagner and Hartman, 2016).
Recently, some colleges and universities have been
using analytics for multiple purposes and for various phases
of students’ academic careers (Ekowo and Palmer, 2016;
Yanosky and Arroway, 2015). In a later section of this
article (see the ‘Deployment of analytics in higher educa-
tion’ section), we cover applications of analytics in colleges
and universities around the United States.
A conceptual model for implementing
analytics in higher education
Before this new technology can really be of benefit to uni-
versities, however, there has to be a fundamental shift in
thinking. Analytics needs to be repositioned in the mindset
of professionals working in the education sector (Ekowo
and Palmer, 2017). Analytics is constantly evolving, has
changed dramatically over the years and is still advancing
rapidly today.
Possible analytics support for institutions during the
student lifecycle
Higher education institutions can leverage analytics to
transform many activities, including enrolment, student
support, alumni engagement, financial aid administration
and other learning and operational functions. To begin this
analysis, it is helpful to consider the engagement with stu-
dents from a lifecycle perspective. In the initial, pre-student
stage, institutions engage with prospective students in var-
ious ways, from assisting primary and secondary education
in developing educational processes to evaluating individ-
ual students for potential acceptance for a higher education
programme. At the next stage of the lifecycle, the student
stage, interactions with students while they are pursuing
their degrees are encapsulated. Finally, there is the post-
student stage, when the student becomes an alumnus of the
higher education programme and may engage with the
institution as a source of information about the efficacy
of its programmes, advise on curricular and programme
development, provide financial support and/or assist in
recruiting future students for the institution. With these
stages in mind, analytics can play a unique role. Figure 3
illustrates our proposed model of the use of analytics to
improve efficiency in higher education institutions. The
following subsections summarize the model.
Pre-student stage: Example of enrolment management (dealing
with potential and incoming students). Big data and big ana-
lytics can be used to help institutions make consistent deci-
sions in the student admission process. At present,
enrolment management departments in universities focus
on a core set of data, often specific to each campus, to make
decisions about which applicants to enrol. Typically, the
data used include standardized test scores (SAT/ACT),
high-school GPA, high-school course patterns, demo-
graphic data and specialized data such as ‘legacy’ connec-
tions. While these data have been used for years, the low
rate of degree completion noted above does raise questions
about how successful their use has been.
Through big data analytics, it is possible to use more of
the considerable quantity of student data and statistics that
campuses already have in their disparate silos to make more
informed decisions. Predicting how a given student profile
will perform can be done much more accurately as the
volume of data from the current application process is aug-
mented by the wealth of information in the various systems.
In addition to the data already collected by the enrolment
management system, information can be added to the anal-
ysis such as aggregate student performance across the range
Attaran et al. 5
of courses (tied to standardized test performance and/or
high-school GPA), the likelihood of being an on-campus
or off-campus resident (based on home address), the like-
lihood of involvement in student organizations and/or of
participating in university functions (based on high-school
activities) and the likely use of campus resources ranging
from the library to tutoring or advisory services (tied back
to high-school activities) – all to make a better estimate of
the likely performance at the university by a given appli-
cant. Of course, these data could also be used to determine
what kind of support a particular student would be likely to
need if accepted.
As more and more data are collected on students, the
information will begin to reveal certain trends with respect
to different types of students. Ultimately, there will be
enough data collected to allow prediction of a student’s
potential via a mathematical calculation, given all the
inputs of ethnicity, residence, SAT score, college entrance
essays, high-school rank and GPA.
Further, the potential for this type of big data analytics is
growing. It is already possible also to make effective use of
qualitative data. For example, natural language processing
technology is now available for reading essays automati-
cally and grading them without the help of an administra-
tor, opening the possibility of automating even the
application essay process (Adams, 2014). This type of ana-
lytics automation is just one of the many ways that univer-
sities can streamline their processes and add to the
collection of information on student profiles. Eventually,
there will be little need for the traditional enrolment
management function and a computer program will be able
to predict a student’s capabilities and will do so in a way
that is likely to be more accurate than the prediction of a
human being. In addition to better decisions for applicants,
this could have immense cost-saving implications if imple-
mented correctly.
Student stage: Example of student performance management
(dealing with current students). More than 30 million people
in the United States have earned some college credits but
no degree (Shapiro et al., 2014). As we focus in on how big
data and big analytics can be used by key stakeholders to
increase student success, the overarching goal is to address
student retention rates, time to degree completion, informa-
tion retention and career preparedness. Big data analytics
can assist in achieving these goals in various ways, includ-
ing collecting data on student performance, identifying
effective teaching methods and implementing predictive
analytics based on performance.
Professors collect a plethora of data on students, such
as homework scores, test scores, classroom participation
and attendance, in determining the overall performance of
a student in a given class. According to Daniel, ‘ ...Big
Data analytics could be applied to examine student entry
on a course assessment, discussion board entries, blog
entries or wiki activity, which could generate thousands
of transactions per student per course’ (Daniel, 2014:
910). Most of the data stay with the professor, and the
student’s overall grade is reported into a student database.
Imagine, however, if all of the student’s work leading up
Figure 3. Using analytics to improve efficiency in higher education institutions.
6Industry and Higher Education XX(X)
to the final grade were also reported into the system and a
profile for each student were built. For some larger
schools, this would create millions of transactions over
the course of a single year, which it would be impossible
for any department to manage.
That said, a complex analytics system would be able to
capture, analyse and generate meaningful data correla-
tions and patterns. This type of system could then make
correlations, such as that between the number of absences
from class and the student’s final grade. If there were a
significant correlation, then the system could be set up to
identify students at risk from frequently missing class.
This type of system could also analyse trends in individual
students across time. If, for example, a student were per-
forming poorly in writing assignments in different classes,
the system could notify the student and campus writing
centre. Further, it could recommend additional course-
work for particular students based on their results across
classes. Using the data collected, administrators could
identify those areas in which they were above the national
average and those in which they were below it, making
curriculum adjustments accordingly.
Colleges could also use a data analytics system to iden-
tify which teaching methods lead to better understanding
and more long-term retention. One way to implement this is
to have the same professor teach a class in two or three
different ways. Perhaps one class would be set up with
primarily student projects, presentations and no exams,
while another would consist primarily of exams and essays.
The effectiveness of teaching methods can then be tested by
giving students an examination at the beginning of the term
to test their baseline knowledge and giving them the same
examination at the end of the term to test their cumulative
retention of concepts. It would also be possible to look
more deeply into the type of learning improvements
required. For example, in the quantitative area, more data
would allow administrators to determine whether a learning
problem was in understanding and framing the question, in
the actual computations or in the analysis afterwards and
making sense of the computations. Rather than a general-
ized finding of a need for better quantitative skills – a
finding that does not really offer a clear prescription – a
more targeted finding would offer a clearer and more effec-
tive course of remediation. After a few years of testing, a
well-developed analytics system can show trend analysis
and demonstrate which teaching methods are most effec-
tive in promoting overall student retention.
Post-student stage: Example of university advancement
(analytics for donor relations and federal funding). Colleges and
universities in the United States can greatly benefit from
the implementation of an analytics system in terms of
gaining more federal funding and improving donor rela-
tions. An environment conducive to such usage has been
developing. For example, while in office, President Obama
worked on a strategy to make colleges more affordable for
the middle class by promoting new policies that specified
performance-based funding (Nisar, 2015). Essentially, the
proposal was to identify specific factors, like time to degree
and affordability, and then to allocate federal funds to the
schools that provided the best balance between low cost
and ability to graduate students. In turn, this would incen-
tivize universities to find ways to keep their costs low and
to graduate students in a timely manner. In response, it is
likely that some universities will adopt analytics systems
that do a better job of tracking student progress, measuring
the most effective teaching strategies and enrolling students
whose predicted potential is the greatest, as well as report-
ing these results to government funding agencies.
