Ijciet 10 01_195-2-3

http://www.iaeme.com/IJCIET/index.asp 2170 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 01, January 2019, pp. 2170–2178, Article ID: IJCIET_10_01_195
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=1
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
©IAEME Publication Scopus Indexed
HARNESSING BIG DATA TECHNOLOGY TO
BENEFIT EFFECTIVE DELIVERY AND
PERFORMANCE MAXIMIZATION IN
PEDAGOGY
E. E. Ekong
Department of Electrical and Information Engineering,
Covenant University, Ota, Ogun State, Nigeria
Q. E. Adiat
Department of Demography and Social statistics,
J. O. Ejemeyovwi
Department of Economics and Developmental studies,
A. M. Alalade
Department of Electrical and Information Engineering,
ABSTRACT
The Big data concept emerged to meet the growing demands in analysing large
volumes of fast moving, heterogeneous and complex data, which traditional data
analysis systems could not manage further. The application of big data technology
across various sectors of the economy has aided better utilization of multiple data
collated and hence decision making. Organizations no longer base operations on
assumptions or constructed models solely, but can make inferences from generated
data. Educational organizations are more efficient and the pedagogical processes
more effective, when multiple streams of data can be collated from the various
personnel and facilitators involved. This data when analysed, maximizes the
performance of administrators andrecipients alike. This paper looks at the
components and techniques in bigdata technology, and how it can be implemented in
the education system for effective administration and delivery.

E. E. Ekong, Q. E. Adiat, J. O. Ejemeyovwi, A. M. Alalade
Key words: Big data technology, Education, ICT, Learning analytics.
Cite this Article: E. E. Ekong, Q. E. Adiat, J. O. Ejemeyovwi, A. M. Alalade,
Harnessing Big Data Technology to Benefit Effective Delivery and Performance
Maximization in Pedagogy, International Journal of Civil Engineering and
Technology (IJCIET) 10(1), 2019, pp. 2170–2178.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1
1. INTRODUCTION
Learning has evolved over time away from the conventional models into more complex
patterns that allow regular and distant learning to be done at the same time, it is apparent that
the world has moved away from the traditional teacher, student and classroom sphere, and
progressed to the use of digital and virtual systems, [1], [2]. While these methods may
produce better learning outcomes, they also generate deeper and more complex facets that
require big data to simplify [3], [4]. Effective education delivery involves employing tactical
means to successfully impart knowledge on students in an institution. This could prove
difficult as it requires that the methods employed in imparting knowledge are in sync with the
students learning abilities. Students are either comprehensive, targeting or sampling learners
[5].
Comprehensive learners are categorized as those who embark on mission to garner in-
depth knowledge, therefore, they concentrate on the entire course content. Targeting learners
on the other hand, focus on assimilating specific aspects of the course contents to perform
well in the examinations and tests, while sampling learners set less goals and exhibit
unpredictable trends hence perform lower than their counterparts [5]. Big data merges benefits
of data mining and predictive analysis [6] and applying this technology to analyse student
learning abilities could ensure that education delivery methods are upgraded to resonate with
each category of students.
High expectations are placed on educational institutions by both the public and private
sector, to provide timely solutions to emerging challenges. This creates competition among
educational institutions, first to attract the best individuals and then ensure that they are able
to handle economic challenges by effectively managing the vast amount of data generated
within and outside the educational sector [1]. Part of the strategies involved in managing this
data is finding hidden information in the large amounts of data through classification,
clustering and association [6], [7]. Essentially, the data mining system filters through the
enormous data from vast sources, to assemble relevant information about student, faculty and
staff and filters such information to fit the context in which they are relevant. It can be used
to determine student area of interest, likely performance and predict grades, hence sound
decisions can be made in the educational sector [6], [8].
Big data is a technology used to classify enormous high speed and complex data sets that
are too complex for conventional data processing systems [9], [10]. At that level of analysis,
an open-sourced java based programming framework; Apache Hadoop is deployed, its
simplified programming models, allows for rapid data transfer rates between computer
clusters and continuous operation in the event of a system failure, thereby reducing the risk of
an apocalyptic system failure even with a considerable amount of system malfunction [11]. It
also prevents data loss through replication of data on servers, making it run applications while
ensuring fault tolerance [12]. The Hadoop framework has the Data storage and Data
processing components and The Hadoop Distributed File System (HDFS) and Map Reduce
Framework sub components. The subsequent sections of this paper are arranged as follows;
section II examines several research works on big data and its integration into the education
system, section III highlights the major components of the big data technology, techniques

