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The main objective of this paper is to present a new E-Learning model. There exists large amount of different types of data in E-Learning environment both structured and unstructured. So, there is a need for enhancing the performance of big data processing in educational systems. Handling big data efficiently is required to serve educational systems and learners. Our suggested model can deal with big data efficiently. Important data will be collected in a smart way through interaction between learners and learning content. The proposed model can determine all learners' behavior in E-Learning system by handling educational big data using hadoop and NoSQL.
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International Journal of Advanced Research in
Computer Science & Technology (IJARCST 2015)
47
Vol. 3, Issue 1 (Jan. - Mar. 2015)
ISSN : 2347 - 8446 (Online)
ISSN : 2347 - 9817 (Print)
www.ijarcst.com © All Rights Reserved, IJARCST 2015
Handling Big Data in E-Learning
IAhmed Ashraf, IIHazem El-Bakry, IIISamir M. Abd El-razek, IVYehia El-Mashad
I,II,IIIInformation Systems Dept., Faculty of Computer Science and Information Systems,
Mansoura University, Mansoura, Egypt
IVDelta Higher Institute for Engineering & Technology, Talkha, Dakahlia, EGYPT
I. Introduction
Big Data, a highly innovative provides a unique forum for
world-class research exploring the challenges and opportunities
in collecting, analyzing, and disseminating vast amounts of data,
including data science, big data infrastructure and analytics, and
pervasive computing[1]. Big Data is a loosely dened term used to
describe data sets so large and complex that they become awkward
to work with using standard statistical software. The rise of digital
and mobile communication has made the world become more
connected, networked, and traceable and has typically lead to
the availability of such large scale data sets. Some of the keepers
of Big Data sets develop interfaces for everyone to access and
analyze some of the data, e.g. Google provides freely available
Google Insights, while others hesitate to offer any access [2].
Big Data coverage includes: Big data industry standards, new
technologies being developed specically for big data, Data
acquisition, cleaning, distribution, best practices, Data protection,
privacy, and policy, business interests from research to product,
The changing role of business intelligence, visualization and
design principles of big data infrastructures, physical interfaces
and robotics.
E-learning is an education via the Internet, network, or standalone
computer. E-learning is basically the network-enabled convey
of skills and knowledge [3-5].Distance education is the most
renowned descriptor used when referencing distance learning.
It often describes the effort of providing access to learning for
those who are geographically distant [27-53].Big data concepts
and analytics can be applied to a variety of higher education
administrative and instructional applications, including recruitment
and admissions processing, nancial planning, donor tracking, and
student performance monitoring [5].
A. Oracle, Hadoop, and NoSQL
Oracle big data sql breaks down those silos by leveraging oracle
sql to enable you to get the insights you need with the skills you
already have. Designed to be used with oracle big data Appliance
and oracle exadata Database Machine, oracle big data sql lets
you use sql queries to seamlessly access data stored in Hadoop,
relational databases, and NoSQL stores [6].
II. Overview about big data and e-learning
We will discuss the meaning of big data and eLearning both
separately.
A. The Big Deal with “Big Data?
Now, Data is available at a larger scale. Obviously, this is a lot
of data. But what exactly is new about it? The short answer is
that data is now available faster, has greater coverage and scope,
and includes new types of observations and measurements that
previously were not available. Modern data sets also have much
less structure, or more complex structure, than the traditional
cross-sectional, time- series, or panel data models that we teach in
our econometrics classes. While business analytics are a big deal
and surely have improved the efciency of many organizations,
predictive modeling lies behind many striking information
products and services introduced in recent years [7].
B. Types of big data which we need
Not only in e-learning or educational system but also in any big
organization needs to be cognizant of the types of data they need
to deal with. The main types of data are structured data and
unstructured data. Generally the term structured data (SD) is
applied to databases (DB) and unstructured data (UD) applies to
everything else.
