Focus, Fiddle and Friends: Sources of Knowledge to Perform the Complex Task of
Teaching 1
Kenneth A. Frank
Yong Zhao
Michigan State University
William R. Penuel
SRI International
Nicole Ellefson
Michigan State University
Susan Porter
Michigan State University
Contact information:
Kenneth A. Frank
Room 462 Erickson Hall,
Michigan State University
East Lansing, MI 48824-1034
phone: 517-355-9567; e-mail: kenfrank@msu.edu.
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This study was made possible by a grant from the Michigan Department of Education (MDE), but views
and findings expressed in this report are not necessarily those of MDE. The following individuals participated in the
design and implementation of this study: Yong Zhao, Ken Frank, Blaine Morrow, Kathryn Hershey, Joe Byers,
Rick Banghart, Andrew Henry, and Nancy Hewat. Although we cannot identify the names of the schools that
participated in this study, we want to thank all the teachers and administrators in these schools. Without their
cooperation and support, this study would not have been possible.
Running head: Focus, Fiddle and Friends
Focus, Fiddle and Friends: Sources of Knowledge to Perform the Complex Task of
Teaching
Abstract
Although knowledge has been linked to productivity within and between organizations,
little is known about how knowledge flows into schools and then diffuses from teacher to teacher
within schools. Here, we hypothesize that the value of different sources of knowledge depends
on a teacher’s current level of implementation. We test our theory using longitudinal network
data from 470 teachers in thirteen schools. From models of change (i.e. first differences) in
teachers’ use of computers over a one year period, we infer that the more a teacher at the lowest
initial levels of implementing an innovation is exposed to professional development focused on
student learning, the more she increases her level of implementation (focus); the more a teacher
at an intermediate initial level of implementation has opportunities to experiment and explore,
the more she increases her level of implementation (fiddle); and the more a teacher at a high
initial level of implementation accesses the knowledge of others, the more she increases her level
of implementation (friends). Concerning the potential for selection bias, we quantify how large
the impacts (Frank 2000) of confounding variables must be to invalidate our inferences. In the
discussion, we emphasize the changing nature of knowledge through the diffusion process.
Keywords: Diffusion of Innovation, Professional Development, Implementation of Technology,
Causal Inference, Social Networks
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Running head: Focus, Fiddle and Friends
INTRODUCTION
The goal of this study is to identify how different experiences contribute to knowledge
required for the complex task of teaching. Generally, workers’ access to knowledge has been
linked to productivity from the level of the state (Romer 1990) down to the individual (Burt
2002). In particular, over the course of the past decade, a number of organizational theorists
have advanced the argument that the processes of knowledge creation, integration, management,
and transfer are fundamental to the performance of an organization (Osterloh and Frey 2000;
Spender 1996; von Krogh, Ichijo, and Nonaka 2000). Knowledge flows within organizations
have also been linked to such outcomes as overall organization performance (DeCarolis and
Deeds 1999; Gupta and Govindarajan 2000), product innovation (Hansen 1999), the transfer of
best practices (Szulanski 1996), and workplace learning (Darr, Argote, and Epple 1995).
But even as knowledge transfer has moved to the fore of organizational theory, there is
no integrated conceptualization of the nature of knowledge as it first permeates the
organizational boundary and then diffuses from person to person within an organization. The
macro economic perspective typically assumes that general knowledge circulates to
organizations through sheer power of competition in the marketplace via generally accessible
media, personnel transfers, etc. That is, the conceptualization is essentially of dissemination of
fixed knowledge through unspecified mechanisms. On the other hand, the micro level
perspective characterizes person to person diffusion within the organization but takes the body of
knowledge within the organization as static and given without recognizing origins outside the
organization. Even those studies that attend to how knowledge permeates the organizational
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Running head: Focus, Fiddle and Friends
boundary (e.g., Abrahamson and Rosenkopf, 1997) do not address the changing nature of
knowledge as it does so.
Here, we address the gap between macro theories of knowledge circulation and micro
processes of person to person diffusion within organizations by estimating the effects of different
knowledge sources from outside and within the organization on changes in production practices.
As we do so we develop a trajectory of knowledge adaptation and evolution, as abstract
knowledge is introduced from outside the organization, adapted as it is implemented on the shop
floor, and then articulated as it circulates throughout an organization.
The empirical example of this study concerns teachers’ attempts to implement computer
innovations (e.g., the Internet, educational software, and the digital camera) in the classroom. Of
course, technology may generally directly contribute to production (Romer 1990). But in
education there are still strong concerns regarding the value of digital technology for learning
(Cuban 1999, 2001; Dynarski et al. 2007). Regardless of the educational value of technology,
there is strong pressure on schools to implement such innovations (Cuban 1999; Loveless 1996;
President's Committee 1997); even if computers do not enhance productivity, computers have
strong institutionalized legitimacy (Rowan 1995).
THEORETICAL BACKGROUND
The implementation of technology in schools, like many reforms, has proven vexing for
reformers and organizational theorists alike (Elmore, Peterson, and McCarthey 1996; Tyack and
Cuban 1995). Part of this resistance is due to the considerable variation within schools in how
technology is implemented (Adelman et al. 2002). This is an example of resistance to
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Running head: Focus, Fiddle and Friends
comprehensive change that has prompted organizational theorists to give considerable attention
to schools, (see Bidwell and Kasarda 1987, Bolman and Heller 1995, and Perrow 1986, for
reviews) from control theory (Callahan 1962) to contingency theory (e.g., Greenfield 1975) and
new institutionalism (Meyer and Rowan 1977; Rowan 1995).
Previous research on the diffusion of computers in schools has generally focused on the
effects of four sets of factors on the adoption of computers. First, hardware and software and
technical support affect implementation by making the technology more reliable (Collins 1996;
Cuban 1999; Zhao et al. 2002). Second, organizational factors, such as scheduling and types of
school leadership, can facilitate conditions affecting teachers’ use of computers (Cuban 2001;
Hodas 1993; Loveless 1996; Zhao et al. 2002). Third, aspects of professional development can
support the implementation of innovations (e.g., Berends, 2007; Little, 1993; Wilson and Berne,
1999). Technology uses that require teachers to adopt more constructivist methods, which
increase the cognitive demands on students present particularly significant professional
development needs, since they require teachers to adopt both new pedagogies and technology
tools (Adelman et al., 2002; Windschitl and Sahl, 2002). Fourth, and perhaps most frequently
cited, characteristics of individual teachers, including the willingness and ability to use
technology and pedagogical style, as well as teacher preparation, affect implementation (Becker
2000; Smith et al., 2007; U.S. Congress, 1995).
Although some studies considered interactions among sets of factors, such as between
teacher characteristics and professional development (Desimone, Smith, and Ueno, 2006; Penuel
et al., 2007), few program designs that aim at technology implementation have seriously
considered the complexity of teaching (Bidwell 1965; Woodward 1965). The complexity arises
from multiple sources: variability in student needs, which can influence decisions about what and
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Running head: Focus, Fiddle and Friends
how to teach (e.g., Barr and Dreeben 1977 1983; Delpit 1988); conflicts among organizational
demands that arise from policies enacted at different levels of organization (e.g., Bidwell and
Kasarda 1987; Honig, 2006); varying levels of coherence among curriculum, pedagogy, and
assessments (Borman et al. 2003; Schmidt 2001); and from teachers’ unique educational
trajectories, which exposes them to varying educational approaches (Lortie 1975). As a result,
implementation may not be a simple function of physical resources, background institutional
factors and individual motivation and preparation. Instead, implementation may depend on the
adaptation of an innovation to the complex task of teaching within the organizational context of
the school (Zhao and Frank 2003).
