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2007-

This is to certify that the
dissertation entitled

Understanding Technology Adoption in Schools: A Social

Ph.D.

 

Approach

presented by

Bo Yan

has been accepted towards fulfillment
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degree in Educational Psychology

 

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UNDERSTANDING TECHNOLOGY ADOPTION IN SCHOOLS:
A SOCIAL APPROACH

By

Bo Yan

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Counseling, Educational Psychology and Special Education

2006

ABSTRACT

UNDERSTANDING TECHNOLOGY ADOPTION IN SCHOOLS:
A SOCIAL APPROACH

By
Bo Yan

This study explores factors that influence technology adoption in schools. A
model consisting of social factors including teachers’ access to expertise through help
and talk as well as psychological factors concerning technology use including perceived
values for teachers, values of students, complexity of operating technology, compatibility
between pedagogical practice and technology use, ease of implementing technology, and
pressure to use technology is posited. The model is tested with survey data of 533
Chinese teachers from 9 elementary schools. In addition, data used in Zhao and Frank’s

(2003) study are analyzed for cross-cultural comparison.

The results suggest that technology adoption is both an individual and social issue.
Individually, technology use is associated with teachers’ perception of technology use.
Socially, teachers’ access to expertise through talk is correlated with their technology use.
The results also suggest that social interaction might indirectly affect technology use
through the mediation of perception. But the effect is complex and fairly small. Cultural
differences seem to be able to explain some of the differences in the effects of perception

and social interaction.

DEDICATION

To my parents, Yan, Huatong (filth—3]) and Chen, Yulan (Féiifié)

. To my wife, Chunrong Katy Lu and our daughter Vivienne Yu Yan

iii

ACKNOWLEDGEMENTS

The past five years have been a journey full of joy, excitement, and fulfillment. At
the same time, it is also filled with anxiety, frustration, and uncertainty. I am fortunate to
have some great people in my life to share my feelings along the journey and guide me
when I felt lost. Without them, I could not have gone this far.

No words can express my thankfulness and appreciation for the help, support, and
guidance from my academic advisor and mentor, Dr. Yong Zhao. He has been such an
inspiration in my life and had an enormous impact on my intellectual development and
personal growth. He taught me to be open, gave me new perspectives to look at the world,
and brought me into the field of educational technology. His creativity, integrity, and
devotion to understanding the world better and making it a better place have greatly
shaped my views of research and philosophy of life and will continue to be the source of
inspiration and power for me to move forward.

I would also like to thank Dr. Kenneth Frank, who has my tremendous respect for
his knowledge and scientific spirit, for guiding me through the maze of the dissertation
process. But my gratitude to him transcends the detailed feedback and suggestions he
gave me on the methodology, data analysis and results interpretation. He helped me go
beyond psychological approach and learn to understand the world from a social
perspective, which I believe will benefit me for the rest of my life.

It has been a great honor and such a joy to work with Dr. Patrick Dickson. I have
had his care, support, and encouragement ever since I entered the program. With his
guidance, getting adapted to American culture and the doctoral program became such an

easy thing. He opened my door to online teaching and learning. His vision of the future of

iv

education and technology has a significant impact on my thinking. To me, he is a well-
beloved mentor and easy-going friend.

On the journey, I am very fortunate to meet Dr. Punya Mishra, who is a creative
researcher and ingenious artist. He encouraged me to conduct authentic research early in
my doctoral study. The guidance and suggestions I received fiom him in and out of the
classroom are invaluable and priceless. What I will never forget is his empathy and
encouragement when I was feeling down, which gave me so much console and helped me
regain my confidence.

Another faculty member I must thank is Dr. Joseph Codde. Although he is not on
my dissertation committee, I have learned so much from him and benefited enormously
from his suggestions. It is such a good feeling when you know there is someone who
cares so much about you. Without him, the doctoral study experience would have been
very different.

The completion of this study is indebted to Counsellor Tongzeng Bao at the
Consulate General of the People’s Republic of China in Chicago, Mr. Xuefeng Zhou, Mr.
Jin Fan, Mr. Dong Yang, Mr. Ping Liao at Sichuan Provincial Education Bureau, and Mr.
Yongheng You, Mr. Zhiquan Li, Taoying Zhong, and Lanping Xiong at Sichuan Normal
University. From picking up and contacting schools to conducting survey and interview
at the schools, I received their unreserved help and suggestions. I would also like to

acknowledge the administrators and teachers from the Gaoxin Experiment Elementary
(gfijfy‘éfifi IJ\'”%’), Dongchenggen Elementary (RENE/b ”#4), Chengdu Experiment
Elementary (fikfillgk’gfi ’l‘ ”3%), Moziqiao Elementary (E 354?? ’J‘ 515’ ), Paotongshu

Elementary (MWM/bfir‘), Tongzilin Elementary (Wfi‘fi/J‘fi’é), Yandaojie Elementary

(£333 49%), Yindu Elementary (fififi’l‘?) and Jitou Elementary (Mifi’b’é‘). Their
trust and support for this project ensured high quality of the survey results, especially the
sociometric part which is usually very hard to obtain.

During my doctoral study, I am also very fortunate to have Stephen Vassallo and
John David Gallagher as my best American fi-iends. They are smart, open, warm-hearted,
and willing to help others. The improvement of my English and understanding of
American Culture is inseparable from their help. The wonderful moments we spent
together and their friendship will be unforgettable and cherished wherever I go.

I am also very appreciative to Lisa Payne, Lisa Roy, JJ Chandler, Dave Dai, Scott
Schopieray, and Ken Dirkin for their support and help all through my doctoral program. I
also want to thank Jamie, Gaoming, Cozi, Chun, Ruhui, Wei, Sandra, Nancy, Yan,
Nichole, J iawen, Ning, Xue, and many others for their care and help.

Before I end, I want to thank my beloved wife, Chunrong Lu, for supporting me

to pursue my dreams. Her love makes my life so enjoyable and meaningful.

vi

TABLE OF CONTENTS

LIST OF FIGURES ........................................................................................................... ix
LIST OF TABLES .............................................................................................................. x
CHAPTER 1 PURPOSE OF THE STUDY ....................................................................... 1
Statement of the Problem ................................................................................................ 1
Goals of the Study ........................................................................................................... 2
Project Significance ........................................................................................................ 3
CHAPTER 2 LITERATURE REVIEW ............................................................................. 5
Conceptualization of Technology Adoption ................................................................... 5
Who/What to Blame? ...................................................................................................... 7
Technology ................................................................................................................. 7
Teacher ........................................................................................................................ 8
School ......................................................................................................................... 9
WhINFII‘IRIYInIQPhHI7 HIth 7 I-HmRfllgy 8 \H’ ................................................... l 1
How to Help Teachers Integrate Technology? ............................................................. 14
Summary ....................................................................................................................... 16
CHAPTER 3 THEORETICAL FRAMEWORh .............................................................. 18
Perceptual Control Perspective ..................................................................................... 18
Social Perspective ......................................................................................................... 22
Combining the Two Perspectives ................................................................................. 24
International Comparison .............................................................................................. 26
Research n uestions ....................................................................................................... 31
CHAPTER 4 RESEARCH METHOD ............................................................................. 33
Participants .................................................................................................................... 33
Measurement ................................................................................................................. 33
Data Collection ............................................................................................................. 35
Data Analyses ............................................................................................................... 37
CHAPTER 5 RESEARCH FINDINGS ............................................................................ 38
Description of Samples ................................................................................................. 38
Findings from Multilevel Analyses .............................................................................. 43
Part IVComputer Use as Outcome ............................................................................ 43

Part IIW’erception as Outcome ................................................................................. 50
Effect of Social Interaction ....................................................................................... 66
CHAPTER 6 CONCLUSIONS AND DISCUSSION ...................................................... 69
Effects of Perception ..................................................................................................... 69
Direct Effects of Social Interaction ............................................................................... 71
Indirect Effects of Social Interaction ............................................................................ 73

vii

Limitations and Future Research .................................................................................. 76

Implications ................................................................................................................... 76
APPENDICES .................................................................................................................. 79
REFERENCES ............................................................................................................... 1 O7

viii

LIST OF FIGURES

FIguU-Il 7HThHY3I-IFHS\IRnRI CRP SulMJBVI ............................................................. 20
FIguUIZ FIFIRU/InIQI-hfing 7W 71-thRngy 8 \H ................................................ 25
Figure 3 Computer Use at the American schools ............................................................. 44
Figure 4 Computer Use at the Chinese schools ................................................................ 44
Figure 5 Values for Teachers of the US Data ................................................................... 51
Figure 6 Values for Teachers of the Chinese Data ........................................................... 52
Figure 7 Complexity of the US Data ................................................................................ 59
Figure 8 Complexity of the China Data ............................................................................ 59
Figure 9 Compatibility of the US Data ............................................................................. 63
Figure 10 Compatibility of the China Data ....................................................................... 63

ix

LIST OF TABLES

Table 1 Variables Employed for Investigation ................................................................. 26
Table 2 Reliability Results of Likert Scale Variables Measurement ................................ 34
Table 3 Characteristics of the Sample Schools ................................................................. 38
Table 4 Results of Eleven Computer Uses in the US and China ...................................... 39
Table 5 Descriptive Statistics of Variables ....................................................................... 41
Table 6 Access to Expertise through near Peer Interaction .............................................. 43
Table 7 Computer UseVResults of Unconditional Model for American Schools ............. 45
Table 8 Computer UseVResults of Unconditional Model for Chinese Schools ............... 45
Table 9 Computer UseVResults of Conditional Model for American Schools ................. 48
Table 10 Computer UseVResults of Conditional Model for Chinese Schools ................. 48
Table 11 Computer UseVResults of Independent Regression Coefficients Difference Test
........................................................................................................................................... 50

Table 12 Values for TeachersVResults of Unconditional Model for American Schools.. 52
Table 13 Values for TeachersVResuIts of Unconditional Model for Chinese Schools 53
Table 14 Values for TeachersVResults of Conditional Model for American Schools ...... 55

Table 15 Values for TeachersVResults of Conditional Model for Chinese Schools ........ 55
Table 16 Values for Teacherkuesults of Independent Regression Coefficients Difference
Test .................................................................................................................................... 56
Table 17 Results of Ceiling Effect Analysis for the American Schools ........................... 56
Table 18 Results of Ceiling Effect Analysis for the Chinese Schools .............................. 57
Table 19 Results of Logistic HLM Analysis for the Chinese Schools ............................. 58
Table 20 ComplexityVResults of Unconditional Model for American Schools ............... 60
Table 21 ComplexityVResuIts of Unconditional Model for Chinese Schools .................. 60
Table 22 ComplexityVResults of Conditional Model for American Schools ................... 61
Table 23 ComplexityVResults of Conditional Model for Chinese Schools ...................... 61
Table 24 ComplexityVResults of Independent Regression Coefficients Difference Test 62
Table 25 CompatibilityVResults of Unconditional Model for American Schools ............ 64
Table 26 CompatibilityVResults of Unconditional Model for Chinese Schools .............. 64
Table 27 CompatibilityVResults of Conditional Model for American Schools ................ 65
Table 28 CompatibilityVResults of Conditional Model for Chinese Schools .................. 65
Table 29 CompatibilityVResults of Independent Regression Coefficients Difference Test

........................................................................................................................................... 66

Table 30 Indirect Effect of Access to Expertise through Talking with Colleagues on
Computer Use ................................................................................................................... 68

CHAPTER 1 PURPOSE OF THE STUDY

Statement of the Problem

After years of heavy and continuous investment at the federal, state, and local
levels, the state of technology in American K-12 schools has improved substantially
(Coley, Cradler, & Engel, 1997; Moursund & Bielefeldt, I999; Parsad & Jones, 2005;
US. Department of Education, 2004). According to a recent report published by NCES
(Parsad & Jones, 2005), nearly 100 percent of public schools in the United States had
access to the Internet in 2003, compared with 35 percent in 1994. The ratio of students to
instructional computers with Internet access in public schools was 4.4 to l, a decrease
from the 12.1 to 1 ratio in 1998. Most public school teachers (84%) reported having at

least one computer in their classrooms in 1999 (US. Department of Education, 2000b).

Along with the enormous investment, numerous efforts have been made to help
teachers integrate technology into their everyday teaching practice. Since 1999, the
3UBDinJ 7RP RIIRw’s 7HH<H§ tR8 5H7 echnology program has invested $337.5 million
to help transform teacher preparation programs to make teachers more prepared to use
technology effectively (US. Department of Education, 2005). In 2003, 82 percent of
public schools with Internet access indicated that their school or school district had
offered professional development to teachers in their school on how to integrate the use
of the Internet into the curriculum (Parsad & Jones, 2005). Till 2005, there are even 10
states requiring technology training or a technology test for recertification, or requiring

participation in technology-related professional development (Education t eek, 2005).

Despite the substantial improvement in hardware and huge efforts in brainware,

however, the adoption of technology innovations by teachers is still by and large
disappointing. Technology remains largely un-used or under-used (Becker, 2002; Cuban,
2001; US. Department of Education, 2000b). Moreover, technology is mainly a tool for
drill and practice, administrative use, or sustaining the existing practice rather than
promoting higher-order thinking when it is used in classrooms (Educational Testing
Service, 1998; i ci & whao, 2005; The National School Boards Foundation, 2005; uhao &
)LDiN 2003). “WHKIMInRt U-IIkHi tI<HSIRP isHRI tH‘IGiRtRIy in HIuFDiRn” (8 .S.
DI-BIIIIP Hit RI ( duFDiRn, 2004, SJ .10) Did “schools are still unable to take full

DIYDitDI-IRI tI-FKanRI y”. (7 KHI Dional School Boards Foundation, 2005)

Goals of the Stuafy

This project intends to achieve two goals. First, it attempts to posit a new
framework based on the past research to complement and advance the extant approach to
technology adoption. Instead of treating teachers mainly as individual learners and users
of technology who are isolated from each other and their social environment, the new
framework puts teachers back into context and network of social interaction, and sees
technology adoption as a complex and dynamic erFH;s in wKiFKtHFKHsI’ SHH-BtiRn RI
technology, interaction with the social environment and technology use interplay and co-
evolve. Second, the framework will be tested using the same dataset collected in whao
Did ) IDiNs (2003) study. 7 KHIHDiRnsKiS 13-1th tI-FIGiRflU y usHDid tI-DKHs’
perception of technology will be examined with a purpose of corroborating the past
research. Furthermore, the relationship beth-h tHEQ-Isi’ intHlPtiRn withKI-ISRFIII)
environment and their use of technology as well as their perception of technology will be

investigated. i ast, the same framework and data analyses will be applied to the data that

will be collected in Chinese schools. It is expected that much could be Ieamed from the

cross-cultural comparison.

Project Significance
t ith regard to technology adoption, the practical endeavor and theoretical inquiry
have been predominated by behavioral and cognitive perspective. Based on these two
perspectives, we have Ieamed a great deal and made some progress. However, it is not
adequate and far from being satisfying. By employing a social perspective, this research
project will complement the extant approaches to technology adoption and further our

understanding of the complex social phenomena.

Methodologically, teacher is not the only unit of analysis and sole focus. The
interaction between teachers, interaction between teachers and other people as well as
interaction between teachers and physical objects will also be carefully examined. In
addition, this research project will transcend the individual teacher level and take the

school and district level into consideration.

In practice, most of the focus has been put on establishing technology standards
which target individual teachers and providing prepackaged technology training which is
characterized by hierarchical dissemination of technology innovation. i ittlc attention is
paid to creating a congenial and collegial social environment where technology adoption
is encouraged and facilitated through mutual support and help from teachers themselves.
The findings of this research project are expected to provide implications for the
improvement of the extant approach, or the initiation of new route tRBsist tI-IFKHS’

abundant and meaningful use of technology.

This research project is an extension of the study conducted by whao and Frank
(2003), which investigated technology adoption in 19 American schools from an
ecological perspective. This study relies on the instrument developed and data collected
in their study. But the analytical framework is different. In their study, all factors are
conceptualized to influence technology adoption directly whereas in this study the effect
is direct for some factors but indirect for some others. In addition, international
comparison that specifically investigates how social interaction among teachers differs
and affects technology adoption differently in China and the US is another contribution

of this study.

CHAPTER 2 i lTERATURE RES lEt

Numerous ideas regarding technology adoption have been proposed, debated, and
tested. The underlying motivation and assumption behind those ideas, the arguments
revolving around them and what has been known about their effectiveness and validity

will be reviewed in this chapter.

Conceptual ization of Technology Adoption
A variety of technological tools has been introduced into schools, including

computer, PDA, SmartBoard, and even cell phone. Yet, in most cases, technology refers
to computer when people talk about technology adoption. There is no strict definition of
technology adoption. d enerally, it means that teachers use computers in their teaching
practice. Nonetheless, in most discussions on technology adoption, though implicit, it
means that computers are frequently used, say on a daily basis and meaningfully used, for
example, used for higher-order thinking rather than drill and practice. Some even suggest

a commitment.

