Modelling Serbian pre-service teachers' attitudes towards computer use

Computers & Education 94 (2016) 77e88

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Computers & Education journal homepage: www.elsevier.com/locate/compedu

Modelling Serbian pre-service teachers' attitudes towards computer use: A SEM and MIMIC approach Timothy Teo a, *, Verica Milutinovi c b, Mingming Zhou a a b

University of Macau, China University of Kragujevac, Serbia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 October 2014 Received in revised form 22 October 2015 Accepted 26 October 2015 Available online 11 November 2015

The purpose of this study is to examine the predictors that influence Mathematics preservices teachers' attitudes toward computers use. Five variables (perceived usefulness, perceived ease of use, subjective norm, facilitating conditions, and technological complexity) were hypothesized to have direct and positive influences on attitudes towards computer use. This study also investigated whether socio-demographic variables (e.g., gender, age, and course of study) had any effect on the attitudes. Data were collected from 419 pre-service teachers through a self-report questionnaire and analysed using the structural equation modelling approach. Results showed that 64% of the variance in attitudes towards computer use was explained by the above five variables. However, only perceived usefulness, perceived ease of use, and technological complexity were found to be significant predictors on attitudes toward computer use while subjective norm and facilitating conditions were not. Using MIMIC modelling, the results showed that gender, age, and course of study had no significant influences on pre-services teachers' attitudes toward computers use. Implications for Mathematics teaching were discussed. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Humanecomputer interface Pedagogical issues Country-specific developments

1. Introduction Any initiative to integrate technology in classroom teaching and learning depends strongly upon the support of teachers involved (Gibson et al., 2014; Teo, 2011). It is reasonable to assume that if teachers do not believe that using computers will fulfil their own and their students' needs, they are likely to avoid using technology to discharge their professional duties. One important factor that has been found to repeatedly predict teachers' intention to use technology is their attitudes toward computer use (Teo, 2009a; Teo & Milutinovic, 2015). Regardless of the state of technological advancement in school, the degree of uptake of technology is strongly reliant on teachers having a positive attitude towards computer use (Huang & Liaw, 2005; Shapka & Ferrari, 2003). This in turn affects how students view the importance of technology (e.g., computers) in schools (Teo, 2008). Researchers and educators generally have emphasized the role teachers' attitudes toward information technology play in the successful use of computers in teaching and learning in Mathematics education (Handal, Cavanagh, Wood, & Petocz, 2011; Hoyles & Lagrange, 2010; Kadijevich, Haapasalo, & Hvorecky, 2005; Reed, Drijvers, & Kirschner, 2010; Yushau, 2006). Particularly, pre-service teachers' attitudes towards computers have been found to be a critical factor with direct influence on

* Corresponding author. Faculty of Education, University of Macau, Avenida da Universidade, Taipa, Macau, China. E-mail address: [email protected] (T. Teo). http://dx.doi.org/10.1016/j.compedu.2015.10.022 0360-1315/© 2015 Elsevier Ltd. All rights reserved.