Given that teaching is complex, a teacher’s implementation of new practices can depend
on her knowledge. Teaching is typically not just a simple matter of organizing a set of activities
or delivering a particular curriculum. To the extent that teaching depends on interacting with
students in context, teaching will depend on a teacher’s knowledge (e.g., Shulman, 1985). For
example, the teacher needs this knowledge to anticipate student difficulties and integrate new
learning with students’ existing understandings.
Where does Teacher Knowledge come from?
Knowledge for teaching has been differentiated primarily in terms of domains such as
pedagogical versus content (e.g., Ball and Bass 2000; Darling-Hammond, 2000; Gess-Newsome
and Lederman, 1999; Hill et al. 2005; Shulman, 1985). But here we differentiate knowledge by
its source so that we may link knowledge flows to innovations as they permeate the
organizational boundary of the school. In particular, we differentiate abstract general knowledge
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Running head: Focus, Fiddle and Friends
generated outside the school from specific knowledge that is locally adapted within the school.
For example, general knowledge might consist of the content of a particular lesson in social
studies that can be conveyed to teachers in different schools, while local knowledge might
consist of how to integrate that lesson given the backgrounds of the students and the previous
units in a particular school. Given this distinction, we characterize the literature on the potential
sources of teacher knowledge.
Externally Provided Professional Development. Teachers typically acquire some
knowledge of core curricular subjects and how to teach them as part of their training. But
innovations, by definition, are not generally part of a teacher’s pre-service training. Therefore, a
large body of literature has accumulated on in-service teacher learning that includes
recommendations for effective approaches to foster teacher change (e.g., Munby, Russell, and
Martin 2001; Richardson 2003; Wilson and Berne 1999).
Here we focus on two important new studies that have empirically analyzed the features
of professional development programs that affect teaching practices: Garet, Porter, Desimone,
Birman, and Yoon (2001); and Desimone, Porter, Garet, Yoon, and Birman (2002). The Garet et
al. and Desimone et al., studies establish direct connections between specific characteristics of
professional development and teacher practices and help us understand the mechanisms by which
certain features of professional development affect teachers’ practices. Analyzing professional
development activities attended by a national sample of math and science teachers, Garet et. al.
(2001) found that professional development that positively affected teacher acquisition of
knowledge and skills included a focus on content knowledge and coherence with other learning
activities and with teachers’ own goals for professional development. Supporting Garet et al.
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Running head: Focus, Fiddle and Friends
(2001), Desimone et al. (2002) found that professional development with a focus on a particular
teaching practice leads to increased use of that practice in the classroom.
Opportunities for Experimentation. Garet et al and Desimone et al also attended to the
format of professional development, finding that teachers were more likely to gain knowledge of
how to implement an innovation when they had opportunities for active, “hands-on” learning.
This active learning provides teachers opportunities to process and adapt the information
provided in the professional development, confirming long standing findings about the
importance of experimentation for effective practices (e.g., Little, 1988).
The studies by Garet et al., (2001) and Desimone et al., (2002) significantly shortened the
list of features that define effective professional development from earlier syntheses (e.g.,
Loucks-Horsley et al. 1998; Wilson and Berne 1999) and drew on longitudinal data to support
inferences about the effects of specific aspects of professional development on teacher practices.
In particular, the studies establish two of the tenets of our theory: that focus on specific practices
and the knowledge needed to support those practices, and fiddling with innovations (through
opportunities for active learning) are strongly related to teachers’ increased implementation of
innovations.
We now extend Garet et al., and Desimone et al., by considering how innovations are
adapted once they permeate the organizational boundary of the school. Thompson (1967)
describes the processes of experimentation and exploration “intensive” because they require each
worker to individually develop the knowledge necessary to modify practices to the local context.
This can be limiting because many workers, such as teachers, will simply not have time to
experiment to acquire all of the local knowledge necessary to improve practices (Coburn, 2001;
Cohen and Hill, 2001).
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Running head: Focus, Fiddle and Friends
Given the limitations of individual experimentation, we turn to a second source of local
knowledge that inheres within the organizational boundary. As a social institution the
organization adds to knowledge creation by facilitating interaction among those who possess
diverse knowledge (Schumpeter, 1934) and by reducing transaction costs among dependent
actors with common social contexts and cognitive schema (Williamson, 1981). From this
perspective, organizations can be characterized by their capacity to create and convey knowledge
without reliance on market mechanisms (Arrow, 1974; Coase, 1937). Within the organization of
the school, teachers can learn from others in their local contexts who have adapted innovations
given similar students, other curricular elements, and organizations contexts, and who have an
interest in supporting others (Frank, Zhao and Borman, 2004). Implementation of an innovation
then becomes a function of accessing the knowledge possessed by others in the local context.
Although other teachers may possess critical local knowledge, it may be difficult to
access because teachers have limited opportunities to observe one another (Glidewell, et al.,
1983; Hargreaves, 1991; Little, 1990, 2002; Lortie 1975). Instead the knowledge teachers gain
from one another likely comes through interactions with others who have implemented practices
in comparable contexts (Frank, Zhao and Borman, 2004; Hansen, 1999; Nonaka, 1994). 2
Consider the teacher below describing a routine of explanation concerning the implementation of
a new pedagogy (Coburn and Russell 2008, page 218):
We talked about, like, the math message and the mental math and how to
coordinate the two and that we should be linking the message to the initial
onset of the mini lesson and how those two are connected and that that would
get the children eventually into their individual work and that we should
connect them and that the math messages is separated from the mental math
after it’s done until we go back to it and use that as a lead in for the lesson.
2
This point has been supported in recent work in the economics of education (e.g., Croninger et al., 2007; Jackson
and Bruegmann 2009; Rothstein 2009)
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Running head: Focus, Fiddle and Friends
Note that the new approach, math message, must be coordinated with the old, mental math,
creating a complex task. The complex task is then articulated and knowledge shared through the
teachers’ talk pertaining to how to implement the new approach, motivate the children,
differentiate the approaches, and structure the lesson. Each of these tasks depends on the local
context defined by the students, curricula, and organizational context, which in this case included
a math coach to whom the teacher was describing her interactions.
Consistent with the anecdotal quote above, the knowledge teachers access from one
another has been shown to be essential for changing complex production adapted to a local
context. For example, using a social capital perspective (Burt 1997; Lin, 1999, 2001; Portes,
1998), Frank, Zhao and Borman (2004) found that elementary teachers were able to implement
computer technology in the classroom the more they could access related expertise through
informal interactions with colleagues (see also Reiser et al., 2000; Penuel et al, 2007). The
potential knowledge teachers can access from others in their social contexts defines the third
tenet of our theory, which we colloquially define as friends.
While studies of knowledge flow add an important new dimension to our understanding
of factors that affect teachers’ practices, they have not been situated within a broader frame of
knowledge creation and adaptation. That is, they attend only to how teachers influenced one
another, but not how teacher learning might also be influenced by other professional
development experiences external to the school. In contrast, studies of external professional
development have not attended to the intra-organizational social processes related to knowledge
flows within the school. Thus, in this study, we seek to understand the interplay of formal
professional development and social experiences as they affect teachers’ implementation of
innovations. By so doing, we evaluate the effects of focus and fiddle in the professional
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Running head: Focus, Fiddle and Friends
development literature alongside the effects of friends as in the literature on intra-organizational
diffusion through social networks.
