Content uploaded by Omer Faruk Sozcu
Author content
All content in this area was uploaded by Omer Faruk Sozcu on Oct 31, 2016
Content may be subject to copyright.
Baltic J. Modern Computing, Vol. 4 (2016), No. 3, 428-440
An Analysis and Comparison of Adoption of
E-learning Systems in Higher Education by
Lecturers at Largest Universities
in Estonia and Turkey
Fatih Güllü1, Rein Kuusik1, Kazbulat Shogenov1, Mart Laanpere2,
Yusuf Oysal3, Ömer Faruk Sözcü4, Zekeriya Parlak5
1Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
2Tallinn University, Narva mnt 25, 10120 Tallinn, Estonia
3Anadolu University, Yeşiltepe, Yunusemre Kampusu, 26470 Tepebaşı/Eskişehir, Turkey
4Fatih University, Büyükçekmece, 34500 Istanbul, Turkey
5Sakarya University, Esentepe Kampüsü, 54187 Serdivan/Sakarya, Turkey
{fatih.gullu, rein.kuusik.01, kazbulat.shogenov}@ttu.ee,
martl@tlu.ee, yoysal@anadolu.edu.tr, ofsozcu@fatih.edu.tr,
zparlak@sakarya.edu.tr
Abstract. In this study, for the first time, we analysed and compared adoption of e-learning by
lecturers in three largest universities in Estonia and Turkey. Total number of students and
academic staff in the Estonian universities is 39,259 and 3,991, respectively, and 1,194,735 and
9,076, respectively, in the Turkish universities. The extended Technology Acceptance Model
(TAM2) was used to analyse results of acceptance and usage of e-learning by 923 lecturers (298
from Estonia and 625 from Turkey) or 22% from the sample subject, took part in the research from
the studied universities. Total number of respondents subjected to the questionnaire distribution
was 4,198 (1,423 in Estonia and 2,775 in Turkey). We found and analysed strong and weak sides
of e-learning and main barriers, which hinder adoption of e-learning in Estonian and Turkish
largest universities. Immediate measures to support development and improvement of e-learning
system at higher education in these universities were suggested.
Keywords: e-learning, Estonia, higher education, TAM2, Technology acceptance model, Turkey
1 Introduction
Every year electronic systems in higher education (e-learning) are going to be
implemented more and more actively by the most reliable universities around the world.
E-learning is phenomenon based on remote collaboration of students and lecturers,
facilitating of access to educational resources and services, enhancing of learning
quality, upgrading of teaching methods and habits using new multimedia technologies
and internet. Fast development of this technology is obliged to global level of
technological progress of information technologies (IT). However, balanced adoption
and integration of e-learning in higher education by main users of the system, lecturers
and students, is controversial. Number of barriers limiting productive implementation
and utilization of e-learning in universities’ everyday routine is still exists: economic,
political, technical, pedagogical, absence of strategic plan and consortia between
universities (Hara, 2003; Kilmurray, 2003; Saadé, 2003; Elloumi, 2004; Surry et al.,
Adoption of E-learning Systems in Higher Education in Estonia and Turkey 429
2005; Park, 2009). Identification of the critical factors related to user acceptance of
technology continues to be an important issue (Yi and Hwang, 2003; Park, 2009).
Number of studies was provided to estimate adoption and integration of e-learning
between students, e.g. (Koohang and Durante, 2003; Grandon et al., 2005; Park, 2009),
and analysing usability of e-learning systems, e.g. (Harms and Adams, 2008; Nielsen,
2012; Genc, 2015). But the main developers and deliverers of e-learning for students are
lecturers, which are in most cases accustomed to use old educational system. Therefore,
there is a high importance of understanding of how lecturers perceive and react to
elements of e-learning along with how to most effectively apply an e-learning approach
to enhance learning. These data can help academic administrators and managers to create
more effective learning environment to adopt e-learning in higher education. It is
necessary to conduct research that provides personal information from lecturers about
their perception of, attitude towards, and intention to use an e-learning.