Harnessing Big Data Technology to Benefit Effective Delivery and Performance Maximization in Pedagogy
adopted in its implementation and examines the relevance of the whole concept as regards
education delivery, section IV concludes.
2. LITERATURE REVIEW
Big data has been universally defined in terms of its ability to organize overwhelmingly large
volumes of structured, unstructured and semi-structured data [1], [12] measured in terabytes
or petabytes [11] and convert them into manageable formats for effective decision making.
This singular quality poses a threat to traditional data warehouse systems [3]. Nonetheless,
some scholars emphasized that the emergence of big data should not be to replace but to
broaden the scope of the data warehouse solutions already in place [1], [5] and recommend
integration of new technologies into what was in existence using hadoop for data ingestion/
staging. Others point to implementing the right big data technology combination based on
“technological needs” and “specific applications‟ requirements” [3].
To effectively explain big data, scholars consider it important to examine its components,
characteristic and techniques. 5Vs; volume, velocity, veracity, variety, volume commonly
describe big data characteristics. [3], [11], [13] while data mining which represents techniques
[6], [8] involves classification, clustering and association. Studies continue to reiterate the
benefits of data mining specifically in student grades and result prediction, academic
performance levels, improved research, evaluation and accountability and effective
management of student records [13], [14]. Big data can be analysed in five phases:
acquisition, extraction, integration, analysing and interpretation [15].
The penetration of big data technology into the education system has been defined in
terms of the massive volumes of data generated from online activities carried out by students
[10], [16], [17] while this has led to an increase in learning management systems over the
years it has also posed major challenge in capturing, analysis, storage, searching, sharing,
visualization, transferring and privacy violations [10], [11], [18]. Again the hadoop, an open
source distributed data processing technology is one prominent and well know solution to
address some of these challenges [10].
Apart from technical challenges, big data also comes with an array of ethical and social
issues, [14], [19] consequentialism and scientism are explained as the immediate and deep
challenge, that arise due to a lack of critical understanding “of the scientific basis” of the big
data technology [14] researchers recommended big data and cloud as a secure, reliable model
and the best solution in handling the fast growing numbers in data and information being
experienced by the current information technology world. The authors reveal the importance
of virtualization as astorage concept and the corresponding changes it has made across the
education, finance and health care sectors [9].
Privacy of student data is also a shared concern among public officials, media outlets and
parents, thus restricting the transmission of data from academia to industry [4]. The authors,
based the study on their personal experiences as academic researchers transitioning into the
industry and present three primary areas of concern related to student privacy which include
institution policy, corporate social responsibility and public opinion.
Providing quality education is the major purpose of an education institution. Achieving
the highest possible form of education depends on: Discarding the conventional teaching
models, identification of abnormal result values in student results, predicting performance of
student and acquiring data for developing a prediction model involving students‟ enrolment in
a particular course [14] certain educational models such as standardized testing, computer
tutoring and online courses generated data that are behavioural in nature and this should pave
the way for the adoption of a standard system for relating these behavioural data to
educational models [19] and have implications on human development of students [24] and

