Despite of the advantages of Information and Communication Technology (ICT) which makes our lives easier, faster, and more connected, the development of ICT is pushing the countries in the direction of ICT applications. The higher education is one of the sectors that try to adopt one of ICT applications through E-learning and using (Moodle) as learning management system. This paper finds out the impact of Moodle on students through examining the students’ acceptance of the system using TAM model. This study was carried out by some teaching members of the Faculty of King Abdullah II School for Information Technology at the University of Jordan during the spring semester of the academic year 2013/2014. The results of this study firm the original TAM’s findings and reveal that the faculties of students and number of previously E-learning courses have an influence on perceive ease of use and perceived usefulness while the level of the academic year and GPA have no significant influence on perceived ease of use. Even though, they have affected on perceived usefulness. Finally, the student’s skills on computer with student’s difficulty in reading from the screen affect perceived ease of use but, it has been found that they have no influence on perceived usefulness.
Nowadays, there is a substantial growth in the use of E-learning platforms in higher education from universities around the world [
The vision of E-learning in Jordan is “Enhancing the quality of education and inspiring lifelong learning through E-learning”. The University of Jordan (http://www.ju.edu.jo) translated this vision and moved it to the ground by using Blackboard in 2005 as E-learning management system. Then, the Blackboard system was replaced by the Open Source Moodle. The University of Jordan started to use Moodle in 2012 instead of Blackboard as its main learning management system, i.e. the teacher could upload the course description, material, assignments, news about the courses, and online quizzes. The rolled student can check these announcements, download the E-material, assignments, solve them and upload them again on Moodle, or perform online quizzes.
The new technologies and systems can fail because the end users do not accept to use them. Moodle is a new system which means that it can be accepted or rejected by end users. There are many theories of technology acceptance used to appreciate the perceptions of end users, i.e. TRA model, several MIS models, and TAM model. TRA is a general model applicable to many regions. Then, many MIS models have been derived from TRA model. They are more specific in the understudy areas. One of these models is called Technology Acceptance Model (TAM) [
・ Perceived usefulness (U) is defined as “the degree to which a user believes that using the system will enhance his or her performance” [
・ Perceived ease of use (EOU) is defined as “the degree to which the user believes that using the system will be free from effort” [
According to TAM [
This paper aims to study the impact of Learning Management System (Moodle) on students of The University of Jordan King Abdullah II School for Information Technology according to TAM model with external variables suggested by the authors. A survey was designed for this purpose. It was spread among students in different classes. The answers of the students were collected and analyzed using SPSS software for statistical analysis. Further specifications of the study steps and analysis will be discussed in details in the coming parts of the paper.
To start with, in 2007 (Ngai, et al.) performed an empirical study for the adoption of WebCT using Tam model, where WebCT is a Web Course Tool used to support E-Learning for The Hong Kong Polytechnic University
students. This study showed that it important to mediate the relationship of technical support with attitude and WEBCT usage, which depends on both perceived ease of use and perceived usefulness according to TAM [
While, in 2007 (Pei-Chen Sun, et al.) studies the critical factors that influence the students satisfaction. Their study was also an empirical one, where they investigate what a successful E-Learning needs using a survey designed for this purpose. Their study showed many critical factors affect learners’ perceived satisfaction. They were listed as: student computer anxiety, instructor attitude toward E-Learning, E-Learning course flexibility, E- Learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments [
In 2008, to understand user behavior towards online learning systems, (ZHANG Sheng et al.) extended TAM model for Online Learning Systems. An online survey was used. The results showed that the variance of online learning system use behavior is higher than that of the original TAM model with 71.3% percentage [
Later, in 2008 S. Y. Park studied the students’ behavioral intention to use E-Learning at universities of Korea. Hiss study evolved 628 students, and then it recommended TAM to be used as a theoretical tool to study and measure the acceptance of E-Learning [
