FACTORS AFFECTING TEACHERS' ADOPTION OF TECHNOLOGY ...

HSIN-KAI WUj, YING-SHAO HSU and FU-KWUN HWANG

FACTORS AFFECTING TEACHERS’ ADOPTION OF TECHNOLOGY IN CLASSROOMS: DOES SCHOOL SIZE MATTER? Received 9 June 2006; accepted 27 September 2006

ABSTRACT. Researchers in educational technology have searched for factors to explain teachers_ acceptance and resistance to using technology for instruction. Among the many identified factors, however, organizational and school factors have not yet been explored and discussed. This study investigates the effects of school size on science and mathematics teachers_ adoption of technology in classrooms. Using national survey data collected from 940 science and mathematics teachers at junior high schools in Taiwan, we employed factor analyses, log-linear analyses, and three-way ANOVA techniques to examine interactions among school factors and teacher factors. Results obtained from the log-linear analyses suggested that both the interactions of school region with school size and school size with technology users were needed to explain teachers_ use of educational technology in classrooms. It appears that teachers at small schools were more likely to use technology for instructional purposes. Additionally, results of the study revealed that teachers at small schools tended to have positive attitudes toward technology use and that among users of educational technology in southern Taiwan, teachers at small schools designed and used significantly more instructional activities with technology. This study suggests that small schools provide a better environment for science and mathematics teachers to implement educational technology in instruction. KEY WORDS: adoption of technology, classroom instruction, school size

INTRODUCTION Integrating technology into instruction has been viewed as a key idea in current education reform in many countries (Demetriadis, Barbas, Molohides, Palaigeorgious, Psillos, Vlahavas et al., 2003; Lim & Hang, 2003; National Research Council, 1996; van Braak, 2001). Investment in educational technology continues to increase and new technologies such as computers, televisions, video players, and projectors have been introduced into classrooms. However, most teachers do not use these technologies in classrooms as frequently as policy makers and researchers expect (Cuban, 1986). Even though some teachers integrate technology into instruction, their use is not innovative but to sustain their existing practice (Cuban, Kirkpatrick & Peck, 2001; Zhao & Frank, 2003). Numerous researchers have searched for factors to explain teachers_ acceptance and resistance to using technology for instruction. Various j

Author for Correspondence.

International Journal of Science and Mathematics Education (2007) 6: 63Y85 #

National Science Council, Taiwan (2007)

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factors that are closely related to teachers_ adoption of technology have been found and examined. These factors include: teachers_ beliefs and attitude about technology (Dwyer, Ringstaff & Sandholtz, 1991; Gallini & Barron, 2001; Windschitl & Sahl, 2002), school support and resources (Czerniak, Haney, Lumpe & Beck, 1999), school culture (Demetriadis et al., 2003; Zhao, Pugh, Sheldon & Byers, 2002), collegiality among teachers (Becker, 1994), characteristics of technology (Gbomita, 1997), and subject taught (Becker & Ravitz, 1999; van Braak, 2001; Yaghi, 2001). Although these studies seem to provide a comprehensive list of factors, some factors and issues that have been identified by research on educational reform have not been discussed and analyzed. For example, lessons learned from recent research on educational reform have raised an issue of school size to prominence (Hargreaves & Fink, 2000; Lee & Smith, 1997). Compared with small schools, large schools are usually more bureaucratic and provide lower levels of social support and intimacy among teachers and students (Lee & Loeb, 2000). This in turn may become a barrier to the procurement of social and technical resources and affect teachers_ enactment of innovative practices such as integrating technology into instruction. Reducing school size is also one of the eight key reform tasks in Taiwan (Ministry of Education [MOE], 2006) but no empirical research has been done to examine the impact of school size on teaching and learning in Taiwan (Huang, 1999). In this study, therefore, school size is considered as a factor that can potentially affect teachers_ use of educational technology in classrooms. In order to explore interactions among school and teacher factors that affect teachers_ use of educational technology in classrooms, we conducted a national, representative survey in Taiwan and collected data from over nine hundred science and mathematics teachers in junior high schools. Two research questions guided the study: (1) Do junior high school region and size affect science and mathematics teachers_ use of technology? (2) Do junior high school region, size, and teachers_ use of technology affect teachers_ beliefs, attitudes, practices, and needs concerning educational technology? The results can provide policy and practical suggestions for implementing educational technology in schools. BACKGROUND Factors Affecting Teachers_ Adoption of Technology A review of the literature on technology and learning has concluded that educational technology has great potential to enhance student achieve-