C. Big data storage technologies
Big Data requires huge amounts of storage space. A typical big
data storage and analysis infrastructure will be based on clustered
network-attached storage (NAS). Clustered NAS infrastructure
requires conguration of several NAS “pods” with each NAS
“pod” comprised of several storage devices connected to an NAS
device. The series of NAS devices are then interconnected to allow
massive sharing and searching of data. Owners of small to medium
sized businesses who are unable to afford adoption of clustered
NAS technology can consider a number of cloud computing
models to meet their big data needs. So we have to know that
data storage using cloud computing is a viable option for small to
medium sized businesses considering the use of Big Data analytic
techniques [8]. Note that cloud computing is becoming a reality
for many businesses, with private cloud deployments often leading
the way. Cloud technology is maturing and addressing barriers
to adoption with improvements in security and data integration,
while IT organizations are evolving to support cloud services
delivery. As a result, businesses are demonstrating growing trust
in cloud delivery models.
Abstract
The main objective of this paper is to present a new E-Learning model. There exists large amount of different types of data in
E-Learning environment both structured and unstructured. So, there is a need for enhancing the performance of big data processing
in educational systems. Handling big data efciently is required to serve educational systems and learners. Our suggested model
can deal with big data efciently. Important data will be collected in a smart way through interaction between learners and learning
content. The proposed model can determine all learners' behavior in E-Learning system by handling educational big data using
hadoop and NoSQL.
Keywords
Big Data, Big Data Analytics, Database, Hadoop, Nosql.
International Journal of Advanced Research in
Computer Science & Technology (IJARCST 2015)
48
Vol. 3, Issue 1 (Jan. - Mar. 2015) ISSN : 2347 - 8446 (Online)
ISSN : 2347 - 9817 (Print)
www.ijarcst.com
© 2015, IJARCST All Rights Reserved
D. Big data analytics
The big data analytics initiative should be a joint project involving
both IT and business. IT should be responsible for deploying
the right big data analysis tools and implementing sound data
management practices. Both groups should understand that success
will be measured by the value added by business improvements
that are brought about by the initiative. [9].
E. The differences between distance learning, e-Learning,
and online learning.
Distance education is the most renowned descriptor used when
referencing distance learning. It often describes the effort of
providing access to learning for those who are geographically
distant. Online learning is direct relationships between previously
described modes and online learning by stating that one uses the
technology used in the other and described by most authors as access
to learning experiences via the use of some technology .E-Learning
education via the Internet, network, or standalone computer.
E-learning is essentially the network-enabled transfer of skills and
knowledge. E-learning refers to using electronic applications and
processes to learn. E-learning applications and processes include
Web-based learning, computer-based learning, virtual classrooms
and digital collaboration. The content of e-learning is delivered
via the Internet, intranet/extranet, audio or video tape, satellite
TV, and CD-ROM [10].
F. E-learning objectives
E-Learning represents an innovative shift in the eld of learning,
providing rapid access to specic knowledge and information
[11]. E-Learning enables organizations to override distance
and other organizational gaps by providing a cohesive virtual
learning environment. Companies must educate and train
vendors, employees, partners, and clients to stay competitive
and E-Learning can provide such just-in-time training in a cost-
effective way [12].
G. Student motivation in e-learning
Students enrolled in higher education courses come from a variety
of backgrounds and have different reasons for studying. While
it is generally accepted that online learning designers should use
intrinsic motivation strategies, extrinsic motivation may also be
used. A university student may be extrinsically motivated in only
doing what is required in order to pass units without a signicantly
deep interest for the subject. Students studying in distance mode
need to feel that they are part of a group of learners and are able
to obtain assistance with the unit’s requirements and technical
difculties. For students who are intrinsically motivated to study
due to a desire to develop a deeper understanding of the subject
matter content which fosters deeper understanding of the subject
and relates to real-life and employment situations should also be
included [13].
III. Big Data in eLearning: The Future of eLearning
Industry
Big data, in terms of the eLearning industry, is the data that is
created by learners while they are taking an eLearning course or
training module.
A. Benefits That Big Data Offer To eLearning
Professionals
There are a variety of benets that big data offer to eLearning
professionals, all of which have the power to impact the future
of eLearning and revolutionize the way we analyze and assess
the eLearning experience [14]. Here are just a few of the most
signicant advantages associated with big data: Allows eLearning
professionals to understand how the learners are digesting the
information and which learning needs appeal the most to them,
Enables eLearning professionals to pinpoint areas that may need
to be ne-tuned within the eLearning course or module, Provides
an analysis of which eLearning modules are visited the most
and in the case of social learning which eLearning modules or
links are shared with other learners and Data is received almost
immediately, rather than having to wait for long periods of time
to receive assessments.
