UNDERSTANDING STUDENTS’ ATTITUDES TOWARD E-LEARNING: EVIDENCE FROM BRUNEIAN VOCATIONAL AND TECHNICAL EDUCATION Afzaal H. Seyal
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Serina Mohd Ali
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Hj. Awg.Yussof Hj. Awg. Mohamad Hj. Mohd Noah Abd. Rahman Department of Computing and Information Systems Faculty of Business and Information Technology Institut Teknologi Brunei
Abstract The study investigates the 220 students’ of technical and vocational institution to assess their attitudes toward e-learning. The study was undertaken in one of the technical institutions in Brunei Darussalam. The study uses the survey methodology and is based upon the questionnaire that was distributed randomly to the students to assess their attitudes towards e-learning and to find out any demographical factors that are significant towards the students’ use of elearning. The result shows that majority of the students have a positive attitudes towards the e-learning with a mean of 3.67. The factor analysis data has suggested two-factor solution on the nine attitudinal items out of the twewntyitems scale that was initially used to assess the students’ attitude. The result of regression analysis further indicates that none of the demographical factors are significant predictor of the students’ attitude towards e-learning. However, ANOVA test has found some difference among the students’ attitudes based on the age. Finally, some conclusions are drawn and recommendations are made for the relevant authorities. Keywords:
e-learning, students of VTET, attitude, Brunei Darussalam
Introduction Globalization, once thought as a concept has become a sheer reality at the end of the twentieth centuary. This phenomenon coupled with the advancement in Information and Communication Technologies (ICT) has revolutionized the business as well as the educational organizations across the globe. More and more organizations are taking competitive advantages to get the edge over their competitors. The current globalization thrust to the World economy is being driven by the Internet technology. The swiftness and accessibility of the network have made the ubiquitous computing (24/7) as a milestone in globalization. This has further resulted in the emergance of an Internet based world of e-economy, e-business and e-education. Infact, educational sector is the one that has radically revised the teaching and learning strategies with the one aim to provide the better service to the learners and to develop the current capacity building of the nations through the intensive use of the ICT. The information technology in teaching and learning has created a need to transform how students from higher institutions learn by using more modern, efficient, effective and cost-effective alternatives in the form of e-learning. E-learning is simply defined as a delivery of course content via electronic media such as Internet, Intranet, Extranet, satelliate broadcast, audio/video tapes, interactive TV and CD-ROMs.(Urdan and Weggen, 2000). E-learning concept has been around for decades and is one of the most significant recent developments in the Information Systems (IS) industry (Wang, 2003). E-learning has been viewed as synonymous with Web-based learning (WBL), Internet-based training (IBT), advanced distributed learning (APL), Webbased instruction (WBI), online learning (OL) and open/flexible learning (OFL) (Khan, 2001). E-learning system is implemented through several ways however; the best practices among the various educational institutions have recommended developing Web-based learning management system (LMS).
A formidable body of literature exists on the computer attitude of students, (Simon and Wilkes, 1997; Seyal et al. 2002) schoolteachers (Federico, 2001), university/college faculty members (Intaganok et al. 2007; Seyal et al. 2000) and general computer users (Chau, 2001). E-learning is a recent phenomenon and little is, however, known regarding the students’attitude studying specifically in technical colleges. From these studies, it is argued that the gender, academic qualifications, prior knowledge training, self-efficacy, experiences and interests of the students may affect the attitude towards e-learning. But none of these studies suggests about the attitude of students studying in technical colleges and are likely to differ from those of schools and colleges. Thus, it is likely that students in technical colleges may develop different views and attitudes towards elearning as compared to those of their counterparts. There is need to study this aspect in a new geographical setting. Overview of Brunei Government Initiative An overwhelming majority of existing studies were undertaken in western countries. Thus, their results may not be applicable to an Asian country like Brunei Darussalam, which is culturally different