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the intention to use technology in future to teach mathematics (Teo, 2012; Teo & Milutinovic, 2015). As Yushau (2006) suggested, one should first help teachers develop positive attitudes toward the computer in order to have a wider use of technology in Mathematics classrooms. Attitude towards technology use has been examined in various models that attempt to explain individuals' intention for technology use, including Technology Acceptance Model (TAM, Davis, Bagozzi, & Warshaw, 1989), TAM2 (Venkatesh & Davis, 2000), and Unified Theory of Acceptance And Use of Technology (UTAUT, Venkatesh, Morris, Davis, & Davis, 2003). Despite the strong theoretical grounding as well as extensive empirical research on attitudes towards technology (Celik & Yesilyurt, 2013; Gibson et al., 2014; Teo, 2008), in this study, we chose to examine the variables that theoretically may have significant influences on Serbian teachers' attitudes towards computer use in teaching Mathematics for four reasons. First, the importance of user attitudes in determining the intentional and actual behaviour has been repeatedly emphasized in recent literature. For example, De Witte and Rogge (2014) pointed out that the mere presence or the use of ICT in school does not warrant good math scores. They stressed the need to look beyond achievement scores and examine teachers' beliefs and attitudes towards the use of ICT in teaching Mathematics, given the central role played by teachers in the way that and frequency of ICT was used for teaching and learning. Similarly, Joubert (2013) made a call for greater attention to be made to teachers' attitudes and beliefs in the successful adoption of technologies in Mathematics teaching and learning. Second, the factors that contribute to attitudes towards technology use was less clear than expected. After reviewing the major models on attitudes toward technology use, Tate, Evermann, and Gable (2015) expressed the concern that appropriate mid-range theory (theories with a more limited scope) has suffered from the lack of constructs that would better explain practitioners' behaviour and contribute to theoretical research. In this study, we consider potential users' (i.e., pre-serviceteachers) personal and environmental characteristics to enhance our understanding of the influences these would have on their attitudes towards using computer use in teaching Mathematics. Third, a variety of demographic variables have been identified in the literature to moderate the relationships between attitudes and intention to technology. In this study, we examined the moderating influences of age, gender, and course of study on pre-service teachers' attitudes towards technology use in teaching Mathematics. Fourth, while studies on attitudes towards computer use in teaching Mathematics in developed countries are pervasive in the literature, the focus has been on computer utilization (Barak, 2014; Dogan, 2010; Joubert, 2013; Ocak, 2005; Pierce & Ball, 2009). In contrast, the current study will focus on studying the factors that influence pre-service teachers' attitudes towards computer use in Mathematics teaching, with a view to understanding ICT adoption in developing countries such as Serbia where technological advancement is low and challenges in implementing computer technology in classroom are vast. 2. Literature review Attitudes towards computer use are influenced by different variables. Among these are the users' beliefs about various aspects of technology use. These interact with one another to impact on attitudes towards computers use (Teo, 2011; Teo & Wong, 2013). For example, Teo, Lee, and Chai (2008) and Teo, Wong, and Chai (2008) found that pre-service teachers' attitude towards computer use to be significantly influenced by the ease and usefulness of technology as well as expectations from significant others around them. In addition to the identified factors above, Teo and van Schaik (2009) also found that facilitating conditions for using technology had an indirect and significant influence on attitudes towards computer use. Furthermore, Teo (2010) found that technological complexity had a direct and significant influence on attitudes towards computer use. Although the influence of the aforementioned variables on attitudes towards computer use was examined in education at a general level, we believe that these variables and their influences could be a good starting point in understanding attitudes towards computer use in teaching Mathematics in particular. 2.1. Attitude towards computer use (ATCU) Venkatesh et al. (2003) defined attitudes toward technology use as an individuals' overall affective reaction to using the system and concluded that it represent individual's liking, enjoyment, joy and pleasure associated with technology use. In this study, attitudes toward computer use represent pre-service teachers' overall affective reactions to using the computer in teaching Mathematics i.e. their liking, enjoyment, joy and pleasure associated with computer use in teaching Mathematics. In the TAM (Davis et al., 1989), perceived ease of use and perceived usefulness are posited to influence individuals' attitude towards using technology (Jan & Contreras, 2011; Teo, 2010, 2012), which is in turn hypothesized to influence their behavioural intention to use technology and, actual use. Since its inception, the TAM has been expanded to include other personal and environmental factors that were found to be significant in explaining teachers' attitudes and these included subjective norm (Jan & Contreras, 2011; Teo, 2010, 2012), facilitating conditions (Lai, Wang, & Lei, 2012; Teo, 2012), technological complexity (Teo, 2009a, 2010, 2012), gender (Adebowale, Adediwura, & Bada, 2009; Agbatogun, 2010; Bakr, 2011; Dogan, & Prokop, 2008; Ocak, 2005), age (Adebowale et al., 2009; Fancovi 2010; Fancovi cova cov a & Prokop, 2008; Ocak, 2005), field of study (Adebowale et al., 2009; Kadijevich, 2006; Teo, 2008), and self-efficacy (Wong, Teo, & Russo, 2012). In the following sections, we described in detail the five predictors of attitudes towards computer use in our model.


