Prioritizing barriers of E-Learning for effective teaching-learning using fuzzy analytic hierarchy process (FAHP)

Abstract

E-Learning has become well preferred and accepted tool in teaching-learning of higher education. This pattern of change is owing to the advancement in computer and teaching-learning methodology. The introduction of E-Learning provides the real-time flexibility of time and place. The success of E-Learning depends upon many factors that must be controlled to accomplish effective E-Learning outcome. Moreover, the E-Learning teaching-learning process is also obstructed by several barriers. Stakeholder of E-learning must study and overcome these barriers for getting the benefits of the system. In the present research, MCDM based analytic hierarchy process in its fuzzy form has been applied to study the influence of barriers on the system. Four main dimensions of E-Learning are selected for the study, which are, Student, Instructor, Infrastructure, and Technology and Institutional Management. Twelve barriers under these dimensions are also selected to study their influences on each other. The twelve barriers of E-Learning are quantified using FAHP method and prioritized in terms of control the barriers. The prioritization of such barriers will help the stakeholders to control the E-Learning teaching-learning system.

Full text

Prioritizing Barriers of E-Learning for Effective Teaching-
Learning using Fuzzy Analytic Hierarchy Process (FAHP)
Quadri Noorulhasan Naveed
Department of Information System,
KICT, International
Islamic University
Malaysia, Kuala Lumpur, Malaysia
quadri.nn@live.iium.edu.my
Mohamed Rafik N. Qureshi
College of Engineering,
King Khalid University,
Kingdom of Saudi Arabia
mrnoor@kku.edu.sa
Alhuseen O. Alsayed
Deanship of Scientific Research,
King Abdulaziz University,
Kingdom of Saudi Arabia
aoalsayd@kau.edu.sa
AbdulHafeez Muhammad
Department of Computer Science,
Bahria U
niversity
Lahore Campus.
ahafeez.bulc@bahria.edu.pk
Sumaya Sanober
College of Arts and Science,
Prince
Sattam Bin Abdula
ziz University,
Kingdom of Saudi Arabia
s.sanober@psau.edu.sa
Asadullah Shah
Department
of Information System,
KICT, International
Islamic University
Malaysia. Kuala Lumpur, Malaysia
asadullah@iium.edu.my
Abstract - E-Learning has become well preferred and
accepted tool in teaching-learning of higher education. This
pattern of change is owing to the advancement in computer and
teaching-learning methodology. The introduction of E-Learning
provides the real-time flexibility of time and place. The success of
E-Learning depends upon many factors that must be controlled
to accomplish effective E-Learning outcome. Moreover, the E-
Learning teaching-learning process is also obstructed by several
barriers. Stakeholder of E-learning must study and overcome
these barriers for getting the benefits of the system.
In the present research, MCDM based analytic hierarchy process
in its fuzzy form has been applied to study the influence of
barriers on the system. Four main dimensions of E-Learning are
selected for the study, which are, Student, Instructor,
Infrastructure, and Technology and Institutional Management.
Twelve barriers under these dimensions are also selected to study
their influences on each other. The twelve barriers of E-Learning
are quantified using FAHP method and prioritized in terms of
control the barriers. The prioritization of such barriers will help
the stakeholders to control the E-Learning teaching-learning
system.
Index Terms - E-Learning, Barriers of E-Learning, Multi-
Criteria Decision Making (MCDM), Fuzzy based Analytic
Hierarchy Process, Higher Education.
I. INTRODUCTION
Nowadays E-Learning has caused many changes in the
higher education system as it plays a role in the modern-day
teaching-learning process, thus changes past learning concept.
E-Learning is a subset of distance teaching and learning
system that is going on since the 1980s. An E-Learning
methodology can exploit instruction process and encourages
learning by developing the information and study material
repositories for all the stakeholders [1].
The advancement of E-Learning offers new conceivable
outcomes for learning and prompts radical changes in teaching
and learning. With the spread uses of the web, E-Learning
turned out to be incomprehensibly broad and numerous
institutes of higher education are using it in their program
[1], [2]. In many countries, numerous candidates do not have
an approach for getting a higher education by attending
regular teaching classes. E-Learning can compensate the
shortcoming of customary learning strategies and bring the
chance to learn by using new development of ICT [3].
These E-Learning frameworks collect a vast information;
which is praiseworthy in assessing students’ performance and
helping the administration of higher educational institutions
and instructors to identify conceivable faults and errors in
improving the present E-Learning system. Around the world,
the E-Learning market has developed very fast, therefore there
exist disappointments [4]. Some researchers stated that as
students of conventional learning many of them are getting
benefits from E-Learning system. E-Learning courses have a
few limits which must be uncovered and modify [5]. For the
moment, numerous E-Learning based courses cannot
adequately propel students to take part actively, in another
word, E-Learning is being adept to isolate learners and this
could cause disappointment. To consider the expansion of
investment on E-Learning arrangements, it is essential to
know the process of calculating the success of E-Learning
programs and to conquer their deficiencies [5], [6]. Numerous
researchers have considered its importance and studied
different aspects of E-Learning system’s performance and
results. One method for confronting these difficulties is
identifying the barriers which affect E-Learning systems’
performance. Past studies have mentioned different
dimensions of barriers that influence the overall execution of
E-Learning system.
This paper has two principal contributions, first,
presenting comprehensive dimensions of barriers of E-
Learning based on in-depth review of literature. Second,
prioritizing the barriers by employing a multi criteria decision
making based analytic hierarchy process in fuzzy
environment. AHP has been widely used in decision making,
however the decision making under crisp environment may
accrue some biasness and vagueness. To overcome such error
in human decision making, the AHP has been applied in fuzzy

