Computer attitudes of non-computing academics:
a study of technical colleges in Brunei Darussalam
Afzaal H. Seyal
1, Md. Mahbubur Rahim
*, Mohd. Noah Abd. Rahman
Department of Computing and Information Systems, Institute Technology Brunei, Permanent Campus, Tungku Link, BSB, BE 1410, Brunei Darussalam
Received 29 October 1998; accepted 29 July 1999
Abstract
Current information systems (IS) literature has paid considerable attention to measuring the computer attitudes of students and schoolteachers. Computer attitudes of non-computing academics working particularly in technical colleges have, however, received scant attention. Moreover, studies on computer attitudes among Asian academics are least reported. Keeping this in view, this study was undertaken by validating an instrument to measure computer attitudes of non-computing academics working among technical colleges in Brunei Darussalam. This study also identi®ed factors that contributed to the formation of computer attitudes of academics. This was achieved by undertaking a survey of 192 non-computing academics from four technical colleges. Attributes related to demographics and education of academics appeared to have little impact on computer attitudes. In contrast, ownership of a personal computer (PC) and level of computer skill were found to be important.#2000 Elsevier Science B.V. All rights reserved.
Keywords:Computer attitudes; Non-computing academics; Technical colleges
1. Introduction
Academic institutions now seek to put a computer on the desk of all their staff [29]. In the past, aca-demics used computers primarily to perform admin-istrative tasks, like compiling students' results and monitoring students' in-class progress. Recently, the concept of using computers has moved beyond this. For instance, several educational institutions in the US have embraced computers in the class rooms, and most
American teachers regard computer as much a part of the classroom as a blackboard [48].
Despite the tremendous educational opportunities, its potential is yet to be fully utilised. Gilbert [15] found that many academics are reluctant to move beyond word processing, while Wilkins and Nantz [54] discovered that teaching uses of the computer network remained low and perceived future use was also low. One plausible explanation is that substantial investment that has already been made in more tradi-tional methods of instruction [27]. More importantly computerisation has brought in anxiety and threats to some teacher's [3] and some educators believe that computers may fail to have an impact on classroom instruction [51]. Consequently, the provision of com-*Corresponding author. Fax:673-2-249036.
E-mail addresses: [email protected] (A.H. Seyal), [email protected] (M.M. Rahim)
1Fax: 673-2-249036.
puter technology is not a suf®cient condition for its success in an educational setting.
A formidable body of literature exists on the com-puter attitude of school teachers and there is also some literature on the attitude of university faculty [19,26,43]. Little is, however, known regarding the computer attitude of academics working in technical colleges. From these studies, it is argued that the quali®cations, working experiences and interests of academics may affect the attitude towards computer. But none of these studies suggests about the attitude of academics employed in technical colleges and are likely to differ from those of schoolteachers and faculty members. Thus, academics in technical col-leges may develop different views and attitudes towards computers as compared to those of their counterparts. There is need to study this aspect in a new geographical setting.
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 north west coast of Borneo island with a total popula-tion of nearly 0.3 million [4]. Its' main economic activity is dominated by the oil and gas sector, and gross domestic product per capita was B$ 23,865 (US$11.58) in 1994. After achieving its indepen-dence in 1984, the government placed considerable importance on technical education. Two engineering colleges, one vocational college, and a technical insti-tute were established to produce technologically oriented professionals at various levels. The govern-ment 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 sector in schools and technical colleges. Against this background, a study was under-taken in late 1997 to examine the computer attitudes of non-computing academics from four different techni-cal colleges.
2. Objectives of study
The central intent of this study was to examine the computer attitudes of non-computing academics
working in technical colleges in Brunei Darussalam. There were two speci®c objectives
1. To develop and validate a suitable instrument to measure computer attitudes of non-computing academics.
2. To identify the factors that significantly affects computer attitudes of these academics.
3. A review of literature
Literature on computer attitudes can be broadly divided into three groups, depending on the type of the target population for whom attitudes were mea-sured. The ®rst group deals with the measurement of computer attitudes of primary and secondary school students. The works of Harvey and Wilson [20], Siann and Macleod [46], Levin and Gordon [30], Martin [33], and Moore [34] fall in this group. Some authors like Koohang [28] and Finnegan and Ivanoff [11] have also studied computer attitudes of students studying in higher institutions.