Activities and strategic development of e-learning in higher education in three largest
Estonian (University of Tartu-UT, Tallinn University of Technology-TUT, and Tallinn
University-TU) and Turkish universities (Anadolu, Sakarya and Istanbul University)
have been already studied and compared in previous studies (Güllü et al., 2014; Güllü et
al., 2015b). The strongest point of Estonian e-learning in higher education is unity
between all participants of e-learning educational system from all the studied
universities. While, studied universities in Turkey have its own interaction platforms
without links and possibility to cooperate between users from different institutions
(Güllü et al., 2015b). Estonia, or “silicon valley of Europe”, as one of the most
developed countries in the field of Information and Communication Technologies in the
world can be a good example for Turkey.
The objectives of this study were to examine and compare quality and issues of e-
learning in Estonia and Turkey at higher education, covering social, pedagogical and
policy aspects. The results of the research would help e-learning systems administrators
and developers to adopt and integrate better e-learning environment for lecturers.
This study proposed an integrated theoretical framework of adoption of e-learning
by university lecturers based mainly on the extended technology acceptance model
(TAM2). TAM is a theoretical model that helps to explain and predict user behaviour of
information technology (Legris et al., 2003). TAM provides a basis with which one
traces how external variables influence belief, attitude, and intention to use. Two
cognitive beliefs are posited by TAM: perceived usefulness and perceived ease of use.
According to TAM, one’s actual use of a technology system is influenced directly or
indirectly by the user’s behavioural intentions, attitude, perceived usefulness of the
system, and perceived ease of the system. TAM also proposes that external factors affect
intention and actual use through mediated effects on perceived usefulness and perceived
ease of use (Davis, 1989; Park, 2009). TAM2 appears to be able to account for 60% of
user adoption (Venkatesh and Davis, 2000). As suggested in TAM2, subjective norm,
one of the social influence variables, refers to the perceived social pressure to perform or
not to perform the behaviour (Ajzen, 1991). It seems important to determine how social
influences affect the commitment of the user toward use of the information system for
understanding, explaining, and predicting system usage and acceptance behaviour
(Malhotra and Galletta, 1999; Park, 2009).
In general, variables related to the behavioural intention to use information
technology or to the actual use of information technology could be grouped into four
categories: individual context, system context, social context, and organizational context.
While social context means social influence on personal acceptance of information
technology use, organizational context emphasizes any organization’s influence or
support on one’s information technology use. Reference (Thong et al., 2002) identified
430 Güllü et al.
relevance, system visibility, and system accessibility as organizational context variables.
They reported that the organizational context affects both perceived usefulness and
perceived ease of use of a digital library. Reference (Lin and Lu, 2000) similarly
reported that higher information accessibility brings about higher use of information and
higher perception of ease of use. In this study, e-learning accessibility refers to the
degree of ease with which a university lecture can access and use campus e-learning
system as an organizational factor (Park, 2009).
In our recent studies (Güllü et al., 2015, 2015a, 2015b) we used EES model and
EES Model-2. TAM2 was selected for further research due to compatibility with
previously implemented models. In this study, for the first time, we analysed and
compared adoption of e-learning by lecturers in three largest universities in Estonia (UT,
TUT and TU), country leading in the field of IT development and integration and three
largest universities in Turkey (Anadolu, Istanbul and Sakarya University), quickly
technologically developing country. Estonian and Turkish universities operated 5,388 e-
courses with 146,067 students and 234 e-courses with 1,401,802 students in 2013–2014,
respectively (Güllü et al., 2015b). Total number of students in 2013 at UT (16,000; 1),
TUT (13,050; 2) and TU (10,209; 3) was 39,259 that is 65% of total students in higher
education in Estonia (59,998; Fig. 1; 2).
Total number of an academic staff in 2013 at UT (1,800; 1), TUT (1,731; 2) and TU
(460; 3) was 3,991 (Fig. 2). Total number of students in Turkish largest universities in
2013 was 1,194,735: >1 mln. in Anadolu 4, 109,901 in Istanbul 5 and 84,834 in Sakarya
6. It is 24% of total number of students in higher education in Turkey (4,9 mln.; Fig. 1;
7). Total number of an academic staff in 2013 at Anadolu University (2,000; 4), Istanbul
University (5,100; 5) and Sakarya University (1,976; 6) was 9,076 (Fig. 2).
