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An analysis of e-learning adoptability in the developing countries: the case of the Kingdom of Bahrain.


E-learning is all-permeate in education and learning as the most significant tool to enhance knowledge in the academic as well as organizations. Since e-learning has several advantage in terms of cost reduction, simplified training programs, flexibility, and convenience; therefore, e-learning become an important component of information dissemination and sharing (Fridrich & Hron, 2010). Nowadays, e-learning emerged as the new paradigm of modern education supported by several favorable trends: for instance, the Global Industry Analyst (2012) predicted that e-learning market will exceed US $600 Billion by 2015. The role of e-learning and information technology in education are expected to expand in scope and complexity. The American Society for Training and Development (2007) reported that organizations spending annually over $40 billion in technology based training. Despite, the positive trends of e-learning, e-learning poses a challenge for both schools and industry; since e-learning requires the integration of information technology into teaching and learning process. For instance, there is no explanations for why some users of e-learning stop using e-learning after their first initial experiences; which impact levels of needed information communication technology innovations (Ozkan, Koseler, & Baykal 2009; Mohamad, Ibrahim, & Mohd, 2010; Kaufman, Sauve, & Renaud, 2011).

Scholars argued these challenges imposes several difficulties in developing theoretical concepts and methodological models to measure the effectiveness of e-learning and levels of information communication innovations (Nishino et al., 2010). Particularly, models concerning e-learning adaptability and learning styles are important in both education and learning literatures (Kay & Knoack, 2008). Although the work of Nishino et al., (2010) involved a process-model with asynchronous learning, use of computers in learning, asynchronous digital communication, and study sequence autonomy are determinants of e-learning adaptability. Nishino et al., (2010) pointed out that e-learning adoptability is tailored to learner's aptitudes; customizability and usability of systems are all designed, developed, and tutored according to user necessity of accessible e-learning. Therefore, users have to ensure usability, ease of use, and attractiveness of interaction. Further, e-learning designed around "students centered learning" considering the students' needs and ability of learning.

This study is to replicate Nishino et al. (2010) e-learning adaptability model within a highly growth-oriented and competitive educational system in an emerging Middle-Eastern economy, namely, the Kingdom of Bahrain.


This section and the following sections provide a review of literatures related to e-learning.

Background of the Study

In the early 1960s, Stanford University psychology professors Patrick Suppes and Richard Atkinson experimented with using computers to teach math and reading to young children in elementary schools in Palo Alto, California. In 1963, Bernard Luskin installed the first computer in a community college for instruction, working with Stanford University and others, developed computer assisted instruction. In early 1993, William Graziadei described an online computer-delivered lecture, tutorial and assessment project using electronic mail. In 1997 Graziadei et al. published an article entitled "Building Asynchronous and Synchronous Teaching-Learning Environments: Exploring a Course/Classroom Management System Solution." They described a process at the State University of New York of evaluating products and developing an overall strategy for technology-based course development and management in teaching-learning. In 1998, the British Prime Minister stated: "Technology has revolutionized the way we work and is now set to transform education. Children cannot be effective in tomorrow's world if they are trained in yesterday's skills. Nor should teachers be denied the tools that other professionals take for granted."

Today many technologies can be, and are, used in e-learning, from blogs to collaborative software, e-portfolios, and virtual classrooms. Most eLearning situations use combinations of these techniques.

E-learning and Information Communication Technology (ICT)

E-learning is an ICT-based learning in which learning materials are delivered electronically to remote learners via a computer network. As the new economy requires more and more people to learn new knowledge and skills in a suitable and effective way, the improvement of computer and networking technologies are providing a diverse means to support learning in more personalized, flexible and portable way (Johnson, Hornik, & Salas, 2008).