All the previously discussed studies were focusing on E-learning Systems, while in 2012 (S. Y. Park et al.) concentrated on Mobile-Learning in a new study that measure the behavioral intention to use mobile learning for a sample of 288 students at Konkuk university at Korea. The results of study showed that the TAM model is acceptable to explain students’ acceptance of Mobile-Learning [
Many Arabian countries researchers developed several studies involving TAM model. For example, in 2013 (A. Tarhini et al.) studied the factors affecting students’ acceptance of E-Learning environments using TAM model in Lebanon among university students. This research used a quantitative methodology approach. The results of the study showed that the significant determinants of students’ behavioral intention are: perceived usefulness, perceived ease of use, social norms, and Quality of Work life [
In the same year, the same authors made another research to extend the TAM model to investigate the students’ behavioral intention to use E-Learning. This study aimed to add to TAM two other factors: social norms, and quality of work life. This makes TAM more for developing countries, such as Lebanon. This time, their study was empirical by spreading a survey for 569 both undergraduate and postgraduate students in Lebanon’s universities. The results showed that policy makers consider that e-learning is affected by social and cultural factors [
These results were also confirmed by Ali Tarhini et al. by their study for user acceptance for Web-based Learning Systems. Their study focused on the role of social, organizational and individual factors. The study considered 604 students who were using web-based learning systems at Brunel University in England. The results showed that individual differences should be taken into consideration when considering success factors such as social, institutional and individual factors in any E-Learning system which should not considered simply as a technological solution [
In the same year, another study was performed by M. A. Shahrabia et al. at Shahid Beheshti University in Iran. Their results have proved that TAM model continued to provide sufficient content validity and reliability [
This study is an extension of a previous study carried out by teaching members of the Faculty of King Abdullah II for Information Technology at The University of Jordan during the spring semester of the academic year 2013/2014 [
The data in this study were gathered via survey distributed to 240 students, 33 of them are excluded because of incomplete data, so the study depended on 207 students from different faculties (medical, scientific, and humanitarian) in different academic years (first, second, third, forth, and fifth) with different GPA’s (excellent, very good, good, and fair) during the summer semester 2014/2015 at The University of Jordan. The questions divided in 2 groups, the first group contains 6 questions including the student’s faculty, level of academic year, student’s GPA, number of previously e-learning courses which depended on Moodle, the student’s skills on the computer, and the difficulty of reading from a computer screen which are shown in “
・ H1: The student’s faculty type has an influence on Moodle perceived usefulness.
・ H2: The student’s faculty type has an influence on Moodle perceived ease of use.
・ H3: The level of student’s academic year has an influence on Moodle perceived usefulness.
・ H4: The level of student’s academic year has an influence on Moodle perceived ease of use.
・ H5: The degree of student’s GPA has an influence on Moodle perceived usefulness.
・ H6: The degree of student’s GPA has an influence on Moodle perceived ease of use.
・ H7: The number of previously E-learning courses has an influence on Moodle perceived usefulness.
・ H8: The number of previously E-learning courses has an influence on Moodle perceived ease of use.
・ H9: The student’s skills on computer and internet affect positively on Moodle perceived usefulness.
・ H10: The student’s skills on computer and internet affect positively on Moodle perceived ease of use.
Variable | Characteristics | Frequency | Percent |
---|---|---|---|
Type of Faculty | Medical | 49 | 23.7 |
Scientific | 93 | 44.9 | |
Humanities | 65 | 31.4 | |
Level of Academic Year | First | 69 | 33.3 |
Second | 61 | 29.5 | |
Third | 44 | 21.3 | |
Forth | 28 | 13.5 | |
Fifth | 5 | 2.4 | |
Student’s GPA | Fair | 18 | 8.7 |
Good | 82 | 39.6 | |
Very good | 80 | 38.6 | |
Excellent | 27 | 13.0 | |
No. of Previous E-Learning Courses | One Course | 42 | 20.3 |
Two courses | 65 | 31.4 | |
Three Courses or more | 100 | 48.3 | |
Student’s Computer Skills | Excellent Skills | 97 | 46.9 |
Moderate Skills | 101 | 48.8 | |
Weak Skills | 9 | 4.3 | |
Difficulties Reading Form the Computer Screen | Yes | 15 | 7.2 |
Moderate | 89 | 43.0 | |
None | 103 | 49.8 |
・ H11: The student’s difficulty reading from computer screen affect negatively Moodle perceived usefulness.
・ H12: The student’s difficulty reading from computer screen affects negatively Moodle perceived ease of use.