inclusive growth of countries at large [25]. Studies relate how big data would serve as a
catalyst to revolutionise education [8], [20] implementing data analytics in the academic
system would answer vital questions related to students motivations, level of satisfaction and
course preferences [3] this would provide management with information relevant to structures
need to be improved for effective planning and administration and predictive analysis[6], [21]
Learning analytics has been defined as the “process of gathering, analysing and reporting
educational big data to provide stakeholders with an in-depth understanding of the learning
process [10, 16] the current condition of learning analytics, has a number of benefits, but at
the same time poses a „‟wicked‟‟ problem to researchers, students, institutions, tutors and the
government [22]. This ranges from the technicalities involved in working with numerous and
very distinct datasets, adapting to continuous technological changes and ethical concerns and
integrating traditional analytics into a learning environment. Addressing these problems calls
for new adaptive forms of leadership, collaboration, policy development and strategic
planning [22]. They also introduced two policy and planning frameworks developed for
complex systems that may offer institutional teams‟ practical guidance in their project of
optimizing their educational systems with learning analytics. There is clear evidence from
research pointing to the reasons why big data continues to spread across various sectors and
declaring that it is a technology that is here to stay [9], [20]
3. BIG DATA COMPONENTS
Big data is used to classify data sets that are so enormous, conventional means of data
processing become inapplicable. Apache Hadoop, an open-sourced java based programming
framework, is one which is frequently used in handling big data including its analytics and
computing. It employs less complex programming models in dealing with large amounts of
data across computer clusters [11]. A major advantage of the technology is that, it has a
distributed file and parallel cluster system which allows for rapid data transfer rates between
computer clusters and continuous operation in the event of a system failure. This reduces the
risk of an apocalypticsystem failure and data loss even if a considerable amount of systems
malfunction. Also, data loss is prevented on Apache Hadoop through replication of data on
servers. This gives Hadoop the ability to run applications while ensuring fault tolerance [12].
The Hadoop framework will be discussed from two sections: Data storage and Data
processing, which consists of two major sub components: The Hadoop Distributed File
System (HDFS) and MapReduce Framework. Users can also add modules on the Hadoop
platform depending on purpose and application requirements.
3.1. The Data Storage
This consists of a file system and a non-relational database called Hadoop distributed file
system (HDFS) and HBase respectively. Hadoop depends on these two components for data
storage.
3.1.1. Hadoop Distributed File System (HDFS)
HDFS is a data storage system which can allow an ample number of systems in a cluster
providing a storage system with high level of reliability and less cost. It has the capability of
handling structured and unstructured data, holding massive amounts of datasets exceeding
terabytes and providing uninterrupted access to program data. HDFS ensures a fault tolerant
file system by enabling data replication, aids in the reduction of network congestion and
system performance increase by ensuring maximum proximity between computations and
data storage. Its structure is designed based on the Master-Slave architecture, where the
master system is called the Name Node and the slave systems are called Data Nodes. The

name node is responsible for managing file system operations while the data nodes are
responsible for managing and coordinating data storage [11], [12].
3.1.2. HBASE
Hbase is a distributed NoSQL database that facilitates storage of data for large tables. It runs
on top of HDFS and enables database user interaction such as Updates, inserts, deletions etc.
Unlike HDFS which is structured for operations with high delay levels (high-latency), HBase
was designed for low-latency operations. Hbase provides a medium for data attributes to be
classified into column families with each column family element stored together. Due its non-
relational database structure, Hbase is majorly employed in many applications such as big
data and data driven websites. It also supports various features such as linear and modular
scalability, queries performed in real-time, and continuous access to big data sources. In
addition, Hbase supports random, read/write operations, a feature which HDFS lacks due to
its batch processing design [11], [12].
3.2. Data Processing
For data processing, which includes a platform for job scheduling and management of cluster
resources, Hadoop employs two main tools: MapReduce and YARN [3]. MapReduce:
MapReduce is a Hadoop data processing platform which is structured as a programming
model allowing a user to write programs that supports parallel processing. It is cost effective
and efficient, making processing of enormous amounts of data simple. As the name implies,
MapReduce consists of two functions that carry out computation of data: Map and Reduce
functions [12]. MapReduce operation depends on;
1. The Map function of the MapReduce framework splits the data inputted into independent
datasets which form key-value pairs.
2. The key-value pairs are then sent into a Mapper by the MapReduce framework where they
are processed individually using an array of parallel map tasks across the cluster. A unique
module is allocated to each data partition. The Mapper produces the outputs for several of the
average key-value pairs while the MapReduce framework is designed to compile these key-
value pairs and categorize them based on key. This procedure results in the production of
several keys with a list of corresponding values.
3. Thirdly, the resulting output data is processed by the Reduce function of the framework.
The Reduce function compiles the values that corresponds to the key suing an already defined
program in the framework. This process is carried out individually for each unique identifier.
4. The framework then stores all output key-value pairs in an output file.
3.3. Yarn
YARN is designed to support a better level of scalability, enhanced parallel computing,
improved resource management and a more generic framework compared to MapReduce.
Where analytical applications of big data may require operating system functions, YARN is
deployed. The Hadoop structure has been re-engineered to include the YARN Resource
Manager and the YARN framework is strategically positioned on top the HDFS. This
strategic positioning enables it execute several applications simultaneously using parallel
computing [12].
The Hadoop framework consists of other tools. Some include [12]: Pig, Mahout,
Zookeeper, Avro, JAQL, Flume, Sqoop, Hive, Spark, Hue and Storm, which are also
classified into:
 Data Access: Flume, Sqoop, Spark and Storm.
 Management: Avro, Ambari, Hue and Zookeeper.