Big data analytics systems can also facilitate stronger
relations between the university and potential donors. For
example, an analytics system can track donor information,
such as residence, income, ethnicity, the amount previ-
ously donated, community affiliations and other metrics
to build a database with all of the information that will
identify trends. Eventually, the system will be able to
predict which neighbourhoods, ethnicities and income
levels are most likely to donate money to the university.
Administrative personnel can then focus on developing
relationships with those who fall into those categories and
avoid those who are not likely to donate. Ultimately, the
university will benefit because it will waste less time con-
tacting individuals who are not likely to donate and will
increase its endowment fund by focusing efforts where
they will do the most good.
Promises and challenges of analytics in
higher education
Universities in the United States are under pressure from
government, parents and students to do a better job of
graduating students. Performance-based funding has
increased the pressure to ensure that every student suc-
ceeds (NCSL, 2015). Besides, universities can minimize
loss of revenue from tuition and fees by retaining students
because it costs less to retain a student than to recruit a
new one (Eduventures, 2013). In this context, analytics
could be viewed as an empowering tool which helps insti-
tutions to create an enriched learning experience for the
students and raise graduation rates. With access to more
data and the availability of easy-to-use predictive analy-
tics tools, more universities can promote academic suc-
cess for students (Yanosky and Arroway, 2015).
However, although universities are sitting on huge
amounts of useful data, these data are stored in different
departments and software systems and are not used to con-
nect the dots. Powered by a cloud computing infrastructure,
which offers cost savings, scalability, agility and moderni-
zation, this situation can be transformed so that universities
are making effective and coordinated use of the wealth of
Attaran et al. 7
data they collect (Attaran et al., 2017). Until recently, much
of the data were collected for accountability purposes and
were merely shared with state and federal agencies that
track the success of universities in graduating their stu-
dents. By using predictive analytics to pull all of those data
together, universities can ascertain, for instance, how often
students interact with online course materials, or whether
freshman students who receive a ‘C’ in certain courses are
more or less likely to graduate. Prognostic analytics can be
used in digital courses to identify what a student is learning
and what components of a lecture plan most effectively
teach them (Wells, 2016).
Likely challenges and setbacks
Although, as the above discussion has emphasized, there
are very real and practical applications of big data and big
analytics for higher education, they certainly will not be
implemented unless a variety of challenges and potential
setbacks are addressed. For example, many universities
lack analytics skills and do not have the internal resources
to take advantage of a wealth of data-driven insights. As a
result, they outsource analytics or, more often, simply fail
to leverage the information they already possess (McGuirt
et al., 2015). In a 2015–2016 Higher Education Industry
Outlook Survey of 102 senior higher education leaders,
41%indicated that they used data/analytics for forecasting,
and 36%of colleges outsourced analytics because they
lacked the necessary skills internally. Moreover, 29%had
the resources to analyse data for strategic and operating
decisions, and 22%said that they had sufficient data but
did not incorporate it effectively in decision-making. The
survey identified the key challenges facing institutions as
effectively using data residing in different functions for
decision-making, data quality, dealing with new types of
data and adopting new or more advanced analytics tech-
niques (McGuirt et al., 2015).
An Educause survey in 2015 identified a host of chal-
lenges in implementing analytics systems, including orga-
nizational behaviour issues such as resistance to change
and a lack of vision, a lack of appropriate financial
resources, a shortage of analysts (digital skills) and insuffi-
cient computing power (Yanosky and Arroway, 2015).
Anderson and Ackerman-Anderson (2001) suggest that
when implementing workplace change, there has to be
some sort of call to action or ‘wake-up call’ in order to
legitimize the change and incentivize leaders to acknowl-
edge its benefit. Further, leaders must take an active role in
‘creating organizational vision, commitment, and capacity’
(Anderson and Ackerman-Anderson, 2001: 40–41). In
other words, the university’s leaders must first understand
the benefit of implementing a complex analytics system
and must then show commitment to and spearhead the
adoption of the new technologies. Furthermore, there has
to be a certain level of capacity in terms of computing
power, human capital and financial resources which must
be allocated to implement the system successfully – some-
thing that can often be difficult and can create dissent.
Finally, some concerns may arise from what the data may
indicate. More specifically, administrators may be hesitant
if they believe that the information might reveal that their
students are underperforming compared to the national
average, that their graduation rates are lower than those
of similar universities or that certain ethnic minorities are
performing below average – all of which might draw public
attention and scrutiny.
Arguments against analytics in higher education
Despite the potential of using big data analytics in univer-
sities, there are some concerns with regard to possible
unfairness in predicting students’ potential and the invasion
of student or applicant privacy. One argument against using
predictive analytics in the admissions decision process is
that the system does not take into consideration specific
circumstances, thus creating a type of automated stereotyp-
ing. Take, for example, the case of a student who performs
very poorly in one class due to a family illness or some
other serious distraction. The student will have a signifi-
cantly lower GPA due to that one class and may well not be
admitted. Another example is the student from a particular
geographical location or ethnicity that, according to the
data, tends to produce below-average performing students.
That student would automatically be disadvantaged solely
because the system had projected a performance consistent
with the dominant trend, creating a kind of systemic
adverse impact. Many would argue that this would be
unfair, unethical and possibly illegal, so there must be strict
limits on what can and cannot be incorporated into an ana-
lytics system.
Another major concern with implementing this type of
system relates to the safeguarding of information about
students and the potential invasion of privacy. Although
college classes have traditionally evaluated students on the
basis of their performance and behaviour, big data changes
the level and scope of the analytics, and so there is a need
for careful evaluation of the implications and potential
impacts (Picciano, 2012). Essentially, there must be safe-
guards in place to ensure that individuals cannot obtain
unauthorized access to the data and that the data are ‘rele-
vant to the purposes for which they are to be used, and, to
the extent necessary for those purposes’ (Watermand and
Bruening, 2014: 90). Such safeguards should include data
encryption and limited authorization to access the data.
That said, there is undeniably an ethical dilemma as to
whether the data should be collected at all, and whether
or not the benefits outweigh the costs. This will ultimately
be decided by a combination of decisions from students,
administrators and policymakers.
8Industry and Higher Education XX(X)
Deployment of analytics in higher
Today’s higher education institutions struggle to obtain
measurable value from their BI tools because of the frag-
mented nature of the data, security gaps and the confusion
caused by incomplete information (Ekowo and Palmer,
2016). Some universities have used data analysis for years,
but their analytics expertise has been contained within
small pockets of the organization. Some teams have lacked
access to analytics, and data management practices have
been inconsistent. To become a data-driven organization, a
university needs a thoughtfully designed analytics platform
that empowers everyone to make data an integral part of
their day-to-day processes and decisions. It needs an ana-
lytics solution that can bring together disparate data in a
governed environment that allows users from different
departments to model, discover, communicate and distri-
bute information easily (Ekowo and Palmer, 2017). In most
cases, universities need to look beyond their own IT staff
for assistance: A knowledgeable and experienced service
provider can provide initial and ongoing assistance to
increase the chance of analytics success (Burroughs, 2016).
A range of recently published research on the implemen-
tation of BI and predictive analytics has been reviewed to
explore the current status, issues and challenges identified
(Asllani, 2015; Ekowo and Palmer, 2017; Loshin, 2017a,
2017b; McNeill, 2014). It is argued that, before deploying
analytics process in an organization, the areas in which
analytics will add business value need to be identified and
a scalable deployment approach must be planned. We have
modified guiding practices suggested by these papers and
added more to create practical implementation steps for the
successful deployment of a predictive analytics process in a
higher education institution. The following summarizes our
recommended implementation steps.
Key factors to consider
The key attributes of a well-designed analytics system are
the following:
1. Vision and plan. Develop a vision and plan for data
use that help to steer a predictive analytics effort.