from the western world. It is a small sultanate located on the northwest coast of Borneo island with a total population of nearly 0.4 million (Brunei Year Book, 2004). Its’ main economic activity is dominated by the oil and gas sector, and gross domestic product per capita was B$ 23,865 (US$1= 1.45) in 2004. After achieving its independence in 1984, the government placed considerable importance on technical education. Two engineering colleges, one vocational college, and a technical institute were established to produce technologically oriented professionals at various levels. The government also recognised the need for broader use of computer technology in the public sector. As such, the Information Technology Division (ITD) was set up to oversee and to support the development of IT projects in the public sectors. In addition, Ministry of Education (MOE) has set up Department of ICT to augment the ICT initiatives not only in the institutions of higher education and technical colleges but
also in the in primary as well as in secondary schools. The Department of ICT is among one of the key players in setting up e-learning project. The e-learning is one of the projects under the e-Education flagship of the MOE, was signed on Jan 26th, 2008 at the cost of B$7.7 millions and to be completed within sixteen months. The project consists of a strategic study on e-learning in MOE, implementation of Learning Management System (LMS), instructional design facility, provision of authoring tools, notebooks, and digital contents. The e-learning project was introduced by MOE with the assumption that this will change the teaching-learning style at the higher institutions and A-level colleges. This new style will support and promote more student centric, problem based, and collaborative approach of learning. The project further hopes that learners and facilitators both will make a good use and to transform the teaching-learning environments in the country (www.moe.gov.bn). We believe that successful adoption of any information system (IS) is depending on the right and favourable attitudes of the potential users such as students and lecturers in case of educational settings. On this basis and on the background cited above, a study was undertaken in March, 2010 to examine the students’ attitudes on e-learning studying in vocational and technical college in Brunei Darussalam. The specific objectives are given below: Objectives of Study The central intent of this study was to examine the students’ attitudes on e-learning studying in technical and vocational educational (TVE) colleges in Brunei Darussalam. There were two specific objectives: (a)
To develop and validate a suitable instrument to measure e-learning attitudes of students.
(b)
To assess the e-learning attitudes of the students and to find out the difference in the attitudes, if any, on the basis of demographics.
Review of Literature Attitudes are positive or negative evaluations of object, people, or situation that predisposes us to feel and behave toward them in positive or negative ways (Ajzen and Fishbein, 1980). The three basic components of attitudes are cognitive (opinions or beliefs segments), affective (emotional or feeling segment) and behavioural (an intention to behave in a certain way or direction) (Rosenberg and Hovland, 1960). Studying attitudes remained an important segment by several researchers of organizational behaviour, management sciences and ICT domains. It has been hypothesized that attitudes affect users’ behavioural intention which affect users’ actual use of the technology (Rainer and Miller, 1996). Significant relationships have been found between computer attitudes and users’ satisfaction with IS/IT, perceived performance and system usage in a number of studies (Compeau and Higgins, 1995; Rainer and Miller, 1996; Thompson et al. 1994). Social scientists such as Fishbein and Ajzen, (1975) in their Theory of Reasoned Action (TRA) that postulates that belief about an object leads to an attitude and this further leads to behavioural intentions regarding the object and finally these intentions affect the actual behaviours toward the object of target. In other words, we can predict the behaviour from attitudes. Some other studies studied the role of attitude on specific IS/IT rather than discussing the attitudes toward computers in general (Taylor and Todd, 1995a, 1995b). Specifically, several other studies have highlighted the school students’ attitudes on computer (Roberston et al, 1995; Todman and File, 1990; Kirkman, 1993; Gattiker and Hlavka, 1992; Jones and Clarke, 1994; Francis, 1993, Selwyn, 1997). Similarly, in the last decade of the twentieth centaury attitudinal studies on the computer and other IS/IT have shifted to e-learning context. Connolly and Stansfield, (2007) have pointed out that e-learning has gone through three distinct generations.