Hypotheses: Knowledge Type and Current Level of Implementation
Our study concerns not just diffusion, but the role of the organizational boundary in
shaping diffusion. Therefore, we consider how the effects of different sources of knowledge
depend on the location of an innovation in the diffusion trajectory relative to the organizational
boundary. In the diffusion trajectory, teachers first access explicit general knowledge through
focused professional development from outside the organization (focus). This general
knowledge is represented by the black puzzle piece in Figure 1. Next, they develop locally
specific knowledge of the innovation through experimentation (fiddle) as they adapt the
innovation to their organizational contexts. The tacit knowledge that permeates the
organizational boundary is represented by the modified, gray puzzle piece in the figure. Finally
teachers can articulate their local knowledge and can share and benefit from colleagues within
their organizational contexts (friends). The new, rearticulated knowledge is represented by the
black border surrounding the modified gray puzzle piece in the figure.
Insert Figure 1 about here
We develop specific hypotheses from the preceding logic that matches the value of each
type of knowledge to where a teacher is located in the trajectory of implementation. When
workers first access outside knowledge they are novices with respect to the innovation. As such,
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Running head: Focus, Fiddle and Friends
their learning is dependent largely on forming new concepts for problem solving and on learning
from initial experiences (e.g., Daley 1999). These experiences are likely to occur during
professional development focused on student learning. Therefore, we hypothesize:
H1: The more a teacher at the lowest initial levels of implementing an innovation is
exposed to professional development focused on student learning, the more she will increase her
level of implementation.
Second, as production workers advance beyond the novice stage they must adapt external
knowledge to their particular organizational contexts. To do so they likely use active concept
integration and self initiated strategies which can occur during experimentation and exploration
(Daley 1999; Ertmer and Newby 1996). Therefore we hypothesize:
H2: The more a teacher at an intermediate initial level of implementation has
opportunities to experiment and explore, the more she will increase her level of implementation.
Finally, to maintain and extend knowledge, experts need access to other experts, to whom
they can turn to for help when they encounter unusual, non-routine problems (Barley, 1990;
Hansen, 1999; Penuel and Cohen 2003). Therefore, we hypothesize:
H3: The more a teacher at a high initial level of implementation accesses the knowledge
of others, the more she will increase her level of implementation.
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Running head: Focus, Fiddle and Friends
Together these hypotheses imply that implementation may depend on access to different
knowledge sources at different points in the diffusion trajectory. As such, the hypotheses have
implications for the changing nature of knowledge (Polanyi 1966) as well as changing behaviors.
It is not just that teachers at one phase of implementation respond to one type of knowledge
while teachers at another respond to a different type of knowledge. Because the underlying
process is of diffusion, there are common elements of knowledge that are transferred (Argote and
Ingram 2000). And yet, in spite of those common elements, we hypothesize that the form of the
knowledge changes through the diffusion process. Explicit codified knowledge in focused
professional development becomes tacit when it is adapted to the specific setting through
experimentation. It then becomes re-codified, but localized, when it is articulated by experts
during interaction. In this last stage the knowledge has the potential to generalize to complex
settings beyond the specifics of a particular classroom.
We also consider whether the source of knowledge has optimal effects when matched
with a teacher’s initial level of implementation. In particular, we will test whether the effect of
focused professional development is strongest for those at initial levels of implementation,
whether the effect of exploration and experimentation is strongest for those at intermediate initial
levels, and whether access to the knowledge of others has the strongest effect for those at high
initial levels. Evidence of the relative effects would inform us as to the nature of the knowledge
types as a set, for example, whether the types form some sort of hierarchy or sequence in which
each form of knowledge becomes less valuable as production workers increase their levels of
implementation.
In the next section we present the context of our current study. We then present our
measures, analytic approach, and results, including quantifying the robustness of our inferences
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Running head: Focus, Fiddle and Friends
with respect to concerns about internal validity. In the discussion, we emphasize the changing
nature of knowledge through the diffusion process.
STUDY CONTEXT: ELEMNTARY TEACHERS’ IMPLEMENTATION OF
TECHNOLOGY
In the precursor to this study, we found examples of successful professional development
programs that featured some or all of the factors: focus, fiddle, and friends (Zhao, Frank and
Ellefson 2006, pages 169-177). But here we shift our attention from isolated professional
development events or programs to the general characteristics of professional experiences to
track how access to sources of knowledge affect teacher practices.
Our instrument and data collection methods offer distinct advantages over most extant
studies. First, our instrument attended to comprehensive measures of the nature of the
professional development experience (e.g., potential foci of professional development,
opportunities for non-interactive professional development). Second, like Desimone et al., and
Garet et al., our study design is longitudinal. Thus we are able to model factors that affect
change in technology use. This allows us to make a stronger case for the inference of effects of
our primary factors (Cook, Shadish and Wong, 2008; Shadish, Clark, and Steiner, 2008). Third,
similar to Zhao and Frank (2003), our dependent measure is defined by the number of uses of
technology for the core tasks of teaching (e.g., did teachers use the innovation for presenting
material; for student to student communication; for student expression) – see O’Donnell (2008).
This differentiates our instrument from others that attend to particular goals of an innovation
(e.g., to have teachers use a particular innovation a given number of times in a week).
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Running head: Focus, Fiddle and Friends
Sample
Because professional development is often organized and offered by school districts, we chose
whole school districts as our first level of analysis. A total of ten districts were selected from one
Midwestern state. Since our example of an innovation was technology, we needed schools that
had technology available to teachers and students. Operationally, the criteria used to select
districts for participation in the study included passage (between 1996 and 2001) of a bond
referendum or receipt of a community foundation grant for implementation of technology or
recent investment in an externally supported professional development program for technology
(and of course the willingness of the superintendent to participate in the study). Because we
wanted to study the social dynamics of technology implementation we focused on elementary
schools that tend to be small and relatively tightly defined social systems. We selected all
twenty five elementary schools within the ten districts.
As reported in Zhao and Frank (2003), the base of our sample had more access to
technology than the national average (Cattagni and Farris 2001). This is valuable because we
wanted to study examples of technology implementation that could anticipate future trends.
Furthermore, students attending the sampled schools came from slightly higher income families
than average in the state in terms of percentage of students who qualified for free or reduced cost
lunch. However, the sampled schools were not substantively different from other elementary
schools on other measures such as per pupil expenditure, student teacher ratio, and school size.
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Running head: Focus, Fiddle and Friends
The first round of data collection was completed in the spring of 2001 for 19 of the
schools and in the Fall of 2001 for six of the schools (referred to as time 1). We then returned to
each school in the Spring of 2002 and re-administered our instrument (referred to as time 2).
In order to obtain a comprehensive representation of technology uses we administered the survey
to all staff within each school. We offered incentives to schools, as well as to individual teachers
for high response rates to come as close as possible to enumerating each faculty population.
Ultimately we achieved a response rate of 92% or greater in each of our twenty-five schools at
each time point.
Data Collected
We collected three types of data: a) survey of all staff; b) interviews with key informants
in focus schools; and c) observations of professional development in each district. The survey
included 33 various format items (e.g., Likert Scale, multiple choice, and open ended). The
interviews were semi-structured, loosely following a set of questions about technology
infrastructure, policy, investment, and attitudes and beliefs regarding technology. The interviews
were conducted with the district superintendent, district technology director (or equivalent),
principal of one focus school in each district, and three to five teachers in each focus school.
Though we do not report directly on the interviews and observations here, they were critical in
informing our understanding of the phenomenon (Zhao and Frank 2003; Zhao Frank and
Ellefson 2006) as well as shaping our survey instruments. A professional independent research
firm was contracted to collect the survey data and conduct some of the interviews. We
conducted the professional development observations.
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Running head: Focus, Fiddle and Friends
Measures3
Dependent Variable: Change in Use of Computers. Our dependent variable represents the
number of times a teacher indicated her students used computers for the core tasks of teaching.