 Data Analytics: Mahout.
 Data Querying: JAQL, Hive and Pig
3.4. Data mining
Data mining techniques that are applicable to education: Classification, Clustering and
Association.
Classification, being the most commonly used technique in data mining, makes use of
already classified examples of dataset structures in developing a class and placing each data
set in that particular class. It involves two processes: Learning and classification. In the
learning aspect, an algorithm used in classification is used to perform analysis on datasets
while the classification process then tests the accuracy of the classification algorithm.
Application of these rules to the data structure, depends on the accuracy levels of the
algorithm. As earlier stated, classification uses already classified data structure examples, this
aids the algorithm in determining the parameter set required for proper data placement. The
type of classification technique deployed differs depending on data to be classified and some
examples of such classification techniques are: Bayesian, decision tree, support vector
machines and neural network classification [6], [16].
In Data mining techniques, identification of data with similar classes involves the
adoption of the clustering technique. It recognizes traits in a datasets, finds appropriate classes
and assigns these datasets to suitable classes. Clustering also identifies areas of relatively high
and low density in a data space including overall distribution patterns and relationships
between datasets [6], [16]. Association is used to identify frequently occurring items in
massive sets of data. The main objective of this technique is to find important relationships
between datasets that could prove beneficial to employers of the technique [6], [16].
4. BIG DATA IN EDUCATION DELIVERY
As a buzzing field of operation, studies have shown that big data is well able to solve the
major challenges facing the educational sector, world over [2], [3]. Some of these pressing
issues are explained in terms of how big data proffers a solution to them.
In most instances, academic performance alone is used to measure student effectiveness,
even though, a number of other factors including study, sleep and social habits, passion for
the course of study and instructor effectiveness determine how well a student performs [21].
In the past, the amount of students that instructors had to assess made them base a number of
their decisions on instinct hence creating a difficulty in identifying students in need of extra
attention. They were either identified late or not at all. Many instructors assess a student‟s
learning ability using test scores which when analysed according to the comprehensive,
targeting or sampling learner classification [5] may not be ineffective in student assessment.
In more recent times using big data in accessing individual students creates a more
personalized learning environment for the students as it provides the teachers with
information on the student‟s interest, prior knowledge of the course being taught and the
student‟s academic ability. This allows the teacher to know which student is not being carried
along and adopt suitable teaching methods to accommodate such students. Big data can
support the classic educational system by improving teacher- student effectiveness [16]
creating an avenue for education instructors to learn new techniques and methods to enhance
effectiveness in their various educational fields.
Big data doesn‟t cater to the needs of educational institutions alone, but bridges the gap
between institutions and their students, both prospective and current. Most of the time,
talented students aim to be admitted in the best university available. Prospective students
search thoroughly for institutions using the internet before actually making contact with them.