The first generation, took place from 1994-1999 and was marked by the passive use of the Internet where traditional materials were simply reformatted to an online format. The second generation took place from 2000-2003 and was marked by the transition to higher bandwidths, increased resources and the move to create virtual learning environments. The third generation that was started from 2004 is marked by the incorporation of greater collaboration, socialization, project-based learning and reflective practices. A formidable support in the literature is available that has studied the various demographical variables such as: gender, age, PC ownership, academic qualifications, and skill and use of the computer and Internet (Katz et al. 1995; Shashaani, 1997; Harvey and Wilson, 1985; Gattiker and Hlavka, 1992; Francis, 1993; Roca et al. 2006; Paris, 2004 and Bertea, 2009). These variables have considerably contributed toward the formation of the computer and e-learning attitudes in educational setting. In the context of e-learning attitudes, Wernet et al. (2000) surveyed students who used WebCT in a social work course; found that all of the respondents considered the online course materials beneficial to their overall learning experience. Sanders and MorrisonShelter (2002) examined student attitudes with regards to the Web-enabled learning components in a general biology course for undergraduate. The results showed a positive effect on student learning, problem-solving skills, and critical thinking skills. Paris, (2004) examined the cognitive, effective and behavioural attitudes of fifty-two year ten students from a public schools in Australia to further assess specific online elearning (OWAL). The result indicates students responded better towards OWAL, however gender based difference in attitudes was noticed. Positive correlation was noticed among the Internet users and OWAL attitudes. Some of the prior studied were confined on the application of TAM within the context of e-learning. Brown, (2002) surveyed 78 first year South African University students with
little prior experience of Internet Technology in the context of Technology Acceptance Model (TAM) and found that individual characteristics of self-efficacy and computer anxiety significantly influenced perceived ease of use (PEOU) as did the Web site characteristics, ease of finding and ease of understanding and also confirmed that in developing country context perceived usefulness (PU) components might not predict adoption and amplified the role of PEOU as the main predictor of both usage and PU. Maslin, (2007) studied TAM and e-learning among Malaysian students and found that both PEOU and PU have relationship towards e-learning adoption. Roca et al. (2006) studied 172 respondents in relation to understand e-learning continuance intention with the modified TAM and found that e-learning intention is determined by satisfaction that in turn is jointly determined by PU, PEOU, system quality, service quality and information quality. Similarly, Watcharawaleem, et al. (2005) in Thailand studied the elearning attitudes of the students based upon the methodology of TAM. In Egypt, AbdulWahab (2008) applied a questionnaire with 24-items that measured the attitudes towards e-learning, the intention of adopting e-learning, the availability of resources, the ease of use and the utility in a Lickert scale. Bertea, (2009) found that presence of TAM in measuring attitudes toward e-learning is very high and it is mainly because of the fact that e-learning as an educational process is entirely based on ICTs. Downey et al. (2005) investigated possible relationship between the national culture and the usability of an e-learning system and found the impact of national culture towards the e-learning usability. Selim, (2005) studied 538 students to specify eight elearning critical success factors as perceived by the students that included; students’ motivation, attitudes and technical competency, instructors’ attitudes about students’ e-learning, instructors’ teaching style and university support for e-learning activities as top five factors out of the eight. Cheng, (2006) studied 180 students’ acceptance towards e-learning in technical college in Taiwan and found positive attitudes of the students about e-learning. Results further indicated the demographical variables; gender, computer skills and school system remained insignificant. However, experience
of applying e-learning for business courses played a key factor in affecting the level of acceptance. Al-Khashab, (2007) studied 276 respondents to find about the Kuwait society’s attitudes toward e-learning. The result shows that there are significantly difference in the attitudes towards e-learning based on educational level and found Kuwaiti students generally have good attitudes towards e-learning. Similarly, another study conducted by Al-Doub et al. (2008) in Kuwait College of Business Studies have indicated that students were keen to use e-learning and there are some significant differences between the male and female students in their attitudes to use of e-learning materials. Buzzetto-More, (2008) studied the students’ perception of various e-learning components by designing a Web-based Course Management System. The results indicate that students find course Websites to be helpful resources that enhances the understanding of course content. In addition, students responded favourable towards online submission of assignments. Safavi (2008) in his Iranian-based study described elearning model and guidelines for developing countries intending to adopt the elearning system. Conclusively, it is evident from this section that researchers across the globe have studied the e-learning usage and organizational adoption. It can be summarized that elearning is becoming a standard for today’s education, as it opens the door to learning focusing on the individuals priorities and learning skills. Unfortunately there is dearth of studies in Asia-Pacific region and unfortunately no study, up to our knowledge has been empirically validated the attitudinal aspects of the students on this important part and a gap in this regard exist. This study therefore fill-in the gap and the results of this study will add up to the knowledge.
