A MODEL FOR ENHANCING THE STRUCTURE AND STRATEGY IN AN
E-LEARNING ENVIRONMENT
S. Subha1, S. Baghavathi Priya2
1
2
Rajalakshmi Institute of Technology, 600124 Kuthambakkam, Chennai
Rajalakshmi Engineering College, 602105 Thandalam, Chennai, Tamil Nadu
Abstract
1.
Introduction
In the domain of E-learning, hundreds of dimensions
E-learning becomes an effective tool in the learning
can be taken and predictions can be made based on the browsing
process which is followed in most of the colleges and
history of the users. Nowadays, web-based learning becomes an
universities worldwide [10]. For example, University of
alternate to face to face education and E-learning becomes an
Maryland, Baltimore Country (UMBC) use a learning analytics
effective tool in the learning process. E-Learning paves way for
tool which is integrated into their institutions’ LMS[17]. It helps
the learners to interact with the digitally delivered content and
them to keep track of the progress of the student.
services. It focuses on making the learners to become more
Handling data in a vast amount becomes a challenging issue
interested and efficient in their study process. This paper aims at
in e-learning platforms. Also research steps are taken to process
designing strategies that will improvise the structure, assessment
the data before uploading in an e-learning platform. Better
criteria and course completion rate in an e-learning environment.
learning can be obtained by better and efficient content [20]. It
To achieve this aim, the following sub-objectives have been
is used to enhance knowledge and performance.
framed. The first objective is to provide a well-defined structure
of the course whereas the second objective focuses on designing
The big data arena has erupted over the last year with
an e-learning system with specific assessment criteria. The third
unbelievable technological advancements. Big data is “high-
objective aims at achieving high completion rates for the
volume, -velocity and -variety information assets that demand
registered students. By framing a defined structure, various
cost-effective, innovative forms of information processing for
assessment criteria can be fixed and calculated for a given
enhanced insight and decision making”. This definition is given
student data set. Finally the active participation of enrolled
by Gartner. Few characteristics which measure the complexity
candidates can be examined frequently. Machine Learning
of current big analysis is depicted in Fig 1. According to a
algorithms can be used efficiently in order to handle data in
survey, more than 90% of world’s data today has been created in
large amount.
the last two years alone. Every day 2.5 Quintillion bytes of data
are created. As data grows big every second, there comes an
Keywords: E-learning, Big Data, MOOC, SPOC, Assessment.
important strategy to provide solutions for the predictive
analytics. It is none other than the deep learning [14]. Solutions
are given with increased processing power and some
enhancements in graphic processors.
c
978-1-5386-9371-1/19/$31.00 2019
IEEE
141
E-learning growth rates in various countries are
depicted in Fig.2. Nowadays people get more awareness on the
effective usage of e-learning methods[8]. Surplus amount of
information are being shared by many individuals in e-learning
platforms. Data can be easily documented. Large amount of data
grows at an extra ordinary rate due to enormous developments
in the fields of social media, web technologies, health care and
mobile devices. As a result, there are many challenging fields in
handling big data such as financial services, health care,
transport, biology, online advertising etc. But better predictions
and smarter decisions can be taken using big data [18].
Fig.1 Four I’s of Big Data
Among 22% of the useful data, only less than 5% of
data was actually analyzed in 2013. By 2020, more than 30% of
data could be made useful for data analytics [9].
Machine learning plays a vital role in the field of data
analytics and there is a need for fast Machine Learning
techniques nowadays [3]. It is defined as “ability of systems to
automatically learn and progress from experience without being
explicitly programmed”. It is an application of artificial
intelligence. According to the survey of the McKinsey Global
Fig.2 Comparison of E-learning growth rates
Institute, Machine learning is considered as one of the main
drivers of Big Data revolution [5]. The two main categories of
learning tasks are the Supervised and Unsupervised learning.
2.1 E-learning platforms
E-learning is a popular field in which big data plays a
2. Literature Survey
very vital role. Creation of individual
Since late 1990s, many worldwide organizations are
developing
standards
and
specifications
for
e-learning
technologies. Better learning can be obtained by better and
efficient content [20]. It is used to enhance knowledge and
materials gives an efficient interaction between the learners and
the content in e-Learning systems [19]. E-learning can be used
anytime, anywhere and is easy to update. It is cost effective and
is well suited for global audience.
performance.
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e-learning
3rd International Conference on Computing and Communication Technologies ICCCT 2019
Some of the renowned e-learning platforms are listed
below.
1.
MOOC (Massive Open Online Course)
2.
SPOC(Small Private Online Course)
3.