Include in your plan the questions you hope to
answer and the goals you aim to achieve. Explore
the potential pitfalls of using student data. Make
sure that data will not be used for discriminatory
purposes. Be sure to include key staff in decision-
2. Scalability. Consolidate disparate data into a shared
repository-based platform that provides scalable
self-service solutions to all decision-makers in the
organization. This facilitates the spreading of data-
driven decision-making and optimization to depart-
ments and other units in the university.
3. User-friendly interfaces. Make analytics easy to use
for everyone. All decision-makers from different
segments of the university must have access to
information normally dependent on complicated
and sophisticated analytics tools. On-demand help
and robust online guides should be built into the
system to answer any questions.
4. Up-to-date. Avoid outdated and irrelevant analy-
tics. Avoid restricting analytics edits. Conduct ana-
lytics directly on real-time data.
5. Real-time collaboration. Democratize data-driven
analytics. Avoid limiting access to analytics and
make sharing analytics and context simple for
decision-makers. Expand the use of data throughout
the university. Allow analytics users to slice the
data and answer their own questions. Ensure con-
sistency and coordination across the organization.
6. Quick installation, maintenance and upgrade.
Analytics tools can be installed in a matter of hours
or days and should be simple for the IT department
to maintain and upgrade.
7. Reliability and security. Make sure your analytics
solutions guarantee that your data are accurate,
available and audited. Create a strong partnership
with your IT department to ensure that data are
accurate. Also make sure that your analytics solu-
tions provide proper security options that allow
users to create and publish their work securely.
Finally, ensure that complete security controls and
a trail of users are available at all times.
Higher education institutions can leverage analytics to
drive a host of business objectives; however, finding the
right analytics solution for your campus can be challenging.
There is no one-size-fits-all option and no plug-and-play
device. There is a wealth of new tools that leverage analy-
tics for specific purposes, and independent standards are
being developed rapidly by vendors. Several companies are
providing analytics solutions in the education sector,
including SAP, Tableau Software, SPSS and Rapid Insight.
Table 2 provides a summary of these services.
Case examples of success
Applications in colleges and universities
Progressive higher education institutions are implementing
analytics across the student lifecycle to attract the right
students, maximize student retention and graduation rates,
gain more federal funding and improve donor relations.
Table 3 offers examples of US universities and colleges
that are already using predictive analytics to optimize key
phases of the student lifecycle and to align their resources
with their organizational goals. For each institution, the
Attaran et al. 9
analytics objectives, the processes targeted to achieve those
objectives and the benefits gained are summarized.
Most colleges do not have an adequate number of stu-
dent advisors, and so it is not possible for them to give
individual students the personalized attentiontheyneed.
Predictive analytics can help colleges to pinpoint those
students most in need of institutional support in two ways:
Early-Alert and Program Recommender Systems (Ekowo
and Palmer, 2016). Early-Alert Systems use predictive
analytics to identify at-risk students. Predictive models
can include high-school and college GPA, demographic
data, class attendance and course-taking patterns. Recom-
mender Systems, on the other hand, use predictive analy-
tics to help identify courses or programmes for students.
Predictive analytics have also been used by universities
for enrolment management (Beckwith, 2016), to identify
struggling students and streamline advising practices
(Hardee, 2016), to anticipate the financial needs of incom-
ing and returning classes and to determine whether or not
a student will accept the financial aid award offered
(Ekowo and Palmer, 2016).
In the following, we provide specific examples of data
analytics applications in US higher education institutions.
Case studies – Targeted student advising
Temple University, a public research university in Phila-
delphia, uses analytics to help identify students who are at
risk of struggling academically and in danger of dropping
out. Between 2001 and 2014, Temple’s use of predictive
analytics resulted in a 24%increase in its 4-year graduation
rate, an 11%increase in its 6-year graduation rate and an
increase of 12%in the proportion of students who returned
for a sophomore year (Felton, 2016).
Officials at Arizona State University are also using ana-
lytics to improve students’ academic experience and have
managed to boost graduation by 20%. The university is
using ‘College Scheduler’ analytics software, a platform
that enables students to enter personal information into a
dashboard. The programme considers students’ personal
and academic obligations and auto-populates the courses
they have to take. The software is valuable because it pre-
vents students from taking courses that do not count
towards their majors, thus wasting their time and financial
aid. College Scheduler has been shown to boost college
completion rates by more than 3%(Zinshteyn, 2016).
Concordia University Wisconsin (CUW) has also suc-
cessfully implemented an analytics programme to identify
at-risk students and help them out. Blackboard intelligence
and learning analytics solutions are used to ascertain how
students are developing in their academic commitments
while there are still opportunities for recovery if they are
doing poorly. Student advisors focus on student perfor-
mance and a variety of risk factors based on the data pro-
vided and with the help of dashboards. Advisors use
dashboards to support students more efficiently. The use
of data analysis has improved CUW’s retention rate by
10%: In 2016, the university had a retention rate of 72%
and a year later, using analytics along with better involve-
ment of faculty and administration, CUW reached an 82%
retention rate.
Officials at the University of Maryland, College Park,
analyse student data, including grades, demographics,
financial aid, course schedules and enrolment status, to
identify at-risk students and improve retention rates. They
use predictive analytics to intervene with struggling stu-
dents before it is too late. Analytics helps to identify bottle-
necks and problems, such as a difficult class or other
pressing issues that could lead a student to drop out (Wells,
2016). For example, one finding from this approach is that
students who enrol in a course very late tend not to perform
well in it. Therefore, the institution’s policy is now not to
let any student enrol in a class in the last few days before it
starts, although it is still possible to drop a class four days
after it has started without a penalty. The university is also
using a data tool called ‘Student Success Matrix’ that has
been developed by the non-profit Predictive Analytics
Reporting Framework. Using this tool, officials can deter-
mine whether a C grade in an introductory marketing
course indicates a low chance of the student graduating
in the major. They then use ‘intrusive advising’ where
appropriate to the student improve grades or change major.
Additionally, the university found that students who
Table 2. Analytics solutions available to educational institutions.
Analytics service
provider Analytics solutions Institution
Tableau Software Tableau California State
University Systems,
University of Texas at
Washburn University
SAP SAP HANA University of Kentucky
QlicView Olic Excelsior College
SPSS SPSS Washburn University
Rapid Insight, Inc. Rapid Insight Sarah Lawrence
Blackboard Blackboard Analytics CUW
Piedmont Community
IBM BM Predictive
Analytics software
Michigan State
SAAS Visual Analytics Sinclair Community
University of Oregon
Civitas Learning College Scheduler Arizona State
University of Arizona
Note: CUW: Concordia University Wisconsin.
10 Industry and Higher Education XX(X)
received bad grades (D–F) used Blackboard 40%less than
those who received A, B or C. Instructional technology
experts built a tool using Blackboard called ‘Check-My-
Activity’, which enables students to compare their Black-
board activity with that of anonymous classmates who
received a higher or lower grade on an assignment. This
use of feedback for intervention is already producing good
results. According to officials, students who use the Check-
My-Activity tool are nearly three times more likely to earn
at least a C grade (Wells, 2016).
Case studies – Adaptive learning
Universities use predictive analytics to develop adaptive
learning courseware which modifies a student’s learning
route to enhance and accelerate learning (Ekowo and
Palmer, 2016). One example is provided by Colorado
Technical University, a private institution that offers
undergraduate and graduate degrees primarily online. In
2012, the university adopted predictive analytics to develop
adaptive learning courseware. It used ‘Intellipath’ to assess
Table 3. Applications of analytics in American colleges and universities.