Research Methodology Design of Instrument A variety of scales were used to measure these demographical independent variables. A five-point Likert scale was used to assess the answer for questions asking about “how important the respondents think the use of the Internet and e-learning for todays’ education”. The measure of the dependent variable (attitude towards e-learning) used the definition of attitu A set of 20-items were initially selected from the literature to measures the students’ attitude toward computer (Selwyn, 1997) and were modified to cater for the need of e-learning. However, this is not a new phenomenon. Paris, (2000) modified CAAS (Computer Attitude Scale for Secondary Students) from Jones and Clarke, (1994) and modified to include Web page reference instead of computer reference. Measuring attitudes has an important role in analyzing behavior because it is known as a fact that there is a strong correlation between attitude and behavior (Bertea, 2009). In the context of e-learning a favorable attitude of students shows a greater probability that they will accept the new learning system. There are two models that measure attitude; one developed by the Rosenberg (Rosenberg and Hovland, 1960) and other by Fishbein, (1975). The Rosenberg model is built on two variables; the perceived utility of the object and the value of importance, however, in the context of measuring students attitude this model require full utility of the object as perceived by the consumer so in that case student should be using the e-learning, so for the purpose of this research this model is discarded. Fishbein model offer a different perspective proposing an analysis of attitudes through the consumers’ belief and evaluations. The consumer’s beliefs refer to the probability accepted that object has certain feature, where evaluations stand for the extent to which these features are important or not. The perceived utility from the Rosenberg model corresponds to the consumer belief in the Fishbein model. In fact,
both require user’s belief while using the object. That is also not applicable to this study so we discard both and adopt Selwyn instrument with modification for this study. Population and Sample size The study employed a survey approach to examine e-learning attitudes of the students. The target population was the students studying in the technical colleges in Brunei Darussalam. The number of students in the college was around one thousand in all the six departments under the Higher National Diploma program. The last week of April and first week of May 2010 was examination period so we selected students from only one faculty rougly about 300 questionnaires were randomly distributed to each session after the examination is over. On final securitny of questionnaires, twenty were dropped because it was not fully filled-in and the remaining 220 questionnaire were retained for the further analysis.uting staff. Thus, the response rate of 73% was found sufficient for exploratory study of this kind. Instrument validation An initial version of the instrument was developed in two parts: Part A collected demographic information, computer exposure, and educational attributes, while Part B contained 20 items to measure e-learning attitudes. These items were carefully selected after reviewing existing literature. The works of by Selwyn, (1997), Francis, (1993), Jones and Clark, (1994), were found to be particularly useful. This initial instrument was pretested using several academics as well as students chosen randomly from the colleges. The participating academics were asked to comment on the format and appropriateness of questions and to suggest additional items that they believed should be included in the instrument. In view of their suggestions, undecided, 4 stands for agree and finally 5 stands for strongly agree. A summary several amendments were incorporated into the instrument, which greatly improved its clarity.
The revised instrument was used for the survey. The responses obtained from the pilot test for Part B, were analysed for accuracy using Churchill‘s item purification technique (1979) and exploratory factor analysis (Hair et al. 1995). Using Churchill suggestions, eleven items were eliminated for which ‘corrected-item-total’ correlation was less than 0.30. While exploratory factor analysis eliminated those four items that loaded on more than one factor at 0.40 or greater. Thus, these multiple phases of instrument development and testing produced a 12-items instrument for measuring computer attitudes, and thus established an initial content validity. Table 1 illustrates these eight items, and their corresponding corrected item-total correlation. Table 1 No Att-2 Att-3 Att-13 Att-15 Att-16 Att-17 Att-19 Att-20
Items measuring e-learning attitudes
Correcteditem Total Correlation
The use of e-learning will enable me to do my studies I could probably teach myself to use an e-learning system I avoid coming in contact with e-learning system in the college* I hesitate to use e-learning system for fear of making mistakes I can’t correct * E-learning enables me to do more interesting and imaginative work with my learning I would use e-learning regularly throughout my college Working with e-learning system makes me uncomfortable* Using e-learning system make it possible to work more productive
.51 .46 .51 .42 .53 .60 .50 .57
(* indicates for reverse scoring because of negative responses)
The instrument was restructured and distributed to the 300 students from the Faculty of Business and Information Technology. A total of 220 responses were received; making a response rate of 73% - which is exceptional. For the study, the researchers were still uncertain about the attitude construct. Traditional factor analysis was used to further explore factor structure; this retained all items. Principal component analysis was used as the means of extraction and varimax was used as the method of rotation that grouped these 8 items into two factors.
The Kaiser Meyer-Olkin measure of sampling was 81% and Bartetts’ test of spheriaty was significant (p>.001). Inter-item all correlations were significant (p>.005). In this connection, several decision rules based on Hair et al. (1995) were used to aid extraction process and to derive these three factors. These rules include (a) minimum Eigenvalue of 1.0, (b) simplicity of factor structure, and (3) exclusion of single item factor from the standpoint of parsimony. The two factors were named as perceived behavioral (Factor 1), and affective (Factor 2), These 8 items, together with their corresponding factor loading, are shown in Table 2. This reveals that the factor loading is quite high and range from .60 to .78; the two factors together explained 56% of total variance.