The use of computers in education started in the 1980's and was mainly focused on teaching about computers rather than teaching through computers. Students were taught about some basic applications that handle information or manipulate texts (Passerini & Granger, 2000). The term ICT (Information and Communications Technology) tends to replace IT (Information Technology), because it shows the importance of electronic communications such as email and the internet as well as the computer aspect. ICT Defined as: "the combination of informatics technology with other related technologies, specifically communication technology" UNESCO (2002). The unique power of ICT enables users today to process, store, retrieve and communicate information in whatever form it may take, unconstrained by distance, time, volume and increasingly by cost. At the same time, ICT adds value to the processes of learning. Many experts believe that ICT can transform education (Somekh, 2001).

Encouragement motivation to learn is one of the key principles for effective education (Bransford, Brown, & Cocking, 2000). Many researchers have indicated positive correlations between learner motivational levels and academic achievement (Maehr & Fyans, 1989). Student motivation is very important with respect to use of cognitive strategies necessary for effective learning (Pintrich & Groot, 1990). Kyong and Theodore (2011) argued that, if students are more motivated to learn, it is less likely to drop out of online courses, which is an indicator of effective e-learning. Effective e-learning defined by Johnson, Hornik, and Salas (2008) as "the integration of instructional practices and the internet capabilities to direct a learner toward a specified level of proficiency in a specified competency". Further, Johnson (2011) defined effective e-learning as the degree to which schools achieve their goals, in comparison with other schools that are equalized in terms of student intakes.

Educational effectiveness is usually researched at both classroom and whole school level. Whole school effectiveness deals with issues like leadership, civic engagement and outcomes, while classroom effectiveness address issues such as motivation, attainment and autonomy. Cheng and Mok (2008) argue that "classroom effectiveness" is a kind of future effectiveness that often refers to the relevance of the "learning environment" to students' multiple and sustainable developments for the future.

Advantages and Disadvantages of E-learning:

Safavi (2008) identified some advantages of e-leaning include: 1) an inexpensive tool to deliver education; 2) it is self-paced(usually, e-leaning courses can be taken when they are necessary); 3) it is faster (learners can skip material they already know); 4) provides consistent content (while in traditional learning different teachers may teach different material about the same subject); 5) works anywhere and anytime (e-learners can take training sessions when they want); 6) can be updated easily and quickly (online e-leaning sessions are especially easy to keep up-to-date because the updated materials are simply uploaded to a server); 7) can lead to an increased retention and a stronger grasp on the subject (because of the many elements that are combined in e-learning to reinforce the message, such as video, audio, quizzes, interaction, etc.); and 8) can be easily managed for large groups of students.

Also Safavi (2008) pointed out that disadvantages of e-learning may include: 1) it may cost more to develop initially; 2) requires new skills in content producers; 3) enabling technology might also be costly, especially in case of advanced visually-rich content; and 4) e-learning requires more responsibility and self-discipline for the leaner to keep up with a more free and unconstrained learning process and schedule.

Learning Preferences and E-Learning

Four learning styles have been identified asking students their preferences in studying, understanding, questioning, and doing homework in terms of asynchronous learning and the use of ICT Nishino et al., (2010). The preferences in e-learning include:

1. Preference for asynchronous learning which concerns the place, time, and the content of asynchronous learning. Asynchronous learning is a student-centered teaching method that uses online learning resources to facilitate information sharing outside among a network of people.

2. Preference for the use of computers in learning is about the use of computers in studying and understanding. Computer-based learning is a method that allows students to obtain information in formats that cannot be presented by teachers and it gives the students control of the information.

3. Preference for asynchronous digital communication which concerns the communication matters which there is no timing required for transmission and in which the start of each character is individually signaled by the transmitting device.

4. Study sequences autonomy concerns the autonomy of deciding the study sequence which is based on the learner's willingness and capacity to control or oversee her own learning. More specifically, someone qualifies as an autonomous learner when he/she independently chooses aims and purposes and sets goals, chooses materials, methods and tasks, exercises choice, and purpose in organising and carrying out the chosen task (Cheng & Mok 2008; Safavi, 2008; Mohamad, Ibrahim, & Mohd, 2010).