Then, why is technology adoption so advocated? Simply put, it is because
technology has the potentiality to provide teachers and students with profuse
opportunities that were not available or even possible in previous generations such as
multimedia presentation, simulation, convenient access to a large volume of up-to-date
information, instant feedback and boundless communication to name a few. Much
research has been conducted to investigate whether the potentiality could really turn into
reality and to what extent. Though there are some doubts about the effectiveness of

technology in education and concerns with its negative impact on children (Alliance for

Childhood, 2000), there is a general agreement tKD studHits’ SHIRIP DIFHFRUG EH
improved when technology is appropriately used (Educational Testing Service, 1998;
d lennan & Melmed, I99S; Kulik, 1994; Sivin-Kachala & Bialo, 1994; Solmon &

t iederhom, 2000).

Another major argument supporting technology adoption is that this generation of
studI-Iits is “ERIii tREHwiIHI” (Whation for Public Broadcasting, 2003; Harris
Interactive and Teenage Research Unlimited, 2003; Horatio Alger Association, 2003;

i enhart, Madden, & Hitlin, 2005). i iving in a world where technology is ubiquitous,
children are approaching their lives and daily activities diIIHHitQ. 7RdQI’s FKiGIIHi DH
very technology savvy and feel strongly about the positive value of technology in their
learning and other aspects of life (US. Department of Education, 2004). In contrast to the
abundant technology use outside school, however, technology is seldom used by students
in classroom because teachers have the full control of when, where, and how technology
is used (Wnao, Tan, & Mishra, 2000). Therefore, there is an urgent call for overcoming
the disconnect between technology-savvy students and school (i evin, Arafeh, i enhart, &

Rainie, 2002; US. Department of Education, 2004).

t ith regard to how the adoption of technology could help achieve the goals
expected by technology advocates, there are two major views. One is that technology
could be used as a powerful tool for the enhancement of the extant practice (Office of
Technology Assessment, 1995). According to this view, there is a good chance that
technology and teaching could marry well because there seems to be much room

technology could fit in. The other view is that technology could serve as a catalyst for the

 

transformation of school and education (Coley et al., 1997; d lennan & Melmed, 199S;
US. Department of Commerce, 2002). t hat this view implies is that technology is more
compatible with new ways of teaching. On the one hand, education has to be transformed
to make full use of technology. On the other hand, technology must be used to catalyze

the change.

Different beliefs and views lead to different approaches and actions toward
technology adoption. Afier decades of hard work and exploration, unfortunately, neither
has come up with a cost-effective and sustainable model that could be disseminated to a
large scale. The use of technology is still disappointingly low. Then, people start to ask

where the problem is.

W ho/What to Blame?
For years, people have been arguing and debating what or who should be
responsible for the problem RI “HiJ K—7 PFKSFKRRG Did LRw-7 I-FK7 HFKinJ ” (&LIH)I,
1998). The discussion has concentrate on three components that are central to technology

adoptionWechnology, teacher, and school.

Technology

First, technology itself is thought to be a major reason for the slow technology
DllStiRn. S W KthDy’s FRP SutHii DHP uFKIDter, smaller than they used to be and
computer programs are more diversified and user-friendly, modern technology still has
many conspicuous disadvantages compared to traditional technology such as paper,
pencil and chalkboard. First, most modern technologies in schools are not usually

dHiJ nI-fl tRDldIHis tI-II’KI-ISI’ SLREOP withI-IIIGISI’ inYlIYI-P Hit (ZKIR 2003). 5 DKHJ

tKl-y DJIRItI-h RtKHBFRSOIs sRGitiRns tRRtIG-USIRSOIS SIREIBms. Therefore, there is no
inherent and natural connection between tI-FKnKRIy Did tHI‘KI-Iii’ SIRHDP . SI-Fer,
computers are much more sophisticated than other technologies available to teachers,
which makes it more difficult to learn and use. In addition, technology is in constant
change. It is just difficult for teachers to catch up. Third, computers are still unreliable
and difficult to maintain (Cuban, 2001). It is a pain to deal with virus, spywarc and
constant updates even for professionals, let alone teachers. t hen using computers in
classroom, the chances of running into problems and chaos are much higher than using

traditional technologies.

In contrast to the limited technology use in education, however, computers and
Internet have been embedded in government and business and become an organic
FRP SRiHit RI tKIkHRU DiizDiRns. 7 I<H“disIZSSI-I1D1FHRI tIFKanRI y” (%IiIiH-I& HRI Di,
1998) in other sectors and the fact that technology is still treated as innovation in
education make people suspect that technology is the sole reason for slow technology

adoption.

Teacher

Naturally, people turn to the other side --- teachers for answers. It is argued that
tHFKHiI’ (DINRI FRP SutHJskills, hostility to technology and resistance to change cause
the problem. The majority of this generation of teachers has their first contact with
computers at their 305, 408 and even 503. Till 2004, there are still 19 percent of schools
where at least half the teachers are "beginners" when it comes to using technology

(Education t eek, 2005). Comparing technology SNGB, emery DtitudHtIthd

 

 

technology and incurred changed seems more tIRuEGhJ. &itinJ LRliliHs (1975) FClllssiF
work on teachers, it is argued that teachers are inherently resistant to change. Historically,
there have been several waves of large scale federal efforts purporting to alter ways of
tI-H‘KHs’ instliliFtiRn (5 DtitFK 1983; 8.8. Department of Education, I993;t elch, 1979).
But they all ended up with innovations only occasionally used. t hen it comes to
technology, things seem to get worse due to the fact that computers are more

sophisticated and more difficult to learn and use than earlier interventions.

Despite the low technology use of the majority of teachers, there are some
teachers who have zealously embraced technology and integrated technology into their
curriculum. Some early adopters even spend their own money to bring computers to their
classrooms (d amer & d illingham, 1998). There has been a large body of research
investigating what factors lead to the variDiFHin tI-IFKHsI’ usHRI tIFKanRI y, wI<iFKwiGD

be discussed in the next section at greater length.

School

Some researchers think that the discussion of this issue should not be confined to
the object (technology) and actors (teachers) without considering the environment in
which the object and actors reside. And it is not fair to put teachers in charge since what
teachers do and how they go about it are largely defined by the institution where they
work --- schools. There has been criticism that the problem has been too narrowly framed
(Cuban, 1998). It is argued that, in fact, the inherent incompatibility between technology
and the extant education constitution is the real deep-seated problem (Cuban, 1999, 2001;

i oveless, 199S; Peck, Cuban, & Kirkpatrick, 2002). Several places of incompatibility are

discussed. First, the uniqueness of teaching as a profession. Teaching is not like other
professions whose tasks could be easily streamlined and automated by technology, which
often results in increased productivity. t hat is more important to note is that education is
not only about productivity which is usually narrowly measured by the quantity of high
school and college graduates. It is also about enculturating our younger generation into
caring, thoughtful and responsible citizens in a democratic society, which requires
guidance, coaching and mentoring that could not be accomplished by technology.
Second, the time conflict. It is de facto that teachers are not well prepared to use
technology in their teaching. However, the current time allocation simply does not give
teachers enough time to learn technology and prepare to use technology. Third, separate
subject departments and cellular classroom arrangements. The existing way in which
schools are structured and organized has long been criticized to result in to rare
communication among members of different departments and thus limits the spread of
innovative. Fourth, competing priorities. Though technology adoption is a concern of
most schools and districts, it ofien gives way to other priorities in the competition for

scare resources and time.

This line of discussion has seriously questioned and criticized the assumptions
undI-IQinJ tI<HIHlHIIDid stDHs I-fluFDiRnal technology policies that the increased
availability of technology will lead to widespread classroom use by teachers and
technology could serve as a catalyst to support school reform, stimulate new teaching
methods, and redefine the role of teachers. Some even go further and assert that when

“&RP SutHiI 0 HI &(BSLRRP : &(IBSIRRP Wins” (8aiEDi, I993).

10

WhaVIFac Wrs Influence Teachers’ Technology Use?

In the research on technology adoption, teachers have gained the most attention
and are thought to be at the center of effective use of technology (Office of Technology
Assessment, 1995). After all, it is teachers who decide on whether technology will be
used and how it will be used. To find out why technology is frequently used by some
teachers but only sporadically used by some others, a variety of factors have been
correlated with technology use. This line of research results in a long list of factors that

are significantly correlated with teachers’ tIFKnROUy usH

One set of factors looks at the technological and curricular condition. It is found
the number of computers and the place where computers are housed P DtHJIRUtI-{FKHiI’
technology use. Teachers are more likely to use computers when they have access to
adequate computers in their own classroom (Becker, Ravitz, & t orig, 1999; Brent,
Brawner, & Dyk, 2002). But at the same time, research also indicates access is only the
necessary rather than sufficient condition for successful technology integration (d ubser,
198$; Hadley & Sheingold, 1993). t ith regard to subject area, Computer, business
education and vocational teachers are found more likely than teachers of core subjects to
use computers (Becker et al., 1999). Besides, it is found that elementary teachers are
more likely to use technology for instruction but less likely to use technology for

administrative work than secondary teachers (US. Department of Education, 2000b).

Another set of factors revolves around individual teDM-U’ WtiFs,
including age, length of teaching experience, and computer skills (Office of Technology

Assessment, 1995). The research findings show that although many teachers have Ieamed

ll

 

information technology skills and put them to use for a number of years, insufficient
technical skills is still a major barrier to technology use (Becker, 2002). Yet, higher level
of technology skills does not necessarily translate to increased instructional use
(Moursund & Bielefeldt, 1999; Russell, Bebell, OIDwyer, & ODIonnor, 2003b). It is
argued that thoughtful, pedagogical uses RI tI-FIGiRIRI y Ull"uiUI“7 H‘KanRI iFEO

3I-IID RI imam . nIhNOIlJ H’ IDKHlKDi simply knowing how to operate a computer
(Mishra & Koehler, in press). Although younger teachers are better at computer skills,
research shows older teachers are no less likely to use computers than younger teachers
(Education t eek, 1999; van Braak, 2001) and experienced teachers tend to use
technology more often for instruction than new teachers (Russell, Bebell, Oll)wyer, &

OKIZonnor, 20033).

t hen studying what factors influence tHH<I-I§’ tI—FKnRflU y usH P Diy UlsHlFKI-Isl
take a cognitive approach which assumes that tHH<H§’ EH<DfiRI§ DJIIIII—FtHI Ey tKHU
cognitive status. The cognitive status that has been examined covers a wide range of
constructs, including teaching philosophy or belief (Becker, 2001, 2002; Harris
Interactive and Teenage Research Unlimited, 2003; Russell et al., 2003b), computer
anxiety and comfort with technology (Becker, 2002; Bradley & Russell, 1997), self-
efficacy with technology (Delcourt & Kinzie, 1993), attitudes toward technology
(d alowich, I999; Meltzer, 199S), perceived relevance of technology for teaching
(Marcinkiewicz, I993), perceived loss of control when technology is used (N isan-Nelson,
2001), so on and so forth. Unsurprisingly, higher technology use is associated with lower
computer anxiety, higher computer efficacy, and positive attitudes toward technology. As

for teaching philosophy or belief, it is found teachers with constructivism tend to use

12

 

more technology than ones who are transmission oriented (Becker, 2002).

Some researchers think technology skills, positive attitudes and comfort toward
technology are not sufficient for teachers to use technology. Moreover, concerns with
using technology must be addressed. This linHRI IRHII-KP DnQ (HM; Rn tKH“&RiFHili
0All-fl $ dRStiRn 0 RdID dI-M-(RSl-B Ey ) uGDIX19S9) and Hall (1978). According to this
P Rdl-Dtl-DKHi’ tI~FI<anRI y DiRStiRn is DSLdicess rather than an event. Rieber and
t elliver (1989) suggest that technology adoption by teachers consists of 5 stagesW)
familiarization; 2) utilization; 3) integration; 4) reorientation; and 5) evolution. Holland
(2001) FDH RIizH; emery usHRI thKnROgy into 5 levelszonreadiness, survival,
mastery, impact, and innovation. Empirical research has found that teachers at different
technology integration levels or stages have different concerns (Atkins & s asu, 2000;
d ershner & Snider, 2001; Rakes & Casey, 2002) and concerns of early stage must be
lowered before people can move on to the next level of innovation adoption (Hall,

(I eorge, & Rutherford, 1998).

Research reviewed in this section takes a large portion of the literature on
technology adoption in education. A caveat has to be given when interpreting the
research findings. Since most studies employ correlation technique to detect the
relationship between technology use and a variety of factors, the results could not be
mistakenly interpreted as causal. For example, it is very likely that the positive attitudes
are the result of frequent technology use rather than the other way around. Nonetheless,

we have Ieamed a great deal from this body of research.

13

e ow to e elp Teachers fntegrate Technology?

t e have Ieamed from practice and research that simply pushing technology in
front of teachers is far from being enough for technology adoption. To foster the change
and transition, resources, help and support must be provided concurrently. In addition to
wiring schools, offering professional development and providing support are the other
two most important interventions aiming at technology integration. t hat we have tried

and what we have known are reviewed in this section.

(I reat efforts have been dedicated to technology professional development (Coley
et al., 1997; Parsad & Jones, 2005). Since 1999, the Preparing TomorrowB Teachers to
Use Technology Program has awarded over 400 grants to education consortia to help
teachers get well prepared to use technology in their instruction. The No Child i eft
%H<ind $ Ft RI 2001 (3 uEIiF LDN I R' 107—110)IHjuires recipients of technology grants
to invest a minimum of 25 percent of the awarded funds in professional development
related to instructional uses of technology. To guide professional development,
technology standards for teachers and administrators have been made at the national

(ISTE, 2002) and local levels.

Many studies have observed that professiRnIIlII-IYHRSP fht FRuQ ERIN tI-IIKI-Iil’
FRP IRIII withI-FKnRERI y, iP SIFt tHFKl-Ié’ attitude toward technology and make them
feel better prepared to integrate technology (Dupagne & Krendl, 1992; Education t eek,
1999; US. Department of Education, 2000b). However, it is not clear to what extent
professional development could be turn teachers into frequent technology users (US.

Department of Education, 2000a; s iadero, 2005). The effectiveness of professional

14

 

development is dependent on many factors.

t orkshop and seminars, in which technology coordinators or specialists
demonstrate technology use (Choy, Chen, & Ross, 1998; Higgins & Russell, 2003b), are
the most available professional development activities. However, this pro-packaged-
solution-selling approach has been seriously criticized for lack of attraction and being
ineffective (i ewis et al., 1999). Research shows that the participation rate of this type of
professional development is rather low (Higgins & Russell, 2003b; McCannon & Crews,
2000; Parsad & Jones, 2005) and demonstration of technology use by technology
coordinators or specialists KB tKHODt iP SIR Rn tI-IIKHSI’ tlFKanRIy DIIEtiRn (HiJ J ins
& Russell, 2003a). In addition, professional development in practice is also characterized
by short life-span, incoherence and lack of follow-up (US. Department of Education,
2000b). Similarly, professional development programs with these characteristics are
found to be ineffective (Desimone, Porter, (1 aret, Yoon, & Birman, 2002; i ittle, I993).
Telecommunication technology is seldom used by teachers for their professional

development (Coley et al., 1997).

To improve professional development, many suggestions have been proposed,
tested, and put forth (Choy et al., 1998; North Central Regional Educational i aboratory,
1997). After years of exploration, there has emerged a professional consensus about
characteristics of effective professional development such as in-depth, intensive, coherent
extended duration, collegial environment, etc. (Choy et al., 1998; Desimone et al., 2002;
d aret, Porter, Desimone, Birman, & Yoon, 2001). But schools and districts ofien do not

have the capacity and infrastructure to manage and implement effective professional

15

 

development (US. Department of Education, 2000a). Usually, effective professional
development programs are managed by research institutions with funding support. The
SIREOP RI “sustDnIEth” Did “sKilt in IHRIP anHsllGS” (&REth, 2003) KI) nRt H+h
successfully tackled. In recent years, it is observed that new paradigms of professional
devel0pment are under way (Fulton et al., 1998; Sparks, 1994), in which informal
learning, such as peer observation, mentoring, teacher collaborative, and working
alongside a technology specialist, is IP SKBizHI. HRwl-MIJ‘tKHUIsI—DIKIM suFK

programs and their effectiveness hasnDkept pace with the rhetRIiF’ (9 iDlHR 2005).

Summary

A large body of research has been accumulated in the quest for a better
understanding of technology adoption and a better solution to the slow progress. t e have
known much more than twenty years ago. But there are still areas unexplored and places
that could be improved. t hat first drew our attention is that the literature is profuse in
studies focusing on the individual aspect of the issue. Accordingly, research in this line is
dominated by behavioral approach, in which teachers are treated as individuals on
separated and solitary islands and knowledge is expected to be delivered to teachers
tI<IRuJ KtIDninJ Did wRUlihKRSs Ey IktHililIJ‘HtSHfls” Did tIDisIHIHl Ey tHH<HsI thKHU
classrooms, and cognitive approach which examines cognitive constructs that are
speculated to affect technology adoption by coUHlIIinJ tKH’ withID‘IGIsl’ tH~I<anRIy
use. The social aspect of the issue has not received much attention. Second, although a
long list of factors has been identified to be associated with technology use, they are
mostly studied in isolation from each other. The interdependence and interaction among

these factors have not been well examined. i ast, technology is a process rather than an

IS

event with many negotiations and adaptations among technology, actors and the
organization to which they belong. There are some pioneering efforts in unraveling the
complex and dynamic process (Bruce, Peyton, & Batson, 1993; i ei, 2005). But more

should be done to gain a deeper understanding of the mechanism.