Institutions Process targeted Objectives Benefits gained
Michigan State University University advancement
Identifying potential donors and
providing deep insight into
an individual alum’s potential
to give
Improved director and associate director
productivity; improved visibility of
donor patterns; improved overall user
productivity; annual labour savings of
CUW Student performance
Identifying at-risk students and
helping them
Increased student retention rate to 82%, a
10% increase in 1 year
University of California –
Santa Barbara
University advancement
Improving visibility of who will
donate and repeat donors
Saved time and money; exponentially
increased yearly revenue from donors
Arizona State University Student performance
Improving students’ course
Graduation rates climbed by 20%
University of Maryland –
College Park
Student performance
Predicting student success or
failure and intrusive advising
Helped narrow achievement gaps for
minority and low-income students;
improved graduation rates; shortened
graduation time
Mount St Mary’s University
in Emmetsburg
Student performance
Predicting which incoming
freshmen were unlikely
to succeed
Boosted graduation rates by helping
struggling students
Georgia State University Student performance
Timely intervention for students
with high chance of dropout
Graduation rates raised by six percentage
points; eliminated achievement gaps for
low-income and minority students
Johns Hopkins University Student performance
Flagging students who are missing
assignments or skipping class
Helped increase graduation rates
University of Texas at
User management and
Timely and accurate intervention
in security threats and
incidence across the distributed
university network
Faster insight into anomalies; improved
security posture; educed organizational
risk; reduced incident investigation time
Washburn University Student performance
Retention and graduation Helped increase graduation rates
Sinclair Community College Student enrolment
Generate notification to send
to individual student
25–33% increases in enrolments year
over year
University of Oregon Financial aid programme Redesign the merit-aid
programme to recruit high
achievers more effectively
Enabled deeper insights into the
behaviour of applicants who were
accepted and offered merit aid, thus
increasing the likelihood that these
students would enrol; learned how
much merit aid is needed in a financial
aid package to make high-achieving,
in-state students more likely to enrol
Delaware State University Student enrolment
Identify students at risk and
streamline best advising
Increased student retention rate to 70%;
strived to improve 4-year graduation
rate by 4 percentage points per year
Source: Beckwith (2016), Ekowo and Palmer (2016), Hardee (2016), Felton (2016), Johnson (2016), Tissot (2017), Wells (2016), Wicom et al. (2011) and
Zinshteyn (2016).
Note: CUW: Concordia University Wisconsin.
Attaran et al. 11
what students did and did not know, and then presented
information to help them meet the course learning goals
quickly. Intellipath improved student engagement and
retention: For example, 81%of students passed the
Accounting I course, 95%completed the course and the
average grade increased from C to B (Johnson, 2016).
Summary and conclusion
Higher education institutions are operating in an increas-
ingly complex and competitive environment. This article
has identified some of the challenges they face and has
explored the potential of analytics to address these chal-
lenges. In addition, the article uses the three lifecycle stages
of student engagement and proposes a conceptual frame-
work for applications of analytics in higher education.
As has been shown, US colleges and universities can use
big data and data analytics in a variety of ways to help them
make better decisions. More specifically, big data analytics
can help enrolment management personnel with their
admission decisions, enable university professionals to
identify students who are in need of campus resources and
help universities to gain more funding through donations.
That said, because only a small proportion of universities
currently use data analytics, there will be a significant need
for standards and best practices when more universities
have introduced these types of programmes. Implementing
such programmes may encounter resistance from those
who do not think that the benefits justify the investment.
For a smooth and effective implementation, therefore, it is
essential that the university’s leaders are all committed to
the initiative and ready to support its development. In addi-
tion, lines will have to be drawn with regard to how much
data universities can collect and, specifically, to what uses
it can be put. Although collecting data to help students
succeed in classes is probably a good idea, collecting
excessive data with a lack of strict usage controls might
be seen as violating student rights and privacy. That said,
examples of successful implementation such as those pro-
vided by CUW and the University of Maryland suggest that
the effort and investment are well worthwhile.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
The author(s) received no financial support for the research,
authorship, and/or publication of this article.
Abbott D (2014) Applied Predictive Analytics: Principles and
Techniques for the Professional Data Analyst. Hoboken, NJ:
John Wiley & Sons.
Adams C (2014) Essay-grading software seen as time-saving tool.
Education Week, 10 March 2014. Available at: https://www.
(accessed 19 March 2018).
Anderson D and Ackerman-Anderson LS (2001) Beyond Change
Management: Advanced Strategies for Today’s Transforma-
tional Leaders. San Francisco, CA: Jossey-Bass/Pfeiffer.
Asllani A (2015) Business Analytics with Management Science,
Models and Methods. Upper Saddle River, NJ: Pearson
Attaran M (2017) Cloud computing technology: leveraging the
power of the Internet to improve business performance. Inter-
national Journal of Information Technology and Management
26(1): 112–137.
Attaran M, Attaran S and Celik BG (2017) Promises and chal-
lenges of cloud computing in higher education: a practical
guide for implementation. Journal of Higher Education The-
ory and Practice 17(6): 20–38.
Bange C, Grosser T and Janoschek N (2015) Big Data Use
Cases: Getting Real on Data Monetization. BARC Research
Study. Available at:
pdf (accessed 19 March 2018).
Basu A (2013) Five pillars of prescriptive analytics success.
Analytics Magazine, 8–12 March–April. Available at: http://
scriptive-analytics-success/ (accessed 20 March 2018).
Bayrak T (2015) A review of business analytics: a business
enabler or another passing fad. Procedia – Social and Beha-
vioral Sciences 195: 230–239.
Beckwith S (2016) Data analytics rising in higher education: a
look at four campus ‘data czars’ and how they’re promoting
predictive analytics. University Business Magazine. Available
rising-higher-education (accessed 19 March 2018).
Bughin J (2016) Big data, big bang? Journal of Big Data 3(2).
Available at:
cles/10.1186/s40537-015-0014-3 (accessed 19 March 2018).
Burns E (2016) How PayPal fights fraud with predictive data
analysis. Available at: http://searchbusinessanalytics.techtar
data-analysis (accessed 19 March 2018).
Burroughs A (2016) Survey: data and analytics in higher ed can
be a one-two punch. EdTech Magazine. 8 April 2016.
Available at:
two-punch (accessed 19 March).
Daniel B (2014) Big data and analytics in higher education:
opportunities and challenges. British Journal of Educational
Technology 46(5): 904–920.
Davenport T and Dyche J (2013) Big data in big companies.
International Institute for Analytics. Available at: http://
pdf (accessed 19 March 2018).
12 Industry and Higher Education XX(X)
DeAngelis SF (2015) Predictive analytics becoming a mainstream
business tool. Available at:
tool/ (accessed 19 March 2018).
DeZyre (2017) How big data analysis helped increase Walmart’s
sales turnover? Available at:
over/109 (accessed 19 March 2018).
Diebold FX (2012) On the origin(s) and development of the term
‘Big Data’. PIER Working Paper 12-037. Penn Institute for
Economic Research, Department of Economics, University of
Pennsylvania. Available at:
sites/ (accessed 19
March 2018).
Eckerson W (2016) Embedded Analytics: The Future of Business
Intelligence. Hingham, MA: Eckerson Group.
Eduventures (2013) Predictive analytics in higher education: data-
driven decision-making for the student life cycle. Available at:
sen/YTL03202USEN.PDF (accessed 19 March 2018).
Eiloart J (2017) Top trends in business intelligence for 2017.
Available at:
trends-in-business-intelligence-for-2017/ (accessed 19 March
Ekowo M and Palmer I (2016) The Promise and Peril of Predic-
tive Analytics in Higher Education. New America report.
Available at:
ments/Promise-and-Peril_4.pdf (accessed 19 March 2018).
Ekowo M and Palmer I (2017). Predictive Analytics in Higher
Education: Five Guiding Practices for Ethical Use.New
America report. Available at: https://na-production.s3.amazo
pdf (accessed 19 March 2018).
Evelson B and Bennett M (2015) Quantify the Tangible Business
Value of BI. Forrester Research report. Available at: http://
ble-Business-Value-of-BI.pdf (accessed 19 March 2018).