Studies of E-Learning

Many developed and developing countries have applied e-learning in education and reported successful results. For the sake of space, limited studies from different regions will be briefly explained below:

E-Learning in Developed Countries:

In USA, a study of high school teachers used a wide variety of tools (content, communication, and management) and approaches (course management system and project-based learning) within an online, secondary-level, social studies course. By the completion of the course, students received credits that could apply toward a high school diploma. Findings from this study indicated that secondary online teachers and students use a variety of tools to engage in meaningful learning in online courses.

In Canada a study was used to determine whether playing an online educational games to improve the secondary school students' cognitive skills as and if there are any differences between males and females in cognitive skills developed by the game. The results of the paired t-tests showed significant improvements in a variety of cognitive skills after students played the game on laptops in their classrooms for 40-60 minutes. No differences were found between males and females. These results are encouraging for teachers who wish to use educational digital games in their classrooms (Kaufman, Sauve, & Renaud, 2011)

A study in Germany was conducted to evaluate the implementing and testing the E-Learning system in German high schools (Grade 11 and 12) which consisted of high-quality learning content in the form of interactive multimedia presentations and subject specific learning tools (lexica, algebra tools, mind mapping software) that could be used inside and outside the classroom. Results perceived usefulness was a significant positive predictor of students' acceptance, with perceived usefulness of the Learning Management Systems having the greatest weight. Computer related attitude, self-efficacy, and gender had no influence on acceptance and the findings indicate the critical importance of perceived usefulness in predicting students' acceptance of the E-Learning system, as known from acceptance research on IT at the work place (Friedrich & Hron, 2010).

E-Learning in Developing Countries

In a study in Malaysia, M-Learning (mobile and wireless technologies) implemented for Smart primary school students in Malaysia by using an open source technology of a new mobile learning environment named Mobile Math which focuses on learning mathematics and allows learners to do lessons, quizzes, tests, and performance tracking with automated graph. The result shows that the mobile phones can be useful in learning mathematics as most of primary school students already use them through many communication activities. Mathematics teachers should start implementing the M-Learning to allow students to independently explore the lesson taught with flexible access to the content and construct the effective teaching environment. The authors propose the M-Learning for mathematics by allowing the extension of technology in the traditional classroom in term of learning and teaching (Mohamad, Ibrahim, & Taib, 2010).

In Jordan, a study in Applied Science University presents conceptual framework architecture of an e-Learning system that could be used to prepare graduated students to take an ETS-like, the international exam during their last semester; which is critical to maintain the quality of higher education among competing private and public universities. Students can prepare for the exam by reviewing the material and taking the mock tests from different locations, such as on campus or off campus; and using different hardware platforms, such as desktop, laptop, or PDA. The study findings confirmed that the system provides students a better preparation, savings in preparation time, system verification, and documenting lessons learned.

In Kuwait, a study investigated the impact of using e-learning models' to enhance the critical thinking skills of students in higher education institutions. The study examines the effectiveness of e-learning model in enhancing critical thinking of students at Kuwait University. The effectiveness is measured by a critical thinking test. The findings confirmed that there was an increase of critical thinking for those who used the e-learning models (Salah & Abdulwahed, 2006).

In Unite Arab Emirates (UAE) a study aims to reflect the development of a new learning environment within the library at the University of Sharjah (UOS). It seeks to discuss e-learning, and how it can be supported by the library web-based services. The findings of this study confirmed that the capabilities of learning management systems (LMS) such as Blackboard have a great effect on libraries and become an active partner in the learning process. On the other hand, strategies adopted by the UOS library place it in a strong position to play an effective role in e-learning environment through the Blackboard platform.


The researcher envisions Nishino et al. (2010) E-Learning Adaptability Model to be applicable in the context of Bahraini schools as shown in figure 1. The model includes asynchronous learning, use of computers in learning, asynchronous digital communication, and study sequence autonomy as determinants of e-learning adaptability. The linkage between the use of computers in learning and e-learning adaptability is based on the logic that a use of computer in learning would translate into better e-learning adoptability, which in turn, would result into better students learning.