environment for the barriers of E-Learning. The rest of the
paper is organized as follows; section II provides a brief
review of literature followed framework for E-Learning
barriers. Section IV provides brief insight of fuzzy set theory
and MCDM based AHP methodology under fuzzy
environment. Section V provides application of FAHP in E-
learning for prioritization. Section VI provides findings and
discussions followed by concluding remarks, limitations, and
future research scopes.
II. LITERATURE REVIEW
There are diverse advancements in the field of education.
The word E-Learning refers to electronic learning [7]. Now
instructors are utilizing different ICT tools for E-Learning,
such as Intranet, Extranet, Internet, broadcast with the help of
satellites, audio/video tape, intelligent, smart TV, CD–ROM,
cellular phones, PDAs, and numerous others devices and
mediums. But with the advancement of the web, idea of E-
Learning has become a common tool in higher educational
institutes where courses are taught with the help of internet
and ICT tools [1], [8]. E-Learning can help in synchronous or
asynchronous learning and can be used beyond the limitation
of time and place [7]. The two terms, time and place, indicate
the degree to which a course is presented by time or/and by
place. Synchronous implies that at least two actions happen at
the same time, while Asynchronous implies that at least two
occasions do not happen at the same time. For instance, when
a student goes to live class or workshop, the occasion is
synchronous because the event and the learning happen at the
same time. Asynchronous learning happens when the student
takes an online course in which the student finishes events at
various times and correspondence also happens at different
times via email or on discussion board. Both types of these
arrangements have unique sorts of challenges and obstructions
which ought to be resolved before starting the uses of E-
Learning System [9]. [10] defined E-Learning as, “Teaching
Learning experience and instructional content are carried out
by using electronic technology”.
III FRAMEWORK FOR E-LEARNING BARRIERS
Incorporating E-Learning into the conventional teaching-
learning educational system is bit a challenging task. Imbibing
E-Learning may involve complexities and challenges. The
obstacles in adoption of E-Learning conventional teaching-
learning system may be referred as E-Leaning Barriers. Many
barriers have been suggested by researchers across the globe.
The research framework for identifying and prioritizing the E-
Learning barriers has been designed as shown in Fig 1. Based
on the research framework, in-depth review of literature on E-
Learning barriers has been carried out. Many E-Learning
barriers dimensions have also been encountered during the
systematic in-depth review of literature. The review of
literature reveals that E-Learning barriers are numerous hence
must be identified and prioritized. Many researchers also tried
to classify and group them in systematic way into the body of
E-Learning literature. The present research adopts the four
main dimensions of E-Learning barriers. The identified barrier
dimensions are related to students known as Student
Dimensions (SD), barriers related to instructors known as
Instructor Dimension (ID). barriers related to infrastructures
and technologies referred as Infrastructure & Technology
dimension (ITD) and barriers related to institutional
management referred as (IMD) [6]. The outcome of review of
literature also revealed twelve barrier factors. The twelves
barriers are grouped under the main dimension barriers for the
present research.
Fig. 1 Framework for E-Learning Barriers Prioritization
The main E-Learning barrier factors are identified by
reviewing literature. The identified 12 barriers factors are
Lack of E-Learning Knowledge (SELK), Lack of English
Language Proficiency (SELP),Lack of Motivation (SM), Lack
of ICT Skills (IICT), Instructors Resistance to change (IRC),
Lack of Time to Develop E-Courses (ITTD), Inappropriate
Infrastructure (TII), Lack of Technical Support (TTS), Lack of
Financial Support (MFS), Lack of Inadequate Policies
(MIP),Lack of Training on E-Learning (MTE), Lack of
Instructional Design (MID). The identified 12 barrier factors
are illustrated with its respective barrier dimension as follows:
A. Students’ Dimensions
Students’ Dimension is an important dimension since E-
Learning is aimed to fulfill students’ needs. In the E-Learning
system, instructors are distantly away from the students and
students may face difficulties during the E-Learning session.
There are many student related barriers. Based on their
importance there important barrier factors are identified. The
identified factor barriers are Lack of E-Learning Knowledge
(SELK), Lack of English Language Proficiency (SELP) and
Lack of Motivation (SM) which are presented in Table I.
TABLE I
BARRIERS RELATED TO STUDENT’S DIMENSION
Barriers Factors Resources / References
Lack of E
-Learning
Knowledge (SELK)
[11]
, [12], [13], [14]
Lack of English Language
Proficiency (SELP)
[15]
, [16], [17], [17]
Lack of Motivation (SM)
[12],
[9], [10], [17], [18], [19],
[20]

