Pre-service teachers' attitudes towards computer use: A Singapore survey

Timothy Teo
Nanyang Technological University

The aim of this study is to examine the attitudes towards use of computers among pre-service teachers. A sample of 139 pre-service teachers was assessed for their computer attitudes using a Likert type questionnaire with four factors: affect (liking), perceived usefulness, perceived control, and behavioural intention to use the computer. The results of this study showed no gender or age differences among pre-service teachers on computer attitudes. However, there were significant differences for computer attitudes by the subject areas that pre-service teachers had been trained during their university education: Humanities, Sciences, Languages and General (Primary). Correlation analyses revealed significant associations between years of computer use and level of confidence, and computer attitudes. Implications for teacher training and suggestions for further research are provided.

Introduction

In support of the importance of teachers' attitude towards computer use, Zhao, Tan and Mishra (2001) provided evidence to suggest that the attitudes of teachers are directly related to computer use in the classroom. For example, teachers often view the computer as a tool to accomplish housekeeping tasks, manage their students more efficiently, and to communicate with parents more easily. The success of student learning with computer technology will depend largely on the attitudes of teachers, and their willingness to embrace the technology (Teo, 2006). Gaining an appreciation of the teachers' attitudes towards computer use may provide useful insights into technology integration and acceptance and usage of technology in teaching and learning.

Teacher's attitudes towards computers

In many developed countries, nearly all schools are equipped with the infrastructure to conduct ICT mediated teaching and learning. Positive teacher attitudes towards computing are critical if computers are to be effectively integrated into the school curriculum. A major reason for studying teachers' attitude towards computer use is that it is a major predictor for future computer use in the classroom (Myers & Halpin, 2002). Khine (2001) studied 184 pre-service teachers and found a significant relationship between computer attitude and its use in the institution. This finding was corroborated by Yuen and Ma (2001) who, using the Chinese Computer Attitude Scale for Teachers (CAST), found that 216 secondary teachers in Hong Kong had reported the instructional use of computers and their results revealed that affective attitudes, general usefulness, behavioural control, and pedagogical use to be significant in determining the use of ICT. Kumar and Kumar (2003) reported that most teachers believe that the amount of computer experience has a positive effect on attitude towards computers. Jackson, Ervin, Gardner and Schmitt (2001) indicated that female users, compared with males, are more inclined to hold negative reactions to computers and such differences may have resulted in the different ways of using computers.

In achieving excellence in schools, it is important to ensure that teachers are able to integrate technology into the curriculum. As such, the groundwork must be laid at the trainee or pre-service teacher's level. To do otherwise is to produce future teachers with underdeveloped skills in the use of technology. In the course of their training, pre-service teachers should be provided with the tools and experiences that will be useful for the regular activities in their future job: classroom instruction, research, and problem solving. Using technology enables pre-service teachers to arrange their environment and adjust their instructional strategies (Zhang & Espinosa, 1997). On the part of teacher educators, there is a need to understand the dimensions that influence pre-service teachers' attitudes towards computers as a means for effective development of teacher training curriculum that will prepare teachers to face the challenges in the information age (Fisher, 2000).

The aim of this study is to examine the profile of a sample of pre-service teachers in Singapore. Specifically, the following questions will be answered:

1. What is the overall profile of pre-service teachers' attitudes towards computer use?
2. Do computer attitudes differ by age, gender, subject domain, years of computer use, and perceived confidence?

Method

Sample

Procedure

Instrument

The CAS was used to measure the pre-service teachers' attitudes towards computer use. It is a 21-item questionnaire that consists of four components of computer attitudes (Table 1). The first component, 'Affect', is composed of six items and measures feelings towards computers. 'Perceived Usefulness' is composed of five items that measure the individual's beliefs about the usefulness of computers in their job. 'Perceived Control', is composed of six items that measure the perceived comfort level or difficulty of using computers. The fourth component, 'Behavioural Intention', is composed of four items that measure behavioural intentions and actions with respect to computers.

Participants responded to the CAS using a five-point scale of strongly disagree (1), disagree (2), neutral (3), agree (4), and strongly agree (5). The scores from the items on each component were aggregated to provide individual scores on each component. In this study, the negative items were reversed coded in order that meaningful analyses at the sub-scale level could be conducted. The CAS has been found to be a reliable instrument to measure attitude towards computer among teacher education students. Using the CAS on 131 undergraduate students in early childhood education, Sexton, King, Aldridge and Goodstadt-Killoran (1999) reported that the CAS possessed high reliability (alpha = 0.90).

