Big Data Analytics: Enhancing Academic Performance in Higher Education

This research report investigates the application of big data analytics to improve student academic performance in higher education. The report begins by defining big data analytics and its benefits, such as uncovering hidden patterns, improving operational efficiency, and enhancing customer services. It then explores the opportunities that big data analytics provides in higher education, including easier data accessibility, cost savings, time savings, improved services, and customization of programs. The report also addresses the challenges associated with using big data analytics, such as data security, data quality concerns, and potential issues with automation. Finally, the report provides relevant solutions to these challenges, aiming to make the higher education sector more effective and student-centric. The report emphasizes the potential of big data to transform education, with the goal of improving student outcomes and creating more efficient and effective learning environments.

Running head: BIG DATA ANALYTICS
Big Data Analytics to Improve Student Academic Performance in Higher Education
Name of the Student
Name of the University
Author’s Note:

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Abstract
The objective of this research report is to understand the importance of big data analytics in
higher education sector for better improvement of academic performances for the students.
With the core ability of gauging all types of customer requirements and satisfaction through
significant analytics, the respective business gets the significant power of providing the
clients, as per their demands. Big data analytics help the organizations in creating new and
varied products for gaining high competitive advantages. The business can easily rely on the
technology for undertaking agile and quicker decisions and staying competitive for getting
involved in the distinct market. The research report has identified five distinctive
opportunities, with their challenges and relevant solutions to resolve these issues for making
the higher education sector much more effective.

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Table of Contents
1. Introduction............................................................................................................................3
2. Discussion..............................................................................................................................4
2.1 Opportunities obtained by Big Data Analytics for Improving Academic Performances
in Higher Education...............................................................................................................4
2.2 Challenges faced by using Big Data Analytics for Improving Academic Performances
in Higher Education...............................................................................................................8
2.3 Relevant Solutions to the Challenges faced by using Big Data Analytics for Improving
Academic Performances.......................................................................................................11
3. Conclusion............................................................................................................................13
References................................................................................................................................15

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1. Introduction
Big data analytics can be termed as the complex procedure for examining larger
varied sets of data for the core purpose of uncovering information like unknown correlation,
customer preference, hidden pattern and even market trend, which could help out the
organizations in making the informed business related decision (Kambatla et al. 2014). It is
often driven by the specialized analytics software and system and is responsible for providing
distinct advantages like new opportunities of revenue, more effective marketing, improvised
operational efficiency, competitive benefits over other competitors and better customer
services. Apart from the businesses, big data analytics, has also provided advantages to
education sector. It encompasses a mixture of unstructured as well as semi structured data to
ensure that the information is absolutely proper and error free.
The various tools and techniques of this big data analytics include YARN,
MapReduce, Spark, HBase, Kafka, Hive and Pig (Hu et al. 2014). The users of this big data
analytics are eventually adopting the entire idea of Hadoop Data-lake, which assists as the
major repository for the incoming raw streams of data. This type of technology is being used
in higher education or universities for gathering and up gradation of student data profiles with
the help of multiple data points. The following research report outlines a brief discussion on
the opportunities as well as challenges confronted by the big data analytics for improvement
of academic performances in higher education. Moreover, the challenges will also be
highlighted in the research report with relevant solutions.

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2. Discussion
2.1 Opportunities obtained by Big Data Analytics for Improving Academic
Performances in Higher Education
The techniques as well as the technologies of data analytics provide a proper means
for better analysis of data sets and even drawing subsequent conclusions regarding them to
help out the businesses in business operations and performance. According to Sin and Muthu
2015, this big data analytics is a specific form of the advanced analytic that majorly involves
the complicated application with various elements like statistical algorithm and predictive
model to gain high performances in education sector. The universities are improvising the
programs of data analytics for increasing academic performances and helping the faculty and
advisors in providing better support to the students. Few of the universities are experimenting
with the real time data collection tools and techniques. It even helps in finding as well as
analysing the patterns in higher volume data streams for making the decisions in education
sector. Reyes 2015 stated that the tools of big data analytics comprise of exceptional
implications for higher education to improve academic performances.
Real time information is collected as well as disseminated so that maximum
advantages are gained for improvement of performance (Kambatla et al. 2014). Several
opportunities are being gained by big data analytics within the higher education sector for
improving academic performances and these opportunities are as follows:
i)
Easier Accessibility of Data: The first and the foremost opportunity that is being
gained in higher education for improving academic performance with big data analytic is its
easier accessibility of data or information (Macfadyen et al. 2014). As big data completely
relies on technological infrastructure for capturing, storing as well as managing confidential
information, it is extremely easy to find the complex data sets without much complexity.
Apart from infrastructure, the organizations could even make it quite difficult in sharing of