The choice of selecting a suitable institution may depend on the name the institution has
carved for itself and social media analysis on the institution which is gotten from individual
sentiments expressed by potential and current students of the institution. This entire search
process generates data, making students admissions not just about the university recruiting the
students they want, but also is dependent on the image that the university has created for itself
on the online platform. The emphasis on university ranking in the educational sphere further
creates competition among universities to portray the best image. [3], [23]. Since the goal of
every university is to acquire the most talented of students, with big data, institutions who
base conclusion solely on the use of scores of aptitude tests, prospect‟s applications and oral
interviews may be at a disadvantage. Application of the big data technology in this process
provides a desired level of information on how well rounded the potential student is and aid
the students in better understanding the operations of the institutions [21].
Furthermore, on eventual admission, student interests vary the application of big data
allow these interests to be identified through analysis of student activities. New courses can be
recommended based on this data and integrated into the teaching curriculum. This ensures
that students are given the option of choosing the fields they are passionate about and ensures
that they are not misguided in making their choice [10].Also, analysis of student activities
provides a basis for behavioural analysis. Proper examination of such data can detect the risk
of a student dropping out from a particular course or institution and fosters the
implementation of concrete measures to prevent such events from occurring. Big data
provides a platform for harnessing data pertaining to recent and important technological
trends which if leveraged upon by educational institutions can create an avenue for its
integration in the education system. This integration, if properly managed allows students to
understand the technology and develop skills needed for its operation.
Also, technology adoption in the education system gives room for deployment of smart
education. This is a type of education system designed to strengthen the capabilities of
modern day learners by providing an intelligent and customized learning solution through the
implementation of information technology. This entails educational institutions actually
knowing the best form of educational system to adopt in education delivery and it involves
the use of IT equipment like computers, smart boards, mobile phones, tablets etc. as a means
of passing knowledge across to the students.
Applying big data to predictive models also serves as a means of forecasting the success
of a research project at an early stage. Undergoing research projects involves application of
large predictive models to analyse data on research derived from traditional databases or data
warehouses. These platforms may not provide a huge amount of heterogeneous data which
connotes the basis for developing accurate predictive models. Studies have established that
traditional data tools and technologies cannot handle the load and analytic process of
converting this data into meaningful information to aid decisions, [1] this emphasizes the need
to adopt big data technology in order to broaden the scope of these platforms. Big data allows
for the analysis of several data sources increasing the data variety scope, and improving the
accuracy of predictive models. Ability to predict the outcome of a research project in due time
can prove vital in determining what procedures to take: If the project should be stopped due to
absence of meaningful results or continued at its current pace, delayed, or accelerated with a
good result in sight. This prevents unnecessary application of time and energy in the wrong
direction. Predicting research success or failure with high accuracy levels can serve as a guide
to assign staff to areas where they are needed leading to better results for both the researchers
and consequently, the institution. Big data technology enables the storage and analysis of
enormous volumes of research data and can serve as a platform for both archiving of data and
performing advanced data analytics. Considering the aspect of funding research projects