We found and analysed strong and weak sides of e-learning and main barriers,
which hinder adoption of e-learning in Estonian and Turkish largest universities.
Immediate measures to support development and improvement of e-learning system at
higher education in these universities were suggested.
Fig. 1. Number of students in largest universities of Estonia and Turkey
1www.studyinestonia.ee
2www.ttu.ee
3www.tlu.ee
4www.anadolu.edu.tr
5www.istanbul.edu.tr
6http://about.sakarya.edu.tr
7www.studyinturkey.com
Adoption of E-learning Systems in Higher Education in Estonia and Turkey 431
2 Methods
Collected data were based on questionnaire sent to participants. The questions were
divided into two parts, (1) participant profile and (2) how participant feels that e-learning
system adopted in his university for education environment (Table 1). Each part consists
of different groups of questions. Groups in the first part contain four items (questions) to
identify demographic attributes of respondents such as date of birth, gender, academic
position and institution facility. Groups of the second part consist of 2-4 questions.
These questions are partly based on TAM2 model (Groups: Perceived ease of use,
Perceived usefulness, Attitude, Behavioural intention, E-learning self-efficacy,
Subjective norm, System accessibility), consisting in total 17 questions. Groups such as
Policy factor, Pedagogical level and Barriers consist in total 10 questions (Table 1) were
developed for this study by author according to discussion and validation by experts
(professors of e-learning study, heads of e-learning centres, developers of e-learning
system, 8, 9, 10, 11, 12, 13) in the field from the studied universities in Estonia and Turkey.
Total item pool of the scale consisted of 31 items, four in the first part and 27 in the
second one. Participants were asked to complete a seven-point Likert-type scale (1-
Strongly disagree, 2-Disagree, 3-Somewhat disagree, 4-Neither agree or disagree, 5-
Somewhat agree, 6-Agree, 7-Strongly agree) describing the level of agreement proposed
by Vagias (2006). Items were adopted to be appropriate for participants (lectures of e-
learning) from studied universities in Estonia and Turkey.
A. Sample subjects
Participants in the study were lecturers in university (professors, associate professors,
professor assistants and lecturers) who use e-learning in their practices. The number of
sample subjects was set at 1423 in Estonian universities and 2775 in Turkish
universities. Total number of respondents subjected to the questionnaire distribution was
1www.studyinestonia.ee
2www.ttu.ee
3www.tlu.ee
4www.anadolu.edu.tr
5www.istanbul.edu.tr
6http://about.sakarya.edu.tr
7www.studyinturkey.com
Fig. 2. Number of academic staff in largest universities of Estonia and Turkey
8 http://www.uzem.sakarya.edu.tr
9 http://auzef.istanbul.edu.tr/
10 https://www.anadolu.edu.tr/en/academics/faculties/2/open-education-faculty/
11 http://www.tlu.ee/en/E-learning-Centre
12 http://www.ttu.ee
13 http://www.ut.ee/en/studies/elearning/learning
432 Güllü et al.
4198. Nine hundred twenty-three respondents from the selected universities in Estonia
(n=298) and Turkey (n=625) voluntarily participated in the study that is 22% from the
sample subject. The overall response rate of about 20% is considered to be satisfactory
and accurate measurement in terms of the statistical reliability (Visser et al., 1996).
B. Statistical procedure
Data collected with the questionnaire were coded by research assistants. The data were
recorded first in Limesurvey application, a free and open source on-line survey
application written in PHP based on a MySQL, PostgreSQL or MSSQL database,
distributed under the GNU General Public License 14. This software gives opportunity to
users to develop and publish on-line surveys, collect responses, create statistics, etc.
Collected data were transferred to MS Excel program for further analysis.
Collected data show that respondents in Turkey were predominantly males P2(1)
(n=354) than females P2(2) (n=265) (Fig. 3). Six respondents from Turkish universities
did not identify their gender. Gender balance of respondents in Estonian universities was
almost equal, but however females predominated (n=150 females vs n=148 males).