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2.2. Perceived usefulness (PU) Davis et al. (1989) defined perceived usefulness as the degree to which a person believes that using a particular system would enhance his or her job performance. It is an important factor that influences individuals' attitudes toward a system, and has a direct effect on the attitude. In this study, perceived usefulness refers to pre-service teachers' perception of the extent to which they believe using technology would enhance their performance in teaching Mathematics. Past research has found that one's beliefs about the benefits of students' learning with technology, i.e. perceived usefulness of technology, may be an important factor in pre-service teachers' attitudes towards computer use (Barak, 2014; Pynoo et al., 2012; Teo, Lee et al., 2008; Teo, Wong et al., 2008) in the Mathematics classroom (Bennison & Goos, 2010; Pierce & Ball, 2009). 2.3. Perceived ease of use (PEU) Perceived ease of use refers to the degree to which a person believes that using a particular technology will be free of effort (Davis et al., 1989). In other words, if teachers perceive the use of computers in teaching Mathematics to be too difficult, the effort of using the technology in the classroom could outweigh the benefits of usage and this will have a negative influence on their attitudes towards use. Past research has found that perceived ease of use did exert a direct influence on attitude in educational contexts (Pynoo et al., 2012; Teo, 2008, 2010; Teo, Wong et al., 2008; Teo & Zhou, 2014). Moreover, Pierce and Ball (2009) found that females were almost three times more likely, than males, to be concerned with unexpected problems on using technology in teaching Mathematics. This study posits that perceived ease of use have a direct influence on attitudes towards using technology in teaching Mathematics and that such influence may be moderated by gender. 2.4. Subjective norm (SN) Fishbein and Ajzen (1975) defined subjective norm as ‘the person's perception that most people who are important to him or her think he or she should or should not perform the behaviour in question’ (1975: 320). In our study, it represents the degree to which pre-service teachers perceive the demands of the ‘important’ or referent others (e.g. leaders, colleagues, pupils, teachers) to use computer in teaching Mathematics. Pierce and Ball (2009) suggested that the views of colleagues may either make the adoption of technology difficult or easier among secondary Mathematics teachers. In other words, preservice teachers' attitudes would be influenced by how they perceive the use of technology would fit into the Mathematics teaching culture of the school. This view was well supported by studies which found SN to be a significant variable explaining teachers' attitudes towards technology use (Jan & Contreras, 2011; Teo, 2009b, 2010, 2012; Teo, Lee et al., 2008). 2.5. Facilitating conditions (FC) Facilitating conditions refers to user perceived availability of support in the environment that encourages and facilitates technology adoption, which is hypothesized to enhance the intention to use technology (Taylor & Todd, 1995). For example, they include technical support and skills training with an educational environment. Facilitating conditions have been found in general educational studies to positively and directly affect students' attitudes toward computer use (Lai et al., 2012; Teo, 2009b; Teo & van Schaik, 2009). 2.6. Technological complexity (TC) Technological complexity refers to the degree to which a system is perceived to be relatively difficult to understand and use (Thompson, Higgins, & Howell, 1991). Thompson et al. (1991) found that there was a significant negative relationship between perceptions about complexity of use and the utilization of PCs. In recent technology acceptance in education studies, technological complexity was found to have a direct significant influence on attitude to computer use (Teo, 2010, 2012). Of the various problems related to using technology, time management stood out as one of the most important factors that could influence attitudes towards computer use (Bennison & Goos, 2010; Lee, Feldman, & Beatty, 2012; Pierce & Ball, 2009). 2.7. Gender, age and course of study (primary versus secondary pre-service Mathematics teachers) Additionally, this study examines the influence of gender, age, and course of study (primary versus secondary) on the variables in the research model (Fig. 1). Research that has considered gender effects in this field has produced mixed results and Prokop (2008) found that, among students in Slovak (Hoyles & Lagrange, 2010; Ocak, 2005). For example, Fan covicova elementary schools, their attitudes towards ICT were positive although gender differences were observed to be small and insignificant (also Dogan, 2010). However, Teo, Fan, and Du (2015) found that female pre-service teachers had lower scores on perceived ease of use, suggesting that technology use was more challenging for female pre-service teachers than for their male counterparts. In terms of age, Ocak (2005) explored the attitudes of Mathematics teachers towards computer use at New York public schools and found that the younger generation of Mathematics teachers had displayed more positive attitudes toward the computer use than their older counterparts. In another study that considered the course of study, Kadijevich (2006) found

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Fig. 1. Research model.

that technical support was necessary to promote interest in achieving educational technology standards in Serbia, regardless of the student teacher type (i.e. course of study). In addition, it was found that the participants' attitudes and interests to learn and use technology were positively and significantly correlated (Kadijevich et al., 2005). 3. Present study The aim of this study is to examine whether the variables discussed above could significantly predict Serbian pre-service teachers' attitudes towards computer use for Mathematics teaching. Pre-service teachers were selected for this study because of their current position as students and future vocation as teachers. Yildirim (2000) noted that teachers who used computers more usually developed positive attitudes and this had promoted further use of the computer in their professional duties such as teaching and administration. Recently, Agyei and Voogt (2014) found that beginning mathematics teachers' positive attitudes towards ICT use was one of the strongest predictor in their usage. The use of pre-service teachers in this study is hence intended to allow us to understand how future teachers might respond to technology. In this study, we focused our analysis on the pre-service teachers' attitudes towards computer use in the teaching of Mathematics in Serbia. In Serbia, as with other similar developing countries, researchers are interested to know what factors are crucial in the implementation of computer technology in teaching Mathematics and facilitating teachers' adoption of technology to perform their professional duties & Prokop, 2008; Kafyulilo, Fisser, Pieters, & Voogt, 2015). (Adebowale et al., 2009; Fan covicova Serbia is a southeastern developing country in Europe with a population of approximately 7.12 million. The primary education in Serbia is free and compulsory, with two four-year cycles: grades 1e4 and grades 5e8. Secondary education is free but not compulsory. Classroom primary school teachers (grades 1e4) obtain their training at the faculties of education, whilst their counterparts in the upper primary (grades 5e8) and secondary schools are trained at the faculties with corresponding subjects (faculties of science, language, arts, etc.) (UNESCO-IBE, 2011). To date, very limited studies were conducted to investigate the factors influencing attitudes towards computer use among Serbian Mathematics teachers. In an international study, Kadijevich et al. (2005) examined Mathematics teachers' interests to achieve educational technology standards in Finland, Serbia and Slovakia; three countries at considerably different levels of technological development in terms of their computer attitudes and professional support. They found the relationship between pre-service teachers' interest to use computer and their attitudes towards computer use was strongest for the Serbian group. Drawing from the research above, we proposed a model to explain attitude towards computer use among pre-service teachers. We posited that attitude towards computer use could be accounted for by PU, PEU, SN, FC, and TC. Fig. 1 shows the research model for this study. Specifically, this study attempts to answer the following research questions. (1) To what extent do perceived usefulness, PU, PEU, SN, FC, and TC influence pre-service teachers' attitude towards computer use? (2) Are there significant differences in each variable in the research model by gender, age, and course of study (primary versus secondary)?