TEACHERS, ADOPTION OF TECHNOLOGY

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ment when it is used appropriately (Kozma, 1991; Wu & Shah, 2004). In this study, educational technology refers to a range of digital hardware and software used to support teaching and learning, including desktop, laptop, and handheld computers and applications. Regardless of the potential educational benefits, however, computer usage in classrooms remains disappointingly low. Technology has usually been used for supporting teachers_ existing practice (Cuban et al., 2001; Loveless, 1996) instead of enacting innovative practice such as creating fundamentally different learning environments in classrooms (Dwyer et al., 1991). To understand how technology can be effectively used in classrooms, numerous researchers have searched for factors (e.g., teachers_ beliefs, school culture, and teachers_ computer literacy) to explain teachers_ acceptance and resistance to using technology for instruction. Based on the characteristics of these factors, they can be categorized into three domains: teacher, technology, and context. Factors in the first domain include those that are strongly associated with individual teachers_ affection, abilities, and skills. A number of studies indicated that teachers_ pedagogical beliefs about learners and technology contribute significantly to the success of classroom technology innovations (Gbomita, 1997; Windschitl & Sahl, 2002). Teachers who believe that technology can more effectively achieve teaching goals than conventional teaching methods are more likely to adopt technology into instruction (Czerniak et al., 1999). Additionally, teachers_ technological skills (e.g., technology proficiency and computer literacy) are critical for successful implementation of classroom technology (Zhao et al., 2002). Teachers should understand the enabling conditions of certain technologies in order to engage students in technology-based learning activities successfully. Teachers who have lower technology proficiency are usually not willing and have less confidence to use technology for teaching (Windschitl & Sahl, 2002). The characteristics of technology also shape teachers_ adoption decisions. For example, van Braak (2001) showed that teachers are reluctant to use computer-mediated communication technology when they realize that there is a mismatch between the nature of the communication technology and their teaching practice. Thus, factors in the second domain concern the nature of technology itself (e.g., the degree of technology innovativeness and the characteristics of computers) and its relationship to existing teaching practice (Gbomita, 1997). For a successful technology implementation to happen, the type of technology and its underlying nature should be aligned with the existing teaching methods and school culture (Zhao et al., 2002). If the technology innovation deviates from

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the status quo, teachers have to change the structure of their classes or practice in order to accommodate innovative technology. A third set of factors that affect teachers_ adoption of technology in classrooms is associated with the context in which the innovations take place. The contextual factors include collegiality among teachers (Becker, 1994), social support and resources (Czerniak et al., 1999), and school culture (Zhao et al., 2002). It has been found that collegiality among teachers plays a critical role in helping computer-using teachers develop high-quality practice with technology (Windschitl & Sahl, 2002). In Becker (1994), a majority of exemplary computer-using teachers (particularly those teaching science and English) worked in a school with many other computer-using teachers. In addition to social support from colleagues, perceived support from the school influences teachers_ adoption decision. Czerniak et al. (1999) found that although many teachers share beliefs that educational technology could promote learning and that the use of technology is desirable, they are reluctant to use educational technology because of insufficient support and resources provided by schools. Another contextual factor is school culture that refers to the common set of values, beliefs, and practices of the teachers and administrators at a school. Technology is likely to be implemented at schools where the use of technology is consistent with the existing beliefs and practices of school members (Zhao et al., 2002). When taken together, the studies reviewed above seem to provide a comprehensive list of factors. But a closer look reveals that few studies systematically investigated the interactions among factors and that organizational structures have not yet been taken into account in the context domain. Drawing upon research in history and sociology of education, some researchers suggested that adoption of educational technology in classrooms involves complex interplays across human, technological, and organizational structures (Cohen, 1987; Kerr, 1996). One structural factor found to have impact on students_ achievement and teachers_ attitude is school size (Lee & Loeb, 2000). In attempting to incorporate educational technology into a broader educational reform context, this study takes structural factors such as school size and school region into consideration and explores how these school factors interact with teachers_ adoption of technology in classrooms. Research on School Size Extending existing empirical work on school structure and organization, research on the issue of school size has received considerable attention in