Major respondents were Lecturers P3(4) in both countries (58% of respondents in
Estonia and 36% in Turkey, Fig. 4). Assistant professors P3(3) represented 32% of all
respondents in Turkish universities, when in Estonian universities only 15%. Associate
professor option P3(2) was selected by 20% and 17% of respondents in Estonian and
Turkey, respectively. Professors P3(1) composed only 7% of questionnaire participants
in Estonian universities and more than two times in percentage professors participated in
Turkish universities (15%, Fig. 4). Fig. 5 shows how respondents answered in average
for presented questions in total. It is showing a general feeling/intention/satisfaction of
users-lecturers of e-learning in their practice. These data shows users adaptation level.
According to presented questions (Table 1), positive answers show how users accept this
technology, or how it was adopted in their environment.
Fig. 3. Participants profile (gender, P2)
14 www.limesurvey.org
Adoption of E-learning Systems in Higher Education in Estonia and Turkey 433
Table 1. Summary of means, concepts and indexes
Concept
index
Group
index
Measurement instrument
index
Participant
profile
P
Date of Birth
P1
Year
-
Sex
P2
Male
1
Female
2
Academic
position
P3
Professor
1
Associate Professor
2
Assistant Professor
3
Lecturer
4
Your
Faculty
P4
For each university different lists of faculties were applied
Adoption
of
e-learning
system
AS
Perceived
ease of use
PE
I find e-learning system easy to use
E1
Learning how to use an e-learning system is easy for me
E2
It is easy to become skilful at using an e-learning system
E3
Perceived
usefulness
PU
E-learning would improve my teaching performance
U1
E-learning would increase my academic productivity
U2
E-learning would make it easier to teach course content
U3
Attitude
AT
Teaching (studying) through e-learning is a good idea
A1
Teaching (studying) through e-learning is a wise idea
A2
I am positive toward e-learning
A3
Behavioural
intention
BI
I intend to post announcements, assignments and learning materials
via e-learning systems frequently
B1
I intend to be an active user of e-learning system
B2
E-learning
self-efficacy
SE
I feel confident finding information in the e-learning system
S1
I have the necessary skills for using an e-learning system
S2
Subjective
norm
SN
What e-learning stands for is important for me as a university
academic staff
N1
I like using e-learning because academic society values it
N2
In order to prepare students for their future jobs, it is necessary to
provide them e-learning courses
N3
System
accessibility
SA
I have no difficulty accessing and using an e-learning system in the
university
SA
Policy
factor
PF
My university has adopted policies for productive implementation
of e-learning at higher education in my country
PF1
Security aspects of e-learning at higher education are covered by
policies in my country
PF2
Financial support mechanisms of e-learning at higher education are
involved in policies in my country
PF3
E-learning policies in higher education are well implemented
through productive cooperation between universities in my country
PF4
Pedagogical
level
PL
E-learning is the main source of pedagogical innovation in higher
education in my country
PL1
My university provides academic staff trainings to develop
innovative pedagogical approaches for e-learning
PL2
Academic staff in my university needs today more training in
pedagogical aspects of e-learning and less in technological skills
PL3
Barriers
BR
The main barrier that hinders adoption of e-learning in my
university is poor technological infrastructure and outdated e-
learning systems
BR1
The main barrier that hinders adoption of e-learning in my
university is poor readiness of academic staff to use e-learning
system
BR 2
The main barrier that hinders adoption of e-learning in my
university is absence of clear vision and policy for e-learning
development
BR 3
434 Güllü et al.
3 Results
Our study showed that the highest satisfaction of usage and adoption of e-learning
system in higher education between studied largest universities of Estonia and Turkey
was demonstrated by respondents from UT. About 87% of lecturers in average from this
university were satisfied-“strongly agree”, “agree” and “somewhat agree”, when
answered for proposed questions. Only 13% in average of all respondents from this
university were dissatisfied-disagree with different levels of confidence (“neither agree
or disagree”, “somewhat disagree”, “disagree”, “strongly disagree) with statements in
questionnaire (Fig. 5). TU is the next Estonian university and next between all studied
universities according to satisfaction of e-learning. About 84% of respondents in average
from TU were agree and 16% in average were disagree with different levels of
confidence when answered for our survey (Fig. 5).
We found that TUT has third place between Estonian largest universities according
to satisfaction of usage and adoption of e-learning system in higher education. About 74
and 26% of respondents in average answered with different levels of confidence in
satisfaction and dissatisfaction mode, respectively (Fig. 5).