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4. Method 4.1. Participants and procedure Participants were 419 pre-service teachers enrolled in two public-funded universities in Serbia. They were selected purposefully from the cohort of undergraduate students of four-year programs at the faculties of education and Mathematics at two Serbian universities with the prerequisite that they have completed the majority of their classes in technology, pedagogy, and Mathematics contents. Participation was voluntary and among the participants, 12.9% were males and the mean sample age was 22.45 (SD ¼ 1.32). The majority of the participants were in their third and fourth year of training (98.1%) and trained to be primary school teachers (74.7%) after graduation. At the end of the term, participants were sent a questionnaire by mail. The purpose of this study and participants' rights to withdraw from the study at any time during or after the completion of the questionnaire were stated in the questionnaire. In the instructions, participants were told to contextualize all the items in the questionnaire on the use of computer in teaching Mathematics. Prior to completing the questionnaire, they had to watch six-minute long video stimulus on the internet. On this video, eight different types of computer use in Mathematics teaching were illustrated: teaching with ready-to-use presentations (e.g. video presentations from the Internet), presentations with MS Office PowerPoint, knowledge testing with ready-to-use tests, designing new tests, teaching with ready-to-use models (e.g. simulation applets from the Internet), making new models (e.g. with GeoGebra), information exchange in a wiki environment and development of group projects in a wiki environment. Excluding the time taken to watch the video, on average, each participant took about 20 min to complete the questionnaire. 4.2. Measures A multiple-item questionnaire was used, including perceived usefulness (PU) (four items), perceived ease of use (PEU) (four items), and attitudes toward computer use (ATCU) (four items), subjective norm (SN) (three items), facilitating conditions (FC) (three items), technological complexity (TC) (four items) and demographics questions. Each statement was measured on a five-point Likert scale with 1 ¼ strongly disagree to 5 ¼ strongly agree. These items were adapted from various published sources (e.g., Davis et al., 1989; Taylor & Todd, 1995; Thompson et al., 1991). The reliability of these items has been well documented (e.g., Teo, 2012, 2014; Teo & Noyes, 2014; Teo & van Schaik, 2009; Wong, Teo, & Russo, 2013). All items were presented in the Serbian language. To ensure the validity of the score, each item underwent the process of translation and backtranslation. The original questionnaire items in English were translated into Serbian by the second author. Next, the Serbian version of the questionnaire was translated into English by a professional translator. The two versions, English and Serbian, were then compared and the changes made to the Serbian version by a faculty member who worked as an English language Professor to ensure that the meaning and intent of each item were kept intact. The items in English and the sources from which they were adapted are listed in the Appendix. 4.3. Data analysis Data were analysed using structural equation modelling (SEM). SEM is aligned with how hypotheses are expressed conceptually and statistically (Hoyle, 2011) and it is useful for analysing the relationships between latent and observed variables. In addition, random errors in the observed variables are estimated directly, something that traditional techniques (e.g., multiple regression, MANOVA) cannot do. Consequently, the use of SEM produces more precise measurements of the items and constructs in research. Following a two-step approach (Schumacker & Lomax, 2010), we began by estimating the measurement model (also known as a CFA model), which describes how well the observed indicators (items in the survey) measure the unobserved (latent) constructs. In the second step, the structural part of the SEM (Fig. 1) is estimated. This part specifies the relationships among the exogenous and endogenous latent variables. In order to obtain reliable results in SEM, researchers recommend a sample size of between 100 and 150 cases (e.g., Kline, 2010). On this basis, the Hoelter's critical N, which refers to the sample size for which one would accept the hypothesis that the proposed research model is correct at the .05 level of significance, was consulted to assess the suitability of the sample size in this study. The Hoelter's critical N for the model in this study is 225 and, given that the sample size of this study is 419, structural equation modelling was regarded as an appropriate technique for data analysis in this study. 5. Results 5.1. Descriptive statistics The descriptive statistics of the items showed that all means were above the midpoint of 3.00 (except for FC3 which was 2.90), and the standard deviations ranged from .68 to 1.06. Following Kline's (2010) recommendations that the skew and kurtosis indices should be within j3j and j10j respectively, the data in this study were regarded as normal with a skewness between 1.26 and .07 and a kurtosis between .73 and 4.43.