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recent years (Fritzberg, 2001; Hargreaves & Fink, 2000; Lee & Smith, 1997). A traditional view of school structure supports large and comprehensive schools because teachers can specialize in specific fields, more types of courses can be offered, and school savings increase through reduced redundancy (Buzacott, 1982). Yet, recent studies have contradicted this traditional view. The findings regarding students_ achievement consistently support the idea that smaller elementary and secondary schools do better to help disadvantaged students excel (Howley, 1996; Lee & Loeb, 2000; Lee & Smith, 1997). A smaller school has various advantages to support learning such as facilitating personalized social interactions, promoting intimacy among school members, increasing accessibility of resources, and enhancing collective responsibility (Fritzberg, 2001; Lee & Loeb, 2000). However, the research on school size has long emphasized its effects on students. Little is known about whether school size has an impact on teachers. One exception is the study conducted by Lee & Loeb (2000). They found that teachers at small elementary schools have a more positive attitude about their responsibility for students_ learning, which in turn influences student learning. This suggests that to better understand how school size affects learning, research on school organization should not ignore its potential impact on teachers_ practice, attitude, and perceived support. Additionally, Bsmall classes, small schools^ has been one of the most important educational policies in Taiwan since 1998 (MOE, 2006). The Commission on Educational Reform in Taiwan believed that this policy could help students have access to more educational resources and a better quality of classroom instruction. Yet, no empirical evidence was provided to support the policy. In this study, therefore, school size is considered as an important factor that can potentially affect teachers_ use of educational technology in classrooms. Using national survey data collected from over nine hundred science and mathematics teachers in Taiwan, we explore interactions among school and teacher factors. Our findings will inform researchers and policy makers about the impact of school size on teachers and their teaching practices. METHODS Sample The population under investigation consisted of all science and mathematics teachers actively teaching in junior high schools (age range 13Y15 years) in Taiwan. The stratified random sampling strategy was

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employed to select the sample. Because school region and school size could affect budgetary decisions, urban schools located in northern Taiwan tended to have more financial and technical resources to support the adoption of computers. Thus, the key strata of interest in our analysis were: school region (northern, central, southern, or eastern) and school size (large, medium, or small). Size of the school was determined by the number of classes in the school: large (having more than 37 classes [over 1300 students]), medium (having 16Y36 classes [560Y1300 students]), and small (less than 15 classes [560 students or less]). Among the total of 892 junior high schools in Taiwan, approximately 11% of them (99 schools) were selected. Of the 2,019 questionnaires mailed out, 1,002 replies from 82 schools were received (82.8% school response rate and 49.6% teacher response rate). The distribution of responding teachers is shown in Table I. The teacher response rate ranged from 34% to 76% across regions and school sizes. Responses with missing values were removed from the sample and the final statistics were examined for 940 respondents. Instrument A questionnaire was developed to collect information on teachers_ use of technology in classrooms. Some of the items were selected from various existing questionnaires that focused on teachers_ beliefs and attitudes toward using computers in classrooms (Becker, 1994; Czerniak et al., 1999). The questionnaire content was divided into four sections: (1) demographic data of teachers, (2) experiences in technology use for instructional purposes, (3) opinions about professional development for technology-based instruction, and (4) attitudes and beliefs about technology-based learning and instruction. Demographic data of teachers included information about age, gender, years of teaching, school size and school region. In the second section, the response format was yes/no to indicate whether the respondent was a user or a non-user of technology for instructional purposes. Items in the latter two sections were rated on a 5-point Likert-type scale from 1 (strongly disagree) to 5 (strongly agree) and factor analytical techniques were used to determine the underlying structure of teachers_ responses to items in these two sections. Principal axis factor analysis with varimax rotations was employed. Both the Kaiser-Meyer-Olkin measure of sampling adequacy (0.898) and Bartlett test of sphericity (c2(496, N=940)=4931.98, pG0.0001) were significant, indicating that factor analysis was suitable in the sample. By

Northern Large Medium Small Central Large Medium Small Southern Large Medium Small Eastern Large Medium Small Total