Felton E (2016) Temple University is spending millions to get
more students through college, but is there a cheaper way? The
Hechinger Report, 18 May 2016. Available at: http://hechin
dents-college-cheaper-way/ (accessed 19 March 2018).
Gaitho M (2017) How applications of big data drive industries.
Available at:
tions-in-industries-article (accessed 19 March 2018).
Gandomi A and Haider M (2015) Beyond the hype: big data
concepts, methods, and analytics. International Journal of
Information Management 35(2): 137–144.
Gartner (2015) Gartner survey shows more than 75 percent of
companies are investing or planning to invest in big data in
the next two years. Gartner, Press Release, 16 September
2015. Available at:
3130817 (accessed 19 March 2018).
Gewirtz D (2016) ‘Volume, velocity, and variety: understanding
the three V’s of big data. Available at:
vs-of-big-data/ (accessed 19 March 2018).
Groenfeldt T (2012) Big data – big money says it is a paradigm
buster. Forbes. Available at:
paradigm-buster/#6120d0bfe389 (accessed 6 January 2012).
Hardee T (2016) Better by the numbers. Available at: http://www.
numbers#/h (accessed 20 March 2018).
Henke N, Bughin J, Chui M, et al. (2016) The Age of Analytics:
Competing in a Data-Driven World. McKinsey Global Insti-
tute Report. Available at:
analytics-competing-in-a-data-driven-world (accessed 19
March 2018).
Hussar WJ and Bailey TM (2013) Projections of Education Sta-
tistics to 2022. NCES 2014-051, US Department of Education,
National Center for Education Statistics. Washington, DC: US
Government Printing Office.
Intel (2017) Guide to Getting Started with Advanced Analytics.
Intel Planning Guide. Available at:
planning-guide.html (accessed 19 March 2018).
Johnson C (2016) Adaptive learning platforms: creating a path for
success. Educause Review, 7 March 2016. Available at: http://
creating-a-path-for-success (accessed 19 March 2018).
Kalakota R (2014) A primer on predictive analytics. Available at:
mer-on-Predictive-Analytics (accessed 19 March 2018).
Lebied M (2017) Top 11 business intelligence and analytics
trends for 2017. Available at:
trends-for-2017/ (accessed 19 March 2018).
Loshin D (2017a) Five steps to build better predictive analytics
applications. Available at:
applications (accessed 19 March 2018).
Loshin D (2017b) Ten steps to start using predictive analytics
algorithms effectively. Available at: http://searchbusinessana
analytics-algorithms-effectively (accessed 19 March 2018).
Lustig I, Dietrich B, Johnson C, et al. (2010) The analytics
journey. Analytics Magazine, 11–18 November/December.
Available at:
ney/ (accessed 20 March 2018).
Marr B (2015) Big data: 20 mind-boggling facts everyone must
read.’ Forbes, 30 September 2015. Available at: https://www.
boggling-facts-everyone-must-read/#56a1b34e17b1 (accessed
19 March 2018).
Mashingaidze K and Backhouse J (2017) The relationships
between definitions of big data, business intelligence and busi-
ness analytics: a literature review. International Journal of
Business Information Systems 26(4): 488–505.
Attaran et al. 13
McAfee A and Brynjolfsson E (2012) Big data: the management
revolution. Harvard Business Review, October. Available at:
(accessed 19 March 2018).
McGuirt M, Gagnon D and Meyer R (2015) Embracing Technol-
ogy: 2015–2016 Higher Education Industry Outlook Survey.
KPMG report. Available at:
2016.pdf (accessed 19 March 2018).
McNeill D (2014) Analytics in Healthcare and the Life Sciences:
Strategies, Implementation Methods and Best Practices.
Upper Saddle River, NJ: Pearson.
Minelli M, Chambers M and Dhiraj A (2013) Big Data, Big Ana-
lytics: Emerging Business Intelligence and Analytic Trends for
Today’s Businesses. Hoboken, NJ: John Wiley & Sons.
NCSL (2015) Performance-based funding for higher education.
In: National Conference of State Legislators, 31 July 2015.
Available at:
mance-funding.aspx (accessed 19 March 2018).
Nisar MA (2015) Higher education governance and performance
based funding as an ecology of games. Higher Education
69(2): 289–302.
Picciano AG (2012) The evolution of big data and learning ana-
lytics in American higher education. Journal of Asynchronous
Learning Networks 16(3): 9–20.
Rosenbush S and Stevens L (2015) At UPS the algorithm is the
driver. The Wall Street Journal, 16 February 2015. Available
driver-1424136536 (accessed 19 March 2018).
Roy KA (2011) Analyse this! Analytics explained and applied.
Available at:
lyse-this-analytics-explained-and-applied/ (accessed 19
March 2018).
Salleh S (2013) Applying predictive analytics in enterprise deci-
sion making. Available at:
(accessed 19 March 2018).
Scott A (2016) Colleges tap big data to help students in school.
Available at:
tion/tapping-big-data-help-college-students-succeed (accessed
19 March 2018).
Shapiro D, Dundar A, Chen J, et al. (2012) Completing College: A
National View of Student Attainment Rates. Signature Report
No 4. Herndon, VA: National Student Clearinghouse Research
Center. Available at:
Signature_Report_4.pdf (accessed 19 March 2018).
Shapiro D, Dundar A, Yuan X, et al. (2014) Some College, No
Degree: A National View of Students with Some College Enroll-
ment, but No Completion. Signature Report No 7. Herndon,VA:
National Student Clearinghouse Research Center. Available at:
ture_Report_7.pdf (accessed 19 March 2018).
Siegel E (2016) Predictive Analytics: The Power to Predict Who
Will Click, Buy, Lie, or Die. Hoboken, NJ: John Wiley & Sons.
Stedman C (2017) Eyeing the future with predictive analytics
can pay dividends now. Available at: http://searchbusinessa
tics-advances-businesses-ahead-of-the-game (accessed 19
March 2018).
Tan HK, Zhan YY, Guojun J, et al. (2015) Harvesting big data to
enhance supply chain innovation capabilities: an analytic
infrastructure based on deduction graph. International Journal
of Production Economics 165: 223–233.
Taylor P (2012) Crunch time for big data. Financial Times,19
June 2012. Available at:
bd5a5ce2-aa57-11e1-899d-00144feabdc0 (accessed 19 March
Tissot L (2017) Concordia University Wisconsin: how to achieve
a record retention rate. E-Learn, February 2018: 29–32. Avail-
able at:
(accessed 23 March 2018).
Wagner E and Hartman J (2016) Welcome to the era of big data
and predictive analytics in higher education. Available at:
(accessed 19 March 2018).
Waterman KK and Bruening PJ (2014) Big data analytics: risks
and responsibilities. International Data Privacy Law 4(2):
Weiss SM and Indurkhya N (1998) Predictive Data Mining: A
Practical Guide. Burlington, MA: Morgan Kaufmann.
Wells C (2016) Maryland universities to use data to predict stu-
dent success – or failure. The Baltimore Sun, 11 June 2016.
Available at:
(accessed 19 March 2018).
Wicom B, Ariyachandra T, Goul M, et al. (2011) The current state
of business intelligence in academia. Communications of the
Association for Information Systems 29(16): 299–312.
YanoskyRandArrowayP(2015)The Analytics Landscape in
Higher Education. Louisville, CO: Educause Center for
Analysis and Research. Available at:*/media/files/library/2015/5/ers1504cl.pdf
Zinshteyn M (2016) The colleges are watching. Atlantic Daily,
1 November 2016. Available at: https://www.theatlantic.
506129/ (accessed 20 March 2018).
Zwilling M (2016) 10 ways to use analytics to supercharge your
business. Huffpost – The Blog, 17 February 2016. Available at:
use-analytics_b_9254166.html (accessed 19 March 2018).