17

CHAPTER3 THEORETICAi FRAMEt ORK

Upon reviewing the literature, it is believed that technology is both a personal and
social business. A holistic view is needed to integrate the knowledge that has been
accumulated. In this chapter, perceptual control perspective and social perspective, which
account for the individual and social aspects of technology adoption respectively, are

introduced. Then, a new model that is based on these two theories is posited.

Perceptual Control Perspective
Simply put, perceptual control theory is an account of the mechanism of how an
RU DiisP ’s E]-I<D{iRUis DIIBted or changed. In contrast to behaviorism that treats
behavior as response depending on stimulus, behavior is thought to be goal-oriented and
purposeful from the perspective of perceptual control theory. In other words, an
RUDiisP ’s BKIMRUis inIGiHiFHI Fut nRt dHermined solely by the environment. Rather,
its behavior is determined by what it perceives in comparison with its internal goal or

reference level (Cziko, 1995).

This theory has two conspicuous implications for understanding technology
DiRStiRn Ey tHE<HsL ) ilst, it is tHI’KHsI’ SHception of technology that matters for them
to use technology. Critically, perception of technology should not be simplistically
understood as just perception of values of technology, which implicitly treats technology
as a neutral tool for teachers to use. Technology is not only a tool but also an agent that
bears social expectation and pressure. It is not neutral either technically (uhao, Alvarez-
Torres, Smith, & Tan, 2004) or socio-functionally (Frank, whao, & Borman, 2004; whao

& Frank, 2003). Thus, teachers perceive not only values of technology, but also problems

18

associated with using technology, and social expectation and pressure attached to
technology. All these perceptions of technology affect how technology is used by
teachers. Second, since teachHil’ SHHEtiRn RI tI-FKanRIy sSUDIs in P uQ'SOI
dimensions, the benefits and costs of using or not using technology usually are not
consistent across dimensions. How the overall benefits and costs are balanced is

dHHP inHI Ey tKHFRP SDisRn EHth thu’ internal goals that are hierarchical.
Teachers are more likely to use technology when it could help achieve some higher-level

goals without disturbing the accomplishment of other higher-level goals.

Upon interpreting and synthesizing research on technology adoption from the
perspective of perceptual control theory, whao and Cziko (2001) suggest that three
conditions are necessary for teachers to use technologyW) The teacher must believe that
technology can more effectively meet a higher-level goal than what has been used. 2) The
teacher must believe that using technology will not cause disturbances to other higher-
level goals that the he or she thinks are more important than the one being maintained. 3)
The teacher must believe that he or she has or will have sufficient ability and resources to

use technology.

In this study, these three conditions are translated into six variables (See Figure 1).
The first variable is perceived tI-FIGiIKRIy’s YIIDH IRUstudHits. 7Kis YIliIHDIFRiH-Iilis
how teachers think using technology could help students. For example, whether using
computers could help students develop new ways of thinking, think critically, be more
creative, and so on. The second variable is SHH-IYHl tIfKnKRIy’s YIEHs IRUHFI<H§

themselves. This variable is about how teachers think they could benefit from using

19

 

 

technology. For example, whether using computers could help teachers teach
innovatively, connect the curriculum to real world tasks, be more productive, etc.
Apparently, teachers might use technology if they perceive technology as a valuable tool
for teaching and learning. But this perception is neither a necessary nor a sufficient
condition of technology use. Even though values of technology are not perceived by
teachers, they might still use technology due to social pressure, which is the third variable.
For example, teachers might use technology because they feel that they are expected to

use it or need to use it to keep up in their school.

)iJuIHl 7HI’KI-Iii’ 3HHBtiRn RI &RP SutHB sH

 

 

 

s alues for s alues for Social
Students Students Pressure

 

 

 

 

Perception of
Computer Use

 

 

 

 

Compatibility Complexity of ’ Ease of
with Pedagogy Operation Implementation

 

 

 

t hile these three variables might lead to use of technology, the other three
variables might prevent from teachers using technology. One is the perceived

compatibility between using technology and tHFKI-Ié’ SHIDRI iFTm-IIH. 5 HsHlFK

20

suggests that using technology often means to take a constructivism approach to teaching
(Becker, 2002), which is usually not compatible with the pedagogy of many teachers. If
teachers perceive that computers do not support what they try to do in classroom, or it is
difficult to integrate computers with their existing teaching style, they might not use
technology. The last two variables that might hinder teachers from using technology are
perceived complexity of operating technology and perceived ease of implementation,
which are about the ability to make the change and deal with a variety of disturbance
aroused by the change. Of which, the former concerns the perceived easiness of operating
computers, such as whether teachers feel they can work with computers effectively, or
have the ability to learn new computer applications, whether they feel they can recall how
to perform tasks on the computer. The latter focuses more on the perceived easiness of
using technology in the school context. For example, whether it is easy to implement new
software or hardware in their school; whether the computer resources in the classroom
are adequate for instructional needs and so on. Using technology means change. Change
often results in disturbance and problems. Since avoiding disturbance and problems is
one higher-level goal of teachers, the degree to which teachers are able to make the
change and deal with a variety of disturbance aroused by the change could have a great

impact on technology use.

,n suP P uy, tHI‘KHs’ tFFKnRCRI y DIRStiRn is affected by their perceptions of
technology use on six dimensionszerceived values for students, perceived values for
teachers, perceived social pressure, perceived compatibility with pedagogical belief,
perceived complexity of operating technology, and perceived ease of implementation.

This conceptualization is in accordance with the past research. Although can not be seen,

21

 

factors that have been identified to be associated with teD‘KI-Isl’ tI-FKanRly DlRStiRn 11H
embedded in the six variables described above. For example, teachers’ FRP SutHJsNGB,
computer anxiety and computer self-efficacy are embedded in perceived complexity of
operating technology. Attitudes toward technology are embedded in perceived
tH‘KanRIy’s YIEHL °/2y UlquinJ the exhaustive list of factors into six variables, this

conceptualization is more coherent and cogent.

Social Perspective
Technology adoption is a personal business. It is also a social business because
learning and using technology involve constant interaction with a social environment. In
contrast to research on learning where social learning approach is widely employed,

research on technology adoption seldom takes a social perspective.

From a social perspective (d reeno, Collins, & Resnick, 199S; Resnick, 1987),
learning and using technology involve constant interaction with social environment
including not only peers, experts but also tools such as books, artifacts and language. As
a result of these interactions, people change their overt behaviors --- technology use.
Similar view could be found in the literature of innovation diffusion research, in which
diffusion of an innovation is considered “tKHSUtFHss Ey wKiFKDi innRYDiRn is
communicated through certain channels over time among the members of a social
systI-P ” (5 RleI, 1995, SJ .10). ( YidHiFI-IIIRP Dlimited number of studies taking this
perspective has suggested that the social interaction could have a considerable impact on
, th’KI-Iii’ tlFKanRl y DlRStiRn (DullinJ tRn, 5 HSP an, & s alente, 2000; Fidler & Johnson,

1984; Frank et al., 2004; d ershner & Snider, 2001; i ei, 2005; whao & Frank, 2003).

22

 

,n DsFKRRiHiYiIRnP Hit, tHIKHiI’ tI-thology adoption is concerned with three
kinds of interaction. The first kind of social interaction is professional development.
Usually, the interaction is through training or workshop run by schoolIdistrict technology
coordinators, technology integration specialists, or external experts several times a
semester. One thing to note is that these experts are usually technology experts who are
good at technology but often lack experiences of using technology in the classroom rather
than implementation experts who know not only how to operate technology but also how
to use technology in the classroom. Over the years, professional development has been
the most important way for teachers to learn new technologies. Through professional
development, teachers can learn how to operate new technologies, how to integrate
technology in curriculum, and receive help when problems occur. The second kind of
interaction is peer interaction which takes place between a teacher and hislher colleagues.
Through this kind of interaction, teachers could learn how to use new technologies and
obtain technical help too. In addition, they could also share knowledge and experience of
how to integrate technology into classroom. Moreover, they could talk about issues
concerning technology use that are difficult to talk about or even could not be talked
about with other people as well as provide encouragement and emotional support.
Exploring technology by practicing and experimenting with the technology, reading
technology journal, and consulting technology manuals is the third kind of interaction.
Through this kind of interaction, the teacher really becomes to understand the technology

and get ready to use it in the classroom.

Social interaction is important because it allows teachers to have access to

expertise of how to use computers in the classroom. This access to expertise, as social

23

 

 

capital, has been found to positively impact tHFKl—Ié’ FRP SutHjisHO IDiNI-I In 2004).
,n tKis study, it is DisuP HI tKD tHFKHsI’ Dcess to expertise of computer use is obtained

through the three kinds interaction discussed above.

Combining the Two Perspectives

In the past, the individual and social aspects of technology adoption by teachers
have been researched separately with unequal balance. (I ranting their contributions, each
line of research delineates only part of the whole picture. Several scholars have urged to
understand technology adoption through a holistic view (Dooley, 1999) or ecological
perspective (Bruce & Hogan, 1998; whao & Frank, 2003). To move beyond this cleavage
Did U‘DTKDFRP SUKHisiYHunstltDidinJ , “Dpsychology of cognition and learning that
includes individual, social, and environmental factors in a coherent theoretical and

SllFtiFIIldeiItDidinJ ” (Gweno et al., 1998) is needed.

Taking a closer look at the two approaches, it becomes apparent that each
approach has its inbuilt weakness that can not be overcome. Research focusing on the
individual aspect of technology adoption IDZB sKRIil RI IkSGDninJ KRw tI-II‘KHiI’
perception of technology is formed. Research focusing on the social aspect of technology
adoption lacks accounts of why social interaction could impact technology adoption. The
development in learning theory indicates that there is an inherent connection between the
development of perception and social interaction. From a Piagetian perspective,
perturbations that a cognizing subject generates relative to a purpose or goal is the driving
force of development (Cobb, 1994). Although learning is considered a process of self-

organization and self-construction from this pelilSIFtiYH “tKHP Ikt frequent source of

24

 

 

perturbations for the developing cognitive subject is interaction with others" (von

d lasersfeld, 1989, pg.13S) and the interaction is a process of mutual adaptation where
individuals negotiate meanings by continually modifying their interpretations
(Bauersfeld, 1980). t ith regard to technology adoption, teachels’ SHFl-BtiRn RI
technology is a process of self-organization and self-construction. At the same time, the
interaction between teachers and the social environment is the most frequent way for
perceptual adaptation and re-interpretation of technology (whao & Frank, 2003). i ooking
from the other side, the impact of social interaction on technology adoption is realized
through the reorganization of perception by individual teachers. Based on the view that
the cognitive perspective and social perspective should complement each other, a model

that incorporates these two perspectives is developed below (See Figure 2).

)iJuUlZ )IFtRls ,nIQHiFinJ 7HFKHsI’ 7H‘KnKRIy 8 sH

Social ¢ Computer
Interaction Use

   
   

  

Perception of
Computer Use

,n tKis P MIDtI-U‘IG-Isl’ tI-FKnKRI y usHis hypothesized to be directly affected by
their social interaction. At the same time, it is also influenced by their perception of

FRP SutHJusH 0 RURYHJtHI‘Kl-Isl’ SHHEtiRn RI FRP SutHJusHis ilel—hFHl Ey tKHU

25

 

 

social interaction. Here, social interaction is an umbrella that encompasses four

interaction variables. Similarly, perception of computer use consists of six perception

variables. Table 1 lists all variables that are employed for investigation in this study.

Table l s ariables Employed for Investigation

 

s ariables

o emarks

 

Computer Use

How frquergly computer is used in teaching

 

Perception of Computer r se

8 alues for Teachers

The degree to which teachers think using computers is
valuable for themselves

 

s alues for Students

The degree to which teachers think using computers is
valuable for students

 

 

 

Complexity The degree to which teachers think computers are
complex to operate

Ease of Implementation The degree to which teachers think it is easy to use
computers in school for instructional and professional
purposes

Compatibility The degree to which teachers think using computers is

compatible with their pedagogies

 

Social Pressure

The pressure teachers feel concerning using computers
in the classroom

 

 

Professional

How frequently teachers participate in professional

 

 

 

 

 

E Development development activities

E Exploring Technology How frequently teachers interact indirectly with social

33‘; environment through tools such as books and artifacts

ii Access to Expertise Access to expertise through help received from

-§ through Help colleagues

9°, Access to Expertise Access to expertise through talking with colleagues
through Talk about ideas of computer use

 

fnternational Comparison

From the framework delineated above, it can be seen that social interaction plays

a pivotal role in shaping and changing tHIKHsI’ tI-FKanRI y usHDid SHIT-BtiRn RI

technology in the evolving process of their professional development. However, research

has suggested that the way American schools are structured and scheduled does not

suSSthl tHPKHs’ SIRII-IssiRnIIllI-M-[RSP Hit through this route (Abdal-Haqq, 1998). This

pattern is in contrast with the approaches found in Asian schools such as China and Japan

ZS

 

 

where time for collegial interaction and collaboration are integrated into the school day
(National Education Commission on Time and i earning, 1994). There have been calls for
improving professional development through creating and sustaining an interactive
culture (Choy et al., 1998; Donahoe, 1993). By comparing how social interaction takes
place and its impact on adoption of innovation between Asian countries and the US,

much could be Ieamed to improve technology adoption here in the US.

In this study, the relationship between technology adoption and social interaction
and perception of technology in the US is compared with that in China. This is because
K-12 schools in China have a better social network and social support due to its structural
and cultural tradition and the variance between these two countries in social network and
social support is adequately large for a meaningful comparison, which will be discussed
in more detail below. In addition, there has been a considerable body of research
examining the differences between these two countries in this regard, upon which this

study could draw.

There are many differences between China and the US in educational system. One
RI tKHP Ikt distinFt Ran is KIM tI-D‘KHsl’ erking space is structured. In the US, teachers
have their own office which is also their classroom. Accordingly, teachers spend most of
their time by themselves, doing teaching, lesson planning, grading, or learning new
technologies. This independence is cherished by American teachers, but at the price of
feelings of loneliness and isolation from each other (Stevenson & Stigler, 1992). In
China, on the contrary, teachers do not have their own office or classroom. Instead,

teachers teach in different classrooms. And after teaching, they return to a large office

27

 

 

shared by teachers of the same subject or same grade, where they prepare lessons, grade
assignment and discuss problems with their colleague. In this shared office, the structural
arrangement makes it more convenient and feasible for teachers to communicate and

collaborate with each other (Cole, 1992).

Another major difference is how teachers’ leN is DIRFDHI. ,n HDP Ihtlly
schools where the participants were recruited for this study, American teachers teach all i
suENFts. $ 5 DIHmGl, P Ikt RI DtHFKHJs daily time is spent teaching in hisIher own

classroom. According to the National Education Commission on Time and i earning the

 

daily working life of most American teachers is one of unrelieved time pressure. In
contrast, Chinese teachers usually teach only one subject. Due to this difference, Chinese
teachers teach fewer have several hours of free time to engage themselves in any
profession related work. This time arrangement results in two important consequences for
American teachers. First, teachers lack the time to play with and try new things. Second,
little time is set aside for teachers to work together for learning, preparation, planning,

and cooperation. In contrast, Chinese teachers teach fewer classes, which give them more
time to learn and experiment innovations in education on the one hand. On the other hand,
DIDIQ (111] HSUSRIJliRn RI 8d<inHHtI-IH<H.<I’ time is allocated for them to work together,

which is often organized by teaching research group.

S FfRIdinJ tR Hu (2005, SJ .680), “DtI-[FKing research group may work as an
independent subject department (i.e., a group made up of all and only the teachers who
teach English in a school) or as a subject teacher unit of larger groups organized by grade

levels (i.e., a subgroup consisting of all the teachers who teach English at a particular

28

 

J IDIHOM-D.” $ tHHGnJ UliI-DIKJ IRuS is Din to a department in an American school.
However, it is much more important than its counterpart here in the US in prevalence and
function. Teaching research group could be found in almost every Chinese K-12 school.
It organizes and engages teachers in two basic kinds of activitieszeekly group meeting
and frequent group activities (Paine & Ma, 1993). Based on their observations, interviews
and correspondence with teachers and researchers, Paine and Ma further give a long list
of group activities organized by the teaching research group, which includes collectively
studying and discussing the national teaching outline that sets the objectives for each
subject at each grade level, designing an instructional plan schedules specific plans to
implement the objectives in the academic year, studying and exploring subject matter,
HLD’ ininJ tHIKinJ P HKRdS, FKHNnJ tI-IIIG-Iii’ wRINinKudinJ HLIP ininJ HIK
tHIKHJs OlisRn SGDis, DJIDiJinJ Did sHtinJ J RDs for peep observation of colleagues, and

SO on.