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information and leadership might be privy to data, which the teachers do not have
accessibility for creation of barriers to better understanding and growth. The data analytics is
also helpful in creation of a collaborative environment (Hu et al. 2014). Due to the easy data
accessibility, the respective educational sector’s individuals or students get the major
opportunity of making data informed decisions with the easy to utilize analytics. Competitive
advantages are being gained by the organizational members to eventually iterate, improvise
and even move much more faster, in comparison to other companies. They can even focus on
the core educational needs, instead of maintenance of respective analytics infrastructures.
Moreover, the students would not face any type of complexity in management of data and
information and hence become successful in getting knowledge quickly without feeling bored
or stuck with any 1 topic (Cen, Ruta and Ng 2015). Big data analytic forms a data-driven
culture, in which the data can lead to effective and efficient actions and decisions.
ii)
Cost Savings: The second important and significant foremost opportunity that is
being obtained in higher education for improving academic performance with big data
analytics is better cost savings. According to Daniel 2015, proper allocation of resources is
quite vital for higher education and the data is the key for efficiency. The confidential data
could even provide insights into the enrolment numbers of separate class sections. The
resources of classroom space, energy consumption and time of teachers could be well utilized
effectively. The cloud based systems could potentially cut the costs of data storage and
ensure higher cost savings. A successful deployment of the tools of real-time big data
analytics are quite expensive and it saves up a lot of money (Macfadyen et al. 2014). There is
also zero waiting time for the teachers as well as the in memory databases that are required
for the real time analytics for reducing the burden over the overall IT landscape of an
organization, before freeing up of resources that are previously devoted to responding to the
report requests. Due to the opportunity of cost savings, each and every student is able to

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afford such education system without any issue for better academic performances and as a
result, big data analytics has obtained maximum advantages or benefits with much more
effectiveness and popularity.
iii)
Time Savings: Another distinctive and vital opportunity that is being obtained in
higher education for improving academic performances with big data analytic would be time
savings (Ellaway et al. 2014). Since, it reduces the complexities of data management to a
high level, it becomes quite easy for the students and educational institutes to save the time
and does not involve any type of complexity related to simplification of the procedure of
obtaining correct information or data that provides the organization the major chance of
reacting efficiently and avoidance of crisis situation. It is even effective in management of the
predictive maintenance such as engine failure prediction and alerting the system
prioritization. Hence, time management is not an issue anymore. With the successful
deployment of big data analytics, the education sector even allows their students to create
better opportunities and identify resources (Lane 2014). Due to the easy data accessibility
with big data analytics, the students do not have invest a lot of time for searching any type of
data or information related to their education or academic performances.

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Figure 1: Big Data Analytics in Higher Education
(Source: Ellaway et al. 2014)
iv)
Improvement of Services and Make them faster: This is yet another vital
opportunity that is being gained in higher education for improving academic performance
with the help of big data analytics. Apart from the cost savings advantage, it comprises of the
core advantage of making the respective services much more improved and faster, in
comparison to other traditional business processes. According to Dubey and Gunasekaran
2015, the information of the educational sector is required to be made available within one
centralized location that saves an incredible amount of time collecting the data for the core
purpose of finding any 1 specified report and information for the respective student. Big data

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analytics is also available in real time for the core purpose of making the decisions faster and
efficient. It is also helpful while student enrolment period and making the process automated.
v)
Customization of Programs: The fifth distinctive opportunity or advantage that is
being obtained by the students to improve their educational performance with the help of big
data would be customization of programs. Big data analytics has the advantages of helping
the students in creation of individual programs for the students and these distinct programs
can even provide the students with the significant scope of achieving better results in their
own specific pace (Daniel 2015). They can use both online and offline sources for this
purpose and these types of mixed learning experiences are much more effective for
improvement of the performances as well as in active participation in the learning
experiences.
2.2 Challenges faced by using Big Data Analytics for Improving Academic
Performances in Higher Education
Although, big data analytics is responsible for producing different types of advantages
and opportunities to the students for gaining better performances in the higher education,
there are some of the most distinctive and noteworthy challenges that are being faced by the
students (Williamson 2016). These challenges to the above mentioned opportunities are
provided in the following paragraphs:
i)
Challenge to Easier Accessibility of Data: According to Anirban 2014, the first
and the most significant opportunity that is being identified for the students is easy
accessibility of data. However, one of the major challenge prevails with this particular
opportunity. Data security is the most degrading and noteworthy issue that is being
confronted if students get the opportunity of easy data access. These data require better
assessment by the big data analysts, wherever they are being used. It becomes quite common