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5.2. Test of the measurement model (confirmatory factor analysis) A mentioned above, the first step in SEM analysis we used to explore data in this study is estimating the measurement model (also known as a CFA model). Once we estimated specified model, it is important to compare theory to reality by checking the validity of the measurement model, i.e. goodness of fit for the model and construct validity. A confirmatory factor analysis using AMOS 21.0 with the maximum likelihood estimation (MLE) procedure was employed to test the congeneric research model for goodness of fit. While the MLE is a robust procedure for use in SEM, this procedure assumes multivariate normality of the observed variables (Schumacker & Lomax, 2010). On this account, the data in this study were examined using the Mardia's normalized multivariate kurtosis value. The Mardia's coefficient (Mardia, 1970) for the data in this study was 139.147, which is lower than the value of 528 computed based on the formula p(p þ 2) where p equals the number of observed variables in the model (Raykov & Marcoulides, 2008). On this basis, multivariate normality of the data in this study was assumed. The overall model fit was assessed using the c2 test and, because it is highly sensitive to sample size, the ratio of c2 to its degree of freedom was also computed (c2/df), with a value of between 3.0 being indicative of an acceptable fit between the hypothetical model and the sample data (Schumacker & Lomax, 2010). In addition, other fit indices such as the Tucker-Lewis index (TLI), comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) were consulted. Hu and Bentler (1999) proposed that TLI and CFI statistics greater than .95 represent a good model fit and those for RMSEA and SRMR, values with less than .06 and .08 would represent an upper limit for acceptable model fit (Steiger, 2007). From the results, there was a good fit for the CFA model in this study (c2 ¼ 349.110; c2/ df ¼ 2.042; TL1 ¼ .953; CFI ¼ .962; RMSEA ¼ .050 [.042, .057]; SRMR ¼ .042). The reliability of the items that were purported to measure each variable in the research model (Fig. 1) was measured using the composite reliability (CR) instead of the Cronbach alpha. Despite its popular usage, Cronbach's alpha was not employed in this study because it was prone to violate key assumptions when used with a multidimensional construct (ATCU) and multi-item scale such as the one used in this study (Teo & Fan, 2013). In assessing the validity of the questionnaire items, the direction, magnitude, and statistical significance of each parameter (t-value) were examined (Schumacker & Lomax, 2010). An item explains its variable well if its standardized estimate was greater than 0.50 (Hair, Black, Babin, & Anderson, 2010). Using a more conservative indicator of validity, an average variance extracted (AVE) for each variable, which measures the amount of variance captured by the factor in relation to the amount of variance attributable to measurement error, was computed. Both the CR and AVE are judged to be adequate when they equal or exceed 0.50 (i.e., when the amount of variance captured by the construct exceeds the variance due to measurement error) (Fornell & Larcker, 1981). From the results, all the t-values, standardized estimates, CR, and AVE of all items and variables meet the recommended guidelines. Table 1 shows the results of the confirmatory factor analysis. 5.3. Test of the structural model Having obtained a good fit for CFA model, in a final stage we tested the validity of the structural model and its corresponding hypothesized theoretical relationships (Fig. 1) The structural model was test for goodness of fit. Using the indices and applying the same goodness of fit criteria as those for the CFA, we found that the structural model had a good fit (c2 ¼ 355.756; c2/df ¼ 2.068; TLI ¼ .952; CFI ¼ .961; RMSEA ¼ .051 [.043, .058]; SRMR ¼ .043). As far as our first research questions concerns, the results showed that ATCU, as an endogenous variable in this study, had its variance explained by perceived usefulness, perceived ease of use, subjective norm, facilitating conditions, and technological complexity with a R2 of 0.624. This means that together, these five variables accounted for 62.4% of the variance found in the attitude towards computer use. Among the predictors of ATCU, PU (b ¼ .360, p < .01), PEU (b ¼ .402, p < .01), and TC (b ¼ .136, p < .05) were significant, and SN (b ¼ .067, p > .05) and FC (b ¼ .073, p > .05) were not. 5.4. MIMIC modelling To answer the second research question, i.e. to assess if significant differences exist in the attitude towards computer use among pre-service teachers by their gender, age, and course of study (primary versus secondary), a multiple indicators, multiple causes (MIMIC) model (Fig. 2) was used. The specification of MIMIC model is a way to estimate group differences on latent variables where factors with effect indicators are regressed on one or more cause indicators which are dichotomous and representing a group membership. MIMIC modelling is useful as an alternative to multiple-group comparisons where bigger sample sizes are required because through specifications of MIMIC model, group differences on latent variables factors could be estimated on the total sample. For the MIMIC analysis, the sample is not partitioned into subsamples, and the identification requirements are the usual ones for single-sample analyses (Kline, 2010). This method allows the measurement of observed variables (gender, age, and course of study) that are manifestations of an underlying latent variable that is affected by other exogenous variables (perceived usefulness, perceived ease of use, subjective norm, facilitating conditions, and technological complexity) which “cause” and influence the latent variable (Joreskog & Goldberger, 1975). In this study, MIMIC modelling was employed for its advantages over the use of traditional techniques for comparing groups (e.g., between male and female) such as the t-tests or ANOVA. First MIMIC modelling allows the simultaneous analysis of a latent variable with observed indicators and second, measurement errors are modelled and


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Table 1 Results of the confirmatory factor analysis.