Table 2 Varimax rotated factor loading and eigen-values with variance explained No Item description Factors Att-2 Att-3 Att-13 Att-15 Att-16 Att-17 Att-19 Att-20
The use of e-learning will enable me to do my studies I could probably teach myself to use an e-learning system I avoid coming in contact with e-learning system in the college I hesitate to use e-learning system for fear of making mistakes I can’t correct E-learning enables me to do more interesting and imaginative work with my learning I would use e-learning regularly throughout my college Working with e-learning system makes me uncomfortable Using e-learning system make it possible to work more productive Percentage of variance
Factor 1 –Perceived behavioral and Factor 2-Affective
1 .72 .60
2
.75 .78
.75 .76 .71 .42
.72 .14
In factor analysis, it is generally desirable to have a larger number of respondents than items. The ratio of sample size to number of items was (16:1), which is above the (10:1) ratio suggested by Nunnally, (1970). Furthermore, the derived instrument was tested for reliability. Chronbach’s (1951) alphas were calculated for the overalls instrument, as well as for each of the three factors and are presented in Table 3. The alpha is considered satisfactory. The comparision with two studies shows that our instrument has sufficient parsimony.
Table 3 Results of reliability analysis Factors No of Reliability items Coefficient (α) Factor 1: Perceived Behavioral Factor 2 : Affective Overall
5 3 8
.81 .77 .78
Reliability Coefficient Paris, (2004) .62 .85 .73
Reliability Coefficent Clark & Jones, (1994) .71 .95 .83
Results Data obtained from the survey were analysed using descriptive statistics, factor analysis t-tests as well as multiple-regression analysis by means of SPSS version 17, a well known statistical package. Background profile The background of the participating academics is summarised in Table 4. The dominance of females is quiet clear. This is not unexpected, because Bruneian tertairy school enrollement have confirmed the more female students comparitive to the male couterpart (Bruneian Year Book, 2008) that is contary to the findings of Bell, (1995). A vast majority (90%) of the participating students fell in the age group between 20-25 years. Majority of the students (59%) possess the advanced level (a-level) qualification. Another interesting finding is that majority of students (92%) owned a personal computer. Apparently students showed keen interest in a PC to perform work at home. Not all these students however had equal number of hours spent on the Internet. Roughly around 33% reported having spent more than ten hours on the Internet. Moreover, only one fifth of the students (21%) actually have a professional certification like International Computer Driving License (ICDL) (www.icdl.org). In summary, even though a majority of the participating students owned a PC, their timing and main use of the Internet is quiet diversified. In fact, more than half of the students (66%) are using the Internet from five to ten hours and 83% reported using the Internet for educational
and research purposes. 90% of the students responded that the Internet is very important for todays’ education and 60% of them thought of e-learning as very important feature for todays’ education. Table 4 Background profile of the academics (%) Gender Male 43 Female 57 Age Less than 20 years 4 Between 20-25 years 90 Over 25 years 6 Educational Qualification Professional diploma 13 O’level 28 59 A’level Time Spent on Internet Less than 2 hours Between 2 –5 hours Above 5- less than 10 hours Over10 hours PC ownership Own one Does not own Basic Use of the Internet* Use for graphics Use for games Use for fun For education For research For e-business For e-shopping For e-government Others *multiple responses
3 32 32 33 92 8 43 58 70 77 83 3 36 10 30
E-learning attitudes The eight statements that were grouped into two factors (via factor analysis) were used to solicit the attitudinal views held by the students. They were asked to indicate their level of agreement/disagreement with each statement on a five-point Likert scale. Their responses were compiled, and a mean rating for each statement was computed. These are listed in Table 5. The mean rating for each of these statements lie fair above the ‘neutral’ position (3.0) on the Likert scale. Table 5 No.
Att-2 Att-3 Att-13 Att-15 Att-16 Att-17 Att-19 Att-20
Mean rating received by each attitude statement Items
Mean
The use of e-learning will enable me to do my studies I could probably teach myself to use an e-learning system I avoid coming in contact with e-learning system in the college I hesitate to use e-learning system for fear of making mistakes I can’t correct E-learning enables me to do more interesting and imaginative work with my learning I would use e-learning regularly throughout my college Working with e-learning system makes me uncomfortable Using e-learning system make it possible to work more productive
Overall
The mean attitude score of these two groups were also computed, and were tested for significant difference. Results of t-test (t = 13.29, p = .000) indicate that difference in attitude score between those students having positive attitudes (n=200), and students with negative attitudes (n= 20) is statistically significant at the 5% significance level.