・ H13: The Perceived ease of use affects positively perceived usefulness of Moodle.
・ H14: The Perceived ease of use affects positively attitude towards using Moodle.
・ H15: The Perceived usefulness affects positively attitude towards using Moodle.
・ H16: The Perceived usefulness affects positively behavioral intention to use Moodle.
・ H17: Attitude towards using affects positively behavioral intention to use Moodle.
・ H18: Behavioral intention to use affects positively actual using Moodle.
Measurement validity in terms of reliability and construct validity was evaluated. The reliability analysis measured the internal validity and consistency of questions used for each construct by calculating Cronbach’s alpha coefficient [
In testing the hypotheses, for the external variables the researchers used one way ANOVA with pre-set level of significance is 0.05 followed by Post Hoc tests to examine the differences between the students in their perceived usefulness and perceived ease of use for the learning management system (Moodle) based on their demographics.
The type of faculty has significant influence on both perceived usefulness and perceived ease of use as shown in “
Item | Number of Items | Cronbach’s Alpha |
---|---|---|
Perceived Ease of Use | 4 | 0.823 |
Perceived Usefulness | 7 | 0.752 |
Attitudes toward Usage | 5 | 0.789 |
Behavioral Intention to Use | 4 | 0.667 |
Actual Use | 5 | 0.844 |
Sum of Squares | df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|
Perceived Ease of Use | Between Groups | 82.951 | 2 | 41.476 | 6.584 | 0.002 |
Within Groups | 1285.116 | 204 | 6.300 | |||
Total | 1368.068 | 206 | ||||
Perceived Usefulness | Between Groups | 334.728 | 2 | 167.364 | 13.384 | 0.000 |
Within Groups | 2550.924 | 204 | 12.505 | |||
Total | 2885.652 | 206 |
Dependent Variable | (I) College | (J) College | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Perceived Ease of Use | Medical | Scientific | 0.89598* | 0.44306 | 0.044 | 0.0224 | 1.7695 |
Humanities | 1.71931* | 0.47485 | 0.000 | 0.7831 | 2.6555 | ||
Scientific | Medical | −0.89598* | 0.44306 | 0.044 | −1.7695 | −0.0224 | |
Humanities | 0.82333* | 0.40578 | 0.044 | 0.0233 | 1.6234 | ||
Humanities | Medical | −1.71931* | 0.47485 | 0.000 | −2.6555 | −0.7831 | |
Scientific | −0.82333* | 0.40578 | 0.044 | −1.6234 | −0.0233 | ||
Perceived Usefulness | Medical | Scientific | 2.17182* | 0.62422 | 0.001 | 0.9411 | 3.4026 |
Humanities | 3.45024* | 0.66901 | 0.000 | 2.1312 | 4.7693 | ||
Scientific | Medical | −2.17182* | 0.62422 | 0.001 | −3.4026 | −0.9411 | |
Humanities | 1.27841* | 0.57169 | 0.026 | 0.1512 | 2.4056 | ||
Humanities | Medical | −3.45024* | 0.66901 | 0.000 | −4.7693 | −2.1312 | |
Scientific | −1.27841* | 0.57169 | 0.026 | −2.4056 | −0.1512 |
Regarding the academic year, the level of academic year is influenced on perceived usefulness (H3 is supported) while no statistically influence on perceived ease of use (H4 is rejected) as shown in “
Sum of Squares | df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|