The cultural differences delineated above have significant implications on how
teachers learn and use technology in schools. Chinese teachers have more time to engage
themselves in learning and teaching activities that are outside classroom such as learning
innovations, designing innovation-integrated lessons, and trying the innovation-integrated
lessons out before implementing them in the classroom. In the American schools,
teachers do not have much free time for exploration and experimentation. To be worse,
time spent on these activities is not even considered productive time in the US (Raywid,
1993; t atts & Castle, 1993). t hat is more important is that these learning and producing
activities in China take place either collectively, or individually but with expertise easily

accessible. In the regular teaching research group meetings, Chinese teachers explore

29

 

new technology together, share experiences of technology use, and discuss how to apply
technology into teaching. Even when teachers work on technology individually, the
shared office makes expertise easily accessible for them to resort to. In addition, enough
free time makes it more likely for teachers to have in-depth and meaningful interactions.
,n FRntIBt, tI<H$ P HiFDi tI-IIIQIsl’ Wing and producing activities usually take place
individually without expertise available. t hen problems occur, teachers have to rely on
themselves to solve them, which is usually difficult for most teachers. Although they can
ask neighbor teachers for help, it is not as convenient and timely as the situation in China.
Aside from this, limited free time makes it hard for teachers to engage in in-depth
interaction. t ith these differences, it is speculated that the social interaction of Chinese
teachers is more intensive and effective than that of American teachers. Specifically,
Chinese teachers have a larger social network, more frequent communication and
collaboration among colleagues, and more routes and opportunities for social interaction.
This higher degree of frequency, depth and width is speculated to have a greater impact

Rn tHI'KHsl’ SHJ'HStiRi RI tH‘KnIIRI y Did tKHUI-FKanRly usH

t hen conducting international comparative research, not only should cultural
difference but also economic and infrastructural difference be taken into consideration.
Although much progress has been made in the past three decades, China is still the largest
developing country in the world. Overall, the state of technology in Chinese K-12 schools
falls behind the US to a considerable degree. In contrast to a student-computer ratio of
4.4“! (U .S. Department of Education, 2000b), only 80% of Chinese schools reach 20W
(ICT in K-12 education research group, 2005). In addition, there is a large gap between

schools in city, town and rural area (ICT in K-12 education research group, 2005; Song,

30

2005). There are still 40% of schools in rural area of China that do not have computers at
all, whereas schools in large cities like Beijing, Shanghai and Chengdu have a technology

infrastructure that is similar to most American schools.

To highlight the cultural difference whiPKis P DiiIHitHl Ey tHIKHsl’ sRFiID
interaction, this study was conducted in schools at Chengdu where the provision of

hardware, software and technology support is similar to that of the schools in whao and

) [DiNs (2003) study.

0 esearch n uestions
This study intends to find out the relationships between teII’KHSI’ DTFI-Iss tR
expertise through social interaction, perception of technology, and their computer use.
t hat is also of interest is how the relationships in the US differ from those in China and
how the difference might be accounted for by the cultural differences. To achieve this

goal, three questions will be asked.

First, to what degree the variance in tI-D‘KHsl’ thKanRly usHFRuGI EHIFIBuntHzl
for by their access to expertise through social interaction and perception of technology?
SSH'iIiFDQ, tI-IFKI-Id’ FRP SutHJusHis tKHRutcome variable; the predictors are social
interaction variables including Professional Development, Exploring Technology, Access
to Expertise through Help, and Access to Expertise through Talk, and perception
variables including 5 alues for Teachers, 5 alues for Students, Complexity, Ease of

Implementation, Compatibility, and Social Pressure.

Second, this study attempts to examinHtKI-llHDiRnsKiS EHwI-Hi tI-[I'KHSI’

3I

 

perception of technology and the social interaction. Namely, how much variance in
tHIKHs’ SHFHStiRn RI tlFKanRIy FRiiGl H-IHrplained their access to expertise through
social interaction. SpecificalQ, tIIIIG-Ifi’ SI-IHEtion of compute use is the outcome
variable. The four social intI-IIFtiRn YIDIHDIi DHtKI-ISIHIiFtRIiI. SinFHtHI‘IQIsI’
perception of computer use lies on six dimensions, each of the six perception variables

will be modeled.

i ast, how the relationships identified above differ between the US and China?
From a statistical point of view, how the regression coefficients obtained from the US
data differ from those obtained from the China data? t hat is of particular interest in this
study is how the effects of access to expertise through peer interaction differ between the

two countries and what might cause the difference?

32

 

CHAPTER 4 RESEARCH METHOD

Participants

Research participants of this study came from two countriesWhe US and. China.
The study conducted by whao and Frank (2003) is relied on for the US data. The China
data were collected by the investigator of this study. The investigator first contacted local
education administration officials at Chengdu in Sichuan Province regarding the project.
After the permission was granted, nine elementary schools were selected with the help of
local educational administrators. To ensure commensurability, the following two criteria
were employedW) all schools must have at least one computer lab for instructional use;

2) computers had been introduced into the selected schools for at least one year.

All teachers, including technology teachers, technology coordinators, or

technology integration specialist, were asked to participate in this study.

Measurement
A total of eleven variables are included in the investigation. In addition to
Computer Use, there are four social interaction variables and six perception variables.
Computer Use and five perception variables are measured by multiple i ikert scale items.
The mean of all items that measure a variable is computed to represent the value of the

variable. One perception variable is measured by a single i ikert scale item.

Social interaction is measured in two ways. Exploring Technology and
Professional Development are measured by i ikert scale items like perception variables.

Access to expertise through Professional Development is comparatively more

33

homogeneous in the sense that teachers in the same school or school district usually
receive training and help from the same source of expertise. Similarly, teachers in the
same school or school district usually use same technology, read same professional
journals about new technologies, and consult same technology manuals. Therefore,
Professional Development is measured by simply asking teachers how often they attend
district or school in-service programs for new technologies (single item). Access to
expertise through exploring technology is measured by simply asking teachers how often
they practice and experiment with district-supported software, read professional journals

about new technologies, and consult technology manuals.

7 KI-IUIiDEith Ulsufils (&IRnEDKs a) of all i ikert scale variables are reported in
Table 2. For both the US and China, most of thHYDJIHPIi’ UfiIEth is J UDHXKDi 0.8
with some between 0.7 and 0.8. Although the reliability of pressure perceived by teaches
for using computers in the US is low, overall, the measurement reliability is not a serious

concern for this study.

Table 2 Reliability Results of i ikert Scale 5 ariables Measurement

 

 

 

Likert Scale 3 ariables r S China
Computer Use 0.80 0.89

s alues for Teachers 0.92 0.91

s alues for Students 0.89 0.88
Complexity 0.71 0.74
Compatibility Single item Single item
Ease of Implementation 0.91 0.89
Pressure 0.49 0.75
Hardware 0.91 0.95
Software 0.93 0.95
Professional Development Single item Single item
Exploring Technology 0.SS 0.79

 

34

In contrast to the homogeneity of professional development and technology
exploration, teachers in the same school or school district usually interact with different
colleagues who have different technology skills and degrees of technology use. The
heterogeneity of peer interaction must be taken into account. As a result, peer interaction
is measured through the sociometric method (see Appendix B for details) by asking
teachers to nominate whom they interact with. This method has been widely used in
research on innovation diffusion and has a high validity (Frank et al., 2004; Rogers,
1995). 7Kus, tl—[HQ-Is’ MISS tRHiSHilisHtKIRuJ Khelp is measured by asking teachers to
write down the names of those teachers to whom they resort for help when they need help
witKIRP SutHjisH 7HH<H§’ IIFHS tRHrpertise through talk is measured by asking
teachers to write down names of those teachers to whom they talk regarding ideas of
FRP SutHJusH $ tHI'KI-Us [HHS tRI-kSHilise is calculated by summing up or averaging
the expertise of those teachers who are nominated. How often a teacher uses computers is

used as a proxy to hisIher expertise of computer use.

a ata Collection
The survey used in whao & Frank (2003)study was translated into Chinese to
collect the China data. To ensure the accuracy of translation, the Chinese version of the
survey was back translated into English by a graduate student whose native language is
Chinese. The disparities between the two versions were resolved by discussion and
consulting other Chinese graduate students and American teachers. Then the revised
version was tested by two local Chinese elementary teachers. The survey was finalized

based upon the problems found in the test and suggestions from the two Chinese teachers.

35

 

Two graduate students from a local university were recruited to help with the data
collection. The investigator of this project first introduced the background and theoretical
framework of this study to the two graduate students. Then, he led the two graduate
students to go through the whole process of data collection in one school before they set
out to collect the data by themselves. Each of the two students collected data from two

schools. Data from the other five schools were collected by the investigator of this study.

Each participating school was visited twice. Two teachers were interviewed in the
first visit of a school. Of the two teachers, one was selected by the school and the other
was randomly picked on site. The interview was conducted individually either in the
KIIIDVDI RUDi RIIiFHtKD’s sl-BIlDI-II IIRP tKe shared office. Notes were taken during the
interview. The purpose of interview was not only to collect qualitative data but also to

KHS J Dn tKHSIltliFiSDinJ tHIKHiI’ tIilist so that valid response could be obtained.

Either one day or two days after the interview, the survey was administered in a
teacher meeting either on a professional development day or at other collective events.
Before the questionnaires were handed out, the purpose of the study was briefly
introduced. 11 uestions concerning the survey and participation in this study were

answered.

The data collection also involved observation. During each visit, things like how
tl-DKHs’ wRINnJ SCIIHIWD DIDiJ HI, wl<HI<IUFRmputers were used in the classroom or
office, what teachers were doing if computers were used, and where computers were

housed were documented.

3S

a ata Analyses

To answer the research questions, two series of multiple regression analysis were
conducted with the survey data. The first series examined the predictive effect of
tHI’lQIsI’ sRFiIIIntHIFtiRn Did SHI'IBtion of technology on computer use. To
differentiate the within school variance and between school variance, Hierarchical i inear
Modeling (Hi M) was employed. At the teacher lerDtKHdIBHidI-ht YIliDEIDIis tl—IFKHiI’
computer use and the independent variables are their three types of social interaction and
perception of technology in the six dimensions. At the school level, the dependent

variable is the regression coefficient obtained at the teacher level.

The second series concerns the relatiRnsKiS Bfwl-Hi tID'Kl-Isl’ SHR-BtiRn RI
technology and their social interaFtiRn. ,n tKis FDH tl<HdHSHidHit YDIDKDIis MEG-13’
perception of computer use and the independent variables are their social interaction
variables at the teacher level. Similarly, the dependent variable at the school level is the

regression coefficient obtained at the teacher level.

All of the above analyses were conducted with both the Chinese and American
data. Then, the results from two independent samples were compared to see how the
relationships between computer use, social interaction, and perception of computer use in

the US differ from those in China.

37

CHAPTER 5 RESEARCH F lNDINd S

a escription of Samples

There are 9 schools in the Chinese dataset and 19 schools in the American dataset.
The total sample size is 533 for the Chinese dataset and 428 for the American dataset
respectively. As Table 3 shows, there are considerable differences between the American
schools and the Chinese schools. The size of the Chinese schools is much larger than that
of the American schools. On average, there are 22.53 teachers in an American school as
compared with 59.22 teachers in a Chinese school. The average number of students in a
Chinese classroom is twice as many as in an American school. In addition, the American
schools are much more homogeneous than Chinese schools in terms of both number of

teachers in a school and number of students in a classroom.

Table 3 Characteristics of the Sample Schools

 

 

 

 

School Size Class Size
(Number of teachers per schoolF flamber of students per classF
d roup Mean Min. Max. S.D. d rand Mean Min. Max. S.D.
US — 22.53 9 34 8.90 22.3S 3 S2 S.89
China 59.22 28 90 21 . l S 49.57 15 70 10.37

 

To give a comprehensive picture of how computer are used in schools, eleven
instructional computer uses are measured in this study. These eleven uses are developed
Rn tI<HEDis RI %IiliI~H& LHiin’s (I997) tDtonomy of educational technology applicationsW
media for inquiry, communication, construction, and expression. Although these uses do
not exhaust all possible computer uses in school, they can adequately characterize the
major instructional uses of computers by teachers. Results of each type of use in both

countries are reported in Table 4.

38

 

Table 4 Results of Eleven Computer Uses in the US and China

 

 

 

 

r S China

Computer use Mean 5.0. Mean So. I
Communication with parents 3.39 1.13 2.32 1.38 13.19CI3
Communication with students 2.78 1.48 2.15 1.34 S.48(I}
Classroom management 288 1.47 2.81 1.40 -1.3S
Record keeping 2.39 1.55 2.85 1.20 -4.99(I}
i esson planning 3.58 1.17 3.89 1.08 -1.50
Communication between students 1.54 1.01 2.17 1.35 -8.15CI3
Students inquiry 2.17 1.14 2.54 1.28 -4.S7CI]
Students expression 2.32 1.18 1.98 1.19 4.29m
Core curriculum skills development 2.98 1.38 2.S4 1.27 3.7703
Remediation 2.42 1 .50 1.91 1 .24 5.48m
Development of basic computer skills 3.02 1.38 2.80 1.48 4.45m
Overall computer use 2.89 0.79 2.52 0.89 3.0233

 

(GResults are significant at the 0.01 level (2-tailed).

GResults are significant at the 0.05 level (2-tailed).

Noter) All types of computer use are measured on a 5 point scaleW -No use, 2-Yearly,
3-Monthly, 4-t eekly, and 5-Daily; (2) the Overall computer use is the mean of the
eleven computer uses.

For both the US and China, technology is not frequently used in schools. None of
the eleven computer uses reaches a weekly level. Most uses are between yearly and
monthly. In the US, computers are mainly used for communication with parents (e.g.,
newsletters, e-mail, class t eb page), lesson planning, and development of basic
computer skills (e.g., keyboarding, mouse skills, trouble shooting). Computers are used
for these three activities more than monthly but less than weekly. The American teachers
use computers least for communication between students (e.g., publish student work on a
t eb page, keypals, e-group projects). In China, computers are mainly used lesson
planning, recording keeping (e.g., grades, attendance), and classroom management (e.g.,

reward for completed work). i esson planning is the only one that is used for more than

monthly. The Chinese teachers us computers least for remediation (e.g., repeat a lesson).

39

 

For both the American and Chinese teachers, computers are most frequently used
for lesson planning, which is somewhere between monthly and weekly use. Amidst the
eleven computer uses, there is no significant difference in classroom management and
lesson planning between the two countries. The American teachers use computers more
often than the Chinese teachers for most activities. The Chinese teachers use computers
more on record keeping, communication between students, and student inquiry (e.g.,
student research using electroniF dDDEDsH, WI-E4 th). 2 YHIIIDtKHS P HilFDi tI-D‘KHs’
computer use is significantly higher than the Chinese teachers and there seems not to be a
systematic difference in how computers are used for instructional purposes between the

two countries.

Table 5 shows the descriptive statistics of the independent variables that are used
for analysis. t ith regard to perception of computer use, the Chinese teachers perceive
computers use as being more valuable for themselves than their American counterpart. At
the same time, the Chinese teachers feel more pressure to use computers than the
American teachers, which might be associated with the presence of better technological
infrastructure (Hardware) in American schools. t hen it comes to perceived values of
computer use for students, complexity of using computers, the degree to which it is easy
to implement computers into the curriculum, compatibility between computer use and

pedagogy, and software, there is no significant difference between the two countries.

In contrast to the small difference in perception between the two countries, the
Chinese teachers have access to more expertise through social interaction than the

American teachers. Specifically, the Chinese teachers spent more time on professional

4o

 

development and technology exploration. Notably, the Chinese teachers have access to

much more expertise through interaction with peers. Teachers interact with each other

concerning technology in two formatsWhrough help received from colleagues and

through talking with colleagues about ideas of computer use. No matter what format,

&IGnHHtI-D‘IQ-Isl’ III-HS tRl-kpertise is significantly higher than that of American

teachers. These results are not surprising because of the differences in how social

interaction is supported and structured between the two countries that are delineated

before.