According to our research the highest satisfaction of usage and adoption of e-learning
between largest Turkish universities has Istanbul University (average 77 and 23% of
answers in satisfaction and dissatisfaction mode, respectively). Lower satisfaction
showed Anadolu University with average 73 and 27% of answers with satisfaction and
dissatisfaction mode, respectively. The most dissatisfied atmosphere of usage and
adoption of e-learning by lecturers between Turkish largest and all studied universities
was found in Sakarya University (average 64 and 36% of answers were satisfied and
dissatisfied, respectively, with different levels of confidence) (Fig. 5). Estonian lecturers
in total more satisfied with usage and adoption of e-learning at higher education in their
everyday work (82% in average of satisfied answers, Fig. 5). Their Turkish colleagues in
average 10% less satisfied of this technology usage and adoption in higher education
(71% in average of satisfied answers, Fig. 5).
Fig. 4. Participants profile (Academic position, P3)
Adoption of E-learning Systems in Higher Education in Estonia and Turkey 435
Fig. 5. Summary table of all answers by respondents from six universities from Estonia
and Turkey
We found that respondents from both countries don’t find usage of e-learning system
in their work difficult and agree in importance of implementation of the system in higher
education to improve academic productivity and teaching performance. In general they
were positively related to e-learning system in higher education and mentioned them self
as active users of the system. However, according to received answers Estonian lecturers
were more active in this practice. Respondents from both countries equally answered
about their good skills and confidence in e-learning.
The biggest difference in answers was found for Policy factor (PF), pedagogical level
(PL), barriers (BR) groups of questions (Table 1). According to policy adaptation,
security, financial support mechanisms and productive cooperation we found that
between Estonian universities TUT respondents showed lower satisfaction than TU and
UT. The lowest satisfaction with questions of policy factor was showed by respondents
from Istanbul University.
Lecturers from TUT less than others support opinion that e-learning system is the
main source of pedagogical innovation in higher education in Estonia. The highest
satisfaction of e-learning staff trainings that proposed at universities was expressed by
Estonian respondents. Istanbul University lecturers showed maximum dissatisfaction in
this question. Respondents from all universities expressed need in pedagogical training
of academic staff.
Poor technological infrastructure and outdated e-learning systems were noted as the
main barrier that hinders adoption of e-learning (BR1, Table 1) in UT and Istanbul
University. Lecturers from TUT, Anadolu and Sakarya universities were disagree and
strongly disagree with this statement. Poor readiness of academic staff to use e-learning
system (BR2, Table 1) was noted as the main barrier by lecturers from Istanbul
University and UT. We found that absence of clear vision and policy for e-learning
development (BR3, Table 1) is the main barrier that hinders adoption of e-learning in
Istanbul University. Also big percentage of respondents from TU has noticed about this
problem.
(%)
436 Güllü et al.
4 Discussion
As expected, we found that lecturers from the largest universities in Estonia are more
satisfied of usage and adoption of e-learning system in their universities than their
colleagues from Turkey (Fig. 5). This is due to Estonian e-learning system in higher
education is advanced and united in the context of technical, pedagogical and
economical aspects, and activities provided by this universities, when Turkish e-learning
needs improvements and unification. United platform (like Moodle system in Estonia)
was recommended to be involved in Turkey to integrate students, lecturers and all
available data for e-learning in higher education from all the studied universities into one
independent e-learning environment (Güllü et al., 2014, 2015b). In this study we
explored weak and strong sides of e-learning system in higher education in Turkey and
Estonia and which aspects need to be improved. Immediate measures for improvement
process were suggested.
Strong sides of e-learning in both countries are total acceptance and understanding
of importance of implementation of the modern educational system by lecturers of
largest universities. Good skills and confidence in e-learning are next strong sides of the
system. These make adaptation process easier. As expected, Estonian respondents
showed more activeness in this practice due to excellence of the country in IT
development and integration.
Problems in policy adaptation, security, financial support mechanisms and
productive cooperation between institutions in Estonian universities were found. Lower
success of these aspects in respondent’s answers, as expected, was found at TUT.