The second group of studies has focused on school-teachers. This group includes the works of Katz and Francis [26] and Savenye et al. [43] among others. While limited studies are available for the third group, which address the computer attitudes of university faculty members. Recently, Harris [19] has made a valuable contribution by measuring computer atti-tudes of academics in a Hong Kong university. The attitude studies were hot however con®ned to students and academics, some authors like Gattiker and Hlavka [14], Nickell and Seado [35], Culpan [8], and Winter et al. [53] made attempts to measure computer atti-tudes of people working in business settings.
4. The research model and development of hypotheses
On the basis of a review of existing literature, a nor-mative model was developed. It is presented in Fig. 1.
Gender: The impact of gender on the formation of a person's computer attitude is still a matter of debate. Some authors like Perolle; [38] and Mankin et al. [32] have reported gender difference in computer attitude in various work settings. Barrier and Margavio [2], who studied gender issues by using the attitude toward computer usage scale (ATCUS) and found that more males exhibited negative computer attitude; have reinforced their ®ndings. On the other hand, Stasz and Shavelson [49] reported little gender difference in a study of computer attitude among teachers. Thus, the following hypothesis is postulated
Hypothesis 1. There exists a relationship between gender and academic computer attitudes.
Age: Several researchers have examined the impact of age on a person's computer attitude. Jay
and Willis [24] reported that young males have most favourable predisposition towards computer. Moreover, Kay identi®ed age as an important variable while assessing the positive attitude towards computer use. This leads to the following hypothesis
Hypothesis 2. There exists a relationship between age and academic computer attitudes.
Educational quali®cation: Several researchers have highlighted the importance of education of academics on their computer attitudes. For instance, Dugan and Thurlow [9] suggested that educational level is likely to have an effect on one's attitude towards computer use. Those who are better educated are more favourably disposed to rapid advance in technology. Kay has reported that people with higher educational quali®cation have favourable predisposi-tion to computer use, while Hignite and Echtenacht [21] found that education effect computer attitude of business education teachers. Thus, the following hypothesis is postulated
Table 1
A summary of factors affected computer attitudes
Factors Source Subject
Computer Literacy Hignite and Echternacht [21] Business education teachers
Woodrow [56] Student teachers
Loyd and Gressard [31] Teachers
Simon and Wilkes [47] Participant of computer literacy course, business major Al-Jabri et al. [1] Undergraduate university business students
Age Woodrow Student teachers
Gender Savenye et al. [37] Pre-service teachers
Woodrow Student teachers
Kay [27] Education students
Robertson et al. [42] Student teachers Shashanni [45] Secondary school students
Teaching experience Wilson [55] Students
Personal locus Kay Education students
Computer Ownership Ray [41] Business owners
Gattiker & Hlavka [14] University students enrolled in computer course Nickell & Seado [35] Business managers
Harvey & Wilson [20] Primary & sec school pupil
Al-Jabri et al., Undergraduate university business students
Prior Training Nickell and Seado Business managers
Igbaria [23] Business managers/part-time MBA students
Organisational culture Harris [19] Faculty members
Hypothesis 3. There exists a relationship between educational qualification and academic computer attitudes.
Teaching experience: Existing literature offers little information about the impact of teaching experience on academic computer attitude. A few studies however provide some indication that beginning teachers in UK schools use computers in their classrooms much less than expected. For instance, HMI [22] found that less than 6% of beginning teachers in UK schools used computer in their ®rst year of teaching, while fewer than 20% of the beginning school teachers were found to be prepared to use computer in their classrooms [18]. Maybe, computer awareness will grow with time. The following hypotheses is postulated
Hypothesis 4. There exists a relationship between years of teaching experience and academic computer attitudes.
Personal computer ownership: Literature strongly suggests that ownership of a PC is related to a favour-able attitude. For example, Pfeffer and Lawles [39], Steers and Porter [50], Harvey and Wilson [20] and Noe [36] have shown the differences in attitude between owners and non-owners of computer, with owners having a more positive attitude. Gattiker and Hlavka [14] have shown that individual's attitude to computer usage depends on ownership of a computer. Thus
ownership of personal computer and academic com-puter attitude.
Prior computer training: Several authors have stu-died the relationship between a person's prior training in computers and his/her subsequent attitude. Clarke and Chamber [6] have shown the signi®cance of prior computing training on the person's attitude, while Dupagne and Krendl [10] have found that computer literacy courses greatly improve teachers' attitudes. Based upon this rationale, the following hypothesis is proposed
Hypothesis 6. There exists a relationship between previous training in computer and academic computer attitudes.