Perceived Ease of Use | Between Groups | 38.741 | 4 | 9.685 | 1.472 | 0.212 |
Within Groups | 1329.327 | 202 | 6.581 | |||
Total | 1368.068 | 206 | ||||
Perceived Usefulness | Between Groups | 181.546 | 4 | 45.386 | 3.390 | 0.010 |
Within Groups | 2704.106 | 202 | 13.387 | |||
Total | 2885.652 | 206 |
Dependent Variable | (I) Year | (J) Year | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Perceived Usefulness | First | Second | −1.50059* | 0.64301 | 0.021 | −2.7685 | −0.2327 |
Third | −1.92161* | 0.70587 | 0.007 | −3.3134 | −0.5298 | ||
Forth | −1.87940* | 0.81982 | 0.023 | −3.4959 | −0.2629 | ||
Fifth | −4.05797* | 1.69450 | 0.018 | −7.3992 | −0.7168 | ||
Second | First | 1.50059* | 0.64301 | 0.021 | 0.2327 | 2.7685 | |
Third | −0.42101 | 0.72367 | 0.561 | −1.8479 | 1.0059 | ||
Forth | −0.37881 | 0.83519 | 0.651 | −2.0256 | 1.2680 | ||
Fifth | −2.55738 | 1.70199 | 0.135 | −5.9133 | 0.7986 | ||
Third | First | 1.92161* | 0.70587 | 0.007 | 0.5298 | 3.3134 | |
Second | 0.42101 | 0.72367 | 0.561 | −1.0059 | 1.8479 | ||
Forth | 0.04221 | 0.88450 | 0.962 | −1.7018 | 1.7862 | ||
Fifth | −2.13636 | 1.72672 | 0.217 | −5.5411 | 1.2684 | ||
Forth | First | 1.87940* | 0.81982 | 0.023 | 0.2629 | 3.4959 | |
Second | 0.37881 | 0.83519 | 0.651 | −1.2680 | 2.0256 | ||
Third | −0.04221 | 0.88450 | 0.962 | −1.7862 | 1.7018 | ||
Fifth | −2.17857 | 1.77635 | 0.221 | −5.6811 | 1.3240 | ||
Fifth | first | 4.05797* | 1.69450 | 0.018 | 0.7168 | 7.3992 | |
Second | 2.55738 | 1.70199 | 0.135 | −0.7986 | 5.9133 | ||
Third | 2.13636 | 1.72672 | 0.217 | −1.2684 | 5.5411 | ||
Forth | 2.17857 | 1.77635 | 0.221 | −1.3240 | 5.6811 |
As shown in “
By conducting Post Hoc test in “
Sum of Squares | df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|
Perceived Ease of Use | Between Groups | 20.877 | 3 | 6.959 | 1.049 | 0.372 |
Within Groups | 1347.191 | 203 | 6.636 | |||
Total | 1368.068 | 206 | ||||
Perceived Usefulness | Between Groups | 329.525 | 3 | 109.842 | 8.723 | 0.000 |
Within Groups | 2556.127 | 203 | 12.592 | |||
Total | 2885.652 | 206 |
Dependent Variable | (I) avg | (J) avg | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Perceived Usefulness | Fair | Good | −1.99458* | 0.92363 | 0.032 | −3.8157 | −0.1734 |
Very good | −3.29306* | 0.92571 | 0.000 | −5.1183 | −1.4678 | ||
Excellent | −4.88889* | 1.07977 | 0.000 | −7.0179 | −2.7599 | ||
Good | Fair | 1.99458* | 0.92363 | 0.032 | 0.1734 | 3.8157 | |
Very good | −1.29848* | 0.55763 | 0.021 | −2.3980 | −0.1990 | ||
Excellent | −2.89431* | 0.78735 | 0.000 | −4.4467 | −1.3419 | ||
Very good | Fair | 3.29306* | 0.92571 | 0.000 | 1.4678 | 5.1183 | |
Good | 1.29848* | 0.55763 | 0.021 | 0.1990 | 2.3980 | ||
Excellent | −1.59583* | 0.78978 | 0.045 | −3.1531 | −0.0386 | ||
Excellent | Fair | 4.88889* | 1.07977 | 0.000 | 2.7599 | 7.0179 | |
Good | 2.89431* | 0.78735 | 0.000 | 1.3419 | 4.4467 | ||
Very good | 1.59583* | 0.78978 | 0.045 | 0.0386 | 3.1531 |
good, good and finally fair which shows positive relationship between GPA and the students’ awareness of the benefits.