14 13 10

9 9 9

6 9 9

1 3 7 99

131 113 90

72 95 77

62 75 78

9 23 67 892

1 2 6 82

4 7 6

9 9 7

12 11 8

100% 67% 86% 83%

67% 78% 67%

100% 100% 78%

86% 85% 80%

Response rate

33 49 35 2019

213 146 56

385 176 86

555 207 78

25 18 23 1006

72 86 25

190 117 48

239 117 46

Responded

Mailed

Responded

Total

Mailed

Number of teachers

Number of schools

TABLE I The distribution of responding teachers

76% 37% 66% 50%

34% 59% 45%

49% 66% 56%

43% 57% 59%

Response rate

24 17 21 940

65 84 25

179 116 44

206 117 42

Final sample


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using the Cattell_s scree test and examining the factor loadings of the items, we removed seven items from the questionnaire, and five factors emerged. According to the items correlating with the factors, we assigned a descriptive name to each of the factors. These factors were: (1) teaching practices with technology (e.g., I have designed activities that allowed students to learn through the Internet), (2) attitudes toward technology-based instruction (e.g., I think technology is helpful for my teaching), (3) beliefs about technology-based instruction (e.g., I believe that technology-based instruction will promote students_ motivation), (4) needs for professional development in technology-based instruction (e.g., I hope that teacher workshops can provide more real-world examples of technology-based instruction), and (5) technical and personnel resources available in school (e.g., In my school, the technology facilities are adequate for technology-based instruction.). The factor loadings of the 32 items ranged from 0.84 to 0.45 (see details in Table VI, Appendix A). Scale reliability was evaluated using Cronbach_s alpha. The internal consistency of the instrument was high (alpha=0.90). Data Analysis The Statistical Package for Social Science (SPSS 12.0 for Windows) was used to analyze the data. Because of the large sample size (N = 940), the statistical coefficients for evaluating the data were set at .05 for the level of significance and 95% for the confidence interval. The five factors emerging from the factor analysis were combined with two demographic factors (school region and school size) and a user factor (educational technology user or non-user). We used descriptive statistical techniques, factor analyses, log-linear analyses, and 3-way ANOVA techniques to examine correlations and interactions among variables and factors. Log-linear model techniques allowed for testing the various contributions of school region, school size and their interactions in explaining whether teachers were users or non-users of educational technology. Three-way ANOVA techniques were employed to examine the effects of school region, school size, and teachers_ use of educational technology on the dependent measures (including practice, attitude, belief, need, and school resource). RESULTS The results are presented in three sections. The first section shows the descriptive statistics associated with three factors (school size, school


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region, and use of educational technology) and five dependent measures (teacher practice, attitude, belief, need, and school resource). In the second section, results of log-linear analyses are presented and interactive effects among school size, school region, and teachers_ use of educational technology are shown. The third section outlines the effects of school size, school region, and use of educational technology on the five dependent measures. Descriptive Analyses Because of the high population density in northern and central Taiwan, 74.9% of the participants came from these two regions (see Table II). Less than 7% of the participants came from the eastern region which was considerably less urbanized compared with the other three regions. Additionally, over 80% of the participants taught at large and mediumsized schools. Approximate 50% of the teachers taught at large schools, while 14% taught at small schools. According to the participants_ responses on the second section of the questionnaire (e.g., experiences in technology use for instructional purposes), 65.2% of the teachers in this sample have used technology for instructional purposes, and were identified as users of educational technology in this study. Across regions and school sizes, the users of educational technology ranged from 56.9% to 83.3%. It seems that participating teachers who taught at small schools were more likely to use educational technology (Figure 1). The significance of the tendency will be later examined by log-linear analyses. Table III outlines the mean scale scores and standard deviations for technology users_ and non-users_ practices, attitudes, beliefs, needs, and resources. Compared with the teachers at medium-sized and large schools, the teachers at small schools tended to have higher mean scores on all of the dependent measures. It is not surprising that users of educational technology seemed to hold more positive beliefs and attitudes toward technology and have more resources to support technology-based instruction. Yet, the mean scores on teachers_ needs were high for both users and non-users. This indicates that a majority of participating teachers felt a need for professional development opportunities in order to gain experience and resources about technology-based instruction. There were intercorrelations among teachers_ practices, attitudes, beliefs, needs, and resources (Table IV). The values of the Pearson correlation coefficient ranged from 0.633 to 0.100 and revealed high positive correlations among practice, attitude, and belief.

n

206 137 69 179 103 76 65 37 28 24 15 9

School region

Northern User Non-user Central User Non-user Southern User Non-user Eastern User Non-user

Large

62.5% 37.5%

56.9% 43.1%

57.5% 42.5%

66.5% 33.5%

% within school size

School size (N=940)

21.91% 14.57% 7.34% 19.04% 10.96% 8.09% 6.91% 3.94% 2.98% 2.55% 1.60% 0.96%

% of total 117 83 34 116 68 48 84 56 28 17 12 5

n

Medium

70.6% 29.4%

66.7% 33.3%

58.6% 41.1%

70.9% 29.1%

% within school size 12.45% 8.83% 3.62% 12.34% 7.23% 5.11% 8.94% 5.96% 2.98% 1.81% 1.28% 0.53%

% of total

42 35 7 44 33 11 25 18 7 21 16 5

n

Small

Descriptive statistics of school region, school size, and teacher_s use of technology