In particular, level of implementation was measured based on responses to the following items:
How frequently [daily=180, weekly=40, monthly=9, yearly=2, never=0] do you or your students
use computers for …classroom management and/or incentives for students; student to student
communication; student inquiry; student expression; core curriculum skills development;
remediation; and basic computer skills (alpha = .76). Thus the measure taps the implementation
of the innovation (technology) for the core functions of teaching.
Key predictors
Hours of Professional Development Focused on Student Learning(focus). We measured
each teacher’s perception of the focus of professional development on student learning from a
composite of items beginning with the stem “What percentage of your technology professional
development …” and concluding with “helped you improve the content you taught”; “helped you
improve student achievement”; “helped you improve your teaching style”; “involved content
directly linked to your curriculum”; “helped you learn how to integrate technology into the
curriculum”; and “engaged you in the planning stages” (scale=0%,25%,50%,75%,100%;
alpha=.81). Thus, like our dependent variable, this variable was defined by the core tasks of
teaching. We then calculated the hours of professional development focused on student learning
by multiplying the percentage focus by the total number of hours of professional development
each teacher reported receiving between time 1 and time 2.
3
See on-line technical appendix for details.
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Running head: Focus, Fiddle and Friends
Days per year Exploring or Experimenting with New Technology (fiddle). We measured
exploration and experimentation by asking teachers to indicate the frequency [daily=180,
weekly=40, monthly=9, yearly=2, never=0] with which they explored new technologies on their
own and with which they experimented with district-supported software between time 1 and time
2. Our measure is the sum of the two items (which were correlated at .31).
Access to Knowledge of Technology Implementation through Talk and Help (friends). We
defined a teacher’s access to knowledge through interaction as the sum of the knowledge of the
others with whom she talked or from whom she received help regarding use of technology in the
classroom. As such it is a measure of the manifestation of social capital because teachers
accessed the resource of knowledge through the social relations of talk and help (Frank, Zhao
and Borman 2004). As a hypothetical example, consider Lisa who indicated talking with Sue
and Bob between time 1 and time 2. If Sue had a knowledge level of 9 at time 1 (out of a scale
from 1 to 10), and Bob had a knowledge level of 3 at time 1, Lisa accessed 12 units (9+3=12) of
knowledge through her interactions with Sue and Bob between time 1 and time 2. We divided
by 180, the number of days in the school year, to create a metric representing the amount of uses
per day of the others with whom a teacher interacted.
We used the self reported level of a teacher’s implementation at time 1 to measure the
knowledge the teacher could potentially convey to others in her school. Therefore “knowledge”
was not simply of the innovation, but of how to implement the innovation in the complex setting
of the classroom (Casson 1994). Access to knowledge through talk and help was then the sum of
access to knowledge through talk and help. We then took the log of the sum (plus one) to reduce
positive skew (see technical appendix for details). We assigned a value of zero to those who did
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Running head: Focus, Fiddle and Friends
not list any others from whom they received help or with whom they talked about computers
because they had no social capital on which to draw. The metric for access to knowledge through
interactions with others is difficult to interpret because it is the logged sum of two products of
frequency of interactions and others’ knowledge. Therefore we interpret primarily the
standardized coefficient for this predictor and compare with estimates for our other key
predictors.
Key Covariate: Teacher Perception of Technology. As in other settings (Rogers 1995),
teacher perceptions of the value of technology are important predictors of use of technology
(Frank, Zhao and Borman 2004). We measured teachers’ perceptions of the value of computers
for their own use in terms of responses to items with the stem “computers can help me...” and
ending with core elements of teaching: “…integrate different aspects of the curriculum”; “…;
teach innovatively”; “…direct student learning”; “…model an idea or activity”; “…connect the
curriculum to real world tasks”; “…be more productive” (responses range from 1=strongly
disagree to 6=strongly agree, alpha=.93).
Key Covariate: Seeking Help from others to Learn about Technology. It could be that access
to knowledge is confounded with knowledge seeking and therefore an estimated effect of access
to knowledge of colleagues may be upwardly biased because those who are more motivated to
engage technology may seek more help and also increase their uses of technology. Therefore in
all our models we controlled for the number of days per year (never=0; yearly=1; monthly=9;
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Running head: Focus, Fiddle and Friends
weekly=40; daily=180) a teacher indicated seeking help from others to learn about new
technologies. 4 This variable was measured retrospectively at time 2.
Other Covariates. Of course, aspects of professional development, motivation for attending
professional development, and adequacy of technology resources may affect technology
implementation. Drawing on the professional development literature (e.g., Adelman et al. 2002;
Wilson Berne 1999), we explored controls for focus of professional development on student
skills (e.g., focus on how to use technology to help students navigate the web); 5 focus of
professional development on teaching skills (e.g., power point); linkage of professional
development to national and state standards; opportunities for general professional development
activities other than as represented by our key predictors; pedagogical style of professional
development (e.g., didactic, interactive, collaborative); location, timing, duration, and instructor
for professional development; support of professional development for other teacher functions
(e.g., managing the classroom); focus of professional development on improving teaching style;
motivation for attending professional development; perceived reliability of technology resources;
perceived adequacy of technology resources; perceived district support for hardware; perceived
district support for software; perceived value of computers for student uses; perceived pressure
to use technology; perceived pace of technology implementation in the school; and teacher
background characteristics.
ANALYTIC APPROACH
4
Seeking Help from others to Learn about Technology is differentiated from Access to Knowledge of Technology
Implementation through Talk and Help because those who seek help may not necessarily receive help.
5
Zhao and Frank, 2003, distinguish uses of computers for the teacher’s purpose versus student skills because teacher
uses help develop student learning while student uses help develop specific skills that might be applied in other
contexts. As such they serve separate purposes form the teacher’s perspective.
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Running head: Focus, Fiddle and Friends
We began by estimating a model of change in uses of computers as a function of our
independent variables as well as our key covariates. We then estimated a second model using
the residuals from the first as the dependent variable and adding other covariates using a
stepwise selection procedure. This was done to identify possible covariates after controlling for
our independent variables and key covariates. Of the possible covariates, three were statistically
significant using a liberal criterion (p < .10): motivated to attend professional development for
technology because of excitement to apply technology in classroom; percentage of time
professional development took place in your school computer lab (negatively related to change
in use of computers) and percentage of time professional development took place at an
intermediate school district facility.
We then divided our sample into three groups: low level implementers (fewer than 58
uses of computers in the time 1 school year, n=209), intermediate level implementers (between
59 and 182 uses of computers in the time 1 school year, n=174), and high level implementers
(more than 182 uses of computers in the time 1 school year, n=197). 6 For each group we report
descriptive statistics before using the SAS PROC MI procedure to impute five data sets for (the
less than 10 cases) with missing values using a Markov chain Monte Carlo method (with a
Jeffrey prior), constraining values for each variable to the range that occurred in the observed
data. The imputed values were based on our key covariates as well as those identified by the
stepwise regression.
6
The sample was divided into approximately equal groups using SAS proc ranked. The sizes are unequal because
of ties in the number of uses of computers.
