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Focus, Fiddle and Friends: Sources of Knowledge to Perform the Complex Task of Teaching 1

Kenneth A. FrankYong Zhao

Michigan State University

William R. Penuel

SRI International

Nicole Ellefson


Susan Porter


Contact information: Kenneth A. FrankRoom 462 Erickson Hall,Michigan State UniversityEast Lansing, MI 48824-1034phone: 517-355-9567; e-mail: [email protected].

1 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.

mailto:[email protected].

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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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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boundary (e.g., 1Abrahamson 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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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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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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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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(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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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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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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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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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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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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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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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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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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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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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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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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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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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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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 over-

dispersion (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.

22


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;

23


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

24


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

26


type of professional development received as well as subsequent changes in practices. This is

also known as concern over 1selection 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= . In this expression, is the correlation between a confounding variable, v (e.g.,

motivation), and the outcome y (e.g., change in computer uses), and 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, (the

correlation between the confound and the outcome) must be greater than 0.21 and (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

27


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 =.187 and =.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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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

29


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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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.,

31


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

32


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

33


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;

35


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’ i’talktalkii’ii’ level of implementationlevel of implementationi’i’ t1 t1. Here talktalkii’ii’ takes a value of one if actor i indicated talking to i’ about technology, zero otherwise and level of implementation i’i’ t1 t1 is the extent to which i’ implemented technology at time 1. A similar

measure was created based on helpii’ii’. 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.

36


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

37


(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

38


classroom teacher; 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?

39


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 statementsThe 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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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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Table 1Descriptive Statistics of Uses of Computers and Predictors for Teachers at Low, Intermediate

and High Levels of Implementation at Time 1

Variable

Level of Implementation at time 1

Low Intermediate High

n Mean Std n Mean Std n Mean Std

Uses of computers for core teaching tasks at time 1

176 30.6 19.4 141 101 33.6 160 318.6 119.1

Change in uses of computers for core tasks for teaching between time 1 and time 2

176 33.4 73.8 141 14.4 93.1 160 -73.3 167.6

Hours of professional development focused on student learning (between time 1 and time 2) Focus

176 5.8 3.6 136 7.4 4.1 157 8.6 4.4

Days per year exploring or experimenting with technology(between time 1 and time 2)Fiddle

175 15.8 25.8 139 19.5 24.4 160 49.7 60.8

Log of access to knowledge of technology implementation through talk and help (between time 1 and time 2)Friends

176 2.1 1.4 141 2.7 1.2 160 2.7 1.4

Perception of extent to which computers can help with core tasks (time 1)

174 4.5 1 140 4.8 .76 159 5.1 .90

Days per year sought help from others to learn about new technologies

174 11.8 28.0 136 11.33 13.2 158 18.7 32.7

Motivated to attend professional development for technology because of excitement to apply technology in the classroom

176 .34 .48 141 .54 .50 160 .61 .49

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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

Low

(n=176)

Intermediate

(n=141)

High

(n=160)

Hours of professional development focused on student learning (between time 1 and time 2)Focus

4.43**

(1.64)[.22]

.32(2.36)[.01]

3.39(3.55)[.09]

Days per year exploring or experimenting with technology (between time 1 and time 2)Fiddleb

-.19(.21)[-.07]

.84*

(.34)[.22]

.70**

(.23)[.25]

Log of access to knowledge of technology implementation through talk and help (between time 1 and time 2)Friendsc

-2.53(4.06)[-.05]

7.16(6.82)[.09]

32.86**

(10.37)[.27]

CovariatesPerception of extent to which computers can help with core tasks (time 1)

11.46*

(5.65)-9.50(11.16)

-9.49(15.13)

Days per year sought help from others to learn about new technologies

.64***

(.20).69(.64)

-.46(.44)

Motivated to attend professional development for technology because of excitement to apply technology in the classroom

-18.81(11.60)

19.28(17.68)

53.13d

(31.24)

R2 .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;

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