which is gotten mainly from grants, big data technology is employed to ensure that the
research is completed within the given timeframe and the allocated funds [21].
The table below gives general overview of the benefits and advantages of implementing
the big data technology in the education system [16].
Table 1 Benefits of implementing Big Data technology in Education
S/N Benefits of big data in Education
1. Effective system
administration
Educational institutions through big data implementation are able to
develop a skillful student base which in turn can aid education
administrators in assigning resources to sustain this development. This
fosters a high performance level and gives room for smart planning in
future education interest.
2. Customized learning
procedures
The use of big data in education makes each student experience the lessons
being given from a personal perspective. Courses can be adjusted to suit
each students learning capability and this gives room for improved student
performance and learning ability.
3. Matching students to
corresponding programs
Using big data analytics, a student can be matched to his/her preferred
program of study. This aids parents in selecting the best institution in that
particular program of study.
4. Matching students to
suitable employment in
their field of study.
Various Companies look for intelligent and capable candidates when
undergoing employment processes. Using education big data analytics,
these companies can sort for students who are best suited for the position in
question. Also students can use data generated from these companies’
activities to find and apply for jobs which aligns with their abilities.
5. Restructuring of learning
curriculum
Education instructors can use big data to design lecture notes to align with
changing trends in various fields of study. This allows the students to be
equipped with up to date knowledge on their areas of study and work
towards developing their skill sets in these areas.
5. CONCLUSIONS
Large amounts of data are constantly generated, sporadically and from numerable sources
with divergent properties so much so that conventional data processing systems continue to
fall short in their functions. Big data ensures effective collecting, processing, mining and
analyses of this data thereby driving the course of sound decisions across all sectors. This
paper examined the big data technology its components, tools and methods, with major focus
on how it role in restructuring education. It emphasized the importance of implementing big
data in the educationalsystem as a means for achieving a successful education delivery and
establishing an effective administration. Big data offers numerous opportunities for education
instructors to reach out and instruct students in a new way by providing in-depth
understanding of students‟ education experiences and aiding in the evaluation of the state of
the education system. It also improves communication between the students, instructors and
the administrative bodies.
ACKNOWLEDGEMENT
This paper is a research work from the collaboration between a faculty in the Department of
Economics and Development Studies and postgraduate students of the department of
Electrical and Information Engineering, Covenant University. Hence, suggestions and
comments from the faculty are acknowledged. In addition, the authors appreciate publication
support from Covenant University Centre for Research, Innovation and Development
(CUCRID)
REFERENCES
[1] L. W. Santoso and Yulia, “Data Warehouse with Big Data Technology for Higher Education,”
Procedia Comput. Sci., vol. 124, pp. 93–99, 2017.