14 Industry and Higher Education XX(X)
... Reference Types Methods Descriptions Strength Weakness [9], [50], [51], [70] Streaming processing Apache Spark streaming Discretized stream, supports machine learning and graph processing algorithms Very fast, automatic parallelization, fault-tolerant for stream processing time-consuming, and lack of file management system [95] Streaming processing manner, from varieties of domain sources such as education data [1], [97]- [100], IoT / sensor data 902 [101]- [103], Social media data [104]- [108], Multimedia data [109]- [111], Text data [7], [112], [113], 903 ...
... shown in Fig. 8 while Table 6 for extra-curricular, alumni etc. [15], [100]. Recently, various studies have utilized the educational data 929 to improve and enhance educational institution and student performances, for instance, Attaran [97] 930 presents a conceptual model for successful implementation of big data analytics in higher education 931 using US higher education as a case study. Higher institutions of learning can be transforming from 932 well harnessed data generated from various institution activities such as enrolment, student support, 933 alumni engagement, financial aid administration, other learning and operational functions. ...
... 1043 are utilized for efficient big educational data analysis. Also, Attaran et al. [97] Big data analytics helps in recording and analyzing sound, long-term recovery results and further 1094 predict recovery process of patients through some enabling sensor devices such as ECG, PPG, and 1095 EEG etc. Heterogeneous data are generated from clinic settings, laboratory, pharmaceutical, sensors, 1096 and hospital management [2], [114]. Also, large dataset are generated from hospital, clinic health 1097 center and medical care, electronic medical records (EMRs) and imaging data are formed from clinic 1098 center, pharmaceutical EMR data, personal practices and preferences data, and financial records data. ...
Full-text available
The study of big data analytics (BDA) methods for the data-driven industries is gaining research attention and implementation in today’s industrial activities, business intelligence, and rapidly changing the perception of industrial revolutions. The uniqueness of big data and BDA has created unprecedented new research calls to solve data generation, storage, visualization, and processing challenges. There are significant gaps in knowledge for researchers and practitioners on the right information and BDA tools to extract knowledge in large significant industrial data that could help to handle big data formats. Notwithstanding various research efforts and scholarly studies that have been proposed recently on big data analytic processes for industrial performance improvements. Comprehensive review and systematic data-driven analysis, comparison, and rigorous evaluation of methods, data sources, applications, major challenges, and appropriate solutions are still lacking. To fill this gap, this paper makes the following contributions: presents an all-inclusive survey of current trends of BDA tools, methods, their strengths, and weaknesses. Identify and discuss data sources and real-life applications where BDA have potential impacts. Other main contributions of this paper include the identification of BDA challenges and solutions, and future research prospects that require further attention by researchers. This study provides an insightful recommendation that could assist researchers, industrial practitioners, big data providers, and governments in the area of BDA on the challenges of the current BDA methods, and solutions that would alleviate these challenges.
... Table 1. Current use of data in universities and higher-education institutions [4][5][6]. ...
... EC.5.1, EC.2.4, ...
... Appendix E. 5 ...
Full-text available
The use of data in decision-making has become prevalent in all sectors, including education. The present paper analyses the steps necessary for a university to become a data-driven organisation and the advantages this transformation has to offer, both in teaching and in management. A qualitative case study methodology was used with a thematic inductive analysis with two groups of participants. The results are a methodology for transformation, identifying the barriers that may arise and actions necessary to overcome them and the advantages the use of data has to offer the university.
... Numerous educational technologies have been suggested to improve teaching and student learning [7][8][9][10]. Research work has developed novel devices (like RFID-based attendance monitoring, remote labs, classroom management, etc.) [9,11], student performance analysis [7,10], learning resource suggestion [7], targeted student advising [8,12], adaptive learning [8], course recommendation [8], campus management [9], enrollment management [8]. Another school of thought considers the insight gained by psychologists on the cognitive processes during learning [13][14][15]. ...
... {∆i,e} (k+s) ⊆ {∆i,e} (k) (12) {∆i,m} (k+t) ⊆ {∆i,m} (k) (13) k + p, k + s, k + t ≤ max iteration, ∀k, p, s, t Figure 6 illustrates the process that addresses equations (11)- (14). Figure 6(a) shows the iterative procedure that adjusts the discriminator network's estimation δ ′ of the generator network's misunderstanding δ. ...
Full-text available
Student diversity, like academic background, learning styles, career and life goals, ethnicity, age, social and emotional characteristics, course load and work schedule, offers unique opportunities in education, like learning new skills, peer mentoring and example setting. But student diversity can be challenging too as it adds variability in the way in which students learn and progress over time. A single teaching approach is likely to be ineffective and result in students not meeting their potential. Automated support could address limitations of traditional teaching by continuously assessing student learning and implementing needed interventions. This paper discusses a novel methodology based on data analytics and Machine Learning to measure and causally diagnose the progress and shortcomings of student learning, and then utilizes the insight gained on individuals to optimize learning. Diagnosis pertains to dynamic diagnostic formative assessment, which aims to uncover the causes of learning shortcomings. The methodology groups learning difficulties into four categories: recall from memory, concept adjustment, concept modification, and problem decomposition into sub-goals (sub-problems) and concept combination. Data models are predicting the occurrence of each of the four challenge types, as well as a student's learning trajectory. The models can be used to automatically create real-time, student-specific interventions (e.g., learning cues) to address less understood concepts. We envision that the system will enable new adaptive pedagogical approaches to unleash student learning potential through customization of the course material to the background, abilities, situation, and progress of each student; and leveraging diversity-related learning experiences.
... Certain parties, on the other hand, are still unwilling to engage with BD as a result of the application's specific constraints. Example: BD sets are frequently too enormous to be collected, stored, and analyzed correctly using typical database approaches [5,2], and there is frequently no consistent structure for the data that has been gathered. According to Reference [6], consumers have evolved into a "continuous initiator of both structured, transactional data as well as contemporary unstructured behavioral data." ...
... It is the goal of this study to further add to the existing research on BD by analyzing the application of BD analytics in HEd institutions. number of useful suggestions and propositions offered to ensure that HEd institutions can flourish in every way possible which can be summed up under the category of increased transparency and accountability involving all stakeholders including students, parents, taxpayers, and others [5] It is important to understand that using BD or LA in HEd is the best method of not only accumulating but also evaluating data regarding the advancement of the students and the educational environment. It not only offers educational content information to instructors, but identifies activities that improve teaching and evaluation procedures [2]. ...
Students, faculties, and other members of the higher education (HEd) system are increasingly reliant on various information technologies. Such a reliance results in a plethora of data that can be explored to obtain relevant statistics or insights. Another reason to explore the data is to acquire valuable insight regarding the novel unstructured forms of data that are discovered and often found to have a connection with elements of social media such as pictures, videos, Web pages, audio files, etc. Moreover, the data can bring additional valuable benefits when processed in the context of HEd. When used strategically, Big Data (BD) provides educational institutions with the chance to improve the quality of education from all the perspectives and steer students of HEd toward higher rates of completion. Further, this will improve student persistence and results, all of which are facilitated by technology. With this aim, the current research proposes a framework that analyzes the data collected from heterogeneous sources and analyzes using BD analytics tools to do various types of analysis that will be beneficial for different learners, faculties and other members of HEd system. Moreover, current research also focuses on the challenges of acquiring BD from various sources.
... One of the evaluation criteria for the success of higher education is the employment situation of students, in which the employment information management of students is one of the important work contents in the employment process. With the arrival of the era of big data, the information construction of college students' employment work faces new requirements [1]. It is extremely important to strengthen the information construction of employment services by effectively using network technology, explore a new road of employment service information suitable for its development, and help students to be employed faster, better, and more harmoniously. ...