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Running head: Focus, Fiddle and Friends
To evaluate our primary hypotheses concerning the match between initial level of
implementation and knowledge gaining experience (H1, H2, H3), we estimated a separate
regression for those at each initial level of implementation, retaining our key predictors as well
the covariates identified by the stepwise regression for the whole sample that were statistically
significant (p < .10). The only covariate to satisfy this criterion for any group was an indicator of
whether a teacher was motivated to attend professional development for technology because of
excitement to apply technology in classroom (statistically significant for the group of teachers
high on initial level of implementation). We retained this variable in each of our models for
comparability. We also controlled for the school in which the teacher taught (using fixed effects
– twenty-four dummy variables for the twenty-five schools). 7 We used interaction terms in an
analysis of the whole data set to test the exploratory hypotheses concerning whether the effect of
the knowledge matched to the specific level of implementation was strongest at that level (e.g,
whether the estimated effect of focused professional development was stronger for teachers at
low initial levels of implementation than for others).
Because our outcome is the change in uses of computers and therefore can be considered
a count variable, we confirmed all of our inferences using Poisson models, correcting for overdispersion (equivalent to a negative binomial), although we report results from linear models for
ease of interpretation (inferences from the Poisson were consistent with those reported). In a
subsequent section we explore the robustness of our inferences to potential selection bias due to
omitted confounding variables.
RESULTS
7
Supplemental analyses indicated that there was essentially zero variation attributable to schools once districts had
been taken into account.
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Running head: Focus, Fiddle and Friends
Table 1 presents descriptive statistics for change in computer uses and for our key
theoretical independent variables as well as the key covariates. Teachers at low levels of
implementation at time 1 used computers roughly 31 times throughout the first year of teaching
and more than doubled their uses between the first and second year of our study (the change is
statistically significant p < .001 using a paired t-test). Teachers at intermediate levels of
implementation at time 1 used computers roughly 101 times throughout the first year of the study
and added thirteen uses on average (the change is borderline statistically significant p < .07).
Teachers at high levels of implementation used computers roughly 319 times throughout the first
year of the study but decreased their uses by about 73 occurrences (the change is statistically
significant p < .001). The changes in uses of computers across the three groups were
significantly different from one another with the decrease in uses for the initial high level group
significantly different form the other two groups (using Tukey’s honestly significant differences,
p < .05). Thus, there is a tendency for regression to the mean, with teachers at the lowest initial
levels increasing and teachers at the highest initial levels decreasing (see the discussion for
further comment).
Insert table 1 about here
Regarding the independent variables, each independent variable increased with initial
level of implementation. That is, teachers at the highest levels of implementation at time 1
subsequently experienced the most hours of professional development focused on student
learning (8.6); spent the most days per year exploring technology (50); accessed the most
expertise of others (2.7); held the highest perceptions of the value of technology (5.1 on a 6 point
likert scale); sought the most help from others to learn about technology – about eighteen days;
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Running head: Focus, Fiddle and Friends
and were most likely (61%) to be motivated to attend technology professional development
because they were excited to apply technology in their classrooms (all differences among the
three groups were statistically significant at p < .001, except days per year sought help from
others to learn about new technologies which was significant at p < .05). Thus, those at the
highest initial level of implementation most actively engaged new sources of knowledge. This
emphasizes the need to control for initial levels of implementation when evaluating our
hypotheses.
Results for the estimated model of change in use of computers are reported in Table 2.
Consistent with hypothesis 1 (focus), the estimated effect of professional development focused
on student learning was statistically significant (p < .01) for teachers at low levels of
implementation at time 1. Teachers in this group increased about four and a half uses of
computers for each hour of technology based professional development focused on student
learning. Consistent with hypothesis 2 (fiddle), the estimated effect of experimentation was
statistically significant (p < .05) for teachers at intermediate levels of implementation at time 1.
For each day exploring or experimenting with computers teachers in this group increased about
one use of computers. Last, consistent with hypothesis 3, the estimated effect of access to
knowledge through interactions with others (friends) was statistically significant (p <.01) for
teachers at high levels of implementation at time 1. A one unit increase in the log of expertise
accessed through help and talk per day (recalling the measure was divided by 180 to reflect
access to expertise per day), resulted in almost thirty three more uses per year of computers for
the core tasks of teaching. For this group, the standardized coefficient for access to knowledge
through interactions was .27, comparable to the coefficient for days per year exploring or
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Running head: Focus, Fiddle and Friends
experimenting and roughly three times the size of the standardized coefficient for focused
professional development.
Regarding the comparison of estimates across initial levels of implementation, the effect
of focused professional development for the low implementers was not significantly different
from the estimated effects for the intermediate and high level implementers. The effect of
experimentation for the intermediate group was not stronger than for the low and high groups.
This is because the high group also benefitted from experimentation. Thus, the estimated effect
of experimentation for the intermediate and high groups combined was statistically different
from the estimated effect for the low group (p < .10). Finally, the effect of access to knowledge
through talk and help was statistically higher for the high group than for either of the other two
groups (p < .001). The effects generally were strongest for the specified levels of initial
implementation, although the effects of each source of knowledge were relatively strong for
those at high initial levels of implementation.
Beyond the effects directly pertaining to our hypotheses, the perceived value of
computers to help with core tasks was statistically significant for teachers at low levels of
implementation at time 1 (and negative for the other two groups). This suggests that low level
users may well be motivated by their perceptions, as in the classic diffusion model. Low level
users were also strongly influenced by how much they sought others for interaction, suggesting
this alternative explanation to the effect of access to knowledge may apply to groups in the early
phases of adoption. Last, the motivation to attend professional development for technology
because of excitement to apply technology in the classroom approached statistical significance (p
< .10) for the high group. Overall, our model explained about 24% of the variation for each
group.
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Insert Table 2 about here
QUANTIFYING THE ROBUSTNESS OF OUR INFERENCES
Any policy or theoretical interpretations we make in our study will depend on the
robustness of our inferences. Recognizing the importance of causal inference, our outcome of a
difference score controls for implementation of technology in the classroom at time 1 (Allison,
1990). Furthermore, we also controlled for other key covariates. But, our inferences may be
invalid because teachers more interested in technology might use technology more as well as
seek more focused professional development, explore and experiment more, and seek more
interactions. That is, our covariates may be inadequate as controls. For example, Heckman and
Hotz (1989) argue strongly for controlling not only for the prior measure of an outcome, but the
prior trajectory that would also forecast a subject’s outcome.
We recognize that no matter how many statistical controls we employ there will be
inevitable concerns about the validity of our inferences. Therefore to inform discourse about our
inferences, we quantify the concerns about the potential to invalidate our inferences. Our
approach can be considered an extension of sensitivity analysis (e.g., Copas and Li 1997; Robins,
Rotnitzky, and Scharfstein 2000; Rosenbaum and Rubin 1983).
Classically, internal validity can be expressed in terms of confounding variables that are
correlated with both the predictor of interest and the outcome (Shadish, Cook, and Campbell
2002). For example, the effect of focused professional development could be confounded with
motivation to attend professional development because motivation could be correlated with the
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type of professional development received as well as subsequent changes in practices. This is
also known as concern over selection bias (Heckman 1978) or identification (Manski 1995).
To express robustness that accounts for the relationship between a confounding variable
and the predictor of interest and between the confounding variable and the outcome, Frank
(2000) defines the impact of a confounding variable on an estimated regression coefficient as
impact= ryv  rxv . In this expression, ryv is the correlation between a confounding variable, v (e.g.,
motivation), and the outcome y (e.g., change in computer uses), and rxv is the correlation
between v and x, a predictor of interest (e.g., hours of technology professional development
focused on student learning). Frank (2000) then quantifies how large the impact must be to
invalidate an inference.
Using Frank’s calculations, the impact of a confound would have to be greater than .047
to invalidate the inference of an effect of focused professional development on change in
implementation for low level implementers. In terms of the component correlations, ryv (the
correlation between the confound and the outcome) must be greater than 0.21 and rxv (the
correlation between the confound and the predictor of interest) must be greater than 0.22 to
invalidate the inference (using Frank’s multivariate correction).