Perceived usefulness

Perceived ease of use

Attitude towards computer use

Subjective norm

Facilitating conditions Technological complexity

Item

UE

t-value*

SE

CRa

AVEb

PU1 PU2 PU3 PU4 PEU1 PEU2 PEU3 PEU4 ATCU1 ATCU2 ATCU3 ATCU4 SN1 SN2 SN3 FC1 FC2 TC1 TC2 TC3 TC4

1.052 1.316 1.280 1.000 1.030 1.227 1.232 1.000 1.000 1.145 1.208 1.212 1.195 .926 1.000 1.000 .798 .939 .836 1.082 1.000

15.318 16.483 16.820 — 13.074 13.908 17.134 — — 18.576 18.314 17.258 10.136 9.993 — — 5.966 13.647 15.525 15.570 —

.783 .939 .870 .735 .755 .810 .822 .690 .771 .859 .848 .805 .796 .675 .622 .798 .644 .695 .790 .793 .756

.90

.70

.85

.59

.89

.67

.74

.50

.69

.53

.84

.58

Notes: *p < .01. UE: Unstandardized Estimate; SE: Standardised Estimate. —: This value was fixed at 1.00 for model identification purposes. P P P a CR ¼ ( l)2/( l)2 þ ( (1 l2)). P P P b AVE ¼ ( l2)/( l2) þ ( (1 l2)).

computed for greater precision in estimating item reliability. The modelling process involved the estimation of two parts: the measurement part (that displays the causal link among the latent variables and the observed causes) and the structural part (which shows how the latent variables are estimated through the observed variables or indicators). The exogenous variables in this study that were assumed to explain attitude towards computer use included perceived usefulness, perceived ease of use, subjective norm, facilitating conditions, and technological complexity. This part of the model can be viewed as five multiple regressions from each of the four factors in attitude towards computer use on gender, age, and course of study. Each of these four factors would be re-coded dichotomously (0s and 1s) prior to the modelling process. Hence, if gender is coded such that males are 0 and females 1, a negative coefficient for gender would indicate that females have a weaker level of attitude towards computer use than their male counterparts. For the age variable, the median value would be computed and this value will be used to convert all ages into 0 and 1 to represent the younger and older participants, respectively. Fig. 2 shows the MIMIC model which represents the influence of gender, age, and course of study (left-hand side) on the latent factor (attitudes towards computer use) that is explained by perceived usefulness, perceived ease of use, subjective norm, facilitating conditions, and technological complexity. The fit of the MIMIC model was estimated using the maximum likelihood (MLE) procedure and assessed using a number of fit indices similar to the ones used to assess the CFA model: c2, c2/df, Tucker-Lewis index (TLI), Comparative Fit Index (CFI), Root Mean Squared Error of Approximation (RMSEA), and Standardized Root Mean Residual (SRMR). The results revealed an acceptable model fit (c2 ¼ 33.836, c2/df ¼ 2.603, TLI ¼ .883, CFI ¼ .946, RMSEA ¼ .062, SRMR ¼ .037). The regression part (left-hand side) of the model showed that no significant differences were in the attitude towards computer use among pre-service teachers by gender (b ¼ .001; p > .01), age (b ¼ .104; p > .01) and course of study (b ¼ .066; p > .01). This are the main results for the second research question. However, a negative sign for all three variables indicated that the male students who were younger and training to be primary school teachers generally displayed a more positive attitude towards computer use than their counterparts. 6. Discussion Dimitrijevic, Popovi c, and Stanic (2012) noted that one of the main obstacles for teachers' better and/or more frequent use of computers in teaching Mathematics was their negative attitude towards using computers. Teo and Milutinovic (2015) pointed out the significance of the role that attitudes towards computers play in Serbian pre-service teachers' intention to use technology to teach mathematics. The study examined the extent to which perceived usefulness (PU), perceived ease of use (PEU), subjective norm (SN), facilitating conditions (FC), and technological complexity (TC) influenced pre-service teachers' attitude towards computer use (ACTU) by determining the significant predictors and the amount of variance in ATCU that was explained by PU, PEU, SN, FC, and TC. Using structural equation modelling, this study found that, among the five variables, PU, PEU, and TC were significant in explaining ATCU while SN and FC were not. Together, these five variables explained 64% of the variance in ATCU.

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Fig. 2. MIMIC model.