Table 5 Descriptive Statistics of s ariables

 

 

 

 

 

 

r S China Likert
s ariables Mean S.a . Mean S.a . t Scale
Perception
s alues for Teachers 4.88 0.90 5.18 0.72 -5.43(I} 1 ~ S
s alues for Students 5.12 0.78 5.14 0.74 -0.34 1 ~ S
Complexity 2.21 0.79 2.25 0.89 —0.72 1 ~ S
Ease of Implementation 3.91 0.92 4.01 1.27 -1.31 1 ~ S
Social Pressure 3.52 1.49 3.88 1.05 -4.05CI3 1 ~ S
Compatibility 4.28 1.33 4.22 1.28 0.81 1 ~ S
Infrastructure
Hardware 3.85 0.94 3.43 0.82 3.7903 1 ~ 5
Software 3.24 1.04 3.19 0.79 0.88 1 ~ 5
Access to Expertise through Social
Interaction
Professional Development 2.19 0.52 2.52 0.91 -S.74CI3 I ~ 5
Exploring Technology 2.14 0.88 2.84 0.98 -8.92CG I ~ 5
Access to Expertise through Help
Summation 1.89 2.88 8.85 2.55 -l 7.33CI3
Average 1.18 1.80 3.02 1.52 -18.23(I1
Access to Expertise through Talk
Summation 3.21 3.77 8.58 5.74 -10.42(13
Average 1 .70 I .58 2.41 1.42 -7.40(I3

 

(GResults are significant at the 0.01 level (2-tailed).

GResults are significant at the 0.05 level (2-tailed).

41

 

For each format of peer interaction, 713915 UBRItls twR Yum RI tI-DTQ-Isl’ IIFHss
to expertise. One is the Summation, which sums up the expertise of teachers that are
nominated. The other is Average, which calculates the average expertise. t hen computer

use is the outcome variable, the first way is employed to compute the values. t hen

tHH<HsP SHI-HStiRn RI FRP SutHJusHis tI<HRutcome variable, the second way is utilized.

One marked distinction between the American and Chinese teachers is that
Chinese elementary teachers are trained and assigned to teach a particular subject area
whereas American elementary teachers teach all subject areas. The past research has
suggested that near peer interaction has a larger effect on behavioral and perceptual
changes. t ith regard to computer use, teachers teaching the same subject area are
thought to be near peers because it might be easier to implement technology when the
knowledge is learned from peers who teach the same subject area. In the U8, any
nominated teacher can be considered as near peer in a broad sense since American
teachers teach all subject areas. In contrast, Chinese teachers who teach the same subject
area are considered near peer in a narrow sense. To find what impact this cultural
dillHHiFHP iJ Kt KIM-[Rn tHFKl-Isl’ FRP SutHJuse, the subsequent analyses use access to
expertise through near peer interaction. Table 8 shows the access to expertise through
near peer interaction. Namely, the expertise of teachers who do not teach the same
subject area is not included in the computation although they are nominated. The values
remain the same for the US data. The ChinHthHIKHiI’ IFFHis tRIkSHllistLRSs
dramatically, especially when the access to expertise is obtained through help received
from colleagues. This is because most Chinese teachers received help from computer

teachers in their school rather than teachers who teach the same subject.

42

Table 8 Access to Expertise through near Peer Interaction

 

 

 

 

r S China
s ariables Mean S.a . Mean S.a .
Access to Expertise through Help
Summation 1 .89 2.88 l .20 2.55
Average 1.18 1.80 0.21 0.43
Talking with Colleagues
Summation 3 .21 3 .77 3 .23 4.13
Average 1.70 l .58 0.43 0.54

 

c indingsfi'om Multilevel Analyses
Hierarchical linear modeling was employed to analyze the nested data. Because
the level-2 units are not adequately large, the estimation results without robust standard
errors are reported here. The robust and model-based standard errors do not differ
substantially, which indicates the Hi M assumptions of multivariate normality are not

violated.

The analyses consist of two major parts. The first part (Part 1) concerns how much
YDJDiFHin tHI‘IG-Isl’ &RP SutHJS sHFRiiG be explained by their perceptions of
technology and social interaction. The second part aims to find out to what extent the
variation of perceptions of technology could be accountI-Ii IRIEy tHFKHsI’ sRFiII)
interaction (including Access to Expertise through Help, Access to Expertise through
Talk, Professional Development, Exploring Technology) and technological infrastructure
wKiFKwDi P I-DuIHI tKIRiJ KtHHG-Isl’ SHIFHStion of Hardware and Software. Then, the

indirect effect of social interaction was calculated.

Part fWComputer r se as I utcome

As reported before, the mean of computer use is 2.89 and 2.52 for the U8 and

43

China data respectively when the measure is on a 5 point scale. The standard deviation of
computer use is 0.31 for the U8 sample and 0.38 for the China sample. As Figure 3 and

Figure 4 show, there is some amount of variation across schools for both samples.

Figure 3 Computer Use at the American schools

 

5.0 -

fill"

Figure 4 Computer Use at the Chinese schools

4s
0
1

Average Computer Use
3.“

 

 

 

 

 

5.0 - O O

o
o
o
t
4.0 - o I 0

Average Computer Use
‘2?
II -
-
I
.-

 

 

School

44

Before running the hypothesized full model, an unconditional model was
analyzed to find out the distribution of variance across the individual teacher and school
levels. As table shows, for both the U8 and China samples, most of the variance is within
schools (See Table 7 and Table 8). The intraclass correlation, which is the proportion of
between variance in total variance, is 0.11 for the American schools and 0.15 for the

Chinese schools.

Table 7 Computer UseVResults of Unconditional Model for American Schools

 

c ixed Eflect Coeflicient se t o atio p s alue oeliability i
School mean use 2.88 0.07 37.49 0.000 0.725

 

s ariance

 

oandom Eflect Component df 12 p s alue
Intercept 0.07 18 89.154 0.000
i evel-I effect 0.55

 

Table 8 Computer UseWtesults of Unconditional Model for Chinese Schools

 

cixed Eflct Coeflicient se toatio Ls alue oeliabilitjy
School mean use 2.47 0.11 21.59 0.000 0.903

 

s ariance
oandom Effect Component df 12 p s alue
Intercept 0. l 2 8 104.94 0.000
i evel-l effect 0.87

 

Next, the following model was analyzed to examine how much variance of
tI-DKHQ’ FRP SutHIisI-IFRJQ EHHLSGDnHI Ey perception and social interaction including
Access to Expertise through Help and Access to Expertise through Talk, Professional

Development, and Exploring Technology.

45

T echnologyr sea. =

,601 + ,6”. (Access to Expertise through c elp”. ) + ,sz (Access to Expertise through T alk”. )
+ ,631. (Professional a evelopment,’ ) + ,6“. (Exploring T echnology” ) + ,651.(Complexity,j )
+ fls, (s alues for Teachers”) + ,8“ (Pressure, ) + [38,. (Ease ] f fmplementationy.)

+ 391(Compatibilityy. ) + r”.

.80) = 700 +uoy

1811:710
.621 =l’zo
1631:730
7641 =740
:65; =l’so
:85} =l’so
[37,- =770
163} =73o
.89,- =79o

At the individual teacher level (level 1), computer use is hypothesized to be
DIthHl Ey tHI‘KHs’ SHI‘IEtiRn RI FRP SutHJuse and their social interaction concerning
FRP SutI-UusH 2 UJinIIlD, tHB<HsI’ SHH-BtiRn RI FRP SutHJusHis Rn six diP HisiRns:

s alues for Teachers, 8 alue for Students, Complexity of Using Computers, Social
Pressure, and Compatibility with Pedagogy. The initial analysis indicates that s alues for

Teachers is highly correlated with s alues for Students for both the China (r0 Z 0.884,

hma

p < 0.001) and US data (r, 5 Z 0.887, p < 0.001 ). As a result, only 5 alues for Teachers is

retained in the model because it is thought to be more relevant to their computer use.

In the literature of social nHwREN Di indiYiduIDs [PH-ks tRHrSHIlisHD sRFrII)
capital has been defined as the summation or mean of the expertise of the people with

whom she nominates to have social interaFtiRn. ,n tKis FDH thm<His D’FHss tR

48

expertise of computer use is measured by summing up the computer use of teachers with
whom she nominated to have two kinds of social interaction. One is that the access to
expertise of computer use is through help. The other is that the access to expertise is

through talk. Thus, access to expertise is defined as follow.

n-I
Access to Expertise through e elp”. = 2 e elp”). x (Provider's Expertise?)
i'=l.i¢r" I

71—]
Access to Expertise through T alk,j = Z T alkm. x (Provider' 5 Expertise.) )

i'=l,i:i'

 

At the school level (level 2), only the intercept is modeled to vary randomly.
Although the mean values of these predictors might vary across schools, no school effect
is theoretically considered to affect the regression coefficients of these predictors. In
other words, although the perceived complexity of using computers might be different in
two schools, its impact on computer use is assumed the same for both schools.
Empirically, the variances of slopes are very close to zero or the p value associated with

the hypothesis of slope homogeneity for the slopes is greater than 0.05.

For the US data, there are 393 observations at level I and 19 observations at level
2. The model accounts for 40.7% of the level 1 variance. Similarly, 47.1% of the level I
variance could be explained for the China data with 482 observations at level 1 and 9
observations at level 2. Detailed results are reported in the following two tables (Table 9

and Table 10).

47

Table 9 Computer Use: Results of Conditional Model for American Schools

 

 

 

Standardized
Fixed Eflect Coefiicient se t Ratio P Value Coefficient
Intercept 0.60 0.33 1.81 0.087
Access to expertise through -0.01 0.01 -0.71 0.478 -0.03
Help
Access to expertise through 0.04 0.01 4.60 0.000 0.19
Talk
Professional Development 0.21 0.06 3.41 0.001 0.14
Exploring Technology 0.21 0.05 3.80 0.000 0.17
Values for Teachers 0.17 0.04 3.72 0.000 0.19
Complexity -0.07 0.05 -1 .38 0.170 -0.07
Ease of Implementation 0.02 0.02 1.05 0.294 0.05
Pressure -0.04 0.03 -1 .29 0.200 -0.05
Compatibility 0.12 0.03 3.61 0.001 0.20
Variance

Random Effect Component (9’ f P Value

Intercept 0.02 1 8 37.99 0.004

Level-1 effect 0.33

 

Table 10 Computer Use: Results of Conditional Model for Chinese Schools

 

 

 

Standardized
Fixed Eflect Coeflicient se t Ratio P Value Coeflicient
Intercept 0.81 0.32 2.57 0.033
Access to expertise through 0.02 0.01 1.50 0.133 0.05
Help
Access to expertise through 0.04 0.01 4.77 0.000 0.18
Talk
Professional Development 0.10 0.04 2.87 0.005 0.11
Exploring Technology 0.29 0.04 8.05 0.000 0.32
Values for Teachers 0.05 0.05 1.09 0.276 0.04
Complexity -0.09 0.04 -2.14 0.033 -0.09
Ease of Implementation 0.01 0.03 0.39 0.695 0.02
Pressure 0.01 0.03 0.33 0.741 0.01
Compatibility 0.08 0.03 2.62 0.009 0.11
Variance

Random Eflect Component df / P Value

Intercept 0.05 8 63.83 0.000

Level-1 effect 0.35

 

What is noticeable in the results is thMDhHV Del-WW I-kSHIW-IWIRugh

48

 

Talking with Colleagues about ideas of computer use is a significant predictor of
computer use for both the American and Chinese teachers. Moreover, the effect is fairly
large comparing to other significant predictors. The standardized regression coefficient of
Access to Expertise through Talk is the second largest for the American schools (0.19)
and it is the largest for the Chinese schools (0.18). In addition, Professional Development
and Exploring Technology are also significant predictors for both the US and China data.
Amidst social interaction variables, Talking with Colleagues has the largest effect (0.19)

for the US data, whereas it is Exploring Technology (0.32) for the China data.

 

With regard to perceptions, Compatibility is a significant predictor for teachers
from both countries. cor the American teachers, Compatibility (0.25) has the largest
effect. It is followed by Values for Teachers (0.20) and Complexity (-0.14). cor the
Chinese teachers, Complexity (-0. I 7) has the large effect. It is followed by Compatibility

(0.13) and Pressure (0.08).

To examine whether the regression coefficients obtained from the analysis of two
independent samples are significantly different from each other, the following test

described by (Cohen & Cohen, 1983) was conducted.

21 ‘22

1 1
+
nl-3 n2-3

z : ln(1 + r(x, y)) - ln(l — r(x, y))
2

, where

 

diffz =

 

 

 

r(x, y) is the partial correlation between a predictor and the outcome variable. It

could be calculated through the following equation, where df is degree of freedom

49

obtained from the HLM model.

I

"(an’)= (7277—:

cor two tailed test, the difference between two regression coefficients is
significant if difz is greater than 1.96 (the critical value for 0.05 significance level). As
the results show in Table 1 1, there is a significant difference between American and
Chinese teaches in Values for Teachers and Exploring Technology. c urther, the effect of
Values for Teachers for American teachers (0.19) is significantly higher than for Chinese
teachers (0.05). In contrast, the effect of Exploring Technology for American teachers

(0.17) is significantly lower than for Chinese teachers (0.32).

Table 11 Computer Use: Results of Independent Regression Coefficients Difference Test

 

 

 

 

US (N=401) China (N=487)

Predictors b Observed t b observed t difl‘t
Access to expertise through Help -0.01 ~0.71 0.02 1.50 -1.531
Access to expertise through Talk 0.04" 4.60 0.04" 4.77 0.197
Professional Development 0.21** 3.41 0.10" 2.87 0.590
Exploring Technology 021'” 3.80 0.29" 8.05 -2.494*
Values for Teachers 017’” 3.72 0.05 1.09 2.005*
Complexity -0.07 -1.38 -0.09* -2.14 0.414
Ease of Implementation 0.02 1.05 0.01 0.39 0.515
Pressure -0.04 -l .29 0.01 0.33 -l .175
Compatibility 0.12" 3.61 0.08" 2.62 0.902

 

** Results are significant at the 0.01 level (2-tailed).
* Results are significant at the 0.05 level (2-tailed).
Part II: Perception as Outcome

The next set of analyses set out to find out thMkVohWMIvDDcI-IRI 110:th
perception of computer use on different dimensions could be accounted for by their
interaction with both the physical and social environment. In other words, the perception

will be the dependent variable. The independent vIIIlIEI-NIIHthY SHiiHSimn RI WH

50

 

technological infrastructure (Software and Hardware), and their social interaction
including the degree to which they explore technology (Exploring Technology), and their

Access to Expertise through both Help and Talk, and Professional Development.

,n WHDER/HDiDyW \MIhI-IY SHiifSWSns of computer use on five dimensions
are examined: Values for Teachers, Complexity, Ease of Implementation, Pressure, and
Compatibility. Since Ease of Implementation and Pressure are not significant predictors,

analysis of perception on the rest three dimensions is reported in the following sections.

Values (or Teachers

1 n a 6 point scale, the mean value of computer use perceived by the American
teachers is 4.88 with a standard deviation of 0.9. The mean value of computer use
perceived by the Chinese teachers is 5.16 with a standard deviation of 0.72. crom c igure

5 and c igure 6, it can be seen that there might be a ceiling effect.

c igure 5 Values for Teachers of the US Data

 

   

 

 

 

51

c igure 6 Values for Teachers of the Chinese Data

 

 

 

 

 

6.0 — g. .,

5.0 — I $— 1' . ,
2
0
.:
iii 4.0 —
0
}— O
‘6
I-
I’ll 3.0 J
a)
2
II
> O

2.0 —

*
1.0 —
I I I I f I I l I
School

Before running the hypothesized full model, an unconditional model was
analyzed to find out the variance of Values for Teachers is distributed across the
individual teacher and school levels. As Table 12 and Table 13 show, for both the U8 and
China samples, most of the variance is within schools. The intraclass correlation is 0.07

for the American schools and 0.04 for the Chinese schools.

Table 12 Values for Teachers: Results of Unconditional Model for American Schools

Fixed Efiect Coefiicient se tRatio p Value Reliability
School mean use 4.87 0.07 69.67 0.000 0.609

 

 

 

 

Variance
Random Effect Comgnent df f p Value
Intercept 0.06 1 8 46. l 9 0.002
Level-1 effect 0.77

 

52

Table 13 Values for Teachers: Results of Unconditional Model for Chinese Schools

 

 

 

Fixed Efiect Coefficient se t Ratio p Value Reliability
School mean use 5.16 0.06 91.66 0.000 0.658
Variance
Random Efiect Component fl [7 p Value
Intercept 0.02 8 24.38 0.002
Level-l effect 0.50

 

Many factors could influence how teachers perceive computer use in terms of its
values for themselves. What this study is particularly interested in is how it could be
lebllofl Ey thV \RcID anth. 7hu\l 111}:th SHIIIBth RI cRP SuibUVvDuI-N
for themselves is modeled to be influenced by Access to Expertise through Help and
Talk, Professional Development, and Exploring Technology.

Values for Teachers :1 =

flu}. + ,0” (Access to Expertise through Help a)

+ ,6” (Access to Expertise through Talk a. )

+ ,831. (Professional Development”) + ,6“. (Exploring Technology 0') + r”.

i601- =7’00 +110]

:81} =710
162; =720
:83,- =73o
164; =740

At the school level (level 2), only the intercept is modeled to vary randomly.
Theoretically, the effects of Access to Expertise through Help and Talk might vary across
schools because the social network might be different across schools. Since most schools
are from the same school district for both the U8 and China sample, the effect of
Professional Development should be close to one another. As for Exploring Technology,

no school level factor is thought to make them vary across schools. Empirically, the

53

 

 

variances of the slopes are very close to zero (<0.01) or the p value associated with the

hypothesis of slope homogeneity for the slopes is greater than 0.05.