Affective component (six items)	AFF1	If given the opportunity to use a computer, I am afraid that I might damage it in some way*
	AFF2	I hesitate to use a computer for fear of making mistakes I can't correct*
	AFF3	I don't feel apprehensive about using a computer
	AFF4	Computers make me feel uncomfortable*
	AFF5	Using a computer does not scare me at all
	AFF6	I hesitate to use a computer in case I look stupid*
Perceived usefulness component (five items)	PU1	Computers help me improve my work better
	PU2	Computers make it possible to work more productively
	PU3	Computers can allow me to do more interesting and imaginative work
	PU4	Most things that a computer can be used for I can do just as well myself*
	PU5	Computers can enhance the presentation of my work to a degree which justifies the extra effort
Perceived control component (six items)	PC1	I could probably teach myself most of the things I need to know about computers
	PC2	I can make the computer do what I want it to
	PC3	If I get problems using the computer, I can usually solve them one way or the other
	PC4	I am not in complete control when I use a computer*
	PC5	I need an experienced person nearby when I use a computer
	PC6	I do not need someone to tell me the best way to use a computer
Behavioural intention component (four items)	BI1	I would avoid taking a job if I knew it involved working with computers*
	BI2	I avoid coming into contact with computers in school*
	BI3	I only use computers at school when l am told to*
	BI4	I will use computers regularly throughout school.
* Item for which scoring is reversed.

Results

Overall profile of the computer attitudes among pre-service teachers

Subscale	No of items	Mean	SD	alpha
Affective	6	4.00	.77	.79
Perceived usefulness	5	3.98	.51	.66
Perceived control	6	3.54	.54	.61
Behavioural intention	4	4.00	.83	.76
Overall computer attitudes	21	3.85	.52	.86

The relationship among the subscales is shown in Table 3. All subscales correlate significantly at the p < .01 level and the coefficients range from .23 to .61. This suggests that the four components were fairly independent to be used as independent variables. This allows us to examine the computer attitudes of pre-service teachers by each subscale.

Subscale	Affective	PU	PC
Perceived usefulness (PU)	.23
Perceived control (PC)	.61	.31
Behavioural intention (BI)	.53	.28	.53
* p < .01 (2-tailed)

A confirmatory factor analysis (CFA) was conducted to test the validity of the factor structure of the CAS. Analysis performed using AMOS 7.0 (Arbuckle, 2006) using maximum likelihood (ML) as the estimation procedure. In terms of the sample size required to use the ML estimator appropriately, Ding, Velicer and Harlow (1995) recommended that the minimum sample size to use MLE appropriately should be between 100 to 150 participants. Following the recommendations of Boomsma (2000) and McDonald & Ho (2002), several fit criteria were applied.

In addition to the chi-square test, other test indices were used. These included the root mean square error of approximation (RMSEA), which is a measure of the discrepancy per degree of freedom between the model and the covariance-matrix in the population, the standardised root mean square residual (SRMR), which is the average difference between observed and reproduced correlations, the non-normed fit index (NNFI), indicating the proportional improvement of the fit of the model relative to the independence model, and the Comparative Fit Index (CFI), which assesses the relative improvement in fit of the researcher's model compared with the baseline model.

The model fit was considered acceptable when both SRMR < 0.08 and RMSEA < 0.06 (Hu & Bentler, 1999). Both the NNFI and CFI should be at least .90. Results of the CFA indicated a good model fit (chi-squared = 199.325, df= 179, p < .142; NNFI = .84; CFI = .98; RMSEA = .03; SRMR = .06). Table 4 shows the regression estimates and the t values of the items and their respective scales.

		Estimate	Standard error	t value	p
Affect	AFF1	1.000**	--	--	--
	AFF2	1.030	.086	12.024	*
	AFF3	1.067	.079	13.509	*
	AFF4	.985	.084	11.700	*
	AFF5	.885	.082	10.751	*
	AFF6	.985	.079	12.421	*
Perceived usefulness	PU1	1.000**	--	--	--
	PU2	1.081	.238	4.545	*
	PU3	.355	.188	1.889	.05
	PU4	1.449	.312	4.643	*
	PU5	1.084	.260	4.167	*
Perceived control	PC1	1.000**	--	--	--
	PC2	1.314	.322	4.080	*
	PC3	.900	.216	4.166	*
	PC4	1.856	.405	4.578	*
	PC5	1.312	.307	4.271	*
	PC6	.867	.260	3.332	*
Behavioural intention	BI1	1.000**	--	--	--
	BI2	.630	.257	2.457	.014
	BI3	1.324	.431	3.070	.002
	BI4	1.245	.371	3.351	*
* p < .001; ** This parameter was fixed at 1.00 during estimation.