The number of previous e-learning courses which depend on Moodle has significant influence on both perceived usefulness and perceived ease of use as shown in “
The student’s computer skills have significant influence on perceived ease of use as shown in “
As shown in “
For testing the original TAM model, the researchers used a regression analyses and found all the hypotheses are supported as shown in “
In linear regression matrix there are five parameters; R2-Value (the coefficient of the correlation or the relation) which shows the strength and direction of the relationship. P-Value indicates the significant of the relationship, P must always equal or less than 0.05 for the relationship to be significant. Beta, β-Value which is another parameter in linear regression shows the slope and the direction of the relationship, standard error SE - Value of β indicates the percentage of error that can happen. The smaller the standard error of β the less likely error can happen while t statistics is the coefficient divided by its error. As reader can see in “
Sum of Squares | Df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|
Perceived Ease of Use | Between Groups | 135.826 | 2 | 67.913 | 11.243 | 0.000 |
Within Groups | 1232.242 | 204 | 6.040 | |||
Total | 1368.068 | 206 | ||||
Perceived Usefulness | Between Groups | 205.112 | 2 | 102.556 | 7.805 | 0.001 |
Within Groups | 2680.540 | 204 | 13.140 | |||
Total | 2885.652 | 206 |
Dependent Variable | (I) E_courses | (J) E_courses | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Perceived Ease of Use | One Course | Two courses | −1.93260* | 0.48657 | 0.000 | −2.8919 | −0.9733 |
Three Courses or more | −2.05952* | 0.45191 | 0.000 | −2.9505 | −1.1685 | ||
Two courses | One Course | 1.93260* | 0.48657 | 0.000 | 0.9733 | 2.8919 | |
Three Courses or more | −0.12692 | 0.39158 | 0.746 | −0.8990 | 0.6451 | ||
Three Courses or more | One Course | 2.05952 | 0.45191 | 0.000 | 1.1685 | 2.9505 | |
Two courses | 0.12692 | 0.39158 | 0.746 | −0.6451 | 0.8990 | ||
Perceived Usefulness | One Course | Two courses | −1.30000 | 0.71764 | 0.072 | −2.7149 | 0.1149 |
Three Courses or more | −2.56000* | 0.66652 | 0.000 | −3.8742 | −1.2458 | ||
Two courses | One Course | 1.30000 | 0.71764 | 0.072 | −0.1149 | 2.7149 | |
Three Courses or more | −1.26000* | 0.57754 | 0.030 | −2.3987 | −0.1213 | ||
Three Courses or more | One Course | 2.56000* | 0.66652 | 0.000 | 1.2458 | 3.8742 | |
Two courses | 1.26000* | 0.57754 | 0.030 | 0.1213 | 2.3987 |
Sum of Squares | df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|
Perceived Ease of Use | Between Groups | 96.568 | 2 | 48.284 | 7.747 | 0.001 |
Within Groups | 1271.500 | 204 | 6.233 | |||
Total | 1368.068 | 206 | ||||
Perceived Usefulness | Between Groups | 1.443 | 2 | 0.721 | 0.051 | 0.950 |
Within Groups | 2884.209 | 204 | 14.138 | |||
Total | 2885.652 | 206 |
Dependent Variable | (I) Skills | (J) Skills | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Ease of Use | Excellent Skills | Moderate Skills | 0.89548* | 0.35492 | 0.012 | 0.1957 | 1.5953 |
Weak Skills | 3.02749* | 0.86994 | 0.001 | 1.3123 | 4.7427 | ||
Moderate Skills | Excellent Skills | −0.89548* | 0.35492 | 0.012 | −1.5953 | −0.1957 | |
Weak Skills | 2.13201* | 0.86848 | 0.015 | 0.4197 | 3.8444 | ||
Weak Skills | Excellent Skills | −3.02749 | 0.86994 | 0.001 | −4.7427 | −1.3123 | |
Moderate Skills | −2.13201 | 0.86848 | 0.015 | −3.8444 | −0.4197 |
Sum of Squares | df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|
Perceived Ease of Use | Between Groups | 95.003 | 2 | 47.501 | 7.612 | 0.001 |
Within Groups | 1273.065 | 204 | 6.241 | |||
Total | 1368.068 | 206 | ||||
Perceived Usefulness | Between Groups | 67.580 | 2 | 33.790 | 2.446 | 0.089 |
Within Groups | 2818.072 | 204 | 13.814 | |||
Total | 2885.652 | 206 |
Dependent Variable | (I) Difficulties | (J) Difficulties | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Ease of Use | Yes | Moderate | −1.61648* | 0.69725 | 0.021 | −2.9912 | −0.2417 |
None | −2.47314* | 0.69038 | 0.000 | −3.8343 | −1.1119 | ||
Moderate | Yes | 1.61648* | 0.69725 | 0.021 | 0.2417 | 2.9912 | |