Though the magnitude of the impact threshold for an unmeasured variable can be
interpreted in terms of general effect sizes in the social sciences (Cohen and Cohen 1983), it is
helpful to compare the threshold to the impacts of measured covariates. Partialling only for
schools as fixed effects, the strongest covariate for the low level implementers is “seeking help
from others to learn technology,” with an impact of .038 (.038=.259 x 148). Comparing .038
with the impact threshold of .047, the impact of an unmeasured confound would have to be about
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Running head: Focus, Fiddle and Friends
50% greater than the impact of our strongest covariate to invalidate the inference of an effect of
professional development focused on student learning.
Using a similar logic for those at intermediate initial levels of implementation, the impact
threshold for the effect of days per year exploring or experimenting with technology is .036, with
component correlations of ryv =.187 and rxv =.193. For this group, excitement to apply
technology in the classroom has the strongest impact, a value of .065 (.065=.127 x .508). Thus if
the impact of an unmeasured confounding variable had about half the impact of the strongest
measured covariate, the inference would be invalid.
Last, for those at high initial levels of implementation, an unmeasured confound would
have to have an impact of .084 (with component correlations of .29) to invalidate the inference
that access to knowledge through talk and help affects change in level of implementation. As
with the intermediate group, excitement to apply technology in the classroom had the largest
impact with a value of .076 (.076=.187 x .404). Thus the impact of an unmeasured confounding
variable would have to be 10% stronger than the impact of the strongest covariate to invalidate
our inference.
Overall, the impact thresholds reveal that the inference for exploring and experimenting
with technology for the intermediate group is less robust than the other inferences. This is
important to acknowledge, although we note that the inference for the effect of experimentation
is more robust for the high group (t-ratio of 3.17 for the high group versus 2.47 for the
intermediate group), supporting the general effect of experimentation and exploration on change
in use of computers.
DISCUSSION
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Running head: Focus, Fiddle and Friends
The macro level link between knowledge and productivity has been conceptualized for
the manufacturing process (Romer 1990). Productivity increases as new knowledge is
transmitted via simple communication or transfer of personnel from one organization to another.
But when the production process is complex, one must pay careful attention to the evolution of
knowledge that supports diffusion. As in the example in this study, the teachers in one school
cannot typically “manufacture” human capital using the techniques learned from teachers in
another school or experts in a particular technology. This suggests a diffusion and evolution of
knowledge intertwined with the implementation of innovations.
Our findings support our primary hypotheses pertaining to the need to match knowledge
source with initial level of implementation. The effect of focused professional development was
statistically significant for those at the lowest levels of implementation. The effect of
exploration and experimentation was statistically significant for teachers at intermediate initial
levels of implementation, and the effect of interactions with others was statistically significant
for those at the highest initial levels of implementation. Note this last finding is especially
important because the highest initial implementers were likely to decrease in their use of
computers. Thus interactions with colleagues may be especially important to sustaining the
implementation of innovations.
Though some of the estimated effects are small, because our dependent variable is
defined as a difference score we emphasize that these are effects on changes in levels of
implementation. As such, the effects may accumulate across years or may complement one
another as teachers develop knowledge and implement innovations. Furthermore, inferences for
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Running head: Focus, Fiddle and Friends
the effects for low and high level implementers are at least moderately robust with respect to
concerns for unmeasured confounding variables (i.e., selection bias).
Our findings suggest a three part evolution in knowledge as innovations permeate
organizational boundaries. Most teachers begin by learning basics from an external agent who
conveys knowledge relevant for the teacher’s goals (focus). This process allows for the diffusion
of knowledge from organization to organization, or from researcher to practitioner. Teachers
may then adapt the innovation to their unique contexts by exploring and experimenting with the
innovation (fiddle). Last, teachers may benefit from further interactions with others who may
support their initiative to innovate, and from whom they can obtain further, context specific,
knowledge (friends).
We emphasize how the organizational boundary shapes the evolution of knowledge
because it defines the salient local conditions and identifies organizational membership. In this
sense, our story is not simply one of a learning theory that includes experimentation and
interaction applied in a professional context (e.g., Cohen, McLaughlin and Talbert, 1993;
Wilson, and Berne, 1999). It is not just that teachers at intermediate levels of implementation
must generally adapt practices, but they must adapt them to the local conditions defined by their
organization – the school. And it is not just that any interaction will do for teachers at high
levels of implementation – school colleagues who know and understand the organizational
context will be able to provide the most local knowledge and support. Our conceptualization also
is not one of novices entering a community of practice in which the local knowledge is already
embedded (e.g. Lave and Wenger, 1991). Instead the novices are vectors of knowledge transfer
as they adapt knowledge generated external to the organization to the organizational context and
then interact with others in that context.
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Running head: Focus, Fiddle and Friends
The question then turns to the value of each type of knowledge when it is not matched to
a specific level of implementation. Our findings provide modest support for the relative effects
of different sources of knowledge depending on initial level of implementation. In particular, the
tacit knowledge gained through experimentation may not be valuable to novices, and the explicit
knowledge embedded in the local context may be valuable only to those who already possess
general and tacit knowledge. The primary departure from these trends was that those at high
initial levels of implementation tended to benefit from all sources of knowledge. Perhaps they
have the scaffolding to filter knowledge from professional development and to extract value
from experimentation. This suggests that knowledge sources are cumulative and not hierarchical
through evolution. Each source is valuable for its targeted level as well as those at higher levels
of implementation.
Limitations
While our research synthesizes the professional development literature with that of
diffusion of innovations, there are still important limitations to recognize. First, we develop our
theory generally with respect to innovations, but our data feature technology as an innovation.
While technology is an important innovation in elementary schools today, it is not clear what the
effects of professional development will be for other innovations. The work by Garret et al., and
Desimone et al., suggests that focus and fiddle will be effective forms of professional
development across innovations and some of our most recent work suggests that social factors
will also be relevant across innovations (Fishman, Penuel, and Yamaguchi 2006; Penuel, et al.,
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2007), but research should explore whether the effects of focus, fiddle and friends, depend on
initial levels of implementation in other contexts and relative to other outcomes.
Second, one of our strengths is that we comprehensively measured professional
development experiences. Yet we do not know their order, nor how elements of specific
programs might interact. For example, an ecological theory (Zhao and Frank 2003) would
suggest that experimentation might have a stronger effect if it comes after focused professional
development, but we were not able to empirically test this.
Even given these limitations, our data have tremendous strengths. Our longitudinal data
allowed us to control for many alternative explanations. Our social network data yielded more
valid measures of the knowledge teachers access through interactions than mere self report. We
measured the full range of professional development as well as of multiple dimensions of
professional development. Therefore, in the next subsection we draw policy implications based
on inferences from our data.
Policy Implications
Our findings and alliterative mnemonic (focus, fiddle and friends) lead to specific
guidelines. Professional development provided by an external agent focused on student learning
will be most effective for teachers or schools just beginning to implement an innovation. This
type of professional development will provide the baseline knowledge necessary to understand
the innovation. As implementation progresses schools should support localized experimentation
and exploration (fiddle). In this phase, schools might do well to devote their resources to teacher
release time for experimentation, and to validating the legitimacy of experimentation, some of
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Running head: Focus, Fiddle and Friends
which may not necessarily be immediately beneficial in the classroom. Last, once teachers attain
high levels of implementation they have the general knowledge of the innovation and the specific
knowledge of adaptation in their classroom to benefit from interactions with colleagues (friends).