TABLE II

76.2% 23.8%

72.0% 28.0%

75.0% 25.0%

83.3% 16.7%

% within school size

4.47% 3.72% 0.74% 4.68% 3.51% 1.17% 2.66% 1.91% 0.74% 2.23% 1.70% 0.53%

% of total

72 HSIN-KAI WU ET AL.


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Log-Linear Analyses

% within School Size

For our analysis of teachers_ use of technology, the potential interactive effects of school region and school size were considered. We analyzed 4 (region)3 (size)2 (technology use) contingency tables by using hierarchical log-linear analyses (Agresti, 1990; Salter, 2003). The analyses tested various hierarchical models, starting with simple main effects and working up to two-way interactions, a combination of twoway interaction, and more complex three-way interaction until the model which best described the data was identified. Results of log-linear analyses indicated that in addition to the saturated model (combining the three-way interaction, two-way interactions, and main effects), three of the log-linear models seemed to offer promising fits to the observed data. The most parsimonious model was: constant+school regionschool size+school sizeuser (G2(9, N = 940) = 9.616, p = 0.382). The likelihood ratio chi-square indicated that a combination of the two-way interactions

90 85 80 75 70 65 60 55 50 45 40 Northern

small medium large

Central

Southern

Eastern

School Region Figure 1. The percentage of technology users (within the same school size) at small, medium-sized, and large schools in the northern, central, southern, and eastern regions

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was not significantly different from the saturated model in accounting for the distribution of teachers_ use of technology in classrooms. To identify outliers, we used residual analyses to examine where the parsimonious model was not fitting well. In the model, technology nonusers at medium-sized schools in the central region were slightly over represented (adjusted deviance residual = 1.236), while technology nonusers at large schools in the northern region were slightly under represented. Yet, none of the absolute adjusted deviance residuals were significant (absolute residuals ranging from 1.236 to 0.056), so overall

TABLE III Mean scale scores and standard deviations for teachers_ practices, attitudes, beliefs, needs, and resources (N=940) Practice Condition Northern Large Medium Small Central Large Medium Small Southern Large Medium Small Eastern Large Medium Small Total Northern Central Southern Eastern Total Large Medium Small Total User Non-user

Attitude

Belief

Need

Resource

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

2.90 3.05 3.19

0.76 0.73 0.60

3.62 3.60 3.87

0.60 0.54 0.49

3.40 3.36 3.60

0.62 0.60 0.61

3.99 4.06 4.14

0.59 0.57 0.55

3.19 3.15 3.16

0.74 0.67 0.71

2.85 2.98 3.09

0.77 0.71 0.53

3.67 3.71 3.77

0.51 0.54 0.41

3.45 3.47 3.57

0.59 0.56 0.54

3.95 3.98 3.97

0.52 0.55 0.53

3.13 3.11 3.50

0.73 0.69 0.71

2.73 2.81 3.13

0.61 0.78 0.85

3.60 3.57 3.84

0.48 0.57 0.60

3.40 3.43 3.50

0.67 0.65 0.77

4.04 3.87 3.98

0.56 0.62 0.46

3.06 3.03 2.90

0.69 0.75 0.77

3.06 2.94 3.10

0.91 0.78 0.59

3.70 3.66 3.77

0.66 0.64 0.48

3.38 3.48 3.55

0.57 0.47 0.61

4.08 4.00 4.15

0.60 0.56 0.52

3.25 3.32 3.16

0.70 0.45 0.58

2.98 2.93 2.83 3.04

0.74 0.72 0.74 0.77

3.64 3.70 3.62 3.71

0.57 0.51 0.55 0.59

3.41 3.47 3.43 3.47

0.61 0.57 0.67 0.56

4.03 3.97 3.95 4.08

0.58 0.53 0.58 0.56

3.17 3.17 3.02 3.24

0.71 0.73 0.73 0.59

2.87 2.96 3.13

0.75 0.74 0.63

3.64 3.64 3.81

0.55 0.55 0.48

3.42 3.42 3.56

0.61 0.59 0.62

3.99 3.98 4.06

0.56 0.58 0.52

3.15 3.11 3.22

0.73 0.69 0.73

3.19 2.46

0.63 0.69

3.76 3.49

0.52 0.55

3.54 3.25

0.57 0.62

4.01 3.97

0.55 0.58

3.21 3.03

0.72 0.69

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the model was a well-fitting one. Parameter estimates showed that all combinations of interacting values were significantly contributing to the explanation of the distribution of data except [school region = southern][school size = small] (Z = 0.589, p = 0.556) and [school region = eastern][school size = medium] (Z = 0.550, p = 0.583). On the other hand, the highest Z values on [school region = northern][school size = large] (Z = 9.831, p G .001) and [school region=central][school size = large] (Z = 9.297, p G.001) showed that the two combinations contributed the most to the overall strength of the relationships in the distribution. Results obtained from the log-linear analyses suggested that both the interactions of school region with school size and school size with technology users were needed to explain teachers_ use of educational technology. The school region affected school size independent of technology use and the school size affected technology use independent of school region. That is, the distribution of large, mediumsized, and small schools was strongly associated with where the school was located, but school region did not affect technology use. On the other hand, the school size factor played an important role in explaining the observed data. Size of the school had significant impact on teachers_ use of technology; teachers at small schools were more likely to use technology for instructional purposes. To further explore interactions between school size and teacher factors, below we examine the effects of school size on teachers_ practices, attitudes, beliefs, needs, and resources. Effects of School Region, School Size, And Use of Educational Technology In order to examine the effects of school region, school size, and teachers_ use of educational technology on the dependent measures (including teacher practice, attitude, belief, need, and school resource), a 4 (region) TABLE IV Correlation matrix for the five teacher factors (N=940) Pearson correlation