Computer attitudes and age, gender, subject domain, years of computer use, and perceived confidence

Preliminary assumption testing was conducted to check for multivariate normality and equality of variance. No violations were found multivariate normality. For the equality of variance, there was a violation for the dependent variable behavioural. Pallant (2005) suggested that if this assumption was violated, a more conservative alpha level for determining the significance for that variable be set. For this study, an alpha value of p < .01 will be used.

A one way, between groups multivariate analysis of variance was performed on the four dependent variables (affective, perceived usefulness, perceived control, behavioural intention) for age and gender. No significant differences were found: F(4, 134)= 1.788, p < .135; Wilks' lambda = 0.949, partial eta-squared = 0.051. There was also no significant difference for gender and the four dependent variable: F(4, 134)= 2.156, p < .077; Wilks' lambda= 0.940, partial eta-squared = 0.060. These results suggest that both male and females pre-service teachers at all ages were similar in their attitudes towards the computer.

A one way, between groups multivariate analysis of variance was performed on the four dependent variables (affective, perceived usefulness, perceived control, behavioural intention) for subject domain (Humanities, Sciences, Languages, and General (Primary)). There was a significant difference by subject domain on the combined dependent variable computer attitude: F(12, 126)= 7.849, p < .001, Wilks' lambda = 0.532, partial eta-squared = 0.190. The four groups (subject domain) differ in their perceptions of how much they like computers (affective): F(3, 135)= 18.087, p < .001; how much control they have over computers (perceived control): F(3, 135)= 9.603, p < .001; and their behavioural intentions in using computers (behavioural intention): F(3, 135)= 29.530, p < .001. For the affect, perceived usefulness and behavioural intention components, there were significant differences between the students in the General (Primary) program and those in the other programs. For the perceived control component, significant differences were found for students in all except those in the language program. The results suggest that while most pre-service teachers were similar in their attitudes towards the computer, they had different views regarding each component within computer attitude. In general, students in the subject specific classes were similar in their computer attitudes compared to those in the General (Primary) classes.

Correlation analyses

The mean years of computer use was 9.63 years (SD = 3.27) and that of computer confidence was 3.73 (SD = .89). Between the 'Year of computer use' and level of computer confidence, there was a significant correlation (r = .23, n = 139, p > .01). To examine the relationship between computer attitudes and years of computer use and level of computer confidence among pre-service teachers, a bivariate correlation was performed. Table 5 shows the summary.

Subscale	Computer use	Computer confidence
Affective	.209*	.314**
Perceived usefulness	-.082	.248**
Perceived control	.195*	.507**
Behavioural intention	.200*	.159
Computer attitudes	.188*	.391**
* p < .05; ** p < .01

The results show a non-significant but inverse correlation between the years of computer use and perceived usefulness. There was also no significant relationship between the level of computer confidence and behavioural intention to use computers. However, there were significant correlations between the overall computer attitudes and years of computer use and level of computer confidence.

Discussion

This study found no significant relationship for age and gender, and computer attitudes. This finding does not support past research which suggested significant differences in computer attitudes by gender (e.g. Margolis & Fisher, 2002; Markauskaite, 2006). For example, Houtz and Gupta (2001) found that males and female had rated themselves on their ability to use the computer in significantly different ways. Other studies have suggested that the masculine image of the computer has deterred females from benefiting from the technology and this has made them less confident or more anxious (Culley, 1988), resulting in females holding more negative attitudes to computers than males (Campbell, 1990). Consequently, female students tended to use computers less even when given equal access (Muira, 1987). The research on gender and computing has often reported, though not conclusively, that males have more experience and make more use of computers (Brosnan & Lee, 1998; Balka & Smith, 2000).

It is usual to consider the issue of gender in the context of other user variables such as self efficacy, computer anxiety, and computer experience. For example, Chua, Chen and Wong (1999) and Coffin and Mackintyre (2000) in their meta-analyses on the relationships between computer anxiety, computer attitudes, computer self efficacy and computer experience stated that most findings usually reinforce the gender effects and suggested that greater levels of computer experience are associated with lower computer experience and more positive computer attitudes. Females usually also have more negative attitudes towards computers (Durndell & Thompson, 1997) and greater computer anxiety (McIlroy, Bunting, Tierney & Gordon, 2001) than males. Research on computer self efficacy in general also revealed that males on average tend to acquire computer self efficacy faster than females (Todman, 2000).