1 ne thing to note in this model is that the access to expertise is defined somewhat
different from before. In the analysis of Computer Use as outcome variable, it is defined
as the summation of the expertise accessed through help and talk. Here, instead, it is
defined as the mean of the expertise accessed through help and talk. This is because
interacting with ten colleagues who have low expertise might be equivalent to interacting
with one colleague who has high expertise when it comes to behavioral change.
However, interacting with ten fellow teachers who have low expertise might not be as
effective as interacting with one fellow teacher who has high expertise when it comes to
perceptual change. Thus, Access to Expertise through Help and Access to Expertise
through Talk are defined as follow. And this definition is used for the rest of the analyses.

Access to Expertise through Help ,1.

n—l
= ____1__ Z Help”? x (Provider' s Expertise,»

n-l

ZHelpn-y i'=l,i:i'

i'=l,ia¢i'

Access to Expertise through Talk ,1.

n-I
= __.1__ 2 Talk ”.1. x (Provider' s Expertise ,4]. )

n—I
i'=1,i¢i'
2 Talk ".1.

i'=l,i¢i'
cor the US data, the model accounts for 6.6% of the level 1 variance. Similarly,
7.9% of the level 1 variance could be explained for the China data. Detailed results are
reported in the following two tables (Table 14 and Table 15). cor both Chinese and

American teachers, Exploring Technology is the only significant predictor.

54

 

 

Table 14 Values for Teachers: Results of Conditional Model for American Schools

 

 

 

Standardized
Fixed Eflect Coefiicient se t Ratio J) Value Coeflicient
Intercept 3.77 0.21 1 7.70 0.000
Access to Expertise through Help 0.01 0.03 0.19 0.851 0.01
Access to Expertise through Talk -0.01 0.03 -0.24 0.811 -0.01
Professional Development 0.1 1 0.09 1.25 0.21 1 0.06
Exploring Technology 0.4] 0.07 5.90 0.000 0.30
Variance

Random Eflect Component cf 12 p Value

Intercept 0.03 18 33.75 0.014

Level-l effect 0.72

 

Table 15 Values for Teachers: Results of Conditional Model for Chinese Schools

 

 

 

Standardized
Fixed Eflect Coefl’icient se tRatio p Value Coefi‘icient
Intercept 4.62 0.1 l 41 .04 0.000
Access to Expertise through 0.02 0.08 0.21 0.833 0.01
Help
Access to Expertise through Talk 0.08 0.06 1.31 0.191 0.06
Professional Development -0.05 0.04 -1 .24 0.216 -0.06
Exploring Technology 0.24 0.04 6.55 0.000 0.32
Variance

Random Effect Component df 12 p Value

Intercept 0.01 8 1 7.47 0.025

Level-1 effect 0.46

 

Again, the independent regression coefficients difference test was conducted to
find out whether the effects of the two significant predictors differ between the U8 and
China samples. As Table 16 shows, there is no difference in the effect of the predictors

between the US and China.

55

 

Table 16 Values for Teachers: Results of Independent Regression Coefficients Difference
Test

 

 

 

 

 

US UV=4I8) China (N=496)
Predictors b Observed t b observed t difi'z
Access to Expertise through Help 0.01 0.19 0.02 0.21 -0.002
Access to Expertise through Talk -0.01 -0.24 0.08 1.31 -1 .061
Professional Development 0.1 1 1.25 -0.05 -l .24 1.756
Exploring Technology 0.41 ** 5.90 0.24" 6.55 -0.078

 

** Results are significant at the 0.01 level (2-tailed).
* Results are significant at the 0.05 level (2-tailed).

Since the results might be distorted by the ceiling effect, the same analysis was
conducted without extreme values to see whether the results change dramatically. Cases
with Values for Teachers greater than 5.9 were removed from the second analysis. The

differences between two analyses for the US data were reported in Table 17.

Table 17 Results of Ceiling Effect Analysis for the American Schools

 

 

 

With extreme values Without extreme values
Fixed Eflect Coeflicient se C oeflicient se
Intercept 3.77" 0.21 3.53" 0.21
Access to Expertise through Help 0.01 0.03 0.01 0.03
Access to Expertise through Talk -0.01 0.03 0.01 0.03
Professional Development 0. 1 1 0.09 0. 16 0.09
ExplorinLTechnology 0.41** 0.07 0.37" 0.07

 

** Results are significant at the 0.01 level (2-tailed).
"‘ Results are significant at the 0.05 level (2-tailed).

There is almost no difference in standard error between the two sets of results.
The regression coefficients changed slightly. What changes dramatically is the proportion
of variance explained by the predictors. Instead of 6.6% with the original data, social
interaction accounts for 18.1% of the total variance when extreme cases are removed

from the analysis.

56

Results from the China data indicate the results from the original data are
somewhat biased, which are presented in Table 18. There is not much difference in
standard error between the two sets of results. But the change of some regression
coefficients was fairly large. Professional Development changed from not significant to
significant. cor the China data, social interaction could explain 27.9% of the total

variance in Values for Teachers in contrast to only 7.9% with the extreme values.

Table 18 Results of Ceiling Effect Analysis for the Chinese Schools

 

 

 

With extreme values Without extreme values
Fixed Eflect Coeflicient se Coefiicient se
Intercept 4.62** 0.1 1 4.75““ 0.13
Access to Expertise through Help 0.02 0.08 -0.04 0.08
Access to Expertise through Talk 0.08 0.06 0.10 0.07
Professional Development -0.05 0.04 --0.1 1* 0.04
ExploringTechnology 0.24" 0.04 0.15" 0.04

 

** Results are significant at the 0.01 level (2-tailed).
"' Results are significant at the 0.05 level (2-tailed).

l verall, there seems not to be a systematic difference in estimating the effects of
the predictors for the U8 sample. But the results are somewhat distorted for the China
sample. Specifically, the effect of Exploring Technology is over estimated and the effect
of Professional Development is under estimated. In addition, much more proportion of

the variance in the outcome could be explained when the ceiling effect is not present.

Next, what social interaction factors might be associated with the probability that
a teacher will respond with a maximum value (>5.9) for Values for Teachers was
analyzed. A new variable called Ceiling was created with a value of 1 if a teacher will
respond with a maximum value (>5.9) for Values for Teachers and 0 otherwise. Then, the

following model was tested with both the U8 and China data.

57

 

 

Prob(Ceiling = 1 I A) = Q9

(0
L0 = .
g[l_¢) 77,,

77,] = .50, + )6” (Access to Expertise through Helpy.)

 

+ :62; (Access to Expertise through T alky ) + ,sz (Professinoal Development 0‘ )
+ ,6“ (Exploring T echnology”. ) + r,j
c or the US data, it took a very long period of time to converge. When looking at

the proportion of cases with extreme values, it was found there were only 59 cases out of
428 in total. They only take 13.8% of the total sample. These two pieces of evidence
suggest that ceiling effect is not a serous problem with the US data. In contrast, 118 cases
out of 533 responded with extreme values. That is 23.4% of the total sample. Therefore,
ceiling effect is a threat to the validity of the estimated results for the China data. The

results of the logistic HLM analysis for the China data are reported in the Table 19.

Table 19 Results of Logistic HLM Analysis for the Chinese Schools

 

 

Fixed Eflect Coeflicient se t Ratio p Value
Intercept -3.52 0.42 -8.29 0.000
Access to Expertise through Help 0.20 0.27 0.75 0.455
Access to Expertise through Talk 0.08 0.22 0.38 0.708
Professional Development 0.1 1 0.13 0.85 0.397
Explorig Technology 0.71 0.13 5.38 0.000

 

Since what is of interest in the average difference between log-odds of responding
with extreme values, the results of the population-average model are reported here.
Exploring Technology is the only significant predictor. In other words, the probability
that a teacher will respond with a maximum value (>5.9) for Values for Teachers is

positively associated with Exploring Technology.

Complexity

58

 

 

The mean value of complexity perceived by the American teachers is 2.21, which
is about the same as the mean value of complexity perceived by the Chinese teachers
(2.25). According to Figure 7 and Figure 8, there might be a floor effect in the
measurement of Complexity.

Figure 7 Complexity of the US Data

 

 

 

 

 

 

6.0-
*
5.0-j
o o
3' .
'i 4.0
2
a
5
O 3.01 g. ,
1.0—
IIIIITIITIITIIITIIWI
School

Figure 8 Complexity of the China Data

 

6.0 - O

5.0 -

Complexity
9
‘2’

 

 

 

 

59

 

 

Before running the hypothesized full model, an unconditional model was
analyzed to find out how the variance of Complexity is distributed across the individual
teacher and school levels. The results reveal that most of the variance is within schools
for both countries (See Table 20 and Table 21). The intraclass correlation of the

American sample (0.03) is the same as that of the Chinese sample (0.03).

Table 20 Complexity: Results of Unconditional Model for American Schools

 

 

 

Fixed Eflect Coefiicient se T Ratio p Value Reliabfly
School mean use 2.22 0.05 44.12 0.000 0.419
Variance
Random fired Component df 12 p Value
Intercept 0.02 18 33.96 0.013
Level-1 effect 0.60

 

Table 21 Complexity: Results of Unconditional Model for Chinese Schools

Fixed Ejfict Coefiicient se tRatio p Value Reliability
School mean use 2.25 0.06 35.29 0.000 0.592

 

 

 

Variance
Random Eflect Component Cy 1‘7 p Value
Intercept 0.02 8 20.29 0.009
Level-l effect 0.78

 

Complexity shares the same group of predictors as Values for Teachers.
Moreover, the model is also the same due to the same reasons. That is, only the intercept

is modeled to vary randomly at the school level (level 2).

For the US data, the model accounts for 17.2% of the level I variance. Nearly half
(42.1%) of the level 1 variance could be explained for the China data. Detailed results are

reported in the following two tables (Table 22 and Table 23)

60

 

 

 

Table 22 Complexity: Results of Conditional Model for American Schools

 

 

 

 

 

 

 

Standardized
Fixed Eflect Coefl'icient se tRatio p Value Coeflicient
Intercept 3.41 0.17 20.17 0.000
Access to Expertise through Help 0.02 0.02 1.04 0.298 0.05
Access to Expertise through Talk 0.01 0.02 0.41 0.685 0.02
Professional Development -0.05 0.07 -0.70 0.484 -0.03
Exploring Technology -0.53 0.06 -9.42 0.000 -0.45
Variance

Random Efi'ect Component df X J Value

Intercept 0.00 18 20.56 0.277

Level-l effect 0.42
Table 23 Complexity: Results of Conditional Model for Chinese Schools

Standardized
Fixed Eflect Coeflicient se t Ratio p Value Coefiicient
Intercept 3.18 0.13 24.50 0.000
Access to Expertise through Help 0.34 0.09 3.65 0.001 0.16
Access to Expertise through Talk -0.26 0.08 -3.49 0.001 -0.16
Professional Development 0.09 0.05 2.00 0.046 0.10
Exploring Technology -0.42 0.04 -10.11 0.000 -0.46
Variance

Random Eflect Component aj’ ){2 p Value

Intercept 0.01 8 15 .51 0.049

Level-1 effect 0.63

 

For the American teachers, Exploring Technology is the only significant predictor
of computer use. For the Chinese teachers, four predictors are all significant. For Access
to Expertise through Talk and Exploring Technology, the effects are negative meaning
that access to more expertise through talking with colleagues about ideas of computer use
and spending more time exploring computers are associated with perception of low
complexity. Interestingly, the effects of Professional Development and Access to
Expertise through Help are positive. In other words, access to more expertise through

help and spending more time on professional development are associated with perception

61

 

of high complexity.

Results of the independent regression coefficients difference test find significant
difference in the effect of access to expertise through Talking with Colleagues between
American and Chinese teachers. That result suggests that the interaction between teachers
through talking with colleagues about ideas of computer use in China has a larger impact
Rn tHIhHY SHFI-Bthn RI FRP SlI-klty RI FRP puter use (decrease the complexity) than in

the US (See Table 24).

Table 24 Complexity: Results of Independent Regression Coefficients Difference Test

 

 

 

 

 

 

UM =41 9) China (N =49 7)
Predictors b Observed t b observed t difie
Access to Expertise through Help 0.02 1.04 0.34" 3.65 -1.689
Access to Expertise through Talk 0.01 0.41 -0.26** -3.49 2.649M
Professional Development -0.05 -0.70 0.09* 2.00 -1.864
Exploring Technology -0.53** -9.42 -0.42** -10.1 1 -0.094

 

** Results are significant at the 0.01 level (2-tailed).
* Results are significant at the 0.05 level (2-tailed).
Compaflbilitv

This variable is a one item measure. The mean of compatibility between computer
use and their pedagogies perceived by the American teachers is 4.28 on a 6 point scale.
This degree of compatibility is shared by the Chinese teachers (4.22). From Figure 9 and
Figure 10, it can be seen that the variability of Compatibility between schools is higher

than that of complexity for both the U8 and China data.

62

 

 

Figure 9 Compatibility of the US Data

 

 

 

 

 

 

 

   

 

 

6.0" I I :2.
5.0- ,1 , _. :’- .11;
= 4.0-
e
‘6
a.
E 3.0—
O
2.0- o
1.0-
ITHTIIIIIIIIIIIIIIII
School

Figure 10 Compatibility of the China Data

 

6.0 -'

5.0 '—

1"
o
l

Compatibility
5»
‘i’

2.0 - O O

1.0- O O O O

 

 

 

School

Still, an unconditional model was analyzed first to find out how the variance of

Compatibility is distributed across the individual teacher and school levels. The results

63

— A

 

 

show that most of the variance is within schools for both countries (See Table 25 and
Table 26). Similar to Complexity, the intraclass correlation of the American sample

(0.05) is the same as that of the Chinese sample (0.05).

Table 25 Compatibility: Results of Unconditional Model for American Schools

Fixed Eflect Coeflicient se t Ratio p Value Reliabilim
School mean use 4.26 0.10 44.42 0.000 0.537

 

 

 

Variance
Random Efect Component of )(2 J Value
Intercept 0.09 40.20 0.002
Level-1 effect 1.68

 

Table 26 Compatibility: Results of Unconditional Model for Chinese Schools

Fixed Effect Coeflicient se tRatio J Value Reliabilioi
School mean use 4.22 0.11 38.76 0.000 0.718

 

 

 

Variance
Random Eflect Component (9’ 12 p Value
Intercept 0.08 8 29.09 0.001
Level-1 effect 1.53

 

Compatibility shares the same group of predictors as Values for Teachers and
Complexity. Due to the same reasons, only the intercept is modeled to vary randomly at
the school level (level 2). Thus, the model for Compatibility is the same as the model for

Values for Teachers and Complexity.

For the US data, the model accounts for 13.8% of the level 1 variance. For the
China data, the model accounts for 12.0% of the level 1 variance. Detailed results are

reported in Table 27 and Table 28.

64

 

 

 

Table 27 Compatibility: Results of Conditional Model for American Schools

 

 

 

 

Standardized
Fixed Eflect Coeflicient se tRatio J) Value Coefiicient
Intercept 2.21 0.30 7.42 0.000
Access to Expertise through Help 0.01 0.04 0.31 0.754 0.02
Access to Expertise through Talk 0.03 0.04 0.80 0.426 0.04
Professional Development 0.14 0.13 l .08 0.28 0.05
Exploring Technology 0.79 0.10 7.98 0.000 0.39
Variance

Random Eflect Component df ){2 p Value

Intercept 0.02 I 8 29.02 0.048

Level-1 effect 1.45

 

Table 28 Compatibility: Results of Conditional Model for Chinese Schools

 

 

 

 

Standardized
Fixed Eflect Coeflicient se tRatio p Value Coeflicient
Intercept 2.97 0.20 14.54 0.000
Access to Expertise through Help -0.31 0.14 -2.27 0.024 -0.11
Access to Expertise through Talk 0.36 0.11 3.23 0.002 0.16
Professional Development 0.03 0.07 0.39 0.698 0.02
Exploring Technology 0.42 0.06 6.69 0.000 0.32
Variance

Random Eject Component df x2 p Value

Intercept 0.07 8 26.43 0.001

Level-I effect 1.35

 

For the US data, Exploring Technology is the only significant predictor. That is,
nchHlHThHY IntHIItion with fellow teachers through help and talk nor professional
development seem to affect their perceived compatibility of computer use. For the China
data, not only Exploring Technology but also Access to Expertise through Help and
Access to Expertise through Talk are significant predictors. Similar to case of
Complexity, the effects of Access to Expertise through Help and Access to Expertise
through Talk are opposite to each other. Again, Access to Expertise through Talk is

positively associated with Compatibility, whereas Access to Expertise through Help is

65

 

 

negatively associated with Compatibility.