The hypothesized relationships in the model are likely to hold in terms of their effects and directions (Kuada & Buatsi, 2005). This is because Bahrain educational system is one of the fastest growing industries not only in Bahrain but also in the entire Middle-East. A free market economic policy led to the emergence of diverse types of schools including public, local-private, western schools. This situation is favorable for e-learning adaptability activities to evolve. Therefore, educational system can be considered a fertile ground for a robust test of Nishino et al., (2010) e-learning adaptability model. Thus, the study formulates the following hypotheses:

Hypothesis 1: There are significant differences between male and female students in their e-learning adaptability in the public schools in Bahrain.

Hypothesis 2: There is significant difference among age groups and their e-learning adaptability in the public schools in Bahrain.

Hypothesis 3: There is a significant relationship between students' preference for asynchronous learning and the e-learning adoptability in the public school in Bahrain.

Hypothesis 4: There is a significant relationship between students' preference for the use of computers in learning and the e-learning adoptability in the public school in Bahrain.

Hypothesis 5: There is a significant relationship between students' preference for asynchronous digital communication and the e-learning adoptability in the public school in Bahrain.

Hypothesis 6: There is a significant relationship between students' study sequences autonomy and the e-learning adoptability in the public school in Bahrain.



Sample and Data Collection

Data were acquired from high school students in Bahrain. A convenient sample of 200 students from four schools participated in this research. The survey instrument was distributed during the class time. 190 students completed the questionnaire resulting in a response rate of 95 percent. 54 percent were male students, and 46 percent of the participants were female.

Data Analysis

To examine the data of the study, descriptive and quantitative analysis were used. The responses to the questionnaire were analyzed using the Statistical Package for Social Science 20.0 software program (SPSS).

Measures of Constructs

Because this study replicates Nishino et al., (2010) e-learning adaptability, their scales for all the constructs have been adopted. All 5 constructs have a total of 46 items. The questionnaire (instrument is presented in Appendix A). All the items in the questionnaire were utilized according to likert 5 point scale ranged from (1 strongly disagree to 5 strongly agree).


Exploratory factor analysis and coefficient alpha were estimated to assess the psychometric proprieties of the scales (Hair et al., 1987). The results of factor analyses identified 4 items with factor loadings less than 0.40 out of 40 items which belong to learning styles dropped from the scale as shown in Appendix A. Cronbach alphas ranged from 0.70 to 0.80 similar to that of Nunnlly (1978) and psychometric proprieties of the scales are similar to those of Hair et al. (1987).


In order to investigate the relationship between the adaptability of e-learning courses and the demographic factors (gender and age) and learning styles includes asynchronous learning, use of computers in learning, asynchronous digital communication, and study sequence autonomy, two multiple regression analysis were conducted. The first multiple regression model was undertaken in response to H1 and H2 that related to age and gender impact on students' e-learning adoptability.

The regression results of model 1 indicate that hypothesis H1 and H2 were not supported. The findings illustrate no variation in the respondents' demographic characteristics. Gender and age of students have no significant impact on their e-learning adaptability. These findings are in contrast with previous studies findings that computer education skewed toward young male students' more than female students. Further, the second multiple regression model was undertaken to investigate the relationship between e-learning adoptability and each of the learning styles in the public school in Bahrain (H3-H6).

Table 1 showed that the regression coefficients of asynchronous learning, use of computers in learning, and asynchronous digital communication are relatively high and the p-value less than 0.01. The regression results indicated that H3, H4, and H5 were supported. While the regression coefficient for study sequence autonomy is low with high p-value, this indicates that H6 was not supported. The study showed that the adaptability to e-learning can be predicted based on student's preference for asynchronous learning, use of computers in learning, and asynchronous digital communication.