Practice

Attitude

Belief

Need

Resource

Practice Attitude Belief Need Resource

1 0.460** 0.430** 0.112** 0.274**

1 0.633** 0.315** 0.266**

1 0.238** 0.175**

1 0.100**

1

**Correlation is significant at the 0.01 level (2-tailed).

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3 (size)2 (technology use) three-way ANOVA was performed. Table III presents the means and standard deviations for the dependent measures. Table V summarizes the 3-way ANOVA results for school region, school size, and technology use. INTERACTION EFFECT. There was no significant 2-way interaction (see Table V). There was, however, a significant 3-way interaction on teachers_ practice, multivariate F (6, 916) = 2.374, pG .05. A series of 2way ANOVA tests were computed to investigate the nature of the interaction. Two simple interaction effects were significant: school sizetechnology use at schools in the southern region F(2, 168) = 4.517, p G .05, and school regiontechnology use at small schools F(3, 124) = 3.182, p G .05. The profile plots (Figure 2) indicated that users and nonusers of educational technology in southern Taiwan displayed opposite tendencies in terms of their teaching practice and that users at small schools in this region expressed stronger agreement on items regarding the implementation of instructional technology. Similarly, users and nonusers of educational technology at small schools demonstrated distinct patterns across regions in terms of their teaching practices (Figure 2). Additional follow-up tests were conducted to examine where the significant differences lay. For technology users in the southern region, school size had a significant simple main effect on teaching practice F(2, TABLE V Summary of three-way ANOVA results for school region, school size, and use of educational technology

Practice Condition Main effect School region School size Technology use Interaction RegionSize RegionUser SizeUser Region Size User *pG.05, **pG.01

F

p

Attitude

Belief

F

F

p

Need p

F

Resource p

F

p

2.00 0.11 0.49 1.36 0.26 3.21 128.09 0.00** 27.85

0.69 0.36 0.04* 0.96 0.00** 25.72

0.78 1.70 0.38 0.78 0.00** 0.35

0.16 4.36 0.00** 0.46 0.07 0.93 0.55 11.29 0.00**

0.85 1.56 0.30 2.37

0.75 0.53 0.29 0.13

0.98 0.80 0.45 0.53

0.32 0.33 0.85 0.72

0.54 0.20 0.74 0.03*

0.57 0.74 1.23 1.66

0.20 0.34 0.81 0.86

1.17 1.16 0.16 0.62

1.72 2.05 0.07 0.13

0.11 0.11 0.93 0.99


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108) = 5.130, p G .01 and post-hoc tests showed that users of educational technology at small schools enacted more technology-based practices than users at large schools. But the school size factor did not affect nonusers_ practice in the southern region. MAIN EFFECTS. Results of the three-way ANOVA revealed a significant main effect for school size on attitude (F(2, 916) = 3.21, p G .05 ). Post-hoc testing showed that school size did not affect the amount of technical and personnel resource available to participating teachers, but influenced teachers_ attitude toward technology-based instruction. Compared to the participants teaching at medium-sized (M = 3.64) and large schools (M = 3.64), teachers at small schools (M = 3.81) showed significantly more positive attitude toward the use of technology in classrooms (both pG.01). There were also statistically significant main effects for the technology user/non-user on attitude (F(1, 916) = 27.85, p G .01), belief (F(1, 916) = 25.72, p G .01), and resource (F(1, 916) = 11.29, pG.01). Compared with non-users of educational technology, users held more positive attitudes and beliefs about technology use in classrooms, and had more technical and personnel resources to support technology-based instruction.