None | −0.85666* | 0.36153 | 0.019 | −1.5695 | −0.1438 | ||
None | Yes | 2.47314* | 0.69038 | 0.000 | 1.1119 | 3.8343 | |
Moderate | 0.85666* | 0.36153 | 0.019 | 0.1438 | 1.5695 |
Independent Variable | β | SE | T | P | R2 | Dependent Variable |
---|---|---|---|---|---|---|
Perceived Ease Of Use | 0.339 | 0.095 | 5.165 | 0.000 | 0.115 | Perceived Usefulness |
Perceived Ease Of Use | 0.455 | 0.071 | 7.324 | 0.000 | 0.207 | Attitude Towards Using |
Perceived Usefulness | 0.326 | 0.052 | 4.937 | 0.000 | 0.106 | Attitude Towards Using |
Perceived Usefulness | 0.214 | 0.046 | 3.144 | 0.002 | 0.046 | Behavioral Intention To Use |
Attitude Towards Using | 0.529 | 0.051 | 8.936 | 0.000 | 0.280 | Behavioral Intention To Use |
Hypothesis | Tested Relationship | Result |
---|---|---|
H1 | The student’s faculty type has an influence on Moodle perceived usefulness. | Supported |
H2 | The student’s faculty type has an influence on Moodle perceived ease of use. | Supported |
H3 | The level of student’s academic year has an influence on Moodle perceived usefulness. | Supported |
H4 | The level of student’s academic year has an influence on Moodle perceived ease of use. | Not supported |
H5 | The degree of student’s GPA has an influence on Moodle perceived usefulness. | Supported |
H6 | The degree of student’s GPA has an influence on Moodle perceived ease of use. | Not Supported |
H7 | The number of previously E-learning courses has an influence on Moodle perceived usefulness. | Supported |
H8 | The number of previously E-learning courses has an influence on Moodle perceived ease of use. | Supported |
H9 | The student’s skills on computer and internet affect positively on Moodle perceived usefulness. | Not supported |
H10 | The student’s skills on computer and internet affect positively on Moodle perceived ease of use. | Supported |
H11 | The student’s difficulty reading from computer screen affect negatively on Moodle perceived usefulness. | Not Supported |
H12 | The student’s difficulty reading from computer screen affect negatively on Moodle perceived ease of use. | Supported |
H13 | The Perceived ease of use affects positively perceived usefulness of Moodle. | Supported |
H14 | The Perceived ease of use affects positively on attitude towards using Moodle. | Supported |
H15 | The Perceived usefulness affects positively on attitude towards using Moodle. | Supported |
H16 | The Perceived usefulness affects positively on behavioral intention to use Moodle. | Supported |
H17 | Attitude towards using affects positively on behavioral intention to use Moodle. | Supported |
H18 | Behavioral intention to use affects positively on actual using Moodle. | Supported |
results showed that the perceived usefulness has a significant influence (R2 = 0.106, b = 0.326) on the attitudes towards using better than its influence on behavioral intention to use. This returns to The University of Jordan students’ awareness to Moodle as learning management system while focusing on its advantages. This research also agrees with other researches to indicate that an attitude towards using is a direct determinant of behavioral intention to use [
This study shows that its obtained results confirm the original TAM Model. It also demonstrates some interesting issues. First: the students of the University of Jordan are highly qualified to use Moodle as learning management system and have sufficient awareness of its benefits. Second: The University of Jordan is working on the improvement of the educational process in its different faculties, so this study can help the decision makers in the University in the development Moodle as learning management system.
Future studies could be conducted to examine TAM with a different sample of students and a wider range of information technology applications, and examine the TAM model with the teachers from the University of Jordan to get more comprehensive view of perception the Moodle.
Nabeel Al-Assaf,Tamara Almarabeh,Lubna Nasir Eddin, (2015) A Study on the Impact of Learning Management System on Students of the University of Jordan. Journal of Software Engineering and Applications,08,590-601. doi: 10.4236/jsea.2015.811056