Answers for questions of Policy factor group of questions by lecturers from TUT, we
suppose, shows that respondents are less informed by TUT governance than lecturers
from TU and UT. We found weak side of e-learning system or barrier that hinders
adoption of e-learning at TU - the absence of clear vision and policy for e-learning
development (BR3, Table 1). We suggest to both universities governance take measures
to eliminate these gaps. Improving productive cooperation between institutions aspect
only can solve consequently other existing problems due to positive experience of UT in
these fields. United e-learning environment (Moodle) that supports productive
cooperation between all participants of e-learning at higher education in Estonian
universities is already exists and successfully implemented in the studied universities.
This environment can be used as prospective tool to rich this aim. (i) Poor technological
infrastructure and outdated e-learning systems and (ii) poor readiness of academic staff
to use e-learning system were noted as barriers which hinder adoption of e-learning at
UT. Those, we suggest to UT administration to renovate technological aspect of e-
learning system, taking as example infrastructure at TUT and TU. The second (ii)
barrier, we suppose, is due to age of lecturers. Using a personal experience, we know
that there is big number of experienced lecturers in the studied universities, whose
experience based on old educational technologies and principles. More experienced
lecturers often are less flexible to accept new technologies than younger ones and prefer
old methods in education. We can suggest a way to solve this problem: to use a systemic
change approach, that is effective measure according to previous studies (e.g. Su, 2009).
One solution for making qualitative change in effective technology integration in the
daily teaching and learning process is to use a systemic change approach. A systemic
change is doable as there are successful cases in the literature (e.g. Fullan, 1993). If
educators use a systemic approach to deal with both first- and second-order barriers,
Adoption of E-learning Systems in Higher Education in Estonia and Turkey 437
success will ultimately come. Reigeluth (1994) points out that systemic change is a
paradigm shift that “entails replacing the whole thing” because “a fundamental change in
one aspect of a system requires fundamental changes in other aspects in order for it to be
successful”. Education as a social enterprise is a very complex system that involves
many stakeholders such as teachers, students, parents, administrators, business partners
and policy makers. To effectively integrate technology, these people will either affect or
be affected by the change (Su, 2009).
Main barriers, which hinder adoption of e-learning in Turkish largest universities,
were found in Istanbul University: (i) poor technological infrastructure and outdated e-
learning systems, (ii) absence of clear vision and policy for e-learning development, (iii)
poor readiness of academic staff to use e-learning system. These results confirmed our
expectations. The suggestion, first of all for Istanbul University, and other Turkish
universities governance (Anadolu and Sakarya University) is to take as example model
of development of e-learning system in Estonian universities. We recommend to begin
with establishment of strong and stable policy, to build consortia between all universities
in the field, significantly finance technological infrastructure, regulate financial support
of projects related to development of e-learning system, support security measures to
provide safe usage of e-learning and develop training system for new and existing
specialists.
We strongly suggest the implementation of measures in a complex. Selection of
suggested tools separately will not guarantee stable, productive result of e-learning
architecture. Wenger et al. (2002) demonstrated that the adoption of e-learning is
actually influencing learning strategy, and that the simple delivery through technology
cannot be sustained as a separate form of training, an appendix to traditional instructor-
led activities. To be successful, it has to be seen as a part of a complete learning
architecture that includes a variety of tools, approaches, and a coherent learning culture.
The analysis shows two emerging phenomena: a different degree of success of the e-
learning initiative depending upon its coherence with the organizational culture, and the
learning strategy; a changing balance of classroom training and e-learning in relationship
to the adoption of the Learning Management System in each department (Kok, 2013).
Similar results were also presented in many studies, e.g. in (Al-Adwan and Smedly,
2012; Chokri, 2012; King and Boyatt, 2015, etc.).
We believe that results of this study will be helpful for improving e-learning
system in higher education in Estonia and Turkey, as well as in other countries that meet
similar barriers.