Computer skill: The skill of academics in using personal computers may have some in¯uence on their computer attitudes. Less skilled academics may develop anxiety; then they view the computer with scepticism. This assertion is partially supported by Loyd and Gressard [31] who reported that the subjects participating in their study developed a more positive attitude once they achieved a certain level of computer skill. Thus, the last hypothesis
Hypothesis 7. There exists a relationship between level of computer skill and academic computer atti-tudes.
5. Research methodology
5.1. Design of instrument
A variety of scales were used to measure these seven independent variables. A ®ve-point Likert scale was
used to measure computer skill and teaching experi-ence. The measure of the dependent variable (com-puter attitude) used the de®nition of attitude suggested by Fishbein and Ajzen [12]: as a learned predisposi-tion to respond in a consistently favourable and unfa-vourable manner with respect to a computer. This is a dimensional psychological concept, with multi-ple items required to capture a psychological factor like computer attitude Nunnally [37]. A set of 27 items was initially selected from the literature to measure computer attitudes of academics. Each academic was asked to indicate their level of agreement/disagree-ment with each stateagreement/disagree-ment on a ®ve-point Likert scale, where 1 stands for strongly disagree, 2 for disagree, 3 for undecided, 4 for agree and ®nally 5 stands for strongly agree. A summary of the de®nitions and scales is provided in Table 2.
5.2. Population
The study employed a survey approach to examine computer attitudes of non-computing academics. The target population was the non-computing academics working among at technical colleges in Brunei Dar-ussalam. The number of academics in all these tech-nical colleges was reported to be 340. Telephone contacts with the Head of these colleges revealed a total of computing staff. Thus, the target population was reduced to 300.
5.3. Instrument validation
An initial version of the instrument was developed in two parts: Part A collected demographic informa-tion, computer exposure, and educational attributes, while Part B contained 27 items to measure computer
Table 2
A summary of research variables
Factors Definition Scale
Computer attitude A disposition to respond favourably to a computer. Five-point Likert
Gender Sex of an academic Dichotomous
Age No. of years since birth Categorical
attitudes. These items were carefully selected after reviewing existing literature. The works of by Selwyn [44], Francis [13], Jones and Clark [25], Popovich [40], and Gressard and Loyd [16] were found to be particularly useful. This initial instrument was pre-tested using several academics chosen randomly from two colleges located in close proximity of the authors' work place. 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, several amendments were incor-porated into the instrument, which greatly improved its clarity.
The revised instrument was further pilot tested among 32 academics selected from three colleges. The responses obtained from the pilot test for Part B, were analysed for accuracy using Churchill's item puri®cation technique [5] and exploratory factor ana-lysis [52]. Using Churchill suggestions, 11 items were eliminated for which `corrected-item-total' correla-tion 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 com-puter attitudes, and thus established an initial content validity. Table 3 illustrates these 12 items, and their corresponding corrected item-total correlation and Varimax factor loadings.
The pilot study proved very effective in eliminating ineffective items, as well as generating a ®rst set of
constructs. Following the pilot study, the instrument was restructured and distributed to the remaining 268 non-computing academics. A total of 192 responses were received; making a response rate of 71% ± which is exceptional. After the pilot study, the researchers were still uncertain about the attitude construct. Tra-ditional factor analysis was used to further explore factor structure; this retained all 12 items. Principal component analysis was used as the means of extrac-tion and varimax was used as the method of rotaextrac-tion that grouped these 12 items into three factors. The Kaiser Meyer±Olkin measure of sampling was 83%. In this connection, several decision rules based on Hair et al. [17] 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 three factors were named as perceived usefulness (Factor 1), affective (Factor 2), and perceived behaviour (Factor 3). These 12 items, together with their corresponding factor loading, are shown in Table 4. This reveals that the factor loading is quite high and range from 0.49 to 0.81; the three factors together explained 57.4% of total variance.