Results of the independent regression coefficients difference test find significant
difference in the effect of access to expertise through help. That result suggests that the
interaction between teachers through talking with colleagues about ideas of computer use
In ChInDhINDngHJIP 8D7t Rn tHIhHY perception of complexity of computer use

(decrease the compatibility) than in the US (See Table 29).

Table 29 Compatibility: Results of Independent Regression Coefficients Difference Test

 

 

2w

 

 

US (N=407) China (N =493)
Predictors b Observed t b observed t difih
Access to Expertise through Help 0.01 0.31 -0.31* -2.27 1.751
Access to Expertise through Talk 0.03 0.80 0.36" 3.23 -1.570
Professional Development 0.14 l .08 0.03 0.39 0.536
Exploring Technology 0.79" 7.98 0.42M 6.69 1.329

 

** Results are significant at the 0.01 level (2-tailed).
* Results are significant at the 0.05 level (2-tailed).
Effect of Social Interaction

AFFRIrlIIng ththhPRIHIFD [LIP HNRUNRI thD/Vudy, tI-IIhHV FRP SutHIiVID/
influenced by their social interaction, especially their access to expertise through help and
talk. Teachers could learn how to operate hardware and how to run a program by help
received from colleagues. Through talking with colleagues about ideas of computer use,
they could not only acquire new ideas from each other but also come up with new ideas
that no one has thought of before. All these could dIIIFtly DII-Ft tHIhHV FRP SutHJAH
At the same time, interacting with colleagues could also indirectly InIluI-IiFI-ItID‘hHY
computer use by changing their perceptions of computer use. For example, a teacher

could develop a positive attitude toward computer use and perceive using computer as

66

 

less complex through talking with colleagues. Subsequently, this positive change in
perception could make the teacher use computers more in her classroom. In this section,

the overall effect of social interaction through help and talk is investigated.

Analyses of Part I indicate that only social interaction through talking with
colleagues about ideas of computer use have a direct effect on computer use. t hen
standardized regression coefficients are considered, the direct effect is 0.19 and 0.18 for

American and Chinese teachers respectively.

This set of analyses also tells us that Compatibility is a significant predictor for
both the US and China data. Apart from that, Values for Teachers is a significant
- predictor for the US data only and Complexity is a significant predictor for the China
data only. The indirect effect is estimated statistically as the products of the direct effects
that comprise it (h line, 1998). For example, the indirect effect of access to expertise
through Talking with Colleagues on Computer Use through Complexity is the product of
the effect of Complexity on Computer Use and the effect of access to expertise through
Talking with Colleagues on Complexity. To test the significance of indirect effects, the
method introduced by Baron C h enny (1986) was employed. Specifically, suppose that a

is the regression coefficient for the direct effect X —> Y, and that SE, is its standard
error. Suppose b is the regression coefficient for the direct effect Y, —> Y2 and that SE ,, is

its standard error. Let the product ab be the estimate of the indirect effect of X on

Y2 through Y, . The standard error of ab is

 

55,, = JszEj + aZSE: + szjsz;

67

T!

 

 

 

Table 30 Indirect Effect of Access to Expertise through Talking with Colleagues on
Computer Use

 

 

Indirect Eflect Routes US China
Via Values for Teachers -0.002 0.003
Via Complexity -0.001 0.015
Via Compatibility 0.008 0.017*

 

** Results are significant at the 0.01 level (2-tailed).
* Results are significant at the 0.05 level (2-tailed).

The results are reported in Table 30 As the results show, only the indirect effect of
access to expertise through Talking with Colleagues via perceived Compatibility for the

China data is significant. The effect is rather small (0.017).

68

 

CHAPTER 6 Cl NCLUSII NS AND DISCUSSII N

The purpose of this study is twofold. First, it attempts to propose a new
framework that connects the psychological and social approaches and apply the new
framework to understanding technology adoption in schools. Second, this study is
particularly interested in investigating the effect of teacherV \RFID IntI-IIFtIRn Rn thHU
technology adoption, especially the interaction between peers. The results of this study

have suggested a very complex picture.

First, the results suggest that teachHY tl-thRley DIRStIRn DJERh D
psychological and a social issue. Psychologically, It D/InlluHiFHI Ey tI-D‘hHY SHHBtIRn
of technology use. Socially, it is influenced by tHIhHY IntHIFtIRn with HttHiIiD HtSHllV
internal peers, and various social artifacts. This conclusion is supported by the results
from both the U8 and China samples, which hDIHP Diy \lP IlDJtIIN 7hD DI tHIhHJI
adoption of technology might follow some general psychological and social principles
regardless of which country they come fi'om and what their culture background is. At the
same time, there are also some disparities between the results from the US and China,
which might be associated with cultural differences. In this chapter, the similarities and
disparities will be summarized and how the disparities might be attributed to cultural

differences will be discussed.

Effects of Perception
According to the perceptual control theory, people perceive things on multiple
dimensions and act based upon the interactions between the perceptions and their

hierarchical goals. Applying this perspective to technology adoption by teachers, it is

69

 

thI-RUzHI thD tI-D‘hHV SI-II'I-BtIRi RI tIfhnology use takes place on six dimensions: 1)
values for themselves, 2) values for their students, 3) complexity of learning and using
technology, 4) case of implementing technology in their classrooms, 5) compatibility
between technology use and their pedagogical practices, and 6) pressure of using
technology. The results reveal that compatibility is a significant predictor both the

American and Chinese teachers and is positively associated with technology use.

This difference in the effect of perception might be due to the fact that the US is
an individual based society whereas China is a collective based society. In the US,
IndIvIduDV dI-FDanVand rights are valued and appreciated, which is especially true in
the teaching profession. Thus, it is commonly thought that whether to use technology and
how to use it should be the results of teacheUVdDFlHIRn. AVdDFlHIRnIly IndIyIduDV
teachers make decisions based upon their perception of technology use and the goals they
pursue. Considering the time that is needed to learn and use technology as well as the
potential problems that might arise, American teachers need to see the values before they
use technology. In contrast to thH8 6’VH’ ShDD/Rn IndIyIduals, collective decisions and
authority are valued and appreciated in China. Though important, individuals are
secondary to collective. As part of a collective, accordingly, Chinese teachers tend to use
technology either because it is encouraged by school administrators or many other
teachers use technology, although they might not necessarily think using technology is
valuable to them. To Chinese teachers, conforming to the authority and collective is not
Rnly IP SRIJlDit Bit D\R“nDuU]”. ,t D/nRt nIFHWlIIy thHresults of perceived pressure
since the perceived pressure is not significant. Rather, it might be something already

InhHHit In ChInI-N-ItI-DhHY P Ind D/DUIsult of adaptation to the environment.

70

 

Direct Effects of Social Interaction

t hat is of particular interest in this study is how the interaction among teachers
themselves might influence their technology use. To achieve this goal, two kinds of peer
interaction concerning technology use are modeled in the study. I ne is the help teachers
receive concerning technology use and the other is the talk between teachers on how to
use technology in teaching. It is found that for both the US and China samples talking
with colleagues about ideas of technology use is significant. In addition, the effect of this
kind of interaction is large comparing to other effects. According to the social learning
theory, people learn greatly from interacting with others. This social interaction gives
people a channel to access expertise, which leads to not only new information but also
resources. Talking with colleagues about how technology could be used helps teachers
learn about new ways of technology use that is not only meaningful but also relevant to
them. In addition, teachers can also learn about where to access the resources that could

be important for them to learn and use technology during the discussion.

Interestingly, access to expertise through help is not a significant predictor of
computer use for both the American and Chinese teachers. In other words, access to
expertise through this channel is not as effective as through the channel of talking with
colleagues about technology use. If we look a careful look at the interaction taking place
when teachers ask for help concerning a technology problem, it is not difficult to find that
this type of interaction is usually shorter and less educative than talking about technology
use. I n the one hand, what a teacher looks for when asking for help with solving a
technology problem is to solve the problem and most likely within a short period of time.

I n the other hand, what a teacher is ready to offer is usually not to teach the other teacher

71

F1

 

 

 

how to solve the problem or how to use technology. As a result, access to more expertise

is not necessarily more beneficial than less expertise for the future technology use.

In addition to access to expertise through help and talk, professional development
and exploring technology are also significant predictors of technology use. Two things
are noticeable here. First, for both the American and Chinese teachers, the effect of
professional development is less than that of talking with colleagues about technology
use. As mentioned earlier, interaction among peers is usually more meaningful and
relevant than interaction between teachers and external experts, which is the most
common format of professional development. Apart from this, the interaction between
peers is either one-to-one or within a small group whereas professional development is
usually one-to-many. This result is important because much attention has been paid to
professional development. However, the results of this study suggest facilitating peer

interaction might be more effectivHIRIlI-D'hHY tIHianng DIRStIRn.

Another thing to notice is that exploring technology has a larger effect for the
Chinese teachers than for the American teachers. This might also has something to do
with the cultural differences. As we know, American teachers spend most of their school
time teaching. Usually, the only one hour when tI-IIhHYan’t tI-D'h D/u\HI IRUlI-MRn
SlDinIng. AVDIHlilt, thI-y an’t hD/HP th [Ice time to explore technology. In contrast,
Chinese teachers teach fewer classes and have more time to spend at their own will.

d iven this difference, Chinese teachers might spend more time exploring technology than
American teachers although the frequency of exploration might be similar. There might

be another cultural reason for the different effects of exploring technology. As discussed

72

earlier in the theoretical framework, American teachers are in their own classrooms most
of their school time. Resulting from this arrangement, when they have a question or a
new idea of technology use pops up in the process of exploring technology, they have no
one to refer to or talk with. In most cases, teachers may let the question or ideas slip away
since they have many other things to deal with. In contrast, Chinese teachers of the same
grade or subject are arranged to share one office, where they spend most of their time.
Due to this setup, Chinese teachers can refer to a colleague nearby if they have question
about learning technology or talk with him/her if a new idea of how technology could be
used springs out. Therefore, exploring technology itself is rather different between the U8

and China, which might lead to the difference in effect.

Indirect Effects of Social Interaction
In addition to these direct effects of social interaction, this study is also interested
in finding out the indirect effects of social interaction through the mediation of
perception. Perceived values of technology use for teachers, complexity of learning and
using technology, and compatibility between technology use and pedagogical practices
were modeled as the outcome variable with social interactions as the predictors. The
UMltVVrggHif thD tHFhHY \RFID IntHIltion have an impact on their perception of

technology use.

I f the four types of social interaction, exploring technology is a significant
predictor for perception on all three dimensions and the effects are large comparing to the
effects of other type of social interaction. In addition, there is no difference in the effects

between the US and China. That is, the difference in exploring technology itself between

73

 

 

 

the American and Chinese teachers could be translated into different degrees of

behavioral change, but not necessarily different degrees of perceptual change.

Professional development is not significant for perceived values for teachers and
compatibility between technology use and pedagogical practices. For perceived
complexity of learning and using technology, its effect is positive for the Chinese
teachers. This might be because professional development is mainly carried out by L
having an external trainer to train teachers to learn how to use certain kinds of programs

or hardware in a short period of time. During the training session, usually, a great amount I

 

of information is presented to teachers, which often lead teachers to feel lost or confused.
I verall, the effect of professional development is marginal across both countries, which
suggests that professional development might nRt hD/HP th IP SIPt Rn tI-Dth-llI
perceptual change. This result is not very surprising. Through professional development,
teachers can learn how to run a piece of software and implement it in their teaching.
However, this does not necessarily mean they change their perception of technology use.
t hen it comes to perception, results of this study suggest exploring technology and peer

interaction are more effective to make an impact.

 

t hat is intriguing about the effects of peer interaction on perception is that the
effects of access to expertise through help and talk are in opposite directions for the
Chinese teachers. Specifically, access to more expertise through talking with colleagues
about technology use is associated with higher compatibility and lower complexity.
However, access to more expertise through help is associated with lower compatibility

and higher complexity. The contrary effects might be due to the knowledge gap between

74

the teachers who seek for help and the teachers who offer help. t hen teachers need help
with technology problems, they often tend to approach colleagues who are commonly
FRanHHI “HrSI-UV’. DIIIHHit IIRP D‘FI-NVtRexpertise through talking with colleagues
in which teachers tend to interact with more colleagues whose technology skills spread

' across a wide spectrum, access to expertise through help usually involves only one or two
colleagues who are very good at technology use. More access to expertise through help
often means there is a large knowledge gap between help-seekers and their helpers.
AlthRugh tHI-hI-IY SIRHIP VHtuld gH \RlvHI, thHI RItHi an’t Ner th thHI DH
solved and feel it is difficult to solve technology problems, which could make them
perceive technology use as being more complex than before. t hen it comes to
compatibility, research has suggested that high level of technology use is often associated
with a change in pedagogy (Becker, 2002; Lei, 2005; Mishra C h oehler, in press). Thus,
when teachers with high expertise provide help in a way that they perceive as being
compatible with their pedagogy, teachers with low expertise might perceive it as being
incompatible. The larger the gap it is, the less compatible help-seeking teachers might

perceive.

The indirect effect of social interaction is estimated statistically as the products of
the direct effects that comprise it. Since peer interaction is the focus of this study, only
the indirect effect of talking with colleagues about technology use is reported. According
to the results, only the indirect effect of talking with colleagues about technology use via
perceived compatibility for the China data is significant and the effect is fairly small.

That is, the indirect effect of social interaction is not very important.

75

Limitations and Future Research

This study is limited mainly in two aspects. First, cross sectional data was
collected and used for analysis. t ithout controlling for the pre-conditions, estimates
obtained from cross sectional data are known to biased (Anselin, 1988; l rd,1975).
Therefore, the effects of the predictors obtained from longitudinal data will be smaller
than what are reported here. Second, the measurement of peer interaction only took the
hI-fl-IRgIthy RI nRP InHIVHrSHilDHIntR PRaneration. The heterogeneity of the social
ties is not captured. In other words, how often teachers interact with their peers was not

measured.

To overcome these limitations, future research should collect and analyze
longitudinal data. In addition, how often teachers interact with peers should be added into
the sociometric measurement and incorporated into subsequent data analysis. It is
speculated that the difference in the effect of social interaction between the US and China
might turn out to be larger when the frequency of interaction is taken into consideration.
This study suggests the effect of near peer interaction might be greater than non-near
SHUV ,t IYD\RwRIIlhwhllHto fiirther investigate the effects of near peer and non-near

peer interaction.

Implications
Since technology adoption by teachers is both a psychological and social issue,
attention should be paid to both aspects. To help teachers use technology in their

classrooms, multiple approaches should be taken.

I n the psychological side, efforts should be devoted to helping teachers see the

76

 

values of technology use and the compatibility between technology use and their
pedagogical practices. In essence, this boils down to two issues. 1 ne is to design better
technologies whose educational values could be easily seen and use is not too radically
deviated from the existing practices. The other issue is to change tHFhI-IY SHFIBtIRnVRI
technology use. In other words, the first issue is to adapt technologies to teachers and the
second one is to adapt teachers to technologies. The past research suggests that
technology adoption is a co-adaptive process (Lei, 2005). To facilitate the co-adaptation,
we need to work hard on both issues. t hen designing educational technology products,
we should take the issues of educational values and compatibility seriously. More

IP SRlllDit, tI-[I'hHY SHJSI-FtIyI-IP u\f EHIHSIFtHI whHi DldlIMIng thI-IDMHII 7RIP SIR

tI-IIhHY SHFIStIRnVRI tthRley u\H aneed to rely on the social aspect.

I n the social side, more peer interaction should be encouraged and facilitated,
especially the conversation between colleagues on how technology could be used for
instructional purposes. In addition, we should give teachers more time to explore
technology rather than requiring them to attend more professional development events.
And the exploration might be better if they can do it in a relatively private place but still

have the access to expertise available.

This study also has implications to future research. After the cognitive
revaluation, measuring and studying psychological traits have gained its predominance
and became a norm in educational research. This approach is characterized by a focus on
individual and cognition. The notion that teachers live in social networks and behavior

could influence behavior directly has not been appreciated. gaining the pioneering

77

studies, this research suggests that combining both psychological and social approaches

could help deepen our understanding of the complex social phenomena.

78

it}

 

 

APPENDICES

APPENDIu A: LIh ERT SCALE MEASURES

Computer Use (1 1 items)

Scale:
1 2 3 4 5
Never v early Monthly t eekly Daily
Items:

1.

2.

10.

II.