It is expected that many students will take fully online courses in the future. However, not all students who take e-learning courses prefer asynchronous learning and the use of ICT. Furthermore, students who do not prefer asynchronous learning and the use of ICT, are expected to score low in the adaptability to e-learning courses and make sure that the system will provide them with instructions before they are enrolling in the online courses (Chen, Shang, Harrris, 2006; Akhtar and Dutta, 2011 ; Ciudad-Gomez, 2012).


In this study, Nishino et al.'s (2010) E-Learning Adaptability model was replicated within a highly growth oriented and competitive industry in an emerging Middle-Eastern economy. Not only e-Learning adaptability research is rare in such context but also the context has the characteristics that allow for a robust test for the complete e-learning adaptability model. The study findings are generally resonate with the results of Nishino et al.'s (2010) and offer one more support for the robustness of Nishino et al.'s (2010) e-learning adaptability model.

However, a closer look into the results reveals some interesting insights. First, the influence of demographic traits, gender and age on e-learning adaptability is not fairly stable across diverse contexts. Gender and age of students have no significant impact on their e-learning adaptability. These findings are in contrast with previous studies findings that computer education skewed toward young males more their counterpart female students. Similarly, asynchronous learning, use of computers in learning, and asynchronous digital communication are fairly stable across diverse contexts.

These findings demonstrated that students' adaptability to e-learning of public schools in Bahrain can be predicted based on student's preference for asynchronous learning, use of computers in learning, and asynchronous digital communication. Similarly to other study findings, study sequence autonomy was insignificant to e-learning adaptability in Bahrain. This finding might be explained by the fact that Bahraini culture is characterized to be high in collectivism and low in individualism. In other words, all of these learning styles preferences are crucial for students' e-learning adoptability. Despite the positive relationships between the dependent and independent variables, a word of caution before applying the asynchronous learning in Bahraini-Arab culture has to be considered.

This culture according to Hofestede's (1997) typology might not be receptive of e-learning. Hofstede identified Arab culture, including Bahrain as having a fixed set of cultural traits such as high in power distance, collectivism, uncertainty avoidance, and femininity that are not conducive for e-learning. Therefore, any implementation for e-learning has to consider such traits and how it enhance or hinder learners' motivation to learn, creating meaningful and memorable experiences, and adjust learners' behavior to non-traditional means of learning. Further, this study confirms that e-learning adaptability may be not a culture-bound. Finally, the study recommend that similar studies to be conducted in Bahrain.

As with any study, there are several potential factors that might limit the generalizability of the study findings. One limitation is that this study is limited contextually where attention should be made not to generalize the findings beyond the empirical findings within limited number of participants; another limitation is that the study findings might be culture bound to oil-rich-Arab-state, namely, Bahrain.
Appendix A

The Result of Factor Analysis of E-Learning and Learning
Styles Questionnaire Data (After Rotation)


                           1       2       3       4

q1) I understand better    0.791   0.110   0.095   0.110
when I study at my
convenient time rather
than learning in class
with other people.

q2) I can familiarize      0.829   0.170   0.181   0.128
myself better when I
study independently at
my convenience than
studying with others
at one place.

q3) I would rather         0.721   0.065   0.219   0.052
study alone at the place
and time convenient to
me than learn in class
with other people.

q4) I can be more          0.693   0.210   0.147   0.129
creative when I study
alone than studying
with others at one

q5) I feel more            0.745   0.231   0.050   0.096
motivated when I study
at my convenience than
learning in class with
other people.

q6) I can learn better     0.862   0.096   0.263   0.053
when I study at the
time I decide than
when I study at the
time decided by others.

q7) I tend to learn        0.655   0.047   0.110   0.092
more actively when I
study alone than
studying with others
at one place.

q8) I study at my own      0.632   0.053   0.320   0.241
pace and do not care
how others study.

q9) I can concentrate      0.745   0.328   0.065   0.213
better when I study
independently at my
convenience than
studying with others
at one place.

q10) I feel less           0.879   0.140   0.290   0.089
tired when I study
independently at my
convenience than
studying with others
at one place.