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. Furthermore, the derived instrument was tested for reliability. Chronbach's [7] Alphas were calculated for the overalls instrument, as well as for each of the three
Table 3
List of items retained during pilot study
No. Items Corrected
item-total correlation
Factor loadings
1 Computer facilitates my teaching 0.74 0.74
2 Computer helps me in designing better and effective assignments for student 0.63 0.75
3 My assignment always requires my students to use a computer 0.32 0.49
4 I use a computer to organize my administrative work 0.76 0.65
5 Computer knowledge is essential for modern life 0.65 0.69
6 Technical teaching without computer is unthinkable now-a-days 0.77 0.72
7 I think that the challenges of teaching using computers is exciting 0.69 0.55 8 I think that working on a computer is a good way to use my spare time. 0.54 0.81
9 Using a computer makes me feel creative 0.51 0.72
10 I think that learning to use a computer needs a lot of patience 0.56 0.64
11 I would like to learn about the computer only if it is essential for my promotion 0.36 0.81
factors and are presented in Table 5. The alpha is considered satisfactory.
6. Results
Data obtained from the survey were analysed using
w2-tests as well as multiple regression by means of SPSS, a well known statistical package.
6.1. Background profile
The background of the participating academics is summarised in Table 6. The dominance of males is clear. This is not unexpected, because nearly 80% of the academics working in the university as well as
technical and vocational colleges are male's [3]. A majority (71%) of the participating academics fell in the age group of 30±50 years. With the exception of doctorate holders, highest educational quali®cations of these academics varied uniformly. This is possibly because, unlike universities, academics in technical/ vocational colleges are not required to possess Ph.D. Degree in their disciplines. Academics with 10±20 years of teaching experience slightly dominated the sample. Only 17% academics can be considered novice, with less than 5 years of teaching experience. Another interesting ®nding is that most academics (79%) owned a PC. Apparently academics showed keen interest in a PC to perform work at home. Not all these academics however had equal computer skills. Only 11% reported having a high level of computer Table 4
Varimax rotated factor loading and Eeigenvalues with variance explaineda
No. Item description Factors
1 2 3
Perceived usefulness (6 items)
1 Computer facilitates my teaching 0.74
2 Computer help me in designing better and effective assignment for student 0.75 3 My assignment always require my students to use computers 0.49 4 I use computer to organise my administrative work in a better way 0.65
5 Computer knowledge is essential for modern life 0.69
6 Technical teaching without computer is unthinkable now-a-days 0.72 Affective component (4 items)
7 I think that the challenges of teaching using computers is exciting 0.55 8 I think that working on computer is a good way to spare time. 0.81
9 Using computer makes me feel creative 0.72
10 I think that learning computer needs a lot of patience 0.64
Perceived behaviour (2 items)
11 I would like to learn about computer only if it is essential for my promotion 0.81
12 I will do as little work with computer as possible 0.80
Eigenvalue 0.25 1.57 1.05
% of variance 35.5% 13.1% 8.8%
aNote : Factor 1 refers to perceived usefulness, Factor 2 refers to affective component, and Factor 3 refers to perceived behaviour.
Table 5
Results of reliability analysis
Factors No. of statements Reliability coefficient (a)
Factor 1: perceived usefulness 6 0.79
Factor 2: affective 4 0.71
Factor 3: perceived behaviour 2 0.60
skill. Moreover, only one-®fth of the academics (21%) actually attended any formal training on the computer. In summary, even though a majority of the participat-ing academics owned a PC, their skill was not high. In fact, half of the academics only felt they had computer skill and most did not receive any formal training.
6.2. Computer attitudes
The 12 statements that were grouped into three factors (via factor analysis) were used to solicit the attitudinal views held by the academics. They were asked to indicate their level of agreement/disagree-ment with each stateagreement/disagree-ment on a ®ve-point Likert scale. Their responses were compiled, and a mean rating for each statement was computed. These are listed in Table 7. The mean rating for each of these statements lie above the 'neutral' position (3.0) on the Likert scale.
The mean attitude score of these two groups were also computed, and were tested for signi®cant differ-ence. Results oft-test (t13.29, df190,p0.000) indicate that difference in attitude score between those academics having positive attitudes (n163), and academics with negative attitudes (n29) is statisti-cally signi®cant at the 5% signi®cance level Table 8.
