Students’ Attitudes Toward E-learning in
Iranian universities
Dr Faranak omidian
Islamic Azad University, Dezfool, IRAN
Abdolhossen omidian Reasearch Scholar, Panjab University, INDIA
E-learning is becoming increasingly prominent in higher education, with universities increasing provision
and more students signing up. This paper examines factors that predict students' attitudes to adopt elearning at the Khuzestan province iran . Data was collected through a survey of 400 post graduate students
at the University of Tehran, yazd , isfahan , khoramabad , dezful , shooshtar , chamran, from different
faculties .The technology adoption model put forward by Davis is utilized in this study. Two more
independent variables are added to the original model, namely, resources availability and e-learning
stressors . The results show that there are four factors that can be used in modeling students' attitudes to
adopt e-learning. These factors are intention toward e-learning, perceived usefulness of e-learning, elearning stressors , and the availability of resources needed to use e-learning.
Keywords : e-learning , adoption , intention , ease of use, , usefulness,
Introduction
Technology is challenging the boundaries of the educational structures that have
traditionally facilitated learning .Recent advances in computer technology and the
diffusion of personal computers , productivity software , multimedia and network
resources over the last decade ,heralded the development and implementation of new and
innovative teaching strategies. Educators who advocate technology integration in the
learning process believe it will improve learning and better prepare students to effectively
participate in the 21 century workplace (Butzin ,2000 ; Hopson , Simms & Knezek , 2002
) . The information society requires the use of technology enhanced learning and
teaching at different educational forms and levels .
In today’s global and competitive environment which is marked by the coming of
information society , using the technologies of e-learning becomes a widely accepted way
of training because of the flexibility and the standardization of the overall educational
process they offer . The concept of e-learning is described in a lot of literature sources .
The basics of these definitions of e-learning are combination , implementation and
relationship of the activities for learning and teaching via different electronic media such
as in distance and open learning , etc (Tuparova ,Tuparov , Karastranova and
Peneva ,2006 ) . As bates (2001) argues , e-learning is being used in three main ways
in universities and colleges :
1) Technology- enhanced classroom :
First , the web and the internet have been integrated into classroom teaching in the same
way as previous technologies . Teachers may build a course Webpage , with links
through the internet to relevant resources on other Websites . Instructors can convert their
1
powerpoint slide presentations to pdf files . (electronic documents) , which students can
download and print from a website , or teacher’s own papers and research materials such
as photographs or slides , as well as links to other relevant sources . Teachers may also
use other web sites for illustration within their classroom lectures . And students may be
asked to participate in on-line discussion forums , to discuss the lecture afterwards
amongst themselves . Enhancing classroom teaching is still by far the most prevalent use
of the web in post – secondary education .
2) Distance education :
Major and highly reputable universities with large on-campus teaching programs , such
as Queen University in Canada , the University of London in the United Kingdom, and
the University of Wisconsin in the USA , have been offering distance education programs
for over 100 years . Institutions such as these that offer both campus-based and distance
education programs are called “ dual – mode “.These institutions have tended to develop
a distance education operation to complement their on – campus teaching . For such
institutions , extension beyond the campus has been critical to their mandate , and hence
distance education is one of the several means used to reach out to farmers , working
professionals , and those who cannot afford to move to a campus away from their homes
or jobs .
In contrast to the dual – mode institutions , Daniel (1998) has described the large ,
dedicated distance education institutions (single – mode ) such as the British open
university . These are characterized by very large enrolments ( usually more than 100,000
students ) , and the use of mass communications technology , such as print and
broadcasting .
3) Distributed learning :
With both technology –enhanced classroom teaching and distance education , the move
to on-line learning could be seen as evolutionary , a natural next step forward in two long
but separate historical processes .The potentially revolutionary development is in
distributed learning , because this will radically change the way that traditional campus
institutions operate . Distributed learning describes a mix of deliberately reduced face- toface teaching and on-line learning (for instance one face- to- face lecture or seminar a
week , with the rest of the teaching and learning done on –line , replacing the traditional
three face -to- face lectures a week ) . Unfortunately , especially in the USA , the term “
distributed learning “ is also commonly used to include fully distance courses taught
totally on – line . It might be more helpful to describe the mix of reduced face – to – face
teaching and on-line teaching as “mixed mode “. Another term , used in Australia , is
flexible learning .While “flexible learning “ may encompass on-line learning , it can also
include face – to-face teaching delivered in the workplace , and other flexible delivery
methods (Bates ,2001) .
However , despite the growing technology in higher education several recent studies
(Link and Marz,2oo6 ; Hayashi , Chen , Ryan and Wa ; 2006 ) have advocated that
universities have been slow to bring computer use , e-learning , on line learning into the
main stream and maximize the potential benefits in the classroom . They discovered that
failing to acknowledge the importance of understanding e-learning was an important
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issue . Many students may lack the necessary skills to use e-learning effectively and are
therefore handicapped . Yet colleges and universities continue to invest large sums of
money in automation and electronic communication facilities . For this reason , Martinze
(2004) suggests that the study of student’s attitude towards e-learning can in many ways
help managers better prepare in light of e-learning for the future . Asan and Koca (2006)
reveal there is a relationship between students attitude towards e-learning and positive
learning outcomes . Perez Cereijo (2006) proposes that students attitude towards elearning provides a beneficial construct to predict learning outcomes . ,
The theory of technology acceptance model was really designed to test user’s attitude to
accept new technology . This theory proposed by davis (1983) explains a variety of
human behaviors based on intentions are jointly determined by attitudes . (figure 1) .
According to TAM , perceived usefulness and perceived ease of use shape an individuals
attitudes for using the technology .