DISCUSSION School Size and Teachers_ Use of Technology Schools are social organizations where the particulars of organizational structures shape and constrain members_ (including both teachers and students) action (Kerr, 1996). This study focuses on one of the structural characteristicsVschool size, and examines whether this structural factor influenced teachers_ use of educational technology in classrooms. Results obtained from log-linear analyses and 3-way ANOVA consistently show that small schools provide a better environment for supporting science and mathematics teachers to implement technology innovations. The results not only echo the findings of research on the topic of school size (Lee & Loeb, 2000), but also expands the body of research in two ways. First, while previous studies on school size generally targeted its effects on high school students (Howley, 1996), this study considered the effects of school size on junior high school teachers. The results indicate that school size has both main and interaction effects on teaching. Secondly, this study included a broader set of teacher factors and associated the school size factor with teachers_ practices and perceived school resources. We find that the size of school

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(a) 3.6

3.4 User

Practice

3.2 3 2.8 2.6 2.4

Non-user

2.2 2 Large

Medium

Small

School Size (Southern)

(b) 3.6 User

3.4

Practice

3.2 3 2.8 2.6

Non-user

2.4 2.2 2 Northern

Central

Southern Eastern

School Region (Small Schools)


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does not affect school resources perceived by teachers but teachers_ attitudes toward the use of educational technology. Why do more teachers at small schools in Taiwan use technology for classroom instruction? We provide two possible explanations. The first explanation is related to collegiality among teachers. Cheng and Wong (1996) indicated that teachers in East Asia highly value personal relationships and are sensitive about how they are viewed by colleagues, students and parents as compared to their counterparts in the West. Taiwanese teachers might change their attitude towards technology and be more willing to use technology for teaching if other teachers do so. And teachers at small schools might have more contact with other computer-using teachers. Therefore, a possible mechanism of the school size effect might be that a smaller school would enhance collegiality among teachers (Fitzgerald, 1997). Through frequent interactions with computer-using teachers, teachers who are not users of educational technology are more likely to have positive attitudes toward technology and initiate change in their teaching practice. This in turn increases the percentage of computer-using teachers at small schools. A possible relation between school size and school culture might provide another explanation. In Taiwan, large schools are usually Bstar schools^ that attract students from across the city or the town because graduates from these schools score high on senior high school entrance examinations. As Aldridge, Fraser & Huang (2001) found, compared to Australian teachers, teachers in Taiwan are more reluctant to use teaching methods that were not teacher-centered and lecture-based because lecturebased methods are the most efficient way to cover the content in the given time frame. In star schools, the school culture is more competitive, and science and mathematics teachers are usually pressured by school administrators and parents to cover all the content and to push students toward higher goals and better test results. The examination-driven culture at large schools might discourage teachers to adopt various teaching methods and implement technological innovations in classrooms. Both explanations can be examined by future research. To investigate whether/how small schools enhance collegiality among teachers and whether/how school culture at small schools encourages educational innovations, researchers can observe interactions among school members, and interview teachers, administrators, and parents from schools with different sizes. Ethnographic methods used by Windschitl and Sahl

RFigure 2.

Profile plots of technology use for (a) schools in southern Taiwan, and (b) small schools across regions

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(2002) might be useful to investigate complex relationships among collegiality, school size, school culture, and technology usage. Factors Affect Teachers’ Use of Technology in Classrooms Five teacher factors (i.e., practice, attitude, belief, need, and resource) were identified and examined in this study. Similar to earlier findings reported by Haney, Czerniak & Lumpe (1996), this study finds that teachers_ implementation of technology innovation was positively correlated with teachers_ attitudes and beliefs about educational technology. But positive attitudes and beliefs were not sufficient for teachers to integrate technology into instruction. The results demonstrated a significant difference in perceived social support and school resources between users and non-users of educational technology. This suggests that teachers need adequate technological facilities and sufficient technical support for successful implementation of classroom technology. Additionally, although approximately 65% of the teachers in this study have used technology for instructional purposes, they still had strong need for professional development opportunities. A majority of teachers in this sample indicated their interests in gaining practical knowledge about using educational technology in classrooms. Teaching workshops should consider providing more real-world examples of technology-based instruction and more classroom observation opportunities. Limitations of the Study There are limitations of the study that derive from the methods. First, the data were collected in Taiwan so the results should not be generalized to other countries where the educational systems are very different from Taiwan. Second, although the questionnaire provided definitions of some keywords (e.g., telecommunication and technology), when answering the questionnaire teachers might hold different interpretations about educational technology, integrating technology in classroom instruction, and learning with technologies. Their interpretations could influence their responses. Additionally, the quantitative methods used in the study cannot address questions such as: What is the nature of technology-based learning tasks used by these teachers? What pedagogical practices are used by the teachers? These limitations provide opportunities for followup studies. We will interview some of the teachers from the two groups (i.e., users and non-users of educational technology) about their definitions and perceptions of integrating technology in classroom


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instruction, observe their pedagogical practices, and combine quantitative and qualitative data to understand why they do or do not use technology in their classrooms.