B. Instructors’ Dimensions
Instructor plays a significant role in the E-Learning
teaching-learning session. Instructor provides the much
needed comforts and user-friendliness. The instructor
knowledge of ICT skill, resistance in adopting changes and
lack of time in developing E-course may prove to be
roadblock to the success E-Learning. Hence barriers factors
like Lack of ICT Skills (IICT), Instructors Resistance to
change (IRC), and Lack of Time to Develop E-Courses
(ITTD) are considered to be significant in E-learning teaching-
learning system. The identified barrier factors are presented in
Table II.
TABLE II
BARRIERS RELATED TO INSTRUCTOR’S DIMENSION
Barriers Factors
Resources / References
Lack of ICT Skills (IICT)
[12], [21], [22], [13], [23], [16],
[10], [24], [25], [26],[27], [28]
Instructors Resistance to change (IRC)
[29], [22], [22] [13], [30], [23],
Lack of Time to Develop E-Courses
(ITTD)
[15], [29], [22], [24], [26], [18],
[27], [31], [19], [28], [14]
C. Infrastructure and Technology Dimensions
The dimension involving Infrastructure and Technology
plays a vital role in the success of E-Learning teaching-
learning. Infrastructure provides an easy access to E-learning
system whereas technology permits the use of stat-of-the art
technology in hardware and software for required
effectiveness in teaching-learning. Infrastructure and
Technology dimension involve the huge investment in
erecting, running and maintaining the E-Learning system. Two
main barriers namely Lack of Inappropriate Infrastructure
(TII) and Lack of Technical Support (TTS) are identified and
presented in Table III.
TABLE III
BARRIERS RELATED TO INFRASTRUCTURE AND TECHNOLOGY
DIMENSION
Barriers Factors
Resources / References
Inappropriate
Infrastructure (TII)
[11], [12], [21] , [32], [13], [23], [16], [33],
[24], [25], [26], [27], [31], [28], [19], [14]
Lack of Technical Support
(TTS)
[12], [15], [29] , [34], [32], [13], [35], [36],
[27], [37], . [31], [20]
D. Institutional Management Dimensions
Institutional Management Dimension involves the
management commitment towards the E-Learning system for
teaching-learning. Any obstacle in achieving the institutional
management towards E-Learning system will be critical for
the success of E-Learning. Many universities consider a state-
of-the-art knowledge thorough E-learning system as a dire
responsibility towards corporate social responsibility (CSR).
Four barriers factors like Lack of Financial Support (MFS),
Lack of Inadequate Policies (MIP), Lack of Training on E-
Learning (MTE), Lack of Instructional Design (MID) as
shown in Table IV, may play a critical role in managing E-
Learning system by the institution.
TABLE IV
BARRIERS RELATED TO INFRASTRUCTURE AND TECHNOLOGY
DIMENSION
Barriers
Factors
Resources / References
Lack of Financial Support
(MFS)
[11], , [21], [12], [38], [13], [30], [39], [10],
[24]
, [17], [25], [27]
Lack of Inadequate
Policies (MIP)
[11], [12], [15] , [38], [21], [13], [23], [24],
[20]
Lack of Training on E-
Learning (MTE)
[11]
, [12] [15], [29], [13], [32], [19]
Lack of Instructional
Design (MID)
[15]
, [31]
IV FUZZY ANALYTIC HIERARCHY PROCESS (FAHP)
The analytic hierarchy process under fuzzy environment
involves the use of fuzzy set theory and extension principle.
Analytic hierarchy process (AHP) is a multi-criteria decision
making (MCDM) proposed by [40]. The process is hierarchy
based and provides scientific decision making approach.
However, the involvement of decision maker during pairwise
comparison may accrue human judgmental error due to
biasness and vagueness in decision making. In order to
remove such flaws, Fuzzy AHP is often practiced in fuzzy
environment. Group decision making (GDM) and Delphi
method may also become fruitful in removing biasness in
decision making. The following section provides some insight
on basic fuzzy set theory. It also provides some basic theory
on the application of extension principles in AHP for
prioritizing the e-learning barriers under fuzzy environment.
(i) Fuzzy Set Theory
In order to achieve the robust decision making under
varying condition, the fuzzy knowledge introduced by Zadie
may prove to be useful in removing the error. The decision
making in crisp mode may have judgmental biasness or it may
be vague. Hence, application of fuzzy knowledge is inevitable.
The imprecise information in crisp decision making is
improved by employing fuzzy membership function ranging
from 0 to 1.
A set of fuzzy numbers (a, b, c) in triangular form or
interval-valued trapezoidal form (a ,b, c, d) may be employed
to facilitate the decision making [41].
Fig. 2 Triangular fuzzy number (M)