B. Big data will Impact the future of eLearning
Offering invaluable feedback, allowing, allowing eLearning
professionals to design more personalized eLearning courses,
Targeting effective eLearning strategies and eLearning goals, and
tracking learner patterns and expanding our understanding of the
eLearning process.
C. Big Data with e-Learning development
In the e-Learning world, when learners interact with content in
your course, they produce data—or Big Data. We’re now able to
collect and track this data through learning management systems
(LMSs), social networks and other media that tracks how learners
interact with aspects of the e-Learning course. The good news is
that we’ve come a long way for the origin of the term Big Data.
Today, Big Data be processed and analyzed, which is especially
helpful for the e-Learning industry [15].
3.3.1 Data driven personalization in e learning
Data driven personalization is a lesson you can apply to your
e-Learning content with the help of big data and new technology
like Tin Can API, you have the opportunity to know more about
your learners and their behavior patterns than ever before. You
can use that knowledge to come up with e-Learning courses that
truly engage your learners, through scenarios that speak to their
job situations and needs, characters they will relate to and more
[16].
D. How to manage data in e learning?
Big Data is a Data Management & Analytics market opportunity
driven by new market requirements. In-Database Analytics – Data
Mining there are used Big Data Connectors to combine Hadoop
and DBMS data for deep analytics. Also there is the need to re-
use SQL skills to apply deeper data mining techniques or re-use
skills for statistical analysis. Everything is all about “Big Data”
instead of RAM-scale data. This is how the predictive learning of
relationships between knowledge concepts and business events
is done [17].
IV. The Potential of Learning Analytics and Big data
The understanding of Big Data is mainly very important. In order
to determine the best strategy for a company it is essential that the
data that you are counting on must be properly analyzed. Also the
time span of this analysis is important because some of them need
to be performed very frequent in order to determine fast any change
in the business environment [18]. The rst international conference
on Learning Analytics and Knowledge was held in 2011 in Alberta
[19] dening Learning Analytics “as the measurement, collection,
analysis and reporting of data about learners and their contexts,
for purposes of understanding and optimizing learning and the
environments in which it occurs”.
International Journal of Advanced Research in
Computer Science & Technology (IJARCST 2015)
49
Vol. 3, Issue 1 (Jan. - Mar. 2015)
ISSN : 2347 - 8446 (Online)
ISSN : 2347 - 9817 (Print)
www.ijarcst.com © All Rights Reserved, IJARCST 2015
A. Improving learning between students and their
teachers
While reection on one’s learning is critical, how do we learn to
reect and make effective decisions about improving our learning?
Personalized feedback is extremely useful but for the teacher it
is time-consuming and cannot always be available in a timely
manner. Personal analytics that potentially identify one’s common
errors in understanding or less-than-appropriate techniques used
by the students can provide them with a context in which to reect
on their own development and to address improvements in a timely
manner. How might we best present such feedback and information
to students? There are several examples of systems and Web sites
that attempt that. There are web site with open resources for learning
mathematics and science through a series of online video and
interactive activities. Detailed bar graphs show students’ progress,
histograms presenting the problems students encountered with
particular activities and a tree structure represent the knowledge.
Can the use of various feedback cases and contextual knowledge
be useful in supporting such feedback? How can teachers leverage
such knowledge to provide the appropriate and effective feedback
efciently? How can the data help students help each other in
developing further understanding and help them improve their
learning experience?[20].
B. Using Learning Analytics to Predict (and Improve)
Student Success
Learning analytics provides one of many methods to not only
document student performance but also to provide tools that
encourage the types of continuous improvement that accrediting
bodies are seeking. On a more national level, institutions of higher
education are experiencing greater demands to retain students.
For example, the American Graduation Initiative (Brandon, 2009)
urges ve million additional higher education graduates by 2020.
Learning analytics can assist with this goal by providing a more
personalized learning experience through the use of data to respond
to students’ needs. This kind of personalization wills likely lead to
greater success in the classroom .Learning analytics can be used
at various levels, including the course, curriculum, institutional,
and national level. There is value in being able to leverage data
analytics at all of these various levels [21].
V. Big data get out of the bottleneck Oracle, Hadoop ,
and NoSQL .