Full-text available
This paper conducts an in-depth research analysis on the precise employment of college graduates in the context of big data using a number-driven approach. The textual information of the study is obtained by using in-depth interviews, and the evaluation index system of college students’ employment quality is constructed by combining the step-by-step coding method with rooting theory. The research on the current situation of employment recommendation platform research and the application status of big data in the employment recommendation platform is explored by using a bibliometric approach. And the innovative use of web crawler technology is used to comprehensively understand the recommendation function and status quo of the same type of recommendation platform, which provides a reference for the research of this platform. Based on the preliminary analysis of platform requirements and overall design, the overall design and functional implementation of the big data employment recommendation platform are carried out by using big data crawler technology, big data architecture technology, text mining technology, database technology, etc. The construction of a recommendation module based on user history information, a recommendation based on real-time user online behavior data, and hybrid recommendation carried out on the recommendation module to grasp all-round the platform is built based on a stakeholder perspective. Based on the platform construction, the initial platform operation and maintenance management mechanism was established from the stakeholder’s perspective. The Pearson correlation coefficient is used to objectively evaluate the current situation of talent supply in universities and talent demand in enterprises from the perspective of image and data. In the research on the development status of the big data education industry, the Lorenz curve and Gini coefficient are used to match the status of new big data majors with their college construction volume in each province and provide data support for the reasonable adjustment of majors setting in each province according to the education level. 1. Introduction One of the evaluation criteria for the success of higher education is the employment situation of students, in which the employment information management of students is one of the important work contents in the employment process. With the arrival of the era of big data, the information construction of college students’ employment work faces new requirements [1]. It is extremely important to strengthen the information construction of employment services by effectively using network technology, explore a new road of employment service information suitable for its development, and help students to be employed faster, better, and more harmoniously. And as there is increased employment recommendation research to focus on user behavior data analysis and text mining, the use of big data technology to deal with the employment recommendation problem has received increased attention in both theoretical research and practical application. Recommendation platforms based on recommendation models are the most effective personalization techniques in the era of big data. However, conventional recommendation means tend to simply analyze and model related to users’ online behavior, while ignoring the development of student users’ attribute characteristics. Most of the activities performed by student users on the Internet are similar, and it is obvious that the way to classify students’ attributes and build recommendation models by simple user’s online behavior does not achieve the expected effect [2]. With the widespread use of social networks in recommendation, and the continuous research on students’ school data, how to effectively use these unstructured data as the constituent attributes of user-profiles in recommendation models will be the key and difficult point to promote the development of personalization technology in the era of big data. In terms of theory, it combines stakeholder theory and platform-related theory and fully explores the big data-related theory, while using bibliometric research methods to review the big data employment recommendation platform and big data employment recommendation algorithm, to comprehensively understand the development status and development trend of employment recommendation platform and employment recommendation algorithm in the context of big data. Big data not only constructs the platform construction technically, but also takes the management strategies such as big data opening and related theories as guidance and fully applies them to the platform operation and maintenance management, which realizes the effective combination of theoretical research and practice [3]. This provides effective theoretical support for the big data employment recommendation platform and employment recommendation algorithm as well as the operation and maintenance management aspect of the big data employment platform. In practice, the research on the construction of employment recommendation platforms and employment information recommendation combined with big data technology is a relatively new research direction, and the employment recommendation platform will be landed under the guidance of theoretical research, and the big data employment recommendation platform in a good operation state will be a new direction of employment recommendation platform research in the era of big data, and it will be a reference for the traditional recruitment industry on how to effectively use the Internet and link big data to better improve employment [4]. It will serve as a reference and guidance for the traditional recruitment industry on how to effectively use the Internet and link big data to better improve the quality and efficiency of employment recommendation. With the popularization of the Internet, increased enterprises are releasing their recruitment information through the medium of recruitment network platform, and the emergence of network recruitment form can effectively solve the problem of the limitations of the existing data statistics, which is also the information channel that can best reflect the market demand for talents; the information is concentrated on the web, with a large amount of data [5]. How to effectively use network technology to strengthen the construction of employment service informatization, explore a new path of employment service informatization suitable for their own development, and help students get faster, better, and more harmonious employment is of extremely important significance. The first is to construct an employment quality evaluation model, which helps college students improve their employment quality by their self-test. This paper obtains textual information through in-depth interviews, combines it with rooting theory to screen indicators of employment quality influencing factors, and, on this basis, constructs an employment quality evaluation model of college students in the new economic background, which can evaluate the employment quality of college students more comprehensively by studying the factors influencing the employment quality of college students from different research perspectives. Secondly, it is to provide a reference for teaching in colleges and universities and provide a reference basis for government policies. By constructing the evaluation model of college students’ employment quality in the context of the new economy and conducting empirical analysis, it is more targeted to analyze the level of college students’ employment quality in Jinan City and the influence indexes, which can provide a reference for the teaching content and curriculum of colleges and universities and provide the basis for government policies. 2. Current Status of Research With the employment of college students changing from the state allocation in the planned economy to the two-way selection in the employment market, the employment situation of college students was once increasingly severe, and employment guidance services have been established to effectively help college students in employment, while information technology and network technology, to promote employment, play an important role in the employment guidance services [6]. Therefore, many scholars began to actively focus on and study the problem of information construction of employment work [7]. It is proposed that colleges and universities should provide information platforms, fully integrate information resources, establish employment information networks connecting various regions, institutions, and industries, and realize information sharing, to effectively establish information communication channels among colleges and universities, employers, and graduates [8]. At the same time, related literature also points out that there are problems in the current informatization construction of employment services, proposing that the construction of employment informatization in colleges and universities lacks overall system planning and the lack of linkage mechanism among colleges and universities, resulting in poor channels of employment information dissemination [9]. It is proposed that the employment informatization construction of colleges and universities is still irregular and out of place, which leads to the lack of good information support and helps graduates in their job search. The necessity of colleges and universities to strengthen the requirements of information construction of college students’ employment services and integrate and share employment information resources is proposed [10]. Because of the current situation faced by the employment information construction of college students, related scholars have studied the application of big data in college employment, which can not only improve the employment rate of schools but also help graduates find better employment by collecting students’ job-seeking intention, professional assessment, personality analysis, career evaluation, and other information, and comprehensively analyzing and correspondingly matching them with the scattered, complex, and huge recruitment information in the social environment [11]. It is considered that, with the continuous improvement of the level of information construction of higher education, universities have accumulated certain data information in the management process and should better serve the employment of graduates through information technology, and these studies provide support for strengthening the information construction of college students’ employment services [12]. After the theoretical exploration of algorithm based on the framework of big data technology [13], the mature research and application practice of big data in technology and the full combination with algorithms have enhanced the possibility and efficiency of big data in solving real-world problems. And in the context of research on big data technology and applications, it is feasible and efficient to study graduate employment recommendation platforms with the perspective of big data technology as a background and technical support. Collecting the theory, the history and development of stakeholder theory are described in detail, further classifying the stakeholders according to different dimensions as primary stakeholders, secondary stakeholders, intended stakeholders, and potential stakeholders. And Mitchell’s scoring method was developed to formulate stakeholders. Mitchell’s scoring method emphasizes three attributes of stakeholders, namely, urgency, power, and legitimacy. Urgency refers to the ability of a group to immediately attract the attention of managers, and it emphasizes the degree to which a group is valued by the platform; power refers to the ability of a group to influence the decision making ability of managers, and it emphasizes the impact of a group’s behavior on the platform thereby causing decision-makers to make decisions about the direction of the platform, and legitimacy refers to the group being given a specific claim on the firm’s resources, and it emphasizes the ability of a group to take up resources in the platform. 3. Analysis of the Precise Employment Situation of Digitally-Driven College Graduates 3.1. Big Data Digitally Driven Algorithm Design The data is crawled according to certain rules for web pages with timed and untimed data. Considering the timeliness and a large amount of data, this study selects the data of full-time section and internship section in the campus recruitment section of WiseLinks Recruitment and MileagePlus, takes software engineering as an example to crawl the data of enterprises’ requirements for software engineering jobs on the platforms of WiseLinks Recruitment and MileagePlus, and conducts relevant text analysis to guide the extraction of keywords of information related to graduates and the matching of recommendation algorithms at a later stage [14]. Overall, in terms of recommendation system, the recommendations of popular search, popular industry, popular enterprise, and popular position are more used in the recommendation section of each job search platform, which also reflects that the core of both college employment platform and general employment platform for job recommendation is focused on the recommendation of industry, enterprise, and position [15]. These three aspects of employment recommendations represent the basic needs of today’s employment class platform recommendation system. At the same time, they have different recommendation preferences in three aspects: popular online application, popular internship, and popular presentation. From the platform side, there is a big difference in the operation and solution of recommendation systems between different platforms, and the homogeneity of the operation of the recommendation system is obvious in the same type of platform. In the missing value processing after the data deduplication process, common data duplication generally includes two kinds, one for the record duplication (that is, there are several records about a certain or some characteristics of the value, which are the same), and the other for the characteristics of duplication (that is, there is a certain or some characteristics of the value, which are the same, but the name of the data features is different). Due to crawler technology reasons, or because the site itself may produce duplicate records, we take the above duplicate records to delete duplicate records to retain only one operation; this data duplication problem only includes the record duplicate type; we traverse the data; the call can complete the data deduplication. Because there is no association between the data, so the data cleaning only after the table misalignment check, due to the absence of information part of the subsequent data forward to deal with the misalignment of the table part of the phenomenon, first determines whether the missing information is the position; if the position information is complete, it is not processed, and vice versa, to delete the data column; this can end the data preprocessing operations, finishing the month as the time unit of the enterprise demand base data table, as shown in Table 1. Attribute name Type Value XH Int 3 XYCJSJ Int 6 HTQX Int 8 DWZGBM Int 113
... The risk assessment for an organization consists of using the past experience and the knowledge in identifying potential risks, and the possible impacts to the success of the business [1], [2] and [3]. Big Data Analytics has been used for effective risk management in energy industry [4], banking [5], engineering and construction [6], business operations [7], and higher education [8]. However, in many such areas, as mentioned in their studies, it is at a nascent or early stage. ...