Discussion Several important findings have emerged from this study. First, a set of eight statements grouped into two factors named affective and behavioral component was identified. This produced a valid instrument to measure e-learning attitude of the students studying in TVE environments. This instrument is shorter than some of the existing ones. For instance,
3.84 3.86 3.66 3.35 3.72 3.58 3.74 3.68 3.67
Selwyn’s (1997) instrument contained 21 statements that were grouped into four factors, where as, Jones and Clark, (1994) CAAS measured forty-items that were grouped into three factors. While Popovich et al. (1987) used instrument in which 40 statements were reduced to 20 items that were also grouped into five factors. Majority of these instruments measured attitudes based upon Breckler, (1984) definition of the attitudes that included items on three components of the attitudes: cognitive, affective and behavioral as supported by three factors solution. The two factors solution as generated by this study were in contrast with those reported by these prior studies, and differ considerably from those five reported by Popovich et al. and Selwyn’s four-factor solution. This opens another debate as why only affective and perceived behavior was found to be two-factor solution? Whereas, Breckler, (1984) and Jones and Clark, (1994) proposed that affective, behavioral and cognitive are distinguishable, yet intended components of attitudes. Cognition or thoughts may vary from favourable to unfavourable statements on e-learning and are not applicable to the Bruneian TVE students. One of the plausible reasons is that as such the elearning is a new phenomenon and students believe on pleasurable to unpleasurable feelings and link them to their supporative or hostile behavior, and that is whytwo-factor solution could be generated from this study. In short, these eight instruments are likely to be easily accepted by students, as it required less time for them to respond. Second, the mean score of the participating students against each statement was well over the neutral value. This indicates that students in general did not hold any unfavourable views about e-learning use. Moreover, the average overall attitude score of 3.67 is moderately high.
Third, a majority of the participating students (92%) were found to own a personal computer. A verbal discussion with some participating students revealed that many of them do not have an access to the PCs in their college laboratories, mainly because the majority of the PCs are slow and outdated, infected with virus, and sometimes their timming doesn’t match so they prefer to bring their own notebook computers because of these constraint. However, this figure is quite high even in comparison to developed nations. For instance,
National Center for Education Statistics, USA (2004) reported that little over 65% of all higher educational students in US now have their own personal computer (http://nces.ed.gov/pubs2004/2004022.pdf). Majority of them had above average Internet skill and mostly didn’t receive any formal training from outside.
Table 6 Results of Multiple Regression Analysis Variables Beta Gender Age Academic Qualifications PC Ownership Hours Spent on Internet R2(adj)= .10 Std. Error =
.058 .027 -.007 .020 .008 .498
t-value
p-value
.692 1.50 -.084 .231 .089 F = .671
.490 1.35 .933 .817 .929 p= .61
Fourth, multiple regression analysis, in Table 6, identified that none of the demographical variables contribute significantly toward e-learning attitude of students. As most of the prior research into gender difference in attitude toward IT have shown that males are more positive in their attitudes than females (Loyd et al. 1987; Durndell, 1991, Shashaani, 1997). However, our findings are inline with Katz et al. (1995) and Paris, (2004) that there is no difference between the attitudinal scores of males and females. Similarly, the PC ownership is reported to be a significant variable and is supported by the authors from various countries. Several authors like Harvey and Wilson, 1985 (UK), Gattiker and Hlavka, 1992 (Canada), Nickell & Seado, 1986 (USA) and Al-Jabri et al. 1997, Saudi Arabia) provided strong support that PC owner have a more positive attitude than non-owners. In a similar fashion, age, and level of computer and Internet skill of students was also found to affect attitude. This finding is in contrast with that of Loyd et al. 1987 (USA), Woodrow, 1991(Canada), Simon & Wilkes, 1997 (USA), DorenKamp, (1993) in Holland, Drundell & Thomson, (1997) in Scotland and Al-Jabri et al. 1997 in Saudi Arabia, Paris, 2004 (Australia) and Bertea, (2009) who reported that subjects participating in his study tended to produce a positive attitude after attaining a certain level of skill. Thus, these two findings seem to be
consistent across various geographical boundaries however, our results are in contrast with these studies and in line to some extent with Cheng, (2006) that these demographical variables remained insignificant. Furthermore, the regression model only explains roughly around 10% of the total variance that does not reflect a very good fit. The low value suggests that this study did not include some important independent variables that have significant impact on e-learning attitudes of the students. Finally, one the basis of ANOVA test some significant difference was noticed on the age (t=2.946, p