SWAYAM
with self-learning ability
themselves.
Used in a varied customer
Used
context.
business context.
Open to a lot of people at
Open to a small group of
same time.
people.
Student participation rate is
Student participation rate is
in
a
business-to-
4.
NPTEL
low.
high.
5.
Course Era
Completion rate is low.
Completion rate is high.
6.
Moodle
Educational Data Mining (EDM) is an effective and practical
2.2 Challenges in MOOC
method and means of applying Big Data Education (BDE).
In relevance to a post published on 25th October 2013,
According to Wei Shang, Shiming Qin [2], educational
the following are the four major challenges in evaluating
platforms are categorized into three types namely Traditional
MOOC.
Online Education Platform, MOOC and SPOC. The challenge is
to further strengthen the research on the educational data mining
of online education.
(i)
No established criteria
(ii)
Low Completion rates
(iii)
Varied Instructor Involvement
(iv)
Accessibility
Amongst the e-learning platforms, MOOC and SPOC
play a predominant role in the past decades. According to Ping
Guo[4], the comparison of MOOC and SPOC is given in Table
1.
No established criteria
Table 1. MOOC and SPOC Comparison
Courses vary greatly and don’t adhere to a standardized
structure. Also there is a lack of validated assessment criteria.
MOOC
SPOC
Fit for non-campus, large-
Special education mode on
scale
Campus.
education
with
sharing,
resource
Low Completion rates
Only 7-10% of enrolled participants are actually
education
finishing their courses on time. They gain knowledge but
fairness as the concept.
sometimes cannot complete the course.
Well adapts for basic theory
Applies
to
education.
skills education.
professional
Varied Instructor Involvement
Sometimes the instructor can be a facilitator or simply
More suitable for people
Suitable for students with
a subject matter expert. Students also can feel disconnected and
weak
unengaged in the course.
ability
to
control
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Accessibility
Table 2. Enrolled Participants
The courses are available online for everyone. Videos,
Presentations, audio lectures, Social media discussions etc. are
to be accessed by everyone.
2.3 Challenges in SPOC
Five main challenges concerning deep learning in
SPOC are addressed by Renee M. Filius [16].
(1) Alignment in learning activities
(2) Insight into students’ needs
(3) Adaptive nature in teaching strategy
(4) Social Cohesion
(5) Creating dialogue.
Roll
Number
Name
Courses taken
101
Abhani
2
102
Abhinav
1
103
Abimanyu
2
104
Bavithraa
3
105
Haritha
1
106
Harshitha
2
107
Indhu
3
108
Jagan
1
109
Kavin
2
110
Niranjana
2
111
Sanjay
3
112
Sri Thiya
1
113
Sujatha
3
114
Thendral
2
115
Varsha
2
The first two challenges in MOOC are taken into
consideration in this paper.
The details of the courses registered by each participant is
depicted in Table 3.
There is no standardized structure in MOOC as there are
Table 3. Students’ Course Details
more number of courses. Also there is no proper criteria for
assessment of student. It is planned to restrict the number of
courses to a limited number for a specific branch of students.
The completion rate can be increased to a considerable range if
the assessment criteria is fixed.
For example, let us consider the following example. Fifteen
students have enrolled for three different courses in the
Computer Science and Engineering stream namely Data
Mining(DM), Artificial Intelligence(AI) and Artificial Neural
Networks(ANN).
The resultant student data set with actual registered courses
count is shown in Table 2.
144
Roll
Number
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
Name
Abhani
Abhinav
Abimanyu
Bavithraa
Haritha
Harshitha
Indhu
Jagan
Kavin
Niranjana
Sanjay
Sri Thiya
Sujatha
Thendral
Varsha
Data
Mining
Y
N
Y
Y
Y
N
Y
N
Y
N
Y
Y
Y
Y
Y
Machine
Learning
Y
N
Y
Y
N
Y
Y
N
Y
Y
Y
N
Y
Y
N
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Artificial
Neural
Networks
N
Y
N
Y
N
Y
Y
Y
N
Y
Y
N
Y
N
Y
3. Proposed System
a.
are given in the form of videos, presentations,
a. Providing a well-defined structure
documents. Weekly analysis report will be
It is a challenging factor when more number of
generated on the basis of their frequent visits to the
participants get enrolled for either a single course or many
online course.
courses. Their intention can be classified under two categorieswhether the participants are willing to write exams or they enroll
How frequently they watch course materials which
b.
to gain knowledge alone. During registration, participants have
Assignments should be submitted in time once in a
fortnight.
to select any of the above given options. The workflow is shown
c.
in Fig.3.