Fuzzy set theory provides arithmetic operations between
two triangular fuzzy number (TFN) using certain rules which
are being stated below [42]: Considering two positive TFNs
may be represented by M1 and M2 as (,,) and
(,,) respectively.
The fuzzy summation and fuzzy subtraction of two fuzzy
numbers may be denoted by  and 4 which gives TFN. In
case of the fuzzy multiplication of any two TFNs, approximate
TFNs may be obtained.
Considering 
=,, and 
=,, as two
TFNs, the following operational rules are described:
(1)
(2)
(3)
(4)
(5)
(ii) Application of Extent analysis principle in AHP under
fuzzy environment
The extent analysis principles may be applied while
comparing two triangular fuzzy numbers (TFNs) [43].
Considering two sets as objective and goal as
={ , ,………, } and ={ , ,………, }
respectively, each object may be derived and extent analysis
for each goal can be performed. As a result m extent analysis
values for each object can be derived as:

,
…
,=1,2,…, (6)
Where 
 (=1,2,…) are TFNs and represented as
(,,). Chang’s extent analysis procedure [43] may be adopted
for fuzzy analytic hierarchy process as follows:
Step 1: Obtain the hierarchy structure for the given goal
The hierarchy structure for the given goal may be
obtained using the help of Expert’s judgement. The goal of
prioritizing the barrier dimensions and barrier factors may be
put at the top hierarchy followed by main barrier dimension
and barrier factors.
Step 2: Obtain the pairwise comparison for main barriers
dimensions and barriers factors using TFNs
The pairwise comparison among E-Learning barriers may
be obtained using Expert’s judgement. TFNs may be used to
decide the relationship between two barriers.
Step 3: Obtain the value of fuzzy synthetic extent
= ∑