In this phase schools might do well to facilitate interactions among teachers, for example, by
designating teachers knowledgeable in a particular area to provide professional development to
others in the school; coordinating release time among sets of teachers to promote knowledge
sharing; and cultivating new sources of knowledge or interactions to insure that knowledge does
not become isolated in social pockets of the school. For example, Frank, Krause and Penuel
(2007) present the intriguing finding that schools as organizations are most responsive to
knowledge flows that are generated by a small number of subgroups but that are distributed
across the school.
Our emphasis on interactions among teachers has profound importance for schools as
social organizations. In our data, teachers found it difficult to sustain high levels of
implementation in the absence of interaction with colleagues. Implied, when interaction
regarding an innovation is concentrated in localized pockets, so too will be sustained
implementation of the innovation. The result could lead to uneven implementation across the
organization (e.g., McLaughlin and Talbert 2001; Zmud and Apple 1992). Furthermore, because
current teaching practices form the landscape for the implementation of new innovations, uneven
implementation of one innovation could generate uneven implementation of future innovations,
creating further coordination problems for the school (Bidwell 2000, 2001; Frank Zhao and
Borman 2004).
Now imagine learning in a school in which one group of teachers wholeheartedly
implements an innovation such as technology while another group does not. Transitions from
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Running head: Focus, Fiddle and Friends
grade to grade or subject to subject could be extremely difficult and likely will be mastered only
by those students with the most advantages and support. For example, a parent who is adept
with technology may help her second grader engage new technology to which the student was
not exposed in first grade. This allows stratification with respect to background to emerge.
Conclusion
The preceding policy recommendations derive from a developmental conceptualization
of implementation. As such, they align the form of professional development with current levels
of implementation. Moreover, the developmental trajectory of implementation informs how
innovations diffuse first between organizations and then within organizations. In this case,
professional development conveys general knowledge between schools that is then adapted
through diffusion within schools. It is through altering these diffusion processes that
organizations such as schools shape the implementation of innovations. That is, the social
institution of the school contributes to society not just as a venue for transmitting knowledge to
students but as an organization that shapes and transforms the knowledge used by teachers.
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On-line technical appendix
Technical Appendix: Measures
Dependent variable: use of computers for student purposes
Based on composite of 7 items (alpha = .76):
How frequently do you or your students use computers for …
classroom management and/or incentives for students;
student to student communication;
student inquiry;
student expression;
core curriculum;
remediation;
basic computer skills.
The scale was (never, yearly monthly weekly daily), recoded into days per year (never = 0;
yearly=2; monthly=9; weekly=40; daily=180).
For independent variables, all responses included 0%, 25%, 50%, 75% ,100% and Don’t Know
(Don’t Know was coded as missing) unless otherwise specified.
Key Predictors
Focus of professional development on student learning (focus)
Teacher’s perception of the focus of the professional development on student learning
(alpha=.81):
What percentage of your technology professional development …
helped you improve students’ engagement in learning;
helped you improve the content you taught;
helped you improve student achievement;
helped you improve your teaching style;
involved content directly linked to your curriculum;
helped you learn how to integrate technology into the curriculum;
engaged you in the planning stages?
We then multiplied by the number of hours attended district or school in-service
programs for new technologies;
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Running head: Focus, Fiddle and Friends
Opportunities for experimentation and exploration (fiddle)
Please indicate the frequency with which you engage in each activity below (never = 0;
yearly=2; monthly=9; weekly=40; daily=180. Items combined (correlated at .31).
Explore new technologies on my own;
Experiment with district-supported software;
Log of Access to knowledge through talk and help (friends)
To measure access of knowledge of others, we developed the following network effects
measure (Marsden and Friedkin 1994). First, we asked each respondent to list the others in the
school with whom they talked about new uses for computers in their teaching. Then, the
helpfulness of another depends on the helper’s use of computers for student purposes at time 1.
Our reasoning is the helper’s “knowledge” as a resource is best measured not by the helper’s
knowledge of computers in the abstract but by the helper’s ability to implement computers in her
own classroom (Zhao and Frank 2003; Frank Zhao and Borman 2004).
Formally, access to knowledge through talk is the sum of extent of implementation of the
others with whom a respondent talked: ∑ i’talkii’ level of implementationi’ t1. Here talkii’
takes a value of one if actor i indicated talking to i’ about technology, zero otherwise and level of
implementation i’ t1 is the extent to which i’ implemented technology at time 1. A similar
measure was created based on helpii’. Access to knowledge through talk and help was then the
sum of access to knowledge through talk and access to knowledge through help. We then took
the log of the sum (plus one) to reduce positive skew.
Key Covariates
Perception of the value of computers for teacher’s own use (responses range from 1=strongly
disagree to 6=strongly agree, alpha=.93):
Computers can help me...
integrate different aspects of the curriculum;
teach innovatively;
teach innovatively;
direct student learning;
model an idea or activity;
connect the curriculum to real world tasks;
be more productive
This measure was obtained at time 1 and time 2 for most teachers. Therefore we took the
average of the non-missing measures, improving reliability and representing the state of their
perceptions roughly between the first and second time points.
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Running head: Focus, Fiddle and Friends
Seeking Help. Please indicate the frequency with which you engage in each activity below
(1=never; 2=yearly; 3=monthly; 4=weekly; 5=daily; items used separately – intended as separate
dimensions):
Seek help from others to learn about new technologies.
Other Covariates
Alternative Foci
Extent to which professional development focused on student skills (alpha=.67):
Approximately what percentage of your technology professional development …
focused on how to use technology with diverse learners;
focused on how to use technology to help students navigate the web;
helped you learn how to teach technology skills to your students.
Focus on teacher skills (items used separately because no combination of 3 or more produced
alpha > .6):
Approximately what percentage of your technology professional development helped you
learn basic technology skills (i.e., word processing, Power Point);
Approximately what percentage of your technology professional development focused
on …
how to use technology to present information (i.e. Power Point);
how to use technology for assessment (i.e. using technology to track and evaluate
student achievement, record keeping);
how to use technology to find information on the web for professional purposes
(i.e. lesson plans).
Linkage to national standards:
Approximately what percentage of your technology professional development was associated
with (items used separately – intended as separate dimensions):
MEAP and/or district testing;
curricular changes;
changes in hardware or administrative software (i.e., electronic report cards, grade books);
state or national curriculum standards.
Opportunities for other professional development activities
Please indicate the frequency with which you engage in each activity below (1=never; 2=yearly;
3=monthly; 4=weekly; 5=daily; items used separately – intended as separate dimensions):
Attend professional-development conferences about new technologies;
Read professional journals about new technologies.
Professional development pedagogical style
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Running head: Focus, Fiddle and Friends
(items used separately – intended as separate dimensions):
Approximately what percent of your technology professional development during this school
year was …
continuation of technology professional development from previous years; didactic (i.e.,
you sat and listened to lectures);
interactive (i.e. instructors responded directly to your needs and requests);
collaborative (i.e., you worked with other participants during the training to achieve
common goals);
experimental (i.e., you experimented with technology during the professional
development).
Structural characteristics
Location (items used separately – intended as separate dimensions):
Approximately what percentage of your technology professional development during this school
year took place …
in your school computer lab;
in your classroom;
at a facility in your district;
at an ISD facility;
outside the district (public/private agency).
Timing of professional development (items used separately – intended as separate dimensions):
Please indicate approximately what percent of your technology professional development
during this school year occurred:
before school;
after school;
during school;
on weekends;
during vacation
don’t know
Duration of professional development (items used separately – intended as separate dimensions):
Please indicate approximately what percent of your technology professional development
during this school lasted …
a single day;
multiple days;
a semester;
a year.