6.3. Test of hypotheses
The impact of academic gender, age, quali®cation, teaching experience, PC ownership, computer skill, and prior computer training, on the dependent variable Table 6
Background profile of the academics
Academics Number (%)
Gender
Male 147 76
Female 45 24
Age
Less than 30 years 28 15
Between 30±50 years 137 71
Over 50 years 27 14
Educational qualification
Diploma 46 24
Bachelor 51 27
Masters 49 25
Ph.D. 8 4
Others 38 20
Teaching experience
Less than 5 years 32 17
Between 5±10 years 51 26
Between 10±20 years 65 34
Over 20 years 44 23
PC ownership
Own one 151 79
Does not own 41 21
Prior PC training
Yes 40 21
No 152 79
PC skill
High 21 11
Above average 30 16
Average 95 49
Below average 30 16
Low 16 8
Table 7
Mean rating received by each attitude statement
No. Items Mean
1 Computer facilitates my teaching 4.04
2 Computer helps me in designing better and effective assignments for student 4.04
3 My assignment always requires my students to use a computer 3.01
4 I use a computer to organise my administrative work 3.91
5 Computer knowledge is essential for modern life 4.30
6 Technical teaching without computer is unthinkable now-a-days 3.60
7 I think that the challenges of teaching using computers is exciting 4.01
8 I think that working on a computer is a good way to use my spare time. 3.51
9 Using a computer makes me feel creative 3.79
10 I think that learning to use a computer needs a lot of patience 4.04
11 I would like to learn about the computer only if it is essential for my promotion 3.81
were investigated using multiple regression analysis. The results, as presented in Table 9, explain 17% variance in the dependent variable, and partially sup-port the model. Out of the seven independent vari-ables, only two, such as PC ownership and computer skill were found to have signi®cant standardised regression coef®cients, and were related to academics' computer attitude. On the other hand, variables like gender, age, quali®cation, teaching experience, and prior computer training had little signi®cant impact on the formation of an academic's attitude. Therefore,
only two hypotheses (e.g., Hypothesis 5 and 7) were supported.
w2-tests at the 5% signi®cance level were performed to examine if there exist any relationships between the seven independent variables and the dependent vari-able. The results shown in Table 10, clearly indicate that, except for PC ownership and computer skill, all the remaining ®ve variables have no signi®cant rela-tionship with academics computer attitude. This observation further reinforces the ®ndings of the regression analysis.
7. Discussion
Several important ®ndings have emerged from this study. First, a set of 12 statements grouped into three factors was identi®ed. This produced a valid instru-ment to measure computer attitude of non-computing academics. This instrument is shorter than some of the existing ones. For instance, Selwyn's instrument con-tained 21 statements that were grouped into four factors, while Popovich et al used instrument in which 40 statements were reduced to 20 items that were also grouped into ®ve factors. The three factors as gener-ated by this study were in line with those reported by Selwyn, and differ considerably from those ®ve reported by Popovich et al. In short, these 12-item instruments are likely to be easily accepted by aca-demics, as it required less time for them to respond. Second, the mean score of the participating aca-demics against each statement was well over the neutral value. This indicates that academics in general did not hold any unfavourable views about computer use. Moreover, the average overall attitude score of 47.6 is reasonably high.
Table 8
Attitude summary
Attributes Values
No. of academics with positive attitude 163 No. of academics with negative attitude 29 Average attitude score for all academics (n192) 45.7 Average attitude score for academics having positive
attitude (n163)
47.6
Average attitude score for academics having negative attitude (n29)
35.2
Table 9
Results of multiple regression analysis
Variables b b p-value
Educational qualification ÿ0.504 ÿ0.110 0.110
Age ÿ0.852 ÿ0.121 0.263
PC ownership ÿ2.84 ÿ0.182 0.014*
Gender 1.438 0.095 0.191
Prior computer training ÿ0.605 ÿ0.038 0.577
Computer skill 0.943 0.153 0.037*
Teaching experience ÿ0.266 ÿ0.042 0.694 R2(adj)0.169 SE5.97 F5.26 p0.000
*Indicates statistical significance at (p< 0.05).