  ∑∑




  (7)
Using fuzzy summation of TFNs, m extent analysis
values∑


 , may be obtained depicted as:
∑


 =∑,

 ∑

 ,∑

  (8)
and ∑∑




 , the fuzzy summation of

(=1,2,…,) values may be performed to get
∑∑




=∑

 ,∑,∑



  (9)
Using the given equation as below, the inverse of the
vector may be derived as:
∑∑




 =  
∑

 ,
∑

 ,
∑

  (10)
Step 4: Obtain the degree of possibility of supremacy for two
TFNs i.e. =(,,)≥=(,,)
(≥)= ((),()) (11)
and can be equivalently expressed as follows:
(≥)=ℎ (∩)=() (12)
()=  1
0

()()  ≥ ,  ≥  (13)
The intersection of two TFNs is shown in Fig.3. The
ordinate d is obtained for the possible highest intersection
shown as D between  and . In order to equate 
and , values of V ( ≥ ) and V ( ≥ ) must be
calculated.
Fig. 3. The intersection of TFNs [41]
Step 5: Obtain the degree of possibility for a given convex
fuzzy number such that it is greater than k convex
Fuzzy number (=1,2,….,) may be defined as
 (≥ ,….)=(≥)  ≥
 ……… (≥) (14)
=min (≥), =1,2,………,
Considering,
()=min(≥)  =1,2,…..,;≠ (15)
Weight vector is may be derived as =
(),(),………,()
Such that (=1,2,…..,) has n elements

Step 6: Obtain the normalized weight vectors.
The normalized weight vector may be obtained using Eq.
(16).
=(),(),………,() (16)
Where W is a non-fuzzy number.
Step 7: Compute the overall score of each barrier dimensions
and barriers factor for the prioritization
The priority weightage of each barrier dimensions and
barrier factors may be calculated using local weightage and
global weightages. The overall score may be obtained on
arranging the global weightages in descending order for the
respective prioritization.
V FAHP FOR PRIORITIZING THE E-LEARNING
BARRIERS
In order to have successful E-Learning system,
stakeholder must study the E-Learning barriers. The E-
Learning barriers hinder the success of E-learning system in
various ways. Hence various barriers of E-Learning system
must be prioritized so that the each barrier may be studied to
control the E-learning system in the best possible manner.
The following steps may be derived to carryout FAHP for
the prioritizing the e-learning barriers as follows:
Step 1: Obtain the Hierarchy structure for the given goal
The E-Learning barriers dimension and barriers factors
obtained through systematic framework will lead to the
hierarchy as shown in Fig.4. The hierarchy is also cross
verified by five experts members having more than 15 years of
E-Learning teaching-learning experience.
Fig. 4. Hierarchy of Barriers Dimensions and Barriers Factors for
Prioritization
The over goal can be framed as the prioritization of the E-
Learning barriers. At the first level the four barrier dimensions
of E-Learning are considered whereas at the second level the
twelve barriers factors are considered. All the main barriers
dimensions and barriers factors for the E-Learning system are
discussed in the previous section.
Step 2: Obtain the pairwise comparison for main barriers
dimensions and barriers factors using TFNs
By employing linguistics scale experts’ judgement may be
obtained for each pairwise decision for main barrier dimension
and barrier factor. Triangular fuzzy conversion scale as shown
in Table V is used for making pairwise comparison. The
experts’ team may be asked to compare one barrier dimension
over other using fuzzy linguistics scale. Later on this
linguistics comparison may be transformed to obtain degree of
possibility. Thus the fuzzy pairwise comparison of each main
barrier dimension and barriers factors may be defuzzified to
get the crisp results.
TABLE V
TRIANGULAR FUZZY CONVERSION SCALE [44], [45]
Linguistic scale
Triangular
Fuzzy Scale
Triangular Fuzzy
Reciprocal Scale
Just Equal
(1,1,1) (1,1,1)
Equally Important
(1/2,1,3/2)
(2/3,1,2)
Weakly
More
Important
(1,3/2,2) (1/2,2/3,1)
Strongly More
Important
(3/2,2,5/2)
(2/5,1/2,2/3)
Very Strongly
More Important
(2,5/2,3) (1/3,2/5,1/2)
Absolutely More
Important
(5/2,3,7/2)
(2/7,1/3,2/5)
The linguistic scale employed may be transformed into
the comparison matrix using respective TFN. The fuzzy
evaluation matrix for main barriers dimension with respect to
the goal can be obtained as shown in Table VI.
TABLE VI
THE FUZZY EVALUATION MATRIX WITH RESPECT TO THE GOAL
USING TFNS
Main Barrier
Dimensions
SD ID ITD IMD
Students Dimension
(
SD)
(1,1,1)
(3/2,2,5/2)
(1/2,1,3/2)
(1/2,1,3/2)
Instructor’s
Dimension (
ID)
(2/5,1/2,2/3)
(1,1,1)
(1,1,1)
(1,1,1)
Infrastructure and
Technology (
ITD
)
(2/3,1,2)
(1,1,1)
(1,1,1)
(1,3/2,2)
Institutional
Management
Dimension (IMD)
(2/3,1,2)
(1,1,1)
(1/2,2/3,1)
(1,1,1)