The lack of computer attitude differences between genders in this study is consistent with research that revealed changing attitudes among female computer users. For example, females may have been socialised differently in today's computer generation to be more comfortable with computers and this may have resulted in lessening the barriers perceived by females, in the lack of training opportunities for them (Ray, Sormunen & Harris, 1999). To a large part, North and Noyes (2002) felt that increased use of computers for teaching and learning in schools has worked against the development of gender differences as reported in previous research, a situation consistent with the use of computers in the Singapore schools (Teo, 2006).

The findings in this study showed that years of computer use and level of computer confidence are positively correlated with positive computer attitudes, supporting previous research (Shashaani, 1997). In part, using computers more frequently and developing a variety of computer related skills and techniques increases one's knowledge of the computer as a whole. This widens one's learning horizon and potential that in turn promotes a positive feeling towards the computer (Houtz & Gupta, 2001). The results of this study shows that years of computer usage is positively correlated with level of computer confidence. While this may seem obvious, it is important that the length of computer use is associated with the successful use of the computer in order that positive feelings can be fostered (Huang & Liaw, 2005). Otherwise, a prolonged unsuccessful period of computer use may serve as a barrier instead of facilitating further usage of the computer (Lim & Khine, 2006). This is corroborated by an observation in this study, although not statistically significant, which suggests an inverse relationship between pre-service teachers' length of computer use and perceived usefulness of the computer.

Participants who majored in different subject domains (Humanities, Sciences, Languages, and General (Primary)) differed in their perceptions of how much they like computers (affective), how much control they have over computers (perceived control), and their behavioural intentions (behavioural) in using computers. Differences in computer attitudes among students who majored in different subject domains are consistent with the literature. For example, it was found that students in sciences such as medicine had less positive attitudes towards the use of computers, due to the fact that medical training was conducted mainly by using face to face methods (Khine, 2001). In this study, there were significant differences in attitudes between students in the General (Primary) course and those who were training to teach specific subjects (e.g. science, humanities). Supporting Khine (2001), it was possible that the former could have been shaped by their vocational expectations. Specifically, students who expect to teach in Primary (General) may hold different perceptions of usefulness and control, relative to the students in the subject specific courses, who are more focused on the computer related tools that they would expect to use when they become teachers in the schools.

However, participants from all the subject areas perceived the computers to be useful in their work. It is reasonable to expect that, given the thrust of the use of ICT in the schools for teaching and learning and their exposure to computers during teacher training, coupled with modelling by the lecturers on computer usage, these participants were in a conducive environment that nurtured a development of positive attitudes towards computers.

Conclusion

There are several limitations in this study. Firstly, the data collected was through self reports and this may lead to a common method variance, a situation that may inflate the true associations between variables, resulting in spurious significant findings. Secondly, the sample size in this study is relatively small, thus limiting the extent to which the findings of this study may be generalised. Thirdly, the data was collected using a cross-sectional, single administration design and it was not possible to establish the stability of the attitudes of the participants. Finally, the variables chosen in this study were determoned by the selection of the CAS for data collection. As a result, other significant variables that influence computer attitudes are excluded, leading to a limited understanding of computer attitudes. For example, Teo, Lee and Chai (2008) and Teo (in press) found evidence to suggest that that computer attitudes may be studied using the Technology Acceptance Model (TAM) with variables external to the model, such as subjective norms, facilitating conditions, and technological complexity. Outside the premise of the TAM, Teo (2007) found the perceived importance of computers, enjoyment, and anxiety to be associated significantly with computer attitudes. Future research may include comparison of the results of this study against a larger sample using a longitudinal design to examine computer attitudes over time. Other variables could be added to examine their impact on computer attitudes.

This study provides a glimpse of selected variables that affect the computer attitudes of pre-service teachers. Future studies could include a systematic examination of all aspects of teacher education and how these interact to impact on pre-service teachers' attitudes, acceptance, and usage of the computer as a tool for instructional purposes and professional development.

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Author: Timothy Teo, Learning Sciences & Technologies National Institute of Education, Nanyang Technological University 1 Nanyang Walk, Singapore 637616. Email: timothy.teo@nie.edu.sg Please cite as: Teo, T. (2008). Pre-service teachers' attitudes towards computer use: A Singapore survey. Australasian Journal of Educational Technology, 24(4), 413-424. http://www.ascilite.org.au/ajet/ajet24/teo.html