Table 10
Relationship between computer attitude and independent variables
Hypothesis Relationship w2value P-value Remarks
H1 Gender has relationship with academics' computer attitude 0.731 0.393 No support H2 Age has relationship with academics' computer attitude 6.35 0.095 No support H3 Educational qualification has relationship with academics' computer attitude 6.29 0.178 No support H4 Teaching experience has relationship with academics' computer attitude 7.13 0.060 No support H5 PC ownership has relationship with academics' computer attitude 11.20 0.000* Support H6 Prior computer training has relationship with academics' computer attitude 2.27 0.131 No support H7 Computer skill has relationship with academics' computer attitude 10.46 0.033* Support
Third, a majority of the participating academics (79%) were found to own a PC. A verbal discussion with some participating academics revealed that many of them do not have a PC on their own desk for their exclusive of®ce use. This constraint encouraged them to own a PC at home. However, this ®gure is quite high even in comparison to developed nations. For instance, Gilbert reported that little over 50% of all higher educational faculties in US now have their own PC. Even though a vast majority of the academics in Brunei owned a PC, nearly half of the academics (49%) had average level of computer skill. Surpris-ingly, most academics did not take any formal com-puter related training.
Fourth, multiple regression analysis identi®ed two variables (PC ownership and computer skill) that affect computer attitude of academics. The PC own-ership is a signi®cant variable and is supported by the authors from various countries. Several authors like Harvey and Wilson (UK), Gattiker and Hlavka (Canada), Nickell & Seado (USA) and Al-Jabri et al. (Saudi Arabia) provided strong support that computer owner have a more positive attitude than non-owners. In a similar fashion, level of computer skill of academics was also found to affect attitude. This ®nding is consistent with that of Loyd and Gressard (USA), Woodrow (Canada), Simon & Wilkes (USA), DorenKamp (Holland), Drundell & Thomson (Scotland) and Al-Jabri et al. (Saudi Arabia) who reported that subjects participating in his study tended to produce a positive attitude after attaining a certain level of skill. Thus, these two ®ndings seem to be consistent across various geographical bound-aries. Furthermore, ownership of PC and computer skill of academics together explained 17% variation in computer attitude. The low value suggests that this study did not include some important independent variables that have signi®cant impact on computer attitudes of academics.
8. Conclusions
This study has produced a reliable instrument to measure computer attitude of non-computing aca-demics working in technical colleges. Using this instrument as a tool, this study further highlighted the prevalence of favourable computer attitudes
among these academics. Thus, it can be suggested that, these academics are likely to have little resis-tance, if college authorities decide to introduce new course structure in order to make use of new innova-tions like multimedia technology. The introduction of such new courses would provide tremendous improve-ment in the ®eld of educational computing, provided the participation of academics in course design and implementation is encouraged.
College authorities should attempt to provide a PC on the desk of each academic and encourage them to use them not only for administrative tasks, but to help in teaching. Authorities should make considerable investment in educational computing. Any effort will not however, be successful without proper training.
The ®ndings of this study bear implications for three groups of people: academics, professional trai-ners and business managers. Academics could be trained for computer-based teaching, learning and operating computer-based classrooms and labora-tories. The prevalence of favourable attitude would thus in¯uence academics' use of these concepts and skills. Academics could even explore the possibility to offer computer-based distance learning programmes. They could even contribute in developing specialised educational computer packages in close collaboration with IT vendors to meet the educational requirements of students. On the other hand, managers in large corporations should re®ne their existing manual-based training and professional development activities with computers. Managers are also encouraged to liase with academics; these managers should send their trainers to academia in order to gain ®rst-hand experience on how to introduce and use computing facilities in classrooms. Lastly, IT vendors and academics should work together in promoting their educational comput-ing products, and to train the users. They should develop the seminar and research based courses that not only include the use of the computer in the class-room, but also concentrate on the effects computer have on learning and schooling from sociological, psychological and conceptual perspectives.
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Dr. Afzaal H. Seyalis a Senior Lecturer at Dept. Computing & Information Systems, Institut Teknologi Brunei. He obtained his B.S. and M.S. from Roose-velt University, USA and Ph.D. from LaSalle University, USA. His research interests include end-user computing, IT application in industry and education and software piracy. He has published a number of papers related to these areas and conferences proceedings. He is a fellow of Institution of Analyst and Programmer (UK). Currently, he is member of Singapore, British and Australian Computer Society. He is also a member of ACM (US).
Md. Mahbubur Rahimis a Lecturer at Department of Computing and Information Systems, Institut Teknologi Brunei. He received M.S. in Computer Science from University Pertanian, Malaysia, in 1992. His research interests includes CASE, and software prototyping. His research papers have appeared in several international journals including IT and People, Information and Software Technology, International Journal of Information Man-agement, Asia-Pacific Journal of Information Management and proceedings at international conferences. Currently, he is a member of the Australian Computer Society.