Step 3: Obtain the value of fuzzy synthetic extent
The weightage of each main barrier dimension and barrier
factors may be obtained by calculating fuzzy synthetic extent
values using Eq. (7). The various values that may be obtained
for the four main barriers dimensions are denoted as SSD, SID,
SITD and SIMD.
 = (2.62, 3.29, 4.5)  (1/22.50,1/17.97,1/15.45)=
(0.12, 0.22,0.29) (17)
 = (4.0,4.0,6.5)  (1/22.50, 1/17.97,1/15.45) =
(0.18, 0.22,0.42) (18)
 = (4.5, 5.17,6.0) (1/22.50, 1/17.97,1/15.45)=
(0.20,0.29,0.39) (19)
 = (4.33,4.9,5.5)  (1/22.50, 1/17.97,1/15.45) =
(0.19,0.27,0.36) (20)
Step 4: Obtain the degree of possibility of supremacy for two
TFNs i.e.=(,,)≥=(,,)
The degree of possibility of Si over Si (i z j) were
calculated by using Eqs. (12) and (13).
V(SIDtSSD)= (..)
(..)(..)=1
The degree of possibility for main barrier dimensions are
calculated and shown in Table VII indicating the degree of
possibility as 1*, similarly, other values are obtained by
comparing other remaining main barrier dimensions.
Similarly, the same procedure may be carried out for
comparing the factor dimension by taking help of experts’
judgement. . The degree of possibility plays a major role in
obtaining the weightages of each barrier.
TABLE VII
DEGREE OF POSSIBILITY
t
V(S
SD
)
V(S
ID
)
V(S
ITD
)
V(S
IMD
)
V(S
SD
)
-
0.95
0.56
0.64
V(S
ID
)
1*
-
0.77
0.82
V(S
ITD
)
1
1
-
1
V(S
IMD
)
1
1
0.91
-
Step 5: Obtain the degree of possibility for a given convex
fuzzy number such that it is greater than k convex
The minimum degree of possibility may be calculated
using Eq. (15). The degree of possibility for main barrier
dimension are calculated and shown as follows:
()= min (0.95,0.56,0.64)=0.56
()=min (1,0.77,0.82)=0.77
() =min (1,1,1)=1
()=min (1,1,0.91)=0.91
Hence W0 = (0.56, 0.77, 1, 0.91).
Step 6: Obtain the normalized weight vectors
The normalized weight vector may be calculated using
Eq.(16).
Thus the weight vector of the Students' dimension,
Instructors' dimension, Infrastructure and technology
dimension and Institutional Management Dimension were
found to be: WG = (0.17, 0.24, 0.31, 0.28)T
Step 7: Compute the overall score of each barrier dimensions
and barriers factor for the prioritization
Using the TFN, the pairwise comparison yields the
priority weight for each barrier dimensions and barriers
factors. The priority weightage of each barrier dimensions and
barrier factors is calculated using local weightage and global
weightages. The products of such weights were obtained to
decide the priority of each barrier. The weightage of barrier
dimension is shown as level-1 priority whereas weightages of
barrier factors is shown as level-2 priorities. The product of
level-1pririty and level-2 priorities gives the final priorities.
On arranging the final priorities in descending order the
priorities of each barrier factor is calculated. The final priority
so obtained is shown in Table VIII.
TABLE VIII
PRIORITY VECTORS FOR THE DECISION HIERARCHY
Dimension
Level
-1 Prior ity
CSF
Level
-2 Prior ity
Final Priorities
Prioritisation
Obtained by
FAHP
Students'
Dimension
(SD)
0.17
SELK
0.345
0.0598
9
SELP
0.097
0.0168
12
SM
0.558
0.0969
3
Instructors'
dimension
(ID)
0.24
IICT
0.418
0.0993
2
IRC
0.376
0.0895
4
ITTD
0.206
0.0490
10
Infrastructure
Technology
Dimension
(ITD)
0.31
TII
0.750
0.2308
1
TTS
0.250
0.0769
8
Institutional
Management
Dimension
(IMD)
0.28
MFS
0.294
0.0826
5
MIP
0.280
0.0788
7
MTE
0.140
0.0393
11
MID
0.286
0.0803
6