This study supports recent research wherein perceived usefulness is one of the key determinants on attitude towards computer use in different educational contexts (e.g., Cheung & Vogel, 2013; Jan & Contreras, 2011; Pituch & Lee, 2006; Teo, 2010; Teo, Lee et al., 2008; Teo, Wong et al., 2008). Perceived usefulness is concerned with the utilitarian aspect of computer use in terms of whether its use would result in higher productivity or not. Our data showed that it was reasonable to assume that when pre-service teachers in Serbia gained successful experiences in computer use (e.g., higher productivity), their attitude towards computer use would be positively reinforced. Perceived ease of use was also shown to be a significant predictor of attitudes towards computer use among pre-service teachers in Serbia. This finding supports existing research that a positive attitude to computers can be explained by users' perception of technology to be relatively free of effort (Teo, 2010; Teo, Lee et al., 2008; Teo, Wong et al., 2008). A recent study by Pynoo et al. (2012) on teachers' acceptance of an educational portal in Belgium found that perceived ease of use was of particular importance in the formation of a positive attitude towards computer use among new and non-heavy users. This finding is relevant to our study because the pre-service teachers are relatively new users of technology in Mathematics education. Thus it is reasonable to believe that their perceptions on the extent to which technology was easy to use had a predictive effect on their attitudes. Consistent with current research (Sime & Priestley, 2005; Teo, 2010), we also found technological complexity a significant predictor of attitudes towards computer use. That is, if the pre-service teachers found technology too complex to use, they would be inclined to be negative about it. It is noteworthy that, despite the fairly low state of advancement in educational technology in Serbian schools compared to their Western counterparts, the pre-service teachers' perceptions of the complexity were similarly related to their attitudes towards computer use, suggesting that such a relationship did exist across cultures. Contrary to some past studies (Teo, Lee et al., 2008; Teo, Wong et al., 2008), subjective norm and facilitating conditions were found to be non-significant predictors of attitudes towards computer use, which suggested that pre-service teachers' attitude in this study was not affected by their important referents and the availability of support. However, Venkatesh and Davis (2000) argued that subjective norm was likely to have a significant influence on user's attitudes only in mandatory settings, not in voluntary settings. It was possible that the pre-service teachers in this study perceived the use of computers to be voluntary, due to a lack of strong mandate from the government to use technology in Serbian schools for instructional, assessment or administrative purposes. From another perspective, it was possible that they were too early in their careers to be affected by significant others (subjective norm) at their workplaces. Two possible reasons were identified for the lack of a significant influence of facilitating conditions on attitudes towards computer use. First, the pre-service teachers in this study might not regard support (e.g. technical, personnel) to an adequate extent that would affect their attitudes towards computer use. In other words, their attitudes could be more anchored on stable aspects, e.g., usefulness, complexity or ease of use of the technology. Second, the pre-service teachers' limited use of technology for Mathematics teaching or the low level of technology that was employed by the pre-service teachers did not require constant support. This would again lead to a non-significant effect on their attitudes. This study also sought to investigate if significant differences existed in pre-service teachers' attitudes towards computer use by gender, age, and course of study (primary vs. secondary). Using MIMIC modelling, this study did not find significant differences in attitudes towards computer use by gender, age, and course of study, although for each variable, the group in the lower threshold had reported a more positive attitude score. These findings were consistent with studies conducted across various countries, wherein effect of gender on attitudes towards ICT was rather weak or absent (Adebowale et al., 2009; & Prokop, 2008). The lack of gender differences may be attributed to the global Dündar & Akçayır, 2014; Fan covicova trend where gender equity in the use of technology is strongly advocated by governments resulting in males and females in all societies being provided with equal opportunities and access to technology training and hardware ownership (Bakr, 2011; Mayer-Smith, Pedretti, & Woodrow, 2000; Sam, Othman, & Nordin, 2005). Due to the relative young age of the sample, the lack of age difference in this study is not surprising. This finding is consistent with Teo and Milutinovic (2015) who examined the variables that influenced Serbian pre-service teachers