[2] M. Huda, A. Maseleno, M. Shahrill, K. A. Jasmi, I. Mustari, and B. Basiron, “Exploring adaptive
teaching competencies in big data era,” Int. J. Emerg. Technol. Learn., vol. 12, no. 3, pp. 68–83,
2017.
[3] A. Manohar, P. Gupta, V. Priyanka, and M. F. Uddin, “Utilizing Big Data Analytics to Improve
Education,” 2016 ASEE Northeast Sect. Conf., 2016.
[4] J. Sabourin, L. Kosturko, C. Fitzgerald, and S. Mcquiggan, “Student Privacy and Educational Data
Mining : Perspectives from Industry,” Proc. Int. Conf. Educ. Data Min., pp. 164–170, 2015.
[5] J. Maldonado-Mahauad, M. Pérez-Sanagustín, R. F. Kizilcec, N. Morales, and J. Munoz-Gama,
“Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in
Massive Open Online Courses,” Comput. Human Behav., vol. 80, pp. 179–196, 2018.
[6] Johina and V. Kamra, “A Review: Data Mining Technique Used In Education Sector,” Int. J.
Comput. Sci. Inf. Technol., vol. 6, no. 3, pp. 2928–2930, 2015.
[7] L. W. Santoso, “Classifier combination for telegraphese restoration,” Proc. Int. Conf. Uncertain.
Reason. Knowl. Eng. URKE 2011, vol. 1, no. March, pp. 79–82, 2011.
[8] N. Priyadarshini, “a Review: Data Mining Techniques in Education Academia,” 2017.
[9] M. M. Sharma, M. S. Ali, and S. Husain, “Implementation of Big data analytics in Education
Industry,” no. November 2017, 2018.
[10] K. Sin and L. Muthu, “"Application of Big Data in Education Data Mining and Learning Analytics
– a Literature Review ",” ICTACT J. Soft Comput., vol. 05, no. 04, pp. 1035–1049, 2015.
[11] Ishwarappa and J. Anuradha, “A brief introduction on big data 5Vs characteristics and hadoop
technology,” Procedia Comput. Sci., vol. 48, no. C, pp. 319–324, 2015.
[12] A. Oussous, F. Z. Benjelloun, A. Ait Lahcen, and S. Belfkih, “Big Data technologies: A survey,”
J. King Saud Univ. - Comput. Inf. Sci., vol. 30, no. 4, pp. 431–448, 2018.
[13] D. M. West, “Big Data for Education_ Data Mining, Data Analytics, and Web Dashboards.pdf,”
no. September, 2012.
[14] J. A. Johnson, “The Ethics Of Big Data In Higher Education,” Int. Rev. Inf. Ethics, vol. 21, no. 7,
pp. 3–10, 2014.
[15] B. Tulasi, “Significance of Big Data and Analytics in Higher Education,” Int. J. Comput. Appl.,
vol. 68, no. 14, pp. 21–23, 2013.
[16] Marconi, “The Use of Big Data in Healthcare,” Big Data Bus. Anal., vol. 11, no. 5, pp. 229–248,
2013.
[17] V. Alonso Secades and O. Arranz, “Big Data and eLearning: A Binomial to the Future of the
Knowledge Society,” Int. J. Interact. Multimed. Artif. Intell., vol. 3, no. 6, p. 29, 2016.
[18] B. Daniel, “Big Data and analytics in higher education: Opportunities and challenges,” Br. J. Educ.
Technol., vol. 46, no. 5, pp. 904–920, 2015.
[19] Z. A. Pardos, “Big data in education and the models that love them,” Curr. Opin. Behav. Sci., vol.
18, pp. 107–113, 2017.
[20] B. Edwards, “Rough Guide to Sustainability A Design Primer,” Futures, 2010.
[21] Oracle, “Improving Higher Education Performance with Big Data,” Oracle Enterp. Archit. Ure
White Pap., no. April, p. 24, 2015.
[22] L. P. Macfadyen and S. Dawson, “Leah P. Macfadyen,” Res. Pract. Assess., vol. 9, no. 2013, pp.
17–28, 2014.
[23] M. Abdous, W. He, and C. J. Yen, “Using data mining for predicting relationships between online
question theme and final grade,” Educ. Technol. Soc., vol. 15, no. 3, pp. 77–88, 2012.
[24] J. O. Ejemeyovwi, E. S. Osabuohien, and R. Osabuohien, (2018). Investment in Technology and
Human Capital Development in ECOWAS International Journal of Economics and Business
Research. 15(4),
[25] J. O., Ejemeyovwi, & Osabuohien, E. S. (2018). Mobile Technology Adoption and Inclusive
Growth in West Africa. Contemporary Social Science, DOI: 10.1080/21582041.2018.1503320
[26] Akhil, Shravya and Dr. K.Uma, Survey on the Challenges and Issues on Big Data Analytics,
International Journal of Mechanical Engineering and Technology 8(12), 2017, pp. 138–149
[27] Patrick Mbassegue, Ma-Lorena Escandon-Quintanilla, Mickaël Gardoni, Knowledge Management
and Big Data: Opportunities and Challenges for Small and Medium Enterprises (SME),
International Journal of Industrial Engineering Research and Development, 8(2), 2017, pp. 05–14.
[28] Nachiyappan .S and Justus Selwyn, Pre Hadoop and Post Hadoop Validations for Big Data,
International Journal of Mechanical Engineering and Technology 8(10), 2017, pp. 608–616.
[29] Dr. Md. Tabrez Quasim and Mohammad. Meraj, Big Data Security and Privacy: A Short Review,
International Journal of Mechanical Engineering andTechnology, 8(4), 2017, pp. 408-412

Ijciet 10 01_195-2-3

Recommended

Recommended

More Related Content

What's hot

What's hot (9)

Similar to Ijciet 10 01_195-2-3

Similar to Ijciet 10 01_195-2-3 (20)

More from IAEME Publication

More from IAEME Publication (20)

Recently uploaded

Recently uploaded (20)

Ijciet 10 01_195-2-3