VI FINDINGS AND DISCUSSION
From the Fuzzy AHP process, the priority weights of four
main barrier dimensions i.e. Institutional Management
Dimension (IMD), Infrastructure Technology Dimension
(ITD), Instructors' Dimension (ID) and Students' Dimension
(SD) are obtained as 0.31, 0.28, 0.24 and 0.17. The graphical
representation of main barrier dimension is shown horizontally
on x-axis whereas y-axis indicates main barrier dimensions
weightages and overall weightages of barrier factors in Fig.5.
The obtained priority weight implies the influence of barriers
as IMD > ITD > ID > SD wherein ‘>’ indicates more
influence of barrier dimension on E-Learning system. The
products of priority weight of four main barrier dimensions
and barriers factors have been calculated. The importance of
priority weightages obtained by FAHP for barriers factors in
order of its importance may be written as: TII > IICT > SM >
IRC > MFS > MID > MIP > TTS > SELK > ITTD > MTE >
SELP with the corresponding priority weights as: 0.2308 >
0.0993 > 0.0969 > 0.0895 > 0.0826 > 0.0803 > 0.0788 >
0.0769 > 0.0598 > 0.049 > 0.0393> 0.0168, wherein ‘>’
indicates more influence of barriers on E-Learning system. On
observing the results obtained, the Infrastructure Technology
Dimension (ITD) has maximum dimension weightages,
whereas Student Dimensions (SD) has minimum weightage.
Observing the barrier factor it has been seen that Lack of
Inappropriate Infrastructure (TII) has highest weightages
whereas barrier factor Lack of English Language Proficiency
(SELP), has the lowest weightage.
VII CONCLUSION, LIMITATION AND SCOPE OF
FUTURE RESEARCH WORK
The present research provides a robust Fuzzy AHP
methodology in prioritizing the barrier dimensions and barrier
factors of E-Learning teaching-learning system.
The linguistics scale in Fuzzy AHP method helps the
decision makers to ascertain the correct pairwise decision
making which is free from vagueness and biases. Thus the
FAHP methodology will provide reliable and robust results
during the pairwise decision making which plays crucial role
in deciding the weightages and subsequently the priorities of
each main barrier dimension and barrier factors. Barriers play
negative role in the E-Learning teaching-Learning process;
hence study of such barriers is inevitable. On prioritizing such
barrier dimensions and barriers factor, stakeholders will get
complete insights and influence of each barrier on the system.
Thus the stakeholders will be able to deploy the scarce
resources in overcoming such barrier dimensions and barrier
factors. Stakeholder may also be able to design the strategy to
achieve the mission and objectives of the E-learning teaching-
learning system depending upon its requirements. Many other
Multi-Criteria Decision Making (MCDM) based
methodologies, such as Analytic Network Process (ANP), and
Technique for Order Preference by Similarity to Ideal Solution
(TOPSIS) [46], may be employed in the fuzzy environment to
quantify the influence of such barriers on the E-Learning
teaching-learning system. The present research reveals the
influence of each barrier dimensions and barrier factors on the
E-Learning system. The future scope of research would also
be to ascertain the influence of such barriers on other barriers
that will influence the E-Learning system.
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