The Oracle Big Data Appliance is an engineered system of hardware
and software designed to help enterprises derive maximum value
from their big data strategies. It combines optimized hardware
with a comprehensive software stack featuring specialized
solutions developed by Oracle to deliver a complete, easy-to-
deploy offering for acquiring, organizing and analyzing big data,
with enterprise-class performance, availability, supportability,
and security [22]. The Oracle Big Data Appliance offers the
following benets: Rapid provisioning of a highly-available and
scalable system for managing massive amounts of data, A high-
performance platform for acquiring, organizing, and analyzing
big data in Hadoop and Control of IT costs by pre-integrating all
hardware and software components into a single big data solution
that complements enterprise data warehouses. Apache Hadoop is
a powerful open source software platform that addresses above
mentioned problems of big data. This is the platform used for
cloud computing by some of the pioneers like Yahoo!, Amazon,
Facebook, eBay, etc. Two major components of Hadoop are:
Hadoop Distributed File System (HDFS) for distributed storage
and Map reduce for parallel processing. Hadoop includes a fault
tolerant storage system called the Hadoop Distributed File System.
HDFS is able to store huge amounts of information, scale up
incrementally and survive the failure of signicant parts of the
storage infrastructure without losing data. Hadoop creates clusters
of machines and coordinates work among them. Clusters can be
built with inexpensive computers. If one fails, Hadoop continues
to operate without losing data or interrupting work, by shifting
computation to the remaining machines in the cluster [23].
NoSql architecture basically tries to achieve the shared nothing
architecture, the term shared nothing means that when the data
repository is being sharded the clusters should not be dependent on
each other for any kind of updates any change inside any cluster
should not affect the others [24].
Big Data is creating new opportunities for organizations to serve
customers and markets while also creating and extracting value
in new ways. There are several technologies to handle big
data such as MapReduce, Hadoop, NoSQL, PIG and Hive. In
this paper we are focusing on the various NOSQL tools. NoSQL
database provides the foundation for many of these systems, not
only as a real-time, operational data store but in ofine capacities
as well. Traditionally, the work of capturing and analyzing data
has required different technologies, different infrastructure and
redundant costs. It is often used for storing Big Data.This is a
new type of database which is becoming more and more popular
among web companies today. Proponents of NoSQL solutions state
that they provide simpler scalability and improved performance
relative to traditional relational databases. These products excel at
storing “unstructured data,” and the category includes open source
products such as MongoDB, and Redis. NoSQL encompasses a
wide variety of different database technologies and were developed
in response to a rise in the volume of data stored about users,
objects and products, the frequency in which this data is accessed,
and performance and processing needs [26].
VI. Framework handling educational big data using
hadoop.
Fig.1: Framework handling educational big data using hadoop
As shown in g.1 a clear map which discuss hdoop environment
with big data starting with gathering structured and unstructured
International Journal of Advanced Research in
Computer Science & Technology (IJARCST 2015)
50
Vol. 3, Issue 1 (Jan. - Mar. 2015) ISSN : 2347 - 8446 (Online)
ISSN : 2347 - 9817 (Print)
www.ijarcst.com
© 2015, IJARCST All Rights Reserved
data to store and manage this. After that looking on products
(data) to help developer write hadoop application or otherwise
analyze this data.
VII. Conclusion
This paper has claried the manipulation of big data in educational
environment. This has been done by using hadoop and nosql.
Furthermore, a propose framework for handling big data in
E-Learning environment has been presented. In addition, the
operation of the proposed model will be accelerated by using
soft computing intelligent techniques. Moreover, its performance
will be enhanced through data mining approaches. We expect that
the proposed technique will develop E-Learning industry.
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procs/siragusa.pdf
... • Synchronous e-learning: is education that takes place over the air or directly, and allows a large group of students to engage in dialogue and interaction at the same time, both among themselves and with the instructor [16]. • Asynchronous e-learning: This form of education is known as indirect education because it does not require the presence of learners at the same time. ...
... • Asynchronous e-learning: This form of education is known as indirect education because it does not require the presence of learners at the same time. Instead, the learner will study at his own pace and with the commitment he desires [16]. ...
... • Co-education: It gives the learner more freedom and achieves a form of socialism in education by becoming asynchronous at times and synchronizing at other times, depending on the activities suggested by the lecturer. [7] [18] [17] [16]. ...
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