q11) I want to study       0.654   0.053   0.082   0.170
at the same pace with
other students.

q12) When I study          0.576   0.011   0.422   0.390
through computers, I
tend not to care how
others study.

q13) I wan to study        0.432   0.230   0.116   0.074
at my own pace.

q14) I tend to learn       0.221   0.531   0.095   0.341
more actively using
computers than
studying in class.

q15) It is easier          0.324   0.572   0.048   0.095
for me to memorize
what is on a computer
rather than to review
printed materials.

q16) I can be more         0.231   0.628   0.265   0.251
creative when I think
on paper than using

q17) I would rather        0.221   0.634   0.192   0.198
do group learning
through computers
than face to face.

q18) I can concentrate     0.342   0.611   0.110   0.094
better looking at a
computer screen than
looking at a
blackboard or a large
screen in a classroom.

q19) I feel more           0.302   0.642   0.257   0.165
motivated when I study
using computers than
learning from
teachers in person.

q20) I understand          0.210   0.582   0.049   0.057
better when I learn
through computers than
when I learn by
reading books.

q21) I can be more         0.027   0.667   0.370   0.022
creative when I think
using computers than
thinking on paper.

q22) It is easier for      0.092   0.698   0.190   0.039
me to communicate
through computers or
cell phones than to
communicate face to

q23) I would rather        0.198   0.510   0.385   0.123
follow the computer
instruction rather
than study reading

q24) I prefer              0.212   0.784   0.345   0.141
learning through
computers to learning
by reading books.

q25) I feel less           0.066   0.349   0.290   0.084
tired looking at a
computer screen than
looking at a
blackboard or a large
screen in a classroom.

q26) It is easier for      0.020   0.310   0.510   0.190
me to take test on a
computer than on

q27) I would rather        0.091   0.114   0.628   0.310
submit my report in
an electronic format
than in a paper and
pencil format.

q28) It is easier          0.301   0.262   0.690   0.210
for me to take test
individually than to
take one in a place
with others.

q29) I would rather        0.112   0.096   0.720   0.073
receive answers later
from teachers via
mail than asking
questions in person
or through chat.

q30) I prefer              0.110   0.120   0.686   0.104
communicating via
email to communicating
through telephones.

q31) I am familiar         0.153   0.053   0.741   0.086
with computers.

q32) I prefer taking       0.231   0.296   0.562   0.271
notes using a computer
than writing on paper.

q33) I would rather        0.105   0.320   0.713   0.832
ask questions using
email or bulletin
boards than asking
teachers in person.

q34) I would rather        0.065   0.215   0.651   0.661
study reading
textbooks rather than
follow the computer

q35) I want to decide      0.011   0.092   0.172   0.578
the study sequence on
my own.

q36) I want to follow      0.124   0.162   0.121   0.496
the study sequence
which my teacher

q37) I prefer being        0.286   0.190   0.110   0.539
assessed individually
upon completion of
the assignment to
being assessed at the
same time with others.

q38) I want to drill       0.201   0.119   0.085   0.290
what I have learnt

q39) It is easier for      0.198   0.085   0.270   0.310
me to tackle with the
project I decide than
the one assigned to me.

q40) I prefer looking      0.223   0.252   0.381   0.298
my grade online to
being given it on paper.


Akhtar, A. & Dutta, M. (2011). E-learning in higher education design and implementation. IJCSI International Journal of Computer Science, 8(4), 1694-0814.

Atkinson, R. (1968). Computerized instruction and the learning process. American Psychologist, 23, 225-239.

Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, Mind, Experience, and school. Washington, D.C., National Academy Press.

Chen, C., Shang, R., & Harris, A. (2006). The efficacy of case method in an online asynchronous learning environment. International Journal of Distance Education Technology, 4(2), 7286.

Cheng, Y. & Mok, M. (2008). What effective classroom? Towards a paradigm shift. School Effectiveness and School Improvement, 19(4), 365-385.