Perceived
usefulness
(PU)
Attitude
toward
USING(A)
Behavioral
intention
(BI)
Perceived ease of
use
(PEOU)
(Figure 1 )
Actual
use
( Based on Davis 1989 )
Davis (1989) defined perceived usefulness (PU) as the degree to which a person believes
that
0 using a particular system would enhance believes that using a particular system
would enhance his or her job performance . He defined perceived ease of use as the
degree to which a person believes that that using a particular system would be free of
effort . (Halawi and McCarthy . 2009 ). In addition , the original TAM has since been
extended and is recognized today as TAM2 (figure 2 ). Davis (1993) mainly suggests
that added external variables be utilized in future research using TAM (halawil ,
mccarthv ; 2007 ) . A substantial amount work has been done investigating a variety of
external factors may be important determinants of the user’s attitude ( jones , 2005 ; sam,
Othman,
Nordin 2005 ; Rezaei , Mohammadi ; Asadi and kalantary , 2008 ) .Hence , the
0
technology acceptance model gives relevance to the current study which investigates the
factors that affect student’s attitude to adopt e-learning . Specifically ,

What is the best group of factors that can be used in predicting students’ attitudes
to adopt e-learning ?
3
Availability of
Resources
Age
Gender
Perceived
usefulness
Pressured
To use
Attitude
toward
External
Factors
using elearning
Perceived ease of
use
Computer
self -efficacy
Technology Acceptance ModeL2
( TAM) 2
(Based on Davis 1989)
( Cited in Halawi ,mccarthy ; 2007)
( Figure 2 )
METHOD
Designing the instrument
To answer the question of the study , a 79 –item scale in 5-point Likert format was
developed
to gain as much information as possible regarding the factors that affect
students ‘attitude to adopt e-learning .
Table 2 shows the reliability of the measurement scale . The results demonstrate that the
questionnaire is a reliable measurement instrument .
4
Table 1
Cronbach’s alpha
Scale
Perceived usefulness of e-learning
0.87
Intention to adopt e-learning
0.75
Ease of e-learning use
0.70
Availability of resources
0.70
E-learning stressors
0.79
Attitude towards e-learning
o.78
survey sample
Stratified sampling technique employed in the present study . 400 post graduate students
at the University of Tehran, yazd , isfahan , khoramabad , dezful , shooshtar , chamran,
from different faculties formed the sample of the study (Table 2).
Table 2
Sample details
Arts
Science
Department
Number
Department
Education
40
Computer science
40
Mass communication
40
Biotechnology
40
Geography
40
Statistic
40
Psychology
40
Physic
40
Political science
40
Chemistry
40
5
Number
Personal characteristics of respondents
Approximately 56%of students who participated in the study here between 19 to 25 years ,
about 31% were between 26 to 30 and only 14% more than 30 years . 53%of respondents
were male and 47% were female .
Analyses of Data
In order to test the research questions data analysis were made throughout the SPSS version
17 . To answer question 1 . A multiple regression model was computed to create a
regression equation to answer the research question : what is the best groups of factors that
can predict students’ attitudes to adopt e-learning ? The model included six predictor
variables(Perceived usefulness of e-learning , Intention to adopt e-learning , Ease of elearning use , availability of resources , E-learning stressors ) simultaneously to determine
the joint effect (attitude towards e-learning ) of these variables .
Results and analysis :
A multiple regression equation was computed to distinguish whether student’s attitude
towards e-learning can be predicted by factors ( Perceived usefulness of e-learning ,
Intention to adapt e-learning , Ease of e-learning use , availability of resources , Elearning stressors ) .
Table 3 reveals that the R2 value for this dataset was.706. This indicated that %70/6 of the
student’s attitude toward e-learning was explained by the independent variables of above
mentioned . figure 2 confirms this results. The statistical significance of the predication
equation was analyzed by looking at the ANOVA(Table 3 ) .The data showed significance
at the P<.000 level (F=189.414) .
Table 3
Analyses of variance : Regression
Sum of squres df
Mean
F
Sig
R Square Adjusted
Square
21884.318
5
4376.864 189.414
Regression
6
R square
.000a
.706
.702
Residual
Total
9104.322
394
30988.640
399
23.107
a. Predictors: (Constant) , intention to use ,e-learning stressors , ease of e-learning use,
availability of resources , perceived use of e-learning
b. Dependent Variable: total attitude mark
Table 2 also shows that the most useful subset of variables that can be used in predicting
students’ attitude towards e-learning includes Usefulness (U) , Intention (I) Ease of Use
(EOU) , E-learning stressors (ES), availability of recourses (A) . Accordingly , the best
regression model that can be used in predicting student’s attitude towards e-learning is:
Attitude = -11.402 +.730 I+ .088 U + .080. (ES) + -.079 (A) + .023 (EOU)
Table 4
multiple regression analysis : Coefficients
Un standardized
Variables
Standardized
t
P
Coefficients
(sig)
Coefficients
B
Std. Error Beta
(Constant)
11.40
2
Perceived usefulness of .053
2.625
-4.344
.000
.021
.088
2.565
.011
.570
.033
.730
17.195
.000
.031
.051
.023
.605
.545
of -.134
.047
-.079
-2.827
.005
e-learning
Intention to adopt
e-learning
Ease of e-learning use
Availability
resources
7
E-learning stressors
.096
.035
.080
2.752
.006
Discussion and conclusion :
The results show that the best subset of predictors that can be used in modeling student’s
attitude to adopt e-learning includes : intention to adopt e-learning, , perceived of
Usefulness, e-learning stressors and availability of resources . factors that are used to
model e-learning explain %70/6 of the variation of the dependent variable (attitude to
adapt e-learning ). Hence , Program managers can focus on these factors that are
expected to affect student’s attitude to adapt e-learning .
On the other hand Further studies should be carried out to explore more variables that can
be used to get better insight into the research questions .
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