5 Conclusion
In this study for the first time we analysed and compared adoption of e-learning by
lecturers in three largest universities in Estonia (Tartu University, Tallinn University of
Technology and Tallinn University) and three largest universities in Turkey (Anadolu
University, Istanbul University and Sakarya University). The extended Technology
Acceptance Model (TAM2) was used to analyse results of acceptance and using of e-
learning by 923 lecturers (298 from Estonia and 625 from Turkey) or 22% from the
sample subject, took part in the research from the studied universities. Total number of
respondents subjected to the questionnaire distribution was 4,198 (1,423 in Estonia and
2,775 in Turkey). We found and analysed strong and weak sides of e-learning and main
barriers, which hinder adoption of e-learning in Estonian and Turkish largest
universities.
438 Güllü et al.
It was found:
that lecturers from the largest universities of Estonia are more satisfied of usage
and adoption of e-learning system and showed more activeness than lecturers
from Turkey
that lecturers from both countries largest universities completely accept and
understand importance of implementation of the modern educational system, such
as e-learning is and showed good skills and confidence in e-learning
gaps in policy adaptation, security, financial support mechanisms and productive
cooperation between institutions in Estonian universities. Less success of these
aspects in respondent’s answers were found at TUT
absence of clear vision and policy for e-learning development at TU
poor technological infrastructure and outdated e-learning systems and poor
readiness of academic staff to use e-learning system at UT
that main barriers, which hinders adoption of e-learning in Turkish largest
universities are in Istanbul University (poor technological infrastructure and
outdated e-learning systems, absence of clear vision and policy for e-learning
development, poor readiness of academic staff to use e-learning system).
We provided suggestions for Estonian and Turkish universities governance to take into
consideration results of our study and to improve current situation in e-learning.
We recommend:
to improve productive cooperation between Estonian institutions. It can solve
existing problems at TUT and TU
to renovate technological aspect of e-learning system at UT, taking as example
infrastructure at TUT and TU; and to use a systemic change approach that is
effective measure to implement new technologies
to take the model of development of e-learning system in Estonian universities as
example for all Turkish universities, beginning with establishment of strong and
stable policy, to build consortia between all universities in the field, to finance
significantly technological infrastructure, guarantee financial support of projects
related to development of e-learning system, support security measures to provide
safe usage of e-learning and develop training system for new and existing
specialists
to implement measures in a complex. Selection of suggested tools separately will
not guarantee stable, productive result of e-learning architecture.
Suggested measures are important to support development and improvement of e-
learning system in higher education in studied universities, as well as in other countries
who meet similar barriers.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50, 179–211.
Al-Adwan, A., Smedly, J. (2012). Implementing E-Learning in the Jordanian Higher Education
System: Factors Affecting Impact. International Journal of Education and Development
using Information and Communication Technology (IJEDICT), 8, 121–135.
Chokri, B. (2012). Factors influencing the adoption of the e-learning technology in teaching and
learning by students of a university class. European Scientific Journal December edition,
8(28), 165–190. (2012)
Davis, F.D. (1989). : Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319–339.
Adoption of E-learning Systems in Higher Education in Estonia and Turkey 439
Elloumi, F. (2004). Value chain analysis: A strategic approach to online learning. In A. Anderson.,
F. Elloumi (Eds.), Theory and practice of online learning, Athabasca, Canada: Athabasca
University, 61–92.
Fullan, M. (1993). Changing Forces: Probing the Depths of Educational Reform. London: Falmer
Press.
Genc, Z. (2015). Usability of a web-based school experience system: opinions of it teachers and
teacher candidates. Proceedings of 9th International Conference on e-Learning (MCCSIS),
81–89.
Grandon, E., Alshare, O., Kwan, O. (2005). Factors influencing student intention to adopt online
classes: A cross-cultural study. Journal of Computing Sciences in Colleges, 20(4), 46–56.
Güllü, F., Kuusik, R., Demiray, U., Laanpere, M. (2014). Comparing implementation patterns of
e-learning for higher education in Turkey and Estonia. Proceedings of ECEL-2014, 644–650.
Güllü, F., Kuusik, R., Laanpere, M. (2015a). Electronic Education System Model-2. Proceedings
of the International Conference E-Learning, Las Palmas De Gran Canaria, Spain, July 21-24,
162–166.
Güllü, F., Kuusik, R., Laanpere, M. and Sozcu, O. F. (2015b). Using EES model-2 for comparison
of e-learning activities of Estonian and Turkish biggest universities. Proceedings of 8th
annual International Conference of Education, Research and Innovation (ICERI 2015),
Seville, Spain, November, 16-18, 6333–6342.