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intention to use technology to teach mathematics and found the lack of age difference. As explained by Dimitrijevi c et al. (2012), younger teachers are leading the way in the implementation of educational technology in Serbia. Their similar exposure to and experience of using technology may have minimized potential age differences. Dimitrijevi c et al. (2012) also suggested that course of study (elementary versus secondary) was not significant in predicting pre-service teachers' attitudes towards computer use in teaching Mathematics. Their findings, together with our results, demonstrated that the types of students may not factor in their attitudes towards technology use. Other characteristics of students need to be explored to further our understanding of technology use intention. 6.1. Limitations of the study and future research A limitation of this study is the sole use of pre-service teachers from a single country. Due to the cultural and socio-political influences, the profile of this sample may not be representative of pre-service teachers in general and this impacts on the generalizability of the results. Future research could focus on comparing practicing teachers with pre-service teachers to understand differences, if any, in their attitudes towards computer use and whether they are influenced by similar variables of interest. For example, Agyei and Voogt (2014) found that beginning mathematics teachers' positive views about ICT use developed during the professional development program seemed to be the strongest predictor in transfer of their learning, i.e. the degree to which they effectively applied the knowledge, skills and attitudes gained in a training context to the job. Longitudinal studies may be designed to trace the stages of attitudinal changes experienced by pre-service teachers when they become practicing teachers. Finally, it is useful to examine whether there are discrepancies between self-reports and actual practice and, if these exist, what are the factors that can explain the gap. For example, obtaining statistical significance in user perceptions may not be the same in predicting user behaviours. 7. Conclusion and implications Teachers need to possess positive attitudes towards computers use since attitudes are linked with successful technology integration in mathematics education (Agyei & Voogt, 2014; Teo & Milutinovic, 2015). This study examined the variables that predict attitude towards computer use in Mathematics teaching among a sample of pre-services teachers in Serbia. The results suggested that, of the five variables, perceived ease of use was found to have the most influence on attitude, followed by perceived usefulness, and technological complexity. It is hoped that this study could serve as a starting point in understanding pre-service teachers' attitudes towards computer use to teach Mathematics in Serbia and societies that share a similar level of technological advancement. 7.1. Implications for theory and practice The findings of this study make unique contributions to both theory and practice. Our data showed that a new model with four components (PU, PEU, TC and ATCU) could be considered and tested in other cultures to find out the percentage of variance explained in attitude towards computer use in teaching Mathematics. Other important variables from the theory and practice should be considered too. Further, this study should help in enhancing debates around attitudes for teaching specific subjects (e.g., Mathematics) in terms of gender, age, course of study and among users in cultures with limited technological development. In terms of practice, the results may be specific to pre-service teachers in Serbia and Mathematics educators in other developing countries. When pre-service teachers possess positive computer attitudes, they tend to be more focused in their use of computers, an ingredient for successful computer usage (Agyei & Voogt, 2014; Shapka & Ferrari, 2003). To harness the positive impact of computer attitudes on computer use, teacher educators and institutional management should provide opportunities to ensure successful interactions with computers among pre-service teachers in teaching Mathematics in such ways that users would acknowledge the usefulness and ease to use in the technology. Grainger and Tolhurst (2005) noted that, among the variables that significantly predict computer use, one's positive attitude towards use could be developed through strong leadership, operational excellence, positive ethos, collaborative culture, and well-motivated and caring peers. On this account, Pierce and Ball (2009) advocated that professional development for Mathematics teachers need to address issues related to their attitudes and perceptions aside from technology skills. Along this line, pre-service teacher training in teaching Mathematics should organize their instructional activities with a view to enable their trainees to develop positive perceptions of the use of computer to strengthen their intention to use technology. On the other hand, Gibson et al. (2014) demonstrated that interventions focussing on teachers' use of technology in the classroom can have an impact on students' attitudes toward the use of computers for educational purposes. For example, Ersoy and Akbulut (2014) used TAM and UTAUT models in determining the variables of interest in investigation of the novel technology adoption process in learning experience. Attempts of this kind are needed in order for a clarification of variables of interest in technology use in mathematics education and examine how different factors affect attitudes in real contexts. This kind of examination would be useful as the outcome could be used for pre-service teacher training and providing necessary support.

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Appendix 1. List of constructs and corresponding items used in this study

Construct

Item

Questionnaire items referring to computers in teaching Mathematics

Perceived usefulness adapted from Teo (2009a,b, 2010)

PU1 PU2 PU3 PU4 PEU1

Using computers will improve my work. Using computers will enhance my effectiveness. Using computers will increase my productivity. I find computers a useful tool in my work. My interaction with computers is clear and understandable. I find it easy to get computers to do what I want it to do. I find computers easy to use. It would be easy for me to become skillful at using the computer Computers make work more interesting. Working with computers is fun. I like using computers. I look forward to those aspects of my job that require me to use computers. People whose opinions I value will encourage me to use computers. People who are important to me will support me to use computers. People who influence my behaviour think that I should use the computers. When I need help to use computers, guidance is available to me. When I need help to use computers, specialized instruction is available to help me. Learning to use the computer takes up too much of my time. Using the computer is so complicated that it is difficult to know what is going on. Using the computer involves too much time. It takes too long to learn how to use the computer.

Perceived ease of use adapted from Teo (2009a,b, 2010)

PEU2 PEU3 PEU4 Attitudes toward computer use adapted from Compeau and Higgins (1995), Thompson et al. (1991), Venkatesh et al. (2003), Teo (2009a,b, 2010)

ATCU1 ATCU2 ATCU3 ATCU4

Subjective norm adapted from Taylor and Todd (1995), Venkatesh et al. (2003), Teo (2009a,b, 2010)

SN1 SN2 SN3

Facilitating conditions adapted from Thompson et al. (1991), Teo (2009a,b, 2010)

FC1 FC2

Technological complexity adapted from Thompson et al. (1991), Teo (2009a,b, 2010)

TC1 TC2 TC3 TC4

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