Conducting On line test at the end of each chapter
in a course.
A maximum of 10, 10 and 30 marks can be awarded
for the above three criteria respectively with the grand total of
50. This assessment can be made once in a month for a three
month duration course. Among the fifteen participants, a sample
of five students’ assessment is calculated based on the above
mentioned criteria. The result is depicted in table 4.
Table 4. Evaluation results
Name of the
Frequent
Assignment
Online
Total
Participant
Visit
(10)
Test
(50)
(10)
(30)
Fig. 3 Registration Workflow
Abhani
5
10
20
35
Abhinav
8
5
20
33
set are shown in Fig.4.
Abimanyu
8
10
25
43
b. Criteria for assessment
Bavithraa
5
10
25
40
Haritha
5
5
15
25
Supervised learning namely Classification can be used to get the
net result of overall registered candidates under two categories.
Number of courses selected by the participants in sample data
The next challenge namely the assessment criteria can
be analyzed. Candidates who have registered for writing exams
will be assessed periodically in the given course period. After
registration, they are provided with the course materials in the
form of videos, presentations, documents and audio (if needed).
Marks can be awarded based on the following criteria.
c. Achieving High Completion Rate
Once the assessment marks are calculated, the overall
results of the participants in a course can be displayed in the elearning platform. Participants who score marks greater than
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40(out of 50) can be crowned as “Platinum Plus” winners.
Those who secured marks between 30 and 40 can be rewarded
as “Gold Plus” participants and those who got an average of 25
to 30 marks can be graded as “Silver Plus” candidates. It is a
motivating factor for the participants to have a continuous
learning with greater involvement. As a result, all the
participants will strive hard to get “Platinum Plus”. Without any
intervention, they will resume their course till the final exams.
So the completion rate of the students will get increased.
Data can be made effective and popular among the
learning community by applying various stages of data mining.
Inaccurate or incomplete data can be determined using data
cleaning.
Merging of new information with the existing
information can be achieved by data integration. Meaningful
Fig.4 Registration count of courses
information can be produced from the data collection using data
Few samples from the given data set are taken and marks are
processing. Rapid Prototyping model called eLab Rapid
awarded for them. Assessment methodologies are applied on the
Prototyping Model is developed by Botturi, Cantoni, Leproi and
students and their performance is evaluated based on the
Tardini[22]. The model is designed in such a way that the
assessment marks. Fig.5 shows the marks obtained by few
teachers focus on pedagogical design instead of course materials
samples during the first week of the course. When the data set
preparation and technology. This can be inculcated in future
grows
LMSs.
Machine)cannot be fast and effective[1].
large
in
number,
an
ELM(Extreme
4. Results and Discussions
By incorporating the data mining techniques and
Machine Learning algorithms, the structure of the e-learning
platform is well defined. Total number of courses selected by
the participants are clearly defined and is depicted in Fig.4.
Fig.5 Assessment Marks of each participant
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3rd International Conference on Computing and Communication Technologies ICCCT 2019
Learning
The bottom line in Big Data learning is that it is unable
to solve both imbalanced and large-volume data learning
Abbas Saeed[23]. Existing learning analytics tools are proposed
to be compared and analyzed.
problems [7]. As data comes from various sources, these data
can have different formats. Also the data to be processed may be
of different types. For example, the categorical data may be
combined with numerical data or the data with images may be
added with categorical data. Before data processing, data preprocessing and data cleaning are done to configure data to fit
within a given model. Works of Wei Ding et al. [17] features the
challenging issues in the data-driven model.
As e-learning
becomes the most renowned technology in the field of
education, inclusion of data analytics is treated as an important
subject. Many methods are evolving to process the data before
uploading in an e-learning environment. Major impact might be
created in future by Open Source LMS with its cost
effectiveness and advanced features[21]. But the installation and
support costs must be considered before using Open Source
LMS.
5. Conclusion and Future Enhancements
Challenges faced by MOOC e-learning platform are
analyzed and provided solutions for few challenges. By
providing a well-defined structure, the registered participants
can get a vivid picture about the e-learning platform. After
registration, they can be assessed by specific, measurable
criteria. Also each participant’s performance is analyzed and
his/her overall grade is calculated. It is observed that there is a
considerable raise in the completion rates of registered
participants as there is a fixed evaluation criteria and welldefined structure. Challenges in other renowned e-learning
platforms namely SPOC and NPTEL will be taken into
consideration for future work. It is planned to develop ideas to
overcome the issues in SPOC and NPTEL e-learning platforms.
While comparing the e-learning platforms, other additional
criteria namely Flexibility, Ease of using, Course delivery tools,
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