CONCLUSIONS

AND IMPLICATIONS

The study situates the issue of teachers_ adoption of technology into the ongoing discourse about the impact of the school structures on teaching and learning and investigates the effects of school size on science and mathematics teachers_ adoption of technology in classrooms. Results obtained from the log-linear analyses suggested that school size had significant impact on teachers_ use of technology, and teachers at small schools were more likely to use technology for instructional purposes. Additionally, results of the three-way ANOVA revealed that among users of educational technology in the southern region, users who taught at small schools reported significantly more use of educational technology and that teachers at small schools tended to have positive attitudes toward technology use. Taken together, these results suggest that small schools provide a better environment for science and mathematics teachers to integrate technology into instruction. To encourage teachers at large schools to use educational technology, administrators at large schools might consider providing more curriculum flexibilities and encouraging teachers to use different teaching methods. Having opportunities to team up with other computer-using teachers might also encourage teachers at large schools to have positive attitudes toward technology and initiate change in their teaching practices. This study also provides evidence to support the policy of Bsmall classes, small schools^ in Taiwan. The Ministry of Education in Taiwan should continue implementing the policy, consider reducing school size in big cities, and coordinate professional development workshops on educational technology.

ACKNOWLEDGEMENT This work was supported by the National Science Council of Taiwan under NSC92-2511-S-003-053. The authors wish to thank Tai-Yih Tso and Yun-Ta Chang for their invaluable assistance and support in developing the questionnaire and collecting data.

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APPENDIX TABLE VI Questionnaire items and factor loadings

No.

Practice e35 e36 e37 e33 e34

e29 e32 e30 e28 e4 Attitude e21 e22 e20 e19 e10 e9 e7 Belief e26 e25 e27

Item and factor

Factor loading

I have designed activities that allowed students to learn through the Internet. I have had students learn collaboratively through the Internet. I have used the Internet to support individual learning. I have used computers and the Internet to collect and grade students_ assignments. I have used computer applications to create pictures, videos and animations and used them in classrooms. I have developed teaching strategies for technology-based instruction. I have used educational software to promote learning. I have used computers to play videos in classrooms. I have used the Internet to discuss with other teachers. I have confidence with integrating technology into instruction successfully.

Alpha reliability

Mean

SD

0.82

2.73

1.04

0.80

2.66

1.01

0.78

2.49

0.98

0.75

2.97

1.12

0.72

3.18

1.07

0.69

2.79

0.93

0.69

3.16

1.08

0.63

3.21

1.09

0.61

2.94

0.97

0.45

3.24

0.82

0.84

3.66 3.64

0.70

0.82

3.63

0.71

0.63

3.48

0.72

0.57 0.56

3.49 3.72

0.74 0.72

0.53

3.90

0.76

0.51

3.77

0.84

0.79

3.42

0.81

0.75

3.71

0.72

0.68

3.78

0.72

0.90

0.86 I should create different teaching strategies for technology-based instruction. I should develop different assessment strategies for technology-based instruction. Using technology can help me share my teaching experiences with others. I think technology is helpful for my teaching. I am willing to follow school policy on implementing technology-based instruction. I think technology-based instruction is one of the future trends in education. I like to search courses-related information on the Internet.

0.81 I believe that technology-based instruction can improve learning achievement. I believe that technology-based teaching can promote students_ motivation. I believe that technology-based instruction can make my teaching lively and energetic.

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(Table VI Continued) No.

e1

e24 Need d5

d6

d7

d3

d4

Item and factor

Factor loading

Alpha Mean reliability

SD

I believe that conventional teaching methods are more efficient than technology-based instruction. I believe that technology-based instruction does not cause a delay in teaching progress.

0.60

3.24

0.88

0.58

3.05

0.94

0.84

4.14

0.73

0.81

4.13

0.71

0.72

4.00

0.74

0.69

4.06

0.68

0.65

3.63

0.87

0.81

3.14

1.00

0.76

3.34

0.89

0.72

3.03

0.97

0.68

3.27

1.14

0.60

2.96

0.86

0.81 I hope that teacher workshops can provide more real-world examples of technology-based instruction. I hope that teacher workshops can provide opportunities to observe technology-based instruction. I hope that teacher workshops can provide both successful and failed examples of technology-based instruction. I hope that teacher workshops can introduce more educational resources for technology-based instruction. I hope that teacher workshops can provide content about learning theories of technology-based instruction.

Resource e12 In my school, the technology facilities are adequate for technology-based instruction. e16 In my school, administrators can provide hardware and software for supporting technology-based instruction. e17 In my school, there are enough technicians to maintain computers. e11 In my school, the computers at labs (or in classrooms) have Internet access. e13 In my school, teachers often discuss topics and exchange ideas about computer hardware and software.

0.79

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