Friedrich, H. & Hron, A. (2010). Factors influencing pupils' acceptance of an e-learning system for secondary schools. Educational Computing Research: Knowledge Media Research Center, 42(1), 63-78.

Graziadei, W. (1993). Virtual Instructional Classroom Environment in Science (VICES) in Research, Education, Service & Teaching.

Graziadei, W., Gallagher, S., Brown, R. & Sasiadek, J. (1997). Building asynchronous and synchronous teaching-learning environments: exploring a course/classroom management system solution.

Hair, J. F., E. Ralph, & R. L. Tatham. (1987). Multivariate Data Analysis with Readings. New York: MacMillan Publishing Company.

Hofstede, G. (1997). Culture and Organizations: Software of the Mind Intercultural Cooperation and its Importance for Survival. McGraw-Hill: New York.

Johnson, R. (2011). Gender differences in e-learning: communication, social presence, and learning outcomes. Journal of Organizational and End User Computing, 23(1), 79-94.

Johnson, R., Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human-Computer Studies, 66(5), 356-369.

Kay, R., & Knaack, L. (2008). An examination of the impact of learning objects in secondary school. Blackwell Publishing Ltd Journal of Computer Assisted Learning, 24, 447-461.

Kaufman, D., Sauve, L. & Renaud, L. (2011). Enhancing learning through an online secondary. School Educational Computing Research, 44(4), 409-428.

Kyong, K., & F. Theodore (2011). Changes in student motivation during online learning. Journal of Educational Computing Research, 44(1), 23 2011.

Maehr, M, & L. Fyans. (1989). School culture, motivation, and achievement. Motivation Enhancing Environments, 16.

Mohamad, S., Ibrahim, M., & Mohd, S. (2010). M-learning: a new paradigm of learning mathematics in Malaysia. International Journal of Computer Science and Information Technology, 2(4), 10.

Nishino, K., Iribe, Y., Aoki, k., & Fukumura, Y. (2010). An analysis of learning preferences and e-learning suitability for effective e-learning architecture. Intelligence Decision Technology, 4, 269-276.

Nunnally, J. (1978). Psychometric Theory, New York: McGraw-Hill.

Ozkan, S., & Koseler, R. (2009). Multi-dimensional students' evaluation of e-learning systems in the higher education context: an empirical investigation, Computers & Education, 53, 1285-1296.

Passerini, K. & Granger, M. (2000). A development model for distance learning using the internet. Computers & Education, 34(1), 1-15.

Pintrich, P., & E. Groot. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 52(1): 33-50.

Safavi, A. (2008). Developing countries and e-learning program development. Journal of Global Information Technology Management. 11(3), 47-64.

Salah, A., & Abdulwahed, K. (2009). Developing critical thinking in e-learning environment: Kuwait University. Assessment and Evaluation in Higher Education, 34(5), 529-536.

Somekh, B. (2001). Pedagogy and Learning With ICT Researching The Art of Innovation. London and New York: Routledge.

UNESCO (2002). Information and communication technology in education. A Curriculum for Schools and Program of Teacher Development, France, UNESCO.

Iman Akour

New York Institute of Technology, Kingdom of Bahrain

Iman Akour is an Assistant Professor of Information Systems at New York Institute of Technology, Bahrain. Dr. Akour holds a DBA in Business Administration/Information Systems from Louisiana Tech University, USA. Her current research areas include applications of Technology Acceptance Model into Arab cultures as well other emerging economies. Additionally, her research covers the impact of information technology, social, political, and economical changes.
Table 1
Regression Results of Model 1

Independent Variable                 Regression      P

Asynchronous Learning                   0.128      0.001
Use of Computers in Learning            0.513      0.004
Asynchronous Digital Communication      0.203      0.001
Study Sequence Autonomy                 0.017      0.643
Multiple R-Square                        0.74
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Author:Akour, Iman
Publication:International Journal of Business, Marketing, and Decision Sciences (IJBMDS)
Geographic Code:7BAHR
Date:Sep 22, 2012
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