Full-text available
Business leaders around the world are using emerging technologies to capitalize on data, to create business value and to compete effectively in a digitally driven world. Among them the risk assessment and the risk management, based on the assessment is a process which can be made using the available past historical data and applying Data Analytics. Although it is being implemented in different business domains, it is at a nascent stage. It is further new and emerging in the area of Education. This paper describes such a process followed in an educational institution of an engineering college and the use of data for risk management. Based on the processes followed, the performance of the students is seen to be improving in academic performance, placement, higher education and entrepreneurship. This also provides a good process and framework for taking strategic initiatives which will give long term benefits in the areas like research and outreach activities.
... BDA helps organisations to fully utilise the data and discover new business opportunities. It assists firms in enhancing the efficiency of their business operations, gaining higher profit margins and achieving higher levels of customers' satisfaction (Attaran et al., 2018). Through the interview of more than 50 business owners in the year 2018, Davenport found that businesses are able to acquire high levels of BDA adoption that includes cost reduction, timely and precise decision making, and rapid promotions of new products and services (SAS, 2018). ...
The availability of big data at universities enables the use of artificial intelligence (AI) systems in almost all areas of the institution: from administration to research, to learning and teaching, the use of AI systems is seen as having great potential. One promising area is academic performance prediction (APP), which is expected to provide individual feedback for students, improve their academic performance and ultimately increase graduation rates. However, using an APP system also entails certain risks of discrimination against individual groups of students. Thus, the fairness perceptions of affected students come into focus. To take a closer look at these perceptions, this chapter develops a framework of the “perceived fairness” of an ideal-typical APP system, which asks critical questions about input, throughput and output, and based on the four-dimensional concept of organizational justice, sheds light on potential (un-)fairness perceptions from the students' point of view.
Higher education systems (HES) have become increasingly absorbed in applying big data analytics due to competition as well as economic pressures. Many studies have been conducted that applied big data analytics in HES; however, a systematic review (SR) of the research is scarce. In this paper, the authors conducted a systematic mapping study to address this deficiency. The qualitative and quantitative analysis of the mapping study resulted in highlighting the research progression over the last decade, and identification of three major themes, 12 subthemes, 10 motivation factors, 10 major challenges, three categories of tools and support techniques, and 16 models for applying big data analytics in higher education. This result contributes to the ongoing research on applying big data analytics in HES. It provides a better understanding of the level of contribution to research as well as identifies gaps for future research direction.
Full-text available
Dynamic education environment have forced universities to adopt state-of-the-art practices to optimize both the cost and operational efficiency of their information technology platform. Cloud Computing Technology (CCT) has emerged as a meaningful technology that could contribute to this optimization by providing infrastructure and software solutions for the whole IT needs of a university via Internet. It is predicted that 2017 will mark the rapid proliferation of educational institutions transitioning to the cloud-based computing technology. This study presents the main principles and potentials of the cloud, as applied to the world of education. It discusses potential strategic benefits of this technology in education, and highlights its evolving trends. Furthermore, this study highlights key adoption factors and illustrates some of the routs that might be taken to implement cloud technology in education. Finally, this study highlights successful implementations of cloud based technology in two universities, Bryant and Roger Williams.
Full-text available
In recent years, Cloud Computing Technology (CCT) has emerged as a meaningful technology that could contribute to operational efficiency of an IT platform by providing infrastructure and software solutions for the whole IT needs of an enterprise via Internet. The cloud has revolutionized IT infrastructure. It is predicted that 2017 will mark the rapid proliferation of enterprises transitioning to the cloud-based computing technology. The utilization of this innovative technology makes collaboration easier among companies and has the potential to create financial and operational benefits. This study discusses potential strategic benefits of this technology, highlights its evolving technologies and trends and their future impact, reviews different phases necessary to deploy the technology, highlights key adoption factors, and surveys its potential application in different industries.
Full-text available
Current trends suggest that academia may be behind the curve in delivering effective Business Intelligence programs and course offerings to students. In December 2009 and 2010, the AIS Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) and the Teradata University Network (TUN) cosponsored the Business Intelligence Congresses and conducted surveys to improve the understanding of the state of BI in academia. This panel report describes the key findings and best practices that were identified. The article also serves as a "call to action" for universities regarding the need to close a widening gap between the BI skills of university graduates in Information Systems and other fields and BI market needs. The IS field is well positioned to be the leader in creating the next generation BI workforce. To do so, it is important for IS to begin moving on this opportunity now. We believe the necessary first step is for BI and IS leaders to advance the BI curriculum.
Full-text available
Business Analytics has emerged as a potential business enabler in both public and private sectors and is one of the fastest growing fields. By implementing Business Analytics initiatives in their organizations, decision makers can integrate disparate data sources, predict trends, improve performance, see key performance indicators, identify business opportunities, and make better and informed decisions. The purpose of this study is twofold: first, it provides a working definition, background, and a review of Business Analytics (BA) / Business Intelligence (BI) / Big Data (BD) theory and practice. Secondly, it discusses if BA/BI/BD is another passing fad or a business enabler.
Full-text available
Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
Advances in technology and increasing data volumes have led to new tools and techniques to exploit data and improve decision-making. Terms used to refer to these tools and techniques include big data (BD), business intelligence (BI) and business analytics (BA). Definitions of these terms are not agreed on. In particular, the terms are used as subsets or special cases of each other and this leads to confusion about their meaning and how they relate. This paper examines both academic and practitioner literature that deals with two or more of the terms to synthesise definitions and describe the relationships between them. BD is data with high volume, variety of sources and high velocity. BI is a set of tools and techniques to use data for decision-making. BA is an advanced form of BI. BD is data that can be used in both BI and BA.