Güllü, F., Kuusik, R., Laanpere, M., Sozcu, O. F. (2015c). Socio-cultural differences of e-learning
in Estonia and Turkey. Proceedings of 8th annual International Conference of Education,
Research and Innovation (ICERI 2015), Seville, Spain, November 16-18, 6325–6332.
Hara, N. (2000). Student distress in a web-based distance education course. Information,
Communication and Society, 3(4), 557–579.
Harms, M., Adams, J. (2008). Usability and design considerations computer-based learning and
assessment. Meeting of the American Educational Research Association (AERA)
Kilmurray, J. (2003). E-learning: It’s more than automation. The Technology Source archives.
Retrieved from http://technologysource.org/article/elearning
King, E., Boyatt, R. (2015). Exploring factors that influence adoption of e-learning within higher
education. British Journal of Educational, 46(6), 1272–1280.
Kok, A. (2013). How to Manage the Inclusion of E-Learning in Learning Strategy: Insights from a
Turkish Banking Institution. International Journal of Advanced Corporate Learning (iJAC),
6(1), Kassel University Press GmbH, 20–27.
Koohang, A., Durante, A. (2003). Learners’ perceptions toward the web-based distance learning
activities/assignments portion of an undergraduate hybrid instructional model. Journal of
Informational Technology Education, 2, 105–113.
Legris, P., Ingham, J., Collerette, P. (2003). Why do people use information technology? A critical
review of the technology acceptance model. Information & Management, 40, 191–204.
Lin, J.C., Lu, H. (2000). Towards an understanding of the behavioral intention to use a Web Site.
International Journal of Information Management, 20, 197–208.
Malhotra, Y., Galletta, D.F. (1999). Extending the technology acceptance model to account for
social influence: Theoretical bases and empirical validation. Proceedings of the 32nd Hawaii
International Conference on System Sciences, 1.
Nielsen, J. (2012). Usability 101: Introduction to usability. Retrieved from
http://www.nngroup.com/articles/usability-101-introduction-to-usability.
Park, S.Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University
Students' Behavioral Intention to Use e-Learning. Educational Technology, Society, 12 (3),
150–162.
Reigeluth, C.M. (1994). The imperative for systemic change. In C.M. Reigeluth., R.J. Garfinkle
(Ed.), Systemic Change in Education. Englewood Cliffs, NJ: Educational Technology
Publications.
Saadé, R.G. (2003). Web-based education information system for enhanced learning, EISL:
Student assessment. Journal of Information Technology Education, 2, 267–277.
Su, B. (2009). Effective technology integration: Old topic, new thoughts. International Journal of
Education and Development using Information and Communication Technology (IJEDICT).
California State University Monterey Bay, USA, 5(2), 161–171.
Surry, D.W., Ensminger, D.C., Haab, M. (2005). A model for integrating instructional technology
into higher education. British Journal of Educational Technology, 36(2), 327–329.
440 Güllü et al.
Thong, J.Y.L., Hong, W., Tam, K. (2002). Understanding user acceptance of digital libraries:
What are the roles of interface characteristics, organizational context, and individual
differences? International Journal of Human-Computer Studies, 57, 215–242.
Vagias, Wade M. (2006). Likert-type scale response anchors. Clemson International Institute for
Tourism and Research Development, Department of Parks, Recreation and Tourism
Management. Clemson University.
Venkatesh, V., Davis, F.D. (2000). A theoretical extension of the technology acceptance model:
Four longitudinal filed studies. Management Science, 46, 186–204.
Visser, P.S., Krosnick, J.A., Marquette, J., Curtin, M. (1996). Mail Surveys for Election
Forecasting? An Evaluation of the Colombia Dispatch Poll. Public Opinion Quarterly, 60,
181–227.
Wenger, E., McDermott, R., Snyder, W. (2002). Cultivating Communities of Practice. Boston,
Mass: Harvard Business School Press.
Yi, M., Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy,
enjoyment, learning goal orientation, and the technology acceptance model. International
Journal of Human-Computer Studies, 59, 431–449.
Received April 26, 2016, accepted May 26, 2016