Type of instructor (items used separately – intended as separate dimensions):
Approximately what percentage of your technology professional development was instructed by
a/an
classroom teacher;
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Running head: Focus, Fiddle and Friends
district technology specialist;
building technology or media specialist;
administrator;
outside specialist/consultant
Support other teacher functions (items used separately – intended as separate dimensions):
Approximately what percentage of your technology professional development helped you
to …
manage the classroom while using technology;
understand procedures for accessing technology resources (i.e. money from the district
for software, technical support);
trouble shoot technology problems;
access technology experts;
understand district technology policies (e.g. acceptable use, restrictions, internet use
permission slips).
Improve teaching style:
Approximately what percentage of your technology professional development helped you
improve your teaching style?
Motivation
Please check any and all of the factors that motivated you to attend technology professional
development this year.
Compensation (i.e., stipends, release time, credits, reimbursement, hardware/software
resources);
Contractual stipulation;
Increased professional opportunities;
Recognition;
My excitement to apply technology in my classroom.
Technology Resources
Reliability of computer resources (items used separately – intended as separate dimensions):
During the current school year what percent of the time have you had technical problems with
the computers in the lab?;
During the current school year, what percent of the time have you had technical problems with
the computer(s) in your classroom?
Of the times you have had technical problems with computers, what percent of the time did you
solve the problem yourself?
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Running head: Focus, Fiddle and Friends
Adequacy of resources (1= strongly disagree to 6=strongly agree; alpha=.84):
Please indicate the extent to which you agree with each of the following statements
The computer resources in my room are adequate for my instructional needs (e.g., lesson and
unit planning, accessing materials such as pictures); The computer resources in my room are
adequate for student uses (e.g., student research, writing, artwork); It is easy to implement new
software in this school; It is easy to implement new hardware in this school.
District Support for Hardware(Please rate the district in terms of the following:1=Poor; 2=Fair;
3=Neutral; 4=Good; 5=Excellent; alpha=.92):
Providing enough hardware;
Choosing appropriate hardware;
Providing a reliable server;
Updating hardware;
Providing technical support for hardware use.
District Support for Software (Please rate the district in terms of the following:1=Poor; 2=Fair;
3=Neutral; 4=Good; 5=Excellent; alpha=.95):
Providing enough software;
Choosing appropriate software;
Updating software;
Engaging teachers in decisions about software purchases;
Providing professional development for software use;
Providing technical support for software use;
Recognition for technological innovation.
Perception of the value of computers for students’ use (responses range from 1=strongly disagree
to 6=strongly agree, alpha=.93):
Computers can help students …
develop new ways of thinking;
think critically;
gather and organize information;
explore a topic;
be more creative;
be more productive
This measure was obtained at time 1 and time 2 for most teachers. Therefore we took the
average of the non-missing measures, improving reliability and representing the state of their
perceptions roughly between the first and second time points.
Pressure to use technology (1= strongly disagree to 6=strongly agree, correlated at .69)
Please indicate the extent to which you agree with the following statements.
I need to use computers to keep up in this school;
Others in this school expect me to use computers
Pace of technology (1= strongly disagree to 6=strongly agree, correlated at .28)
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Running head: Focus, Fiddle and Friends
Please indicate the extent to which you agree with the following statements.
We introduce many new things in this school;
It is difficult to implement all of the new things in this school
Teacher background
Grade=highest grade taught;
Seniority= years in the school;
Gender=1 for female, 0 for male;
Tendency to innovate: (1= strongly disagree to 6=strongly agree. Alpha=.74)
Please indicate the extent to which you agree with the following statements.
I try new things in the classroom;
I am the first to try something new in the classroom;
I enjoy introducing something new in the classroom).
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Running head: Focus, Fiddle and Friends
References
Abrahamson, E., and Rosenkopf, L. (1997, May-June). Social network effects on the extent of
innovation diffusion: A computer simulation. Organization Science, 8(3).
Abdal-Haqq, I. 1996. Making Time for Teacher Professional Development. ERIC Digest. Access
ERIC: FullText (071 Information Analyses--ERIC IAPs No. EDO-SP-95-4). District of
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Table 1
Descriptive Statistics of Uses of Computers and Predictors for Teachers at Low, Intermediate
and High Levels of Implementation at Time 1
Level of Implementation at time 1
Low
Intermediate
High
Variable
n
Mean
Std
n
Mean Std
n
Mean Std
Uses of computers for core
teaching tasks at time 1
Change in uses of computers
for core tasks for teaching
between time 1 and time 2
Hours of professional
development focused on
student learning (between time
1 and time 2)
Focus
Days per year exploring or
experimenting with technology
(between time 1 and time 2)
Fiddle
Log of access to knowledge of
technology implementation
through talk and help (between
time 1 and time 2)
Friends
Perception of extent to which
computers can help with core
tasks (time 1)
Days per year sought help
from others to learn about new
technologies
Motivated to attend
professional development for
technology because of
excitement to apply technology
in the classroom
176
30.6
19.4
141
101
33.6
160
318.6 119.1
176
33.4
73.8
141
14.4
93.1
160
-73.3
167.6
176
5.8
3.6
136
7.4
4.1
157
8.6
4.4
175
15.8
25.8
139
19.5
24.4
160
49.7
60.8
176
2.1
1.4
141
2.7
1.2
160
2.7
1.4
174
4.5
1
140
4.8
.76
159
5.1
.90
174
11.8
28.0
136
11.33 13.2
158
18.7
32.7
176
.34
.48
141
.54
160
.61
.49
.50
51
Running head: Focus, Fiddle and Friends
Table 2
Regression of Change in Uses of Computers between Time 1 and Time 2 for Teachers at Low,
Intermediate and High Levels of Implementation at Time 1a
Level of Implementation at Time 1
Variable
Hours of professional development focused on
student learning (between time 1 and time 2)
Focus
Days per year exploring or experimenting with
technology (between time 1 and time 2)
Fiddleb
Log of access to knowledge of technology
implementation through talk and help (between
time 1 and time 2)
Friendsc
Covariates
Perception of extent to which computers can help
with core tasks (time 1)
Days per year sought help from others to learn
about new technologies
Motivated to attend professional development for
technology because of excitement to apply
technology in the classroom
R2
Low
Intermediate
High
(n=176)
(n=141)
(n=160)
4.43**
(1.64)
[.22]
-.19
(.21)
[-.07]
-2.53
(4.06)
[-.05]
.32
(2.36)
[.01]
.84*
(.34)
[.22]
7.16
(6.82)
[.09]
3.39
(3.55)
[.09]
.70**
(.23)
[.25]
32.86**
(10.37)
[.27]
11.46*
(5.65)
.64***
(.20)
-18.81
(11.60)
-9.50
(11.16)
.69
(.64)
19.28
(17.68)
-9.49
(15.13)
-.46
(.44)
53.13d
(31.24)
.24
.25
.23
Standard errors in parentheses, standardized coefficients in square brackets.
a
All estimates based on ordinary least squares regression with multiple imputation for missing
data. All inferences based on statistical significance confirmed with a Poisson model with over
dispersion.
b
Estimate is statistically smaller for low group than others (p < .10).
c
Estimate is statistically larger for high group than others (p < .001).
d
p < .10; * p < .05; ** p < .01; *** p < .001;
52
Running head: Focus, Fiddle and Friends
Figure 1: the Transformation of Knowledge
As an Innovation Permeates the School’s
Organizational Boundary
General, abstract
knowledge conveyed
during Focused
professional
development
Knowledge becomes tacit as it
is adapted to local context
through exploration and
experimentation
(Fiddle)
Knowledge
articulated and
integrated
through
interactions with
colleagues
(Friends)
53