I use computers for communication with parents (e.g., newsletters, e-mail, class
t eb page)

I use computers for teacher-student communications (e.g., response to written
work, posting schedules and activities)

. I use computers for classroom management and/or incentives for students (e.g.,

reward for completed work)
I use computers for record keeping (e.g., grades, attendance, IEP)

. I use computers for preparation for instruction (e.g., lesson and unit planning,

downloading materials such as pictures)

I use computers for remediation (e.g., repeat a lesson, Accelerated Math, gistens)
I use computers for student to student communication (e.g., publish student work
on at eb page, keypals, e-group projects)

I use computers for student inquiry (e.g., student research using electronic
databases, t ebn uest)

I use computers for student expression (e.g., Hyperstudio, Power-Point collections
of artwork, h idPics, i-movies)

I use computers for core curriculum skills development (e.g., drill and practice on
MathBlaster or Reader Rabbit)

I use computers for development of basic computer skills (e.g., keyboarding,
mouse skills, trouble shooting)

Values for Teachers (6 items)

Scale:

1 2 3 4 5 6
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

Items:
1. Computers help me integrate different aspects of the curriculum
2. Computers help me teach innovatively
3. Computers help me direct student learning
4. Computers help me model an idea or activity
5. Computers help me connect the curriculum to real world tasks
6. Computers help me be more productive

79

Values for Students (6 items)

Scale:

1 2 3 4 5 6
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

Items:
1. Computers help my students develop new ways of thinking
2. Computers help my students think critically
3. Computers help my students gather and organize information
4. Computers help my students explore a topic
5. Computers help my students be more creative
6. Computers help my students be more productive

Complexity (4 items)

Scale:

I 2 3 4 5 6
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

Items:

1. I can work with computers effectively (Reversed)

2. Computers are flexible (Reversed)

3. I have the ability to learn new computer applications (Reversed)
4. I can recall how to perform tasks on the computer (Reversed)

Ease of Implementation (2 items)

Scale:

1 2 3 4 5 6
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

Items:

1. It is easy to implement new software in this school
2. It is easy to implement new hardware in this school

Social Pressure (4 items)

Scale:

1 2 3 4 5 6
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

Items:

1. Using computers helps a teacher advance his/her position in this school
2. I need to use computers to keep up in this school

3. l thers in this school expect me to use computers

4. My colleagues use computers more than I do

80

 

 

Compatibility (single item)

Scale:

1 2 3 4 5
Strongly Moderately Slightly Slightly Moderately
Disagree Disagree Disagree Agree Agree

Item:

1. It is easy to integrate computers with my teaching style

Exploring Technology (3 items)

Scale:
I 2 3 4
Never v early Monthly t eekly
Items:

1. I explore new technologies on my own
2. I read professional journals about new technologies
3. I consult technology manuals

Professional Development (single item)
Scale:
I 2 3 4
Never v early Monthly t eekly
Item: ’

6
Strongly
Agree

Daily

5
Daily

1. Attend district or school in-service programs for new technologies

81

APPENDIu B: 81 C11 METRIC MEASURES

1. During the current school year, who has most frequently helped you with computers
in the lab? (t rite first and last names in the space provided below, then fill in the
circle where they work.)

From this In district, but not Out of

Name school from this school district

 

 

 

 

 

 

bbbbbbb
bbbbbbb
Pbbbbbb

 

2. During the current school year, who has most frequently helped you with the
computer(s) in your classroom? (t rite first and last names in the space provided
below. Then check where they work.)

From this In district, but not Out of

 

 

 

 

 

 

Name school from this school district
A A A
A A A
A A A
A A A
A A A
A A A
A A A

 

82

APPENDIu C: SURVEv (ENd LISH VERSII N)

Technology Use
This section focuses on your use of various technologies. We are interested in frequency,
purposes, performance, etc.

1. Please indicate how much you use each of the following technologies for educational

 

 

 

 

 

 

 

 

or professional purposes: -
Never Yearly Monthly Weekly Daily

Phone system ....................... U U U U D
Voice mail ........................... U U U D D
Video/TV network ............... U U U U Cl
t orld t ide t eb ................ U D U Cl U
E-mail .................................. Cl El Cl [:1 El
$21383??? ............... D E' D D 0
Computers in your El Cl E] l] C]
classroom .............................

 

 

 

 

 

 

 

 

2. How would you rate your computer use this year as compared to last year?
Much less Less Same More Much more
El El [:1 E] El

3. Please indicate your intent to use each of the following technologies for educational
purposes in the next school year compared to your current use.

Much less Less Same More Much more

t orld t ide t eb ............... U U U U '3
E-mail ................................. D D D D D
Computers in your [:1 D E] [I [I
\FhRRI’VlIE ........................

Computers in your E] El Cl [:1 CI
classroom ...........................

Computers in the Lab

The next set of questions looks at your experience using comSXWLVbr yRXUI/FhRROVdE

83

. During the current school year what percent of the time have you had technical
problems with the computers in the lab?
Cl Less than 25%
D 25%-50%
D 51%-74%
Cl 75% or more

. l f the times you have had technical problems with computers in the lab, what percent
of the time did you solve the problem yourself?
El Less than 25%
Cl 25%-50%
D 51%-74%
El 75% or more

. l f the times you have had problems with computers in the lab, what percent of the
time did you turn to others to solve the problem?
Cl Less than 25%
Cl 25%-50%
D 51%-74%
U 75% or more

. l f the times you have had problems with computers in the lab, what percent of the
time was the problem solved in an acceptable time frame?
13 Less than 25%
Cl 25%-50%
EJ 51%-74%
El 75% or more

. During the current school year, who has most frequently helped you with the
computers in the lab? (Write first and last names in the space provided below, then
check where they work.)

 

 

 

 

 

 

From this In district, but not Out of
Name school from this school district
El El E]
El El El
E1 El Cl
1] El El
El E] Cl
E] El El
E1 D El

 

Computers in the Classroom
The next set of questions looks at your experience with using computers in your
classroom.

9. During the current school year, what percent of the time have you had technical
problems with the computer(s) in your classroom?
[3 Less than 25%
El 25%-50%
El 51%-74%
Cl 75% or more

10. l f the times you have had problems with the computer(s) in your classroom, what
percent of the time did you solve the problem yourself?
El Less than 25%
El 25%-50%
El 51%-74%
El 75% or more

I l. l f the times you have had problems with the computer(s) in your classroom, what
percent of the time did you turn to others to solve the problem?
El Less than 25%
El 25%-50%
El 51%-74%
E] 75% or more

12. l f the times you have had problems with the computer(s) in your classroom, what
percent of the time was the problem solved in an acceptable time frame?
Cl Less than 25%
El 25%-50%
D 51%-74%
El 75% or more

13. During the current school year, who has most frequently helped you with the
computer(s) in your classroom? (Write first and last names in the space provided
below. Then check where they work.)

From this In district, but not Out of

 

 

 

 

Name school from this school district
El E1 El
El El 13
El E1 El
El El El
El E] El

 

 

Instructional and Professional Uses of Technology

The next section focuses on specific uses of computers for instructional and professional

purposes.

14. How frequently do you or your students use computers for each of the following:

Activity

Communication with parents (e.g.,

newsletters, e-mail, class t eb page) ........

Teacher-student communications (e.g.,
response to written work, posting

schedules and activities) ............................

Classroom management and/or
incentives for students (e.g., reward for

completed work) .......................................

Record keeping (e.g., grades,

attendance, IEP) ........................................

Preparation for instruction (e.g., lesson
and unit planning, downloading

materials such as pictures) ........................

Student to student communication (e.g.,
publish student work on at eb page,

keypals, e-group projects) .........................

Student inquiry (e.g., student research

using electronic databases, t ebn uest) .....

Student expression (e.g., Hyperstudio,
PowerPoint collections of artwork,

h idPics, i-movies) .....................................

Core curriculum skills development
(e.g., drill and practice on MathBlaster

or Reader Rabbit) ......................................

Remediation (e.g., repeat a lesson,

Accelerated Math, gistens) .......................

Development of basic computer skills
(e.g., keyboarding, mouse skills, trouble

shooting) ...................................................

Sources of Professional Knowledge

Never Yearly Monthly Weekly

Cl

E]

El

El

El

El

El

E1

Daily
[:1

1:1

This section focuses on your professional interactions and sources of new knowledge,

especially with regard to your use of technology.

86

15. v our name: ' (Reminder: N o identifiable
information will be released to your school or anyone else.)

 

16. t ho are your closest colleagues in your school? (Please write first and last names in
the spaces provided. List as many individuals as you wish. You do not have to use all
the spaces provided.)

17. t ith whom do you talk about new uses for computers in your teaching? (Please
write first and last names in the spaces provided, then check where they work. List as
many individuals as you wish. You do not have to use all the spaces provided.)

From this In district, but not Out of

 

 

 

 

 

 

Name school from this school district
1:1 [I 13
CI El El
El El E1
El El E]
13 El D
E] El Cl
E] El E1

 

18. t ith whom do you talk about new ideas for the curriculum? (Please write first and
last names in the spaces provided, then check where they work. List as many
individuals as you wish. You do not have to use all the spaces provided.)

From this In district, but not Out of

 

 

 

 

 

Name school from this school district
El El El
El El E1
El [:1 El
El Cl C]
Cl E] E]
El [:1 El

 

Attitudes and Experiences with Technology
This section focuses on factors that may affect your use of technology. These include
your attitude towards technology, experiences with technology, district support, etc.

87

 

Iii

 

19. Please indicate the extent to which you agree with each of the following statements.

Strongly Moderately Slightly Slightly ModeratelyStrongly
Disagree Disagree Disagree Agree Agree Agree

I try new things in the El [3 Cl E] El E1
classroom ............................
I can work with computers El Cl [:1 El El E1
effectively ...........................
Computers support what I Cl [:1 Cl El El [I
try to do in the classroom....
I am intimidated by D El D D U D
computers ............................
Computers distract students
from learning what is D D U D U D
essential ...............................

I am one of the first to try

something new in the D D Cl U El Cl
classroom ............................

Computers are flexible ........ U U Cl U Cl Cl
1 have the ability to learn El E] El El El El

new computer applications .

It is easy to integrate

computers with my D U U Cl El E1
teaching style ......................
I can recall how to perform Cl E] El El El [:1

tasks on the computer ..........

I enjoy introducing
something new in the U U D U Cl El
classroom ............................

Learning computers takes

too much time ..................... D D D D D D

The next section focuses on your attitudes regarding the value of technology.

Value of Technology for Teachers
20. Please indicate the extent to which you agree with each of the following statements.

88

Strongly Moderately Slightly Slightly ModeratelyStrongly
Computers can help me... Disagree Disagree Disagree Agree Agree Agree

integrate diflerent aspects E] E] El El E1 CI
of the curriculum .................

teach innovatively ...............
direct student learning ........
model an idea or activity .....

connect the curriculum to
real world tasks ...................

El Cl UCIEI
Cl Cl DUE]
Cl El CIDEI
El Cl CIDEI
El E1 EICICI
El E1 ElElEl

be more productive .............

Value of Technology for Students

21. Please indicate the extent to which you agree with each of the following statements.

Computers can help Strongly Moderately Slightly Slightly ModeratelyStrongly
students... Disagree Disagree Disagree Agree Agree Agree
develop new ways Of D D D D D D
thinking ...............................

think critically ..................... U U D U U U
gather and organize [:1 El 1:] El Cl C]
information ..........................

explore a topic ..................... D D D U D D

be more creative .................. D D D D U D

be more productive ............. D D U D D D

Time Spent Learning about New Technologies

22. Please indicate the frequency with which you engage in each activity below.

Activity Never Yearly Monthly Weekly Daily
Explore new technologies on my CI El CI El 1:]
own .....................................................

Attend district or school in-service El El El El [:1

programs for new technologies ..........

89

 

Experiment with district-supported El Cl E] E] El
software ..............................................

Seek help from others to learn E] E] El [:1 El
about new technologies ......................

Attend professional-development

conferences about new D U D D D
technologies .......................................
Read professional journals about Cl E] El U [1
new technologies ................................
Consult technology manuals .............. D D D U U

Ease of Implementation

23. Please indicate the extent to which you agree with each of the following statements.

Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

The computer resources in

my room are adequate for

my instructional needs Cl C] C] C] U Cl
(e. g., lesson and unit

planning, accessing

materials such as pictures) .

The computer resources in

my room are adequate for E] El El El [I E]
student uses (e.g., student

research, writing, artwork).

It is easy to implement new D E] El Cl El El
software in this school ........

It is easy to implement new E] [1 Cl Cl Cl
hardware in this school ....... D

District Involvement and Support for Implementation - Hardware

24. Please rate the district in terms of the following:
Poor Fair Neutral Good Excellent

Providing enough hardware ................. D U D U D
Choosing appropriate hardware .......... D D D D U

90

Providing a reliable server ................... D U U U C]

Updating hardware .............................. D E] D D D
Providing technical support for Cl E] El El El
hardware use ........................................

District Involvement and Support for Implementation - Software

25. Please rate the district in terms of the following:
Poor Fair Neutral Good Excellent

Providing enough software ................. D D D D D
Choosing appropriate software ............ D D U D D
Updating software ................................ D D D D D
Engaging teachers in decisions about C] I] E] D E]
sofiware purchases ...............................

Providing professional development

for D D D D D
sofiware use ..........................................

Providing technical support for Cl E] E] Cl C]
software use ..........................................

Recognition for technological U E] E] Cl C]

innovation .............................................

Views of Computer Use in Your School

26. Please indicate the extent to which you agree with the following statements.

Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

Using computers helps a
teacher advance his/her D D U D D D
position in this school .........

Others in this school are

manna/mm El III D U D D
computers ............................
I need to use computers to El Cl E] El Cl El
keep up in this school ..........

9l

 

Others in this school expect [I U E] Cl [3
me to use computers ............

My colleagues use El Cl E] E] El

computers more than I do

We introduce many new E] E] Cl C] E]
things in this school ............

It is diflicult to implement
all of the new things in this D D D U U
school ..................................

Background
In this section, we ask about some background characteristics that may be related to
technology use.

27. In what subject area(s) do you currently teach? Please check all that apply.
Cl Math
El Science
B Social Studies

28. hat grade levels do you currently teach? Please check all that apply.

h
l
2
3
4
5
6
7

8
Not applicable

UDDDUUDUDD"

29. t hat is your gender?

30. Race/ethnicity (Please check all that apply.)
El African American/Black
El Native American
El Asian/Pacific Islander
Cl Hispanic/Latino
Cl Caucasian/t hite
E] l ther

92

31. v ears in teaching:
32. v ears teaching in this school:

33. How heavy is your workload this year?
Cl Minimal .
El Engaged
I] Busy
El 1 verwhelmed

34. How many students are in your typical class?

 

35. Are you teaching a different grade than last year?
El v es
Cl No

36. Are you teaching a different subject than last year?
El v es
El No

37. t hat percentage of the curriculum you teach changed from last year?
%

 

38. Do you have access to a computer at home?
El v es (continue)
El No (STl P here!)

39. How long have you had a computer at home? years months

40. Do you have Internet access at home?
El v es
El No

41. t hat year did you first use a computer?

42. How frequently do you use your home computer for work related to your job as a
teacher?
El Never
D V early
CI Monthly
E] t eekly
El Daily

93

APPENDlu D: SURVEV (CHINESE VERSI] N)

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103

APPENDlu E: Cl NSENT LETTER (ENd LISH VERSll N)

Dear teachers,

t e are asking you and other teachers in your school to participate in a research project
conducted by Professor vong whao and Bo v an from the College of Education at
Michigan State University. t e are trying to learn about the processes through which
teachers implement technology in the classroom. We are not evaluating the eflectiveness
of your teaching or your school, and administrators will not have access to your
individual responses. t e will use the results of the research to generally help schools
implement innovations.

t e plan to collect the following data as part of this research project:
I This 15-20 minute survey regarding technology use and factors that affect
technology use.
' 30 minutes interviews of some teachers (The interviews will be audio taped with
your permission).

Completing this survey indicates your consent as a participant in this study insofar as
your responses will be analyzed. Participating in this study is voluntary. v ou may choose
not to participate at all, or you may refuse to participate in certain procedures or answer
certain questions or discontinue your participation at any time without penalty or loss of
benefits.

The results from the study may be used in reports, in published articles, and in
presentations at conferences. All of the information we collect will be treated with strict
confidentiality. All of the information we collect will be only accessed by the two
investigators. v our privacy will be protected to the maximum extent allowable by the law.

If you have any questions about this study, please contact Professor v ong whao at 517-
432-7729 or by email at zhaoyo@msu.edu or Bo v an at 517-355-3801 (A local phone
number will be provided after Bo v an arrives at the participating school) or
boyan@msu.edu. If you 'have questions or concerns regarding your rights as a study
participant, or are dissatisfied at any time with any aspect of this study, you may contact
— DanyP RuVy, II yRu th — 31-11119 Dill-11M Ph.D., Chair of the University Committee
on Research Involving Human Subjects (UCRIHS) by phone: (517) 355-2180, fax: (517)
432-4503, e-mail: ucrihs@msu.edu, or regular mail: 202 1 lds Hall, East Lansing, MI
48824. ”

Thank you very much for your time and consideration!

104

v ong whao, Professor Bo v an

Consent for Participation

v ou are being asked to give permission to collect information from you through the
following activities (please circle the number that you permit):

1. Survey regarding technology use and factors that affect technology use.

2. Audio taped interview.

3. Interview with notes taken by the investigator.

Name: Date:

 

 

105

APPENDlu F: Cl NSENT LETTER F (CHINESE VERSll N)
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106

 

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