factors affecting teachers’ adoption of technology in classrooms: does school size matter?

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 intro-

duced into classrooms. However, most teachers do not use these tech-

nologies 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

jAuthor for Correspondence.

International Journal of Science and Mathematics Education (2007) 6: 63Y85# National Science Council, Taiwan (2007)

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 in-

timacy among teachers and students (Lee & Loeb, 2000). This in turn

may become a barrier to the procurement of social and technical re-

sources 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-

HSIN-KAI WU ET AL.64

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 funda-

mentally different learning environments in classrooms (Dwyer et al.,

1991). To understand how technology can be effectively used in class-

rooms, 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 technol-

ogy 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_ techno-

logical 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 deci-

sions. 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 tech-

nology 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 technol-

ogy 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

TEACHERS, ADOPTION OF TECHNOLOGY 65

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


recent years (Fritzberg, 2001; Hargreaves & Fink, 2000; Lee & Smith,

1997). A traditional view of school structure supports large and compre-

hensive 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, promot-

ing 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


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


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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


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 medium-

sized 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 edu-

cational 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.


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Log-Linear Analyses

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 two-

way 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 region�school

size+school size�user (G2(9, N = 940) = 9.616, p = 0.382). The likelihood

ratio chi-square indicated that a combination of the two-way interactions

40

45

50

55

60

65

70

75

80

85

90

Northern Central Southern Eastern

School Region

% w

ithin

Sch

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ize

small

mediumlarge

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


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 non-

users at medium-sized schools in the central region were slightly over

represented (adjusted deviance residual = 1.236), while technology non-

users 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)

Condition

Practice Attitude Belief Need Resource

Mean SD Mean SD Mean SD Mean SD Mean SD

Northern

Large 2.90 0.76 3.62 0.60 3.40 0.62 3.99 0.59 3.19 0.74

Medium 3.05 0.73 3.60 0.54 3.36 0.60 4.06 0.57 3.15 0.67

Small 3.19 0.60 3.87 0.49 3.60 0.61 4.14 0.55 3.16 0.71

Central

Large 2.85 0.77 3.67 0.51 3.45 0.59 3.95 0.52 3.13 0.73

Medium 2.98 0.71 3.71 0.54 3.47 0.56 3.98 0.55 3.11 0.69

Small 3.09 0.53 3.77 0.41 3.57 0.54 3.97 0.53 3.50 0.71

Southern

Large 2.73 0.61 3.60 0.48 3.40 0.67 4.04 0.56 3.06 0.69

Medium 2.81 0.78 3.57 0.57 3.43 0.65 3.87 0.62 3.03 0.75

Small 3.13 0.85 3.84 0.60 3.50 0.77 3.98 0.46 2.90 0.77

Eastern

Large 3.06 0.91 3.70 0.66 3.38 0.57 4.08 0.60 3.25 0.70

Medium 2.94 0.78 3.66 0.64 3.48 0.47 4.00 0.56 3.32 0.45

Small 3.10 0.59 3.77 0.48 3.55 0.61 4.15 0.52 3.16 0.58

Total

Northern 2.98 0.74 3.64 0.57 3.41 0.61 4.03 0.58 3.17 0.71

Central 2.93 0.72 3.70 0.51 3.47 0.57 3.97 0.53 3.17 0.73

Southern 2.83 0.74 3.62 0.55 3.43 0.67 3.95 0.58 3.02 0.73

Eastern 3.04 0.77 3.71 0.59 3.47 0.56 4.08 0.56 3.24 0.59

Total

Large 2.87 0.75 3.64 0.55 3.42 0.61 3.99 0.56 3.15 0.73

Medium 2.96 0.74 3.64 0.55 3.42 0.59 3.98 0.58 3.11 0.69

Small 3.13 0.63 3.81 0.48 3.56 0.62 4.06 0.52 3.22 0.73

Total

User 3.19 0.63 3.76 0.52 3.54 0.57 4.01 0.55 3.21 0.72

Non-user 2.46 0.69 3.49 0.55 3.25 0.62 3.97 0.58 3.03 0.69


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 = sou-

thern]�[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 indepen-

dent of technology use and the school size affected technology use

independent of school region. That is, the distribution of large, medium-

sized, 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 1

Attitude 0.460** 1

Belief 0.430** 0.633** 1

Need 0.112** 0.315** 0.238** 1

Resource 0.274** 0.266** 0.175** 0.100** 1

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


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 2-

way ANOVA tests were computed to investigate the nature of the

interaction. Two simple interaction effects were significant: school

size�technology use at schools in the southern region F(2, 168) = 4.517,

p G .05, and school region�technology use at small schools F(3, 124) =

3.182, p G .05. The profile plots (Figure 2) indicated that users and non-

users 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 non-

users 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

Condition

Practice Attitude Belief Need Resource

F p F p F p F p F p

Main effect

School region 2.00 0.11 0.49 0.69 0.36 0.78 1.70 0.16 4.36 0.00**

School size 1.36 0.26 3.21 0.04* 0.96 0.38 0.78 0.46 0.07 0.93

Technology use 128.09 0.00** 27.85 0.00** 25.72 0.00** 0.35 0.55 11.29 0.00**

Interaction

Region�Size 0.85 0.54 0.57 0.75 0.20 0.98 1.17 0.32 1.72 0.11

Region�User 1.56 0.20 0.74 0.53 0.34 0.80 1.16 0.33 2.05 0.11

Size�User 0.30 0.74 1.23 0.29 0.81 0.45 0.16 0.85 0.07 0.93

Region�Size � User

2.37 0.03* 1.66 0.13 0.86 0.53 0.62 0.72 0.13 0.99

*pG.05, **pG.01


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 non-

users_ 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 consis-

tently 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


(a)

(b)

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

Northern Central Southern Eastern

School Region (Small Schools)

Pra

ctic

e

User

Non-user

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

Large Medium Small

School Size (Southern)

Pra

ctic

eUser

Non-user


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 exam-

inations. 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 lecture-

based 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 mem-

bers, and interview teachers, administrators, and parents from schools

with different sizes. Ethnographic methods used by Windschitl and Sahl

Figure 2. Profile plots of technology use for (a) schools in southern Taiwan, and (b)

small schools across regions

R


(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 technol-

ogy. But positive attitudes and beliefs were not sufficient for teachers to

integrate technology into instruction. The results demonstrated a signif-

icant 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, al-

though 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 educa-

tional 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 follow-

up 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


instruction, observe their pedagogical practices, and combine quantita-

tive 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 curricu-

lum 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.


APPENDIX

TABLE VI

Questionnaire items and factor loadings

No. Item and factor Factor

loading

Alpha

reliability

Mean SD

Practice 0.90

e35 I have designed activities that allowed

students to learn through the Internet.

0.82 2.73 1.04

e36 I have had students learn collaboratively

through the Internet.

0.80 2.66 1.01

e37 I have used the Internet to support

individual learning.

0.78 2.49 0.98

e33 I have used computers and the Internet to

collect and grade students_ assignments.

0.75 2.97 1.12

e34 I have used computer applications to create

pictures, videos and animations and used

them in classrooms.

0.72 3.18 1.07

e29 I have developed teaching strategies

for technology-based instruction.

0.69 2.79 0.93

e32 I have used educational software to

promote learning.

0.69 3.16 1.08

e30 I have used computers to play videos

in classrooms.

0.63 3.21 1.09

e28 I have used the Internet to discuss

with other teachers.

0.61 2.94 0.97

e4 I have confidence with integrating

technology into instruction successfully.

0.45 3.24 0.82

Attitude 0.86 3.66

e21 I should create different teaching strategies


0.84 3.64 0.70

e22 I should develop different assessment strategies


0.82 3.63 0.71

e20 Using technology can help me share my

teaching experiences with others.

0.63 3.48 0.72

e19 I think technology is helpful for my teaching. 0.57 3.49 0.74

e10 I am willing to follow school policy on

implementing technology-based instruction.

0.56 3.72 0.72

e9 I think technology-based instruction is one

of the future trends in education.

0.53 3.90 0.76

e7 I like to search courses-related information

on the Internet.

0.51 3.77 0.84

Belief 0.81

e26 I believe that technology-based instruction

can improve learning achievement.

0.79 3.42 0.81

e25 I believe that technology-based teaching can

promote students_ motivation.

0.75 3.71 0.72

e27 I believe that technology-based instruction can

make my teaching lively and energetic.

0.68 3.78 0.72


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