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that the data becomes inefficient and loses its security and privacy. Several students can even
use the data with wrong intention in their studies for unethically improving their
performances in academics (Demchenko, Gruengard and Klous 2014). This particular
challenge is extremely vulnerable for both the students and the respective university or school
and thus data efficiency is lost completely.
Figure 2: Needs, Challenges and Opportunities of Big Data Analytics in Education
Sector
(Source: Anirban 2014)
ii)
Challenge to Cost Savings: The second distinct opportunity that is identified for
students to improve their academic performances is cost savings (MacNeill, Campbell and
Hawksey 2014). Although, all categories of students get equal opportunity of accessing the
technology for their education without any issue; the quality of data gets degraded in this
process. The education system should not only focus on the costs and expenses, but also on
the quality of data or information that is being provided to the students. In spite of the fact
that the data would not incur huge costs, the process of automation and outsourcing with big

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data analytics is extremely costly and could bring out major issues related to reliability
(Ogata et al. 2017). Due to this particular challenge of cost savings, the academic
performances of the students are hampered and they do not get proper scope for improving
their performances.
iii)
Challenge to Time Savings: The process of producing efficient and effective data
could be extremely time consuming with big data analytics. Moreover, security and
controlling the overall accessibility of the data incurs huge time and for several users, there
exists few protocols for data access (Archenaa and Anita 2015). These protocols are quite
problematic and the actual practices of managing these data and providing security are
extremely inefficient, even if the users are manually securing the unstructured data or dealing
with the data in an automated manner. For providing maximum security, the education sector
requires proper assessment of the location, in which data is being kept and also developing an
inventory to keep these data, so that students do not face any issue while using them.
However, the entire process consumes maximum time and the students face issue if they
would have to access the big data inventory urgently (Vaitsis, Nilsson and Zary 2014). They
might not get accurate data that they are searching for and this particular issue can even make
their academic performance in a negative slope.
iv)
Challenge to Improvement of Services and Make them faster: Another
effective opportunity that is being obtained by students with big data analytics would be a
major improvement of services and making them much faster than the rest (Jacobi et al.
2014). Although, the main purpose of big data analytics is to help out the students in
improving their academic performance, it is evident that they try to improve the services.
However, excessive improvement of services and making them even faster is not always a
better option for students. The data trails can be utilized for wrong intention and when the
education sector monitors these data trails, there is a high chance that they could be sensing

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major issues of data loss and lack of privacy (Picciano 2014). As a result, efficiency and
quality of the data gets degraded for every integrated process and there is a consistent
improvement in the education culture.
v)
Challenge to Customization of Programs: The challenge to the customization of
programs for students would be providing excess scope for blended learning experiences. In
spite of the fact that the students get an opportunity of achieving better results, it does not
always provide active participation of the learning experiences (Ogata et al. 2015). The
students might utilize program customization for any type of illegal intention and might not
notify their professors and even might not work under the supervision of their professors
under any circumstance. Program customization even is responsible for allowing them in
hacking the data from big data inventory and database of their respective university or
college. Lack of security to data is considered as one of the most basic and significant
challenge to program customization.
2.3 Relevant Solutions to the Challenges faced by using Big Data Analytics for
Improving Academic Performances
This type of data analytics helps in harnessing the data and then utilize it for
successful identification of the new opportunities. It even leads to smart business moves, high
profits, better customer satisfaction and more effective and efficient business operations (Yu
and Jo 2014). This particular technology can provide some of the most significant and
important advantages, which include reduction of expenses, better and faster process of
decision making, better customer satisfaction, easy implementation of new services and
products and many more.
The above provided challenges could be effectively resolved with relevant solutions
and these solutions are as follows: