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Branding Educational Data Use through Professional Learning:

Findings from a Study in Three School Districts

Jo Beth Jimerson, Ph.D.

Texas Christian University

Jeffrey C. Wayman, Ph.D.

The University of Texas at Austin

This study was made possible through funding provided by The Spencer Foundation.

Paper presented at the 2012 Annual Meeting of American Educational Research Association,

Vancouver, British Columbia.

We welcome feedback. Please send questions, comments, and requests to

Jo Beth Jimerson: [email protected]

mailto:[email protected]

Branding Educational Data Use 2

ABSTRACT

In order to learn more about how school districts support educator data use, we examined the

intersection of data use and professional learning in three school districts. We conducted a

qualitative study, relying on interview data from n=110 individuals across the three districts, as

well as documents from those districts, to inform our analysis. We found that a chasm exists in

how educators frame ―data use,‖ with some framing data use as a student-oriented improvement

process, and others framing it as a mere exercise in the accountability ratings chase. Further,

these perceptions seemed to impact educators‘ willingness to invest time and effort in data-

informed practice. District leaders often spoke of data use as improvement-oriented; however,

participants‘ descriptions of data-related professional learning opportunities consistently

underscored a focus on accountability system concerns and an overall accountability orientation.


Branding Educational Data Use through Professional Learning:

Findings from a Study in Three School Districts

In one way or another, educators have always been expected to use data, and they always

have used data: Even in the one-room schoolhouses of the past, teachers ―took data‖ by

providing assignments and issuing grades. In the last few decades, however, an increasing focus

on school accountability at the state and federal levels has escalated the expectations on

educators to attend to particular types of data in addition to the gamut of information already in

play. In the accountability era, educators are expected to use a range of local and broad-scale

standardized data to inform what happens in classrooms and schools on a daily basis (Anderson,

Leithwood, & Strauss, 2010; Means, Padilla, DeBarger, & Bakia, 2009; Park & Datnow, 2009).

Despite increasing expectations to engage in data-informed practice, many educators

struggle with aspects of data use (Goertz, Olah, & Riggin, 2010; Means et al., 2009; Wayman,

Jimerson, & Cho, in press). A variety of factors contribute to this difficulty, from user-unfriendly

data systems (Wayman & Cho, 2008), to the lack of a clear vision for the role of data use data

use (Louis, Leithwood, Wahlstrom, & Anderson, 2010), to mistrust among teachers related to

past abuses and misuses of data (Earl & Fullan, 2003; Louis et al, 2010). Also, obstacles to

effective data use can be linked to the scarcity of data use-related knowledge or supports in some

educational contexts. These supports include time dedicated to learning about and practicing

collaborative data use (e.g., Ikemoto & Marsh, 2007) and professional learning that aims at

improving data use capacity of teachers and school leaders (Jimerson & Wayman, 2011).

Initially, we set out to learn more about how districts could support improved data use

among teachers. We were particularly interested in how professional learning might serve as a

catalyst for improved data use capacity among educators. In early explorations of this issue, we


noted that: (1) teachers and administrators (including district leaders) seemed to articulate a

range of rationales for data use; (2) some of these rationales seemed in conflict; and (3) in

several instances, the descriptions provided by educators of data-related professional learning

experiences seemed related to these rationales (Wayman, Cho, Jimerson, & Snodgrass Rangel,

2010; Wayman, Jimerson, & Cho, 2010). We decided to press more on this notion of whether

districts intentionally or inadvertently brand data use through leadership and professional

learning structures. Accordingly, the purpose of the present study was to examine the

intersection of data use and professional learning with particular attention to the role that

rationales for data use play in garnering educator commitment to engage in data-informed

practice. Our research was guided by two questions:

(1) How do educators conceptualize the ―purpose‖ or ―rationale‖ for data use?

(2) Does the structure of data-related professional learning influence these conceptions and,

if so, how?

Data Use, Educators, & the Accountability-Improvement Divide

In this section, we describe the context of the research in which this study is situated. We

first discuss the research that focuses on the value of data use itself. Then, we examine factors

that facilitate educator data use, and which might be well-addressed via professional learning

structures. Finally, we discuss two rationales toward which educator data use might be oriented.

Data Use & School/District Effectiveness

Prior to delving into the supports that facilitate or hinder data use, or the perspectives that

inform how data use is shaped in schools or districts, we think it important to consider whether

data use matters to improving student outcomes.


Is data use by any other name still data use? A challenge in reviewing the research

context for the efficacy of data use is that similar structures exist under various names. In line

with much of the research, we assert that data use is at the core a social venture through which

educators interact with a variety of data, engage in collaborative meaning-making, and adjust

practice accordingly (e.g., Coburn & Turner, 2012; Coburn, Honig, & Stein, 2009; Datnow, Park

& Wohlstetter, 2007; Kerr, Marsh, Ikemoto, Darilek, & Barney, 2006; Knapp, Copland, &

Swinnerton, 2007; Supovitz, 2010; Wayman et al., in press). With this definition of ―data use,‖

we note that several other decision-making models make similar use of data to inform practice.

First, teams of educators who engage in action research reflect on and examine data to

address problems of practice in very context-specific ways (Ferrance, 2000; Mills, 2007).

Second, continuous improvement models (e.g., Langley et al., 2009) make use of work teams

which collect and analyzing information in the hopes of improving situation-specific practice.

Third, professional learning communities depend on collaborative inquiry processes (DuFour &

Marzano, 2011). Because these processes parallel data informed decision making, or ―data use,‖

we think it important that questions of effectiveness and impact not be limited to studies that

explicitly examine connections between the use of broad-scale or standardized data and

outcomes, but should include studies of how continuous improvement teams, professional

learning communities, and school-based action research teams make use of data and the

outcomes associated with these highly contextualized processes.

Does data use matter? If we understand ―data use‖ as a concept at the core of data-

informed decision making, action research, continuous improvement models, and professional

learning communities in that these all rely on similar data-informed inquiry processes, we must

still contend with the question, ―Does data use matter?‖


To this question, evidence is mixed. In terms of classroom-level use of formative

assessment data, Black and Wiliam (1998) reviewed over 250 studies and concluded that the use

of formative assessment by classroom teachers had a strong and statistically significant and

positive impact on student achievement. Using assessment to inform changes in practice is at the

heart of data use, and Black and Wiliam‘s metaanalysis points out that attending to the

information gleaned from classroom-based formative assessment plays a significant role in

addressing student needs.

Other studies have examined schoolwide effects of data use. Marsh, McCombs, and

Martorell (2010) examined the effects of instructional coaches on data-driven decision-making.

While data analysis support was but one of many supports provided to teachers by the coaches,

the authors noted that the majority of coaches focused considerable attention on data use, and

that data analysis support was associated with higher student achievement outcomes on the

Florida Comprehensive Achievement Test (FCAT) at the middle school level and with perceived

improvements in teaching.

In a six-year study that drew from data collected in nine states, 43 schools, and 180

campuses, Louis et al. (2010) looked at breadth and patterns of data used by principals. The

authors concluded:

When schools are considered in the aggregate, typical approaches to data use by districts

and principals have no measurable influence on student achievement. But variations in

data use, specifically in elementary schools, explain a significant amount of variation in

student achievement. (p. 179)

A more mixed portrait of data use emerged in a study conducted by Anderson, et al.

(2010), in which the authors examined the relationship between data use and student


achievement outcomes. In that study, the authors determined that statistical evidence of a

relationship between data use (i.e., principals‘ view of district data use, principals‘ own data use,

and teachers‘ perceptions of principals data use) and student achievement was weak at best, and

limited to the elementary school level. However, the authors noted that qualitative data suggested

that due, in part, to accountability pressures, educators were focusing data use efforts narrowly

on struggling students and schools, whereas:

… efforts to improve student learning are more likely to have a positive effect with the

data and the analysis performed by local educators goes beyond the identification of

problem areas to an investigation of the specific nature of and factors contributing to the

problem for the students and settings where it is situated. (p. 321)

The Anderson et al. study underscores the challenge in parsing out the effects of data use, as the

authors noted a variety of factors (such as principal leadership in the area of data use and a

general school culture that supports data use) that affect how educators use data.

Hamilton, Halverson, Jackson, Mandinach, Supovitz, and Wayman (2009) point out that

as of yet, there are few established causal links between educator data use and student

achievement outcomes. Coburn and Turner (2012) note that even where studies do point towards

promising outcomes, we know little about why data use seems to work in some contexts and not

in others. They suggest that the question is less, ―Does data use matter?‖ and more, ―When and

under what conditions does data use contribute to improved outcomes?‖ To this point, the

message we take from the literature is that the benefits of data use inhere in the informed

collaboration that can happen when teachers and administrators come together around a table of

data (or ―evidence,‖ or ―information‖) to explore problems of teaching and learning.

Educator Data Use


If we accept that collaborative data use can contribute to positive outcomes in at least

some contexts, then we must ask what factors within the influence of district leaders can

contribute to improved educator data use capacity. Here, we focus on four such factors: (1)

Vision for data use; (2) Data-able leadership; (3) Trust; and (4) Collaborative inquiry structures.

Vision for data use. Organizational learning literature (e.g., Senge, 2006) asserts that

people move toward goals more effectively if they can buy into an agreed-upon destination.

Research on data use similarly suggests that effective data use gains traction when teachers,

administrators, and other leaders co-construct and operate from common understandings about

the purposes end goals for data use (Datnow et al., 2007; Louis et al., 2010; Park & Datnow,

2009; Wayman et al., in press). Whether this takes the shape of a formal ―vision statement‖ or

abides throughout a network of learners, a vision that embodies common beliefs about teaching,

leaning, and the role of data use in supporting practice helps guide efforts at data use. Wayman et

al. (in press) point out that the work of co-constructing and revising a vision for data use has no

destination, but is always a work in progress, particularly as educators enter and exit educational

systems.

Data-able leaders. Nearly ubiquitous in the literature on effective data practice is the

finding that principal leadership matters tremendously to whether and how data use is

implemented at the school level (e.g., Anderson et al, 2010; Datnow et al., 2007; Park &

Datnow, 2009). Louis et al. (2010) noted that principals fill a middle role in that their own data

practices are greatly influenced by district leadership, but that the engagement of teachers on a

campus are similarly influenced by whether the principal can and does model effective data use.

In other studies, teachers reported that a major source of support in learning to use data systems


effectively was encouragement by a principal (Gallagher, Means, & Padilla, 2008) and that

leaders in high data-use schools hold a clear rationale for data use (Louis et al., 2010).

Trust. Less positive is the finding in several studies that principals often lack the data-

use skills they need to effectively lead teachers to use data in constructive ways (e.g., Earl &

Fullan, 2003; Means et al., 2009; Wayman, Cho, & Johnston, 2007). When principals do possess

skills for personal data use, the ways in which they engage others in data use sometimes

illustrates misuse or abuse, rather than the creation of trusting collaborations where teachers are

willing to lay bare weaknesses as well as strengths (Earl & Fullan, 2003; Ingram, Louis, &

Schroeder, 2004; Valli & Buese, 2007). Along these lines, Daly (2009) investigated responses to

accountability pressures, and found that leadership approaches that supported trusting, inclusive

working climates predicted lower levels of ―threat-rigid‖ responses. As he explains, ―The threat-

rigidity thesis postulates that when faced with significant threat, organizations (like individuals)

may close down, reduce information flow, engage in poor decision-making, and limit divergent

views‖ (p. 173). School leaders must work to create an atmosphere where data are not used to

shame or punish, but to support healthy dialogue, if they aim at engaging teachers in rigorous

data work (Firestone & Gonzelez, 2007; Louis, 2007; Wayman & Stringfield, 2006).

Collaborative inquiry teams. In schools where constructive data use is the norm, data

work typically happens in teams (Datnow et al. 2007; Louis et al, 2010; Park & Datnow, 2009).

To engage in such work, teachers must have adequate time dedicated to collaborative data use

(e.g., Ikemoto & Marsh, 2007; Louis et al., 2010; Wayman & Stringfield, 2006). Unfortunately,

research tells us that time for data use is typically lacking. Gallagher et al. (2008) reported that

only 23% of teachers in a national sample reported having time available in their regularly

scheduled workday devoted to data use. Valli and Buese (2007) found that teachers identified the


lack of time to engage in data use a formidable barrier to improvement efforts, and other studies

(e.g., Wayman et al., 2007) similarly identify time as an important—but often lacking—

facilitator of data use. This research suggests that leaders who envision a prominent role for data

use must take into account that robust data-informed inquiry can be time-consuming, particularly

when teachers are first exploring ways to engage in collaborative data use.

The Accountability-Improvement Divide

If collaborative data use, under certain conditions, can prove ―effective,‖ we must still

ask, ―Effective for what?‖ At the heart of this study was a key question about the ―end game‖ for

data use in our study districts, and how leaders and planners of professional development

communicate that rationale for data use through language and data-related professional learning.

In this section, we describe two divergent, but not mutually exclusive, purposes spurring data use

in schools: system-focused accountability and student-focused improvement.

Accountability. State and federal accountability requirements have gained steam in the

last 30 years, as proponents looked for ways to shine light onto populations previously

underserved (or simply disregarded) by schools (Beadie, 2004; Ravitch, 2010, Wells, 2009).

Accountability policies generally turn on a theory of action that by collecting and publicizing

student performance data, pressure will be brought to bear on the school(s) by internal and

external forces, and capacity that is already present in the system will be effectively activated

(Elmore, 2009; Loeb, Knapp, & Elfers, 2008). Elmore (2009) asserts that the assumption that

schools possess the capacity to respond constructively to accountability policies can be faulty.

Research affords mixed evidence on the usefulness of accountability exam data to

informing instruction for individual students. For example, For example, Carnoy and Loeb

(2002) conducted a cross-state analysis and concluded that states with more stringent


accountability requirements (as evidenced by the exams in place) had significantly greater gains

on the National Assessment of Educational Progress (NAEP) than states with less stringent

requirements. In contrast, Richards, Jimerson, & Cohen, (2010) reviewed the literature on high

stakes exit exams and concluded that the mere presence of high stakes exams does not ensure

benefit to students, and in some ways, may hinder achievement outcomes. Beadie (2004)

suggests that accountability data are ill-suited to informing instruction, because the types of data

collected for accountability and compliance purposes are typically focused at the system level,

while interventions for students need to be informed by multiple and timely data elements

focused at the student level. Thus, even though school personnel may ―break down‖

accountability data, we know little about the specifics of what they do with those data that may

accrue positively to student achievement (Coburn & Turner, 2012).

Improvement. Whereas accountability focuses on systems as the units of analyses, and

subsequently labels those systems (i.e., districts and campuses) largely in accordance with the

results of standardized exams, an approach to data use characterized by a focus on improvement

can take a system, school, collective, or even an individual student as the unit of analysis. For

example, Young (2006) describes teacher groups using data to analyze and inform classroom-

level practice. The work of DuFour and Marzano (2011) discusses several improvement-oriented

situations in laying out how professional learning communities function as school-level inquiry

groups. Response to Intervention models place the individual student at the center of data-

informed improvement processes (National Center on Response to Intervention, 2010). Bryk,

Sebring, and Allensworth, Luppescu, & Easton (2009) describe continuous improvement

concepts applied at the system level.


Research literature has afforded more credibility to practices which use accountability

exams as one component of a broad palate of data in decision-making (e.g., Anderson et al.,

2010; Coburn & Turner, 2012; Knapp et al., 2007). It is worth noting that in our review of the

literature, we did not find a single study or trade text that recommended that educators focus

exclusively (or even nearly exclusively) on state accountability data for decision-making.

Does the difference matter? An accountability orientation and an improvement

orientation to data use overlap to some degree, but we think the difference matters. The literature

is replete with findings of the negative effects of an over-emphasis on accountability, at the

expense of improved instruction (e.g., Booher-Jennings, 2005; Daly, 2009; Holme, 2008;

Vasquez Heilig and Darling-Hammond, 2008). In contrast, where data use is focused on

catalyzing improvements in teaching and learning, student achievement tends to increase (e.g.,

Bryk et al., 2009, Copland, Knapp, & Swinnerton, 2008; Marsh et al., 2010).

We note that while data use may be oriented towards issue of accountability or towards

issues of improvement, these orientations are not mutually exclusive. Accountability itself is not

inherently ―bad‖ just as improvement is not inherently ―good‖—we think what is being

measured or ―improved‖ matters tremendously, as does how improvements are facilitated.

Theoretical Framework

Our approach to this study was informed broadly by organizational theory and

specifically by Morgan‘s (2006) description of complexity theory and Senge‘s (2006) concept of

―mental models.‖

In describing how organizations function and change, Morgan (2006) notes that

organizations are complex, nonlinear systems—there is inherent complexity due to the multiple

actors, programs, procedures, and internal/external pressures that are at constant interplay. Yet,


―despite all the unpredictability, coherent order always emerges out of the randomness and

surface chaos‖ (p. 251). This order emerges, Morgan notes, because the systems ―get caught in

tensions…falling under the influence of different attractors‖ that help define context and

establish norms for actors in the system (p. 254). Attractors can cement norms and patterns in an

organization, or disrupt norms and patterns, pushing a system to change (for better or worse).

Attractors, Morgan notes, can push a system out of equilibrium—to a point at which the

system encounters a ―bifurcation point,‖ or a proverbial ―fork in the road.‖ Beyond that point,

the outcome for the system is much different depending on which path toward a new equilibrium

is elected. As Morgan addresses systems theory and complexity within the framework of

organizations and leadership, he asserts that leaders can attempt to ―jar‖ the system, or introduce

attractors that push the organization into new patterns.

Senge (2006) takes s lightly different approach in describing how leaders might catalyze

change. Instead of attractors and organizational patterning, he focuses on organizational learning

(including the learning of individuals as a key component of a learning organization). Senge

asserts that a critical consideration in learning is the presence of ―mental models,‖ or ―deeply

ingrained assumptions, generalizations, or even pictures or images that influence how we

understand the world and how we take action‖ (p. 8). He notes that in a learning organization,

members are committed to unearthing the mental models held throughout the system, to holding

these up to new questioning and evidence, and to reforming those mental models as needed.

As we approached this study, we reflected on these ideas of attractors and organizational

patterns of behavior and on the notion of malleable mental models, and considered how these

theories apply to current thinking about data use. Viewing the research through this theoretical


lens gave rise to three assumptions that help us consider the intersection of data use and

professional learning:

1. How teachers (and leaders) engage in data use is affected by their ―mental models‖

for data use—i.e., what they think data use is about.

2. Because mental models and patterns of organizational behavior are malleable, the

ways in which educators frame and engage in data use is also subject to change.

3. Professional learning—as an intended vehicle for individual growth and change

within a school system—provides a key opportunity to reframe or ―rebrand‖ data use

in a way that engages teachers and administrators in constructive collaboration

around data that benefits teaching and learning.

Figure 1 illustrates the range of how educators may conceptualize ―data use‖: Some

construe data use as an exercise necessitated by accountability requirements, while others

construe data use as wholly an improvement enterprise. Still others may perceive data use to be

about both—at times, data may be collected for reporting purposes and little else; at other times,

data may be collected to inform practice, but with no accompanying requirements related to

accountability policies.

Figure 1: The Accountability-Improvement Divide.

Accountability & Compliance

Improvement


In thinking about the interplay of a rationale for data use and how educators engage in

data use, we consider that data use may sometimes be more about accountability, other times

more about improvement, and at still other times, data use may fill a dual role. What is key is

that systems theory and Senge‘s concept of mental models both suggest that these orientations

need not be static—that leaders can work to shape and reshape (or brand and rebrand) data use.

Methods

With these concepts and assumptions in mind, we conducted a study that examined the

intersection of data use and professional learning in three central Texas school districts with

particular attention to the rationales articulated by various participants for engaging in data use.

Here, we provide some information on the context for each district1 and describe our data

collection and analysis procedures.

Participant Selection

This study was conducted under the auspices of a broader study that examined multiple

issues related to educator data use over the course of three years. Several districts in central

Texas were invited to participate in the broader study, and three were selected in order to capture

variety in district size and demographics. None was selected because of assumed data use

proficiency or general district effectiveness. In fact, each district‘s leadership volunteered to

participate because they wanted to improve data use practices in their respective districts. Thus,

sampling at the district level was neither random nor purposeful in the sense of selecting for data

use or general district effectiveness. Table 1 provides a comparison of the demographics of

participating districts; Table 2 provides an at-a-glance of overall district performance for a

window preceding and including the year of the study.

1 Pseudonyms are used for all participating districts.


Table 1. Comparison of participating districts.

The present study was conducted in these same districts, though we used stratified random

sampling to select nine study campuses: one elementary, one middle school, and one high school

within each district.

Procedures

We collected data through interviews, focus groups, observations, and document analysis.

In line with our research questions, we focused on identifying perceptions of data use and

rationales for data use—we wanted to know not only what individual participants thought

constituted the ―end game‖ for data use in the district, but also how they understood the district‘s

purposes and expectations for data use. We also focused on how participants described their

personal experiences with professional learning that involved elements of data use.

Data collection. We interviewed key campus and district-level personnel and conducted

focus groups that included teachers and campus teacher-leaders. Table 3 provides a description

of participants by role and district. In addition, we collected documents to triangulate data

collected via interviews and focus groups. All interviews and focus groups were recorded and

transcribed; transcriptions and collected documents were loaded into Atlas.ti software to

facilitate coding and analysis.

District

Enrollment

Economically

Disadvantaged

Limited

English

Proficient

White

African-

American

Hispanic

Boyer 7,500 3% 2% 81% 1% 7%

Gibson 22,750 49% 16% 29% 22% 39%

Musial 43,500 28% 8% 51% 11% 26%


Table 2. Texas accountability system outcomes for participating districts.

At the school level, we used stratified random sampling to identify one high school, one

middle school, and one elementary school within each district. We interviewed campus leaders at

each site, using a semi-structured protocol. We conducted two focus groups at each study

campus. The first consisted of teachers randomly selected from among all campus teachers. The

second consisted of ―exemplary users‖ (individuals seen as ―go-to‖ persons for data use by their

peers). These educators were selected through a peer nomination process through which we

solicited the names of teachers who exhibited particular characteristics (e.g., ―The person who

brings interesting data to a team conversation is .‖)

At the district level, we used a snowball method to identify persons tasked with planning

or supporting professional learning and/or data use. We triangulated our interview data by

collecting documents that informed district leaders regarding expectations and supports specific

to professional learning or data use. To facilitate the collection of appropriate documents, we

searched district websites and district-level online policy manuals using terms such as ―data,‖

―data use,‖ ―professional development,‖ ―professional learning,‖ and ―training.‖ We conducted

District Rating (Texas Accountability System)

District 2007 2008 2009 2010 2011

Boyer

Recognized Academically

Acceptable

Exemplary Exemplary Exemplary

Gibson

Academically

Acceptable

Academically

Acceptable

Academically

Acceptable

Recognized Academically

Acceptable

Musial

Academically

Acceptable

Academically

Acceptable

Recognized Recognized Academically

Acceptable

% of All Students passing All Tests Taken (TAKS)

District 2007 2008 2009 2010 2011

Boyer 94 95 96 97 96

Gibson 72 73 74 78 76

Musial 83 83 86 86 86


observations of data use-related professional learning experiences and of district-level meetings

where data use was discussed.

Focus groups and interviews were conducted guided by semi-structured protocols.

Sample questions included, ―What should teachers know to be effective users of data?‖; ―How

do you best learn any new skill?‖ and ―Describe some data-related professional learning in which

you have participated.‖ Questions changed slightly depending on participant role, but all

protocols addressed similar concepts.

Analyses. Data analysis proceeded as suggested by Miles & Huberman (1994). A list of

starter codes (e.g., ―rationale,‖ ―professional learning: content,‖ ―perceptions of data,‖

―compliance‖) was drafted based in our literature review, and these codes evolved as we

proceeded with analysis. As we moved through the early stages of data collection and

transcription, we used dialogues and summary memos to flesh out our list of codes for use during

final coding and analysis. We engaged in this process for each district and then followed with a

similar process to generate a cross-case analysis.

Table 3. Study participants by role.

Findings

In each district, we found educators articulated differing conceptions about what

constituted ―data use,‖ though every educator described some form of data-informed practice. In

Participant Role Boyer ISD Gibson ISD Musial ISD

Central Office 6 11 12

Campus Principals 3 3 3

Teachers 16* 17 14

Campus-based support personnel

(Assistant Principals, Instructional

Coaches, Interventionists) 12 6 7

TOTAL (by campus) n=37 n=37 n=36

Study Total n=110


this section, we present our findings in two parts: (1) How educators construed ―data use‖ and

(2) The intersection of professional learning and articulated rationales for data use.

How Educators Construe “Data Use”

In each district, several teachers reported that they ―didn‘t really use much data‖ in

planning for their classes. However, in almost every case, these same educators went on to

describe various data they did, in fact, use. Teachers talked about using classroom assessments,

teacher-made quizzes, common assessments (assessments created with team or department

members), and district benchmark exams (assessments written by central office leaders and

designed to predict accountability test results or assess learning for a particular grading period).

This phenomenon of simultaneously distancing themselves from ―data use‖ while

describing a range of data use in practice was most pronounced in Boyer, where student

achievement was strong and TAKS was not considered an acceptable measure of student

progress. In Boyer, and to varying degrees in the other districts, educators primarily associated

the term ―data use‖ with analysis and decision-making based on TAKS results. Other pieces of

information did not fit with this narrow construal of ―data use.‖

To underscore the strength of the association of ―data use‖ with accountability exams, we

note that we began each interview and focus group by laying out an intentionally broad

definition of data. We told participants that we define ―data‖ to mean any information that helps

teachers know and serve their students, and pointed out that this included tests, quizzes, locally-

developed assessment, disciplinary data, student demographic data and more. Yet it was not

uncommon for teachers to quickly (within two to three protocol items) revert to speaking of data

use as a process predominantly concerned with TAKS. A campus administrator in the district


began telling us about the value of data, but quickly slipped into expressing dissatisfaction with

the accountability ―dance‖:

... to create valuable data for me—I think you have to have assessments that are valued

also, and not for the wrong reasons. We value it because we like to be exemplary. Some

stupid descriptor laid out there by some really stupid and destructive people that have us

dancing like crazy, our whole profession. I‘m so sad for it—sad for my child ... who grew

up with standardized testing and that‘s all she believes education is and—a big part of it

is TAKS prep, and that‘s what they believe education is.

A few leaders in each district asserted that teachers would invest more time and effort in

data use if they understood the benefits that could come from analysis. When asked why some

teachers may be reluctant to use data, a district leader in Musial ISD told us, ―They don‘t

understand it. They don‘t understand what it will do for them. They think that it‘s just a lot of

extra work and [they think that] they know how their kids are doing but they really don‘t.‖ Yet

in each study district, when asked to describe how teachers and campus leaders were using data,

leaders themselves most frequently described data use in TAKS-oriented ways. They noted that

teachers and principals should be ―drilling down‖ in TAKS or predictive benchmark data in

order to reteach or remediate. As these efforts were oriented toward TAKS or benchmarks

(which covered items similar to those frequently assessed via TAKS), we surmise that efforts

prioritized areas of the curriculum believed to impact accountability test results, rather than a

broader effort to identify and address student learning gaps related to broader curricula.

We offer an important caveat: While many educators at all levels and in all districts

tended to talk about ―data use‖ in TAKS-oriented ways, a few teachers in each district articulated

a broader construal of ―data use.‖ Thus, whereas one teacher dismissed data use as ―…a PR


game. And, you know, because things are in the paper, and in the news, as to who scored

Exemplary—that plays into it,‖ while another teacher passionately asserted:

As a teacher, most teachers are looking at, ―OK, let me find out where they are, because I

need to take them from where they are to where I need them to be.‖ So, to me, that‘s

ingrained in being a teacher. And before it was called ―data-driven instruction,‖ I think

good teachers have always done that—―OK, let me find out where they are, this is where

I need them to be, now how am I going to get them there?‖ So to me, that‘s just what

good teachers have always done and do anyway.

Data Use: Rationales & Professional Learning

In each district, there was a lack of a clear, cohesive rationale for why educators should

use data to inform instruction. Numerous teachers in each district said they wanted to know the

―end goal‖ for data use: They wanted to know how data use fit with their roles as educators and

how leaders expected them to use data use in teaching. In this section, we discuss findings

related to the absence of systemic rationales for data use; the presence of passionate, values-

driven rationales articulated by individual teachers; and the intersection of messaging, data use,

and professional learning.

Absence of systemic rationales for data use. Educators in each district frequently said

that they did not know, and had not been told, of the desired purpose or ―end goal‖ for data use.

They described some data-related training, but said that facilitators were not explicit in how data

use fit with educators‘ roles, how data use could be integrated into everyday work, or why

educators should even want to use data. One Gibson teacher implored, ―Let me see the end

picture so I know where I‘m supposed to go with it.‖


Helping teachers understand a systemic rationale for data use was complicated in that a

commonly understood purpose for data use was absent even amongst district leaders. When

asked what would help better prepare teachers to use data, a district leader in Gibson remarked:

There were not real clear expectations of how this data was supposed to be used. So some

very clear expectations from upper administration on, ―You will be expected to use these

reports to do these things and monitor these types of activities.‖ Test scores—whatever.

I‘m not sure those have been published and put out there—they may have been,

unbeknownst to us, but if they have been, we‘re not necessarily aware of them.

Our evidence did reveal some promising practices at the campus level, though these were

not shared broadly and were limited to campuses with leaders who championed data use. At one

elementary, teachers spoke positively of the benefits of data use and described several ways they

collaborated around data. Their commitment was echoed in the principal‘s interview, where she

spoke about data not in accountability terms, but as an integrated part of teacher work: ―…one of

the things I try to talk to [teachers] about in terms of collecting data is, ‗Don‘t collect your data

for me. Your data needs to be personal to you. It needs to drive how you do your work.‘‖

Presence of passionate individual rationales for data use. When we asked participants

if they thought data use was worthwhile, a few teachers in each district gave impassioned

articulations of why data use was not only worthwhile, but why they thought it critical to doing

their jobs. These rationales for data use were more complex and passionate than the messages

seemingly communicated by day-to-day data use and through existing district professional

learning structures.

Teachers said that data use helped them serve their students, allowing teachers to zero in

on student strengths and weaknesses quickly. This was important, they noted, because the sooner


issues could be identified and addressed, the chances were reduced that a student‘s problem

would worsen, or that the student would fall further behind. Teachers also said that data helped

them collaborate with others more effectively: Data helped multiple educators, all with various

pieces of a puzzle, pool knowledge and experience while problem-solving. A few talked about

data use in terms of ethics or a form of moral duty or due diligence. For example, a middle

school teacher in Gibson stated:

For me, the more data I have, whether just my instruction or evaluations—it helps me

make ethical and justified decisions about students, about their programs, about what they

need, about labels. So the more I have, the better. Because I can pull and analyze it and

look at different parts, and use the whole thing to just make better decisions all around.

Some teachers in each district said that data use gave them tools to be reflective

practitioners. They took their responsibility to help students to heart, and data use provided them

a way to more accurately meet that responsibility. A Musial middle school teacher explained:

Ultimately I‘m responsible for whether or not [students] are learning that material. Their

responsibility is—I guess that‘s also a viewpoint, yes, there are teachers out there who are

going to say, ―It‘s the kids‘ responsibility to learn.‖ But if it‘s their responsibility to learn,

then why are we even here? If they could just learn on their own, they don‘t need the

teachers. Our job is to teach. Our job is to make sure they know that information, so they

can be prepared for the following years. So if that‘s my job, the responsibility is mine.

And yes, there‘s a give-and-take in that relationship, but if I haven‘t made an effort to see

where I may have made a mistake, and to correct that mistake, how can I then blame the

student for anything?


These rationales seemed oriented toward educators‘ sense of identity as teachers and toward their

professional and ethical responsibilities to make the best decisions possible for every student. In

general, campus educators who spoke passionately about data use, or who at least said they

thought data use was important to their roles as teachers, tended to couch rationales in teaching

and learning-oriented ways.

Educators who were committed to data use also talked about using data to support

reflective practice. A Musial teacher admitted:

In all honesty a lot of times I look back at the data and I‘ll find, I‘ll look at that test

question and I‘ll realize it‘s my problem. It‘s not the student‘s problem, it‘s something

that I may not have covered specifically or in the way the test was given. And it helps me

as a teacher know that I‘m going to have to reteach that, number one, and then next year

I‘m going to have to revamp how I taught it.

An elementary teacher similarly noted that taking several types of data over time helped her

reflect on her teaching practice and how instructional choices intersected with student progress:

[Using data] is opening my eyes to maybe what […] interventions need to be put into

place. Or maybe my teaching methods are not working. Or there could be something

interfering with [student] learning that‘s keeping them from progressing like the other

students. … So it‘s […] a way for me to not only track my own teaching methods, but the

student‘s progress.

While educators said they were not sure what the ―end goal‖ of data use was in each

district, teachers themselves voiced some fairly robust reasons to use data. And, in discussing

data, educators who were positive about data use appeared to broaden an otherwise narrow

construal of data. One elementary teacher in Musial laughingly said that she thought data use


―got a bad rap‖ and that the name should be changed to ―Party Time‖ so people would get

excited about it. Her assessment was not far off though—the disparity between how educators

talked about the potential for data use and how they understood (or did not understand) the

district‘s ―end goal‖ for data use suggested that data use itself was ripe for rebranding. However,

we found no evidence that districts had taken advantage of the potential to ―rebrand‖ data use in

line with how some teachers positively and passionately framed data use.

Messaging, data use, and professional learning. Despite these bright spots, analyses

suggested that most contexts lacked a clear, systemic, and agreed-upon rationale for data use. We

found no formal planning documents or structures that established an intentional message about

data use: Leaders worked from their individual conceptions of ―data use‖ as they planned

professional learning aimed at improving educator capacity for data use. Sometimes this worked

out well, as several teachers credited specific campus leaders or instructional coaches with

helping them learn how to engage in data use in constructive ways. In many other instances, this

lack of intentional messaging or structure meant that professional learning supports for data use

varied depending on the capacity of individual facilitators. Without a clear understanding of the

rationale, purposes, and expectations for data use throughout each district, planning for data-

related professional learning failed to address the need to communicate to teachers how they

were expected to use data, to what ends they were expected to use data, or even why data use is

considered a fundamental component of teaching and learning.

Rationales for data use expressed by some teachers were vivid, but the data-related

professional learning experiences most teachers described were often more sterile and oriented

toward accountability and compliance-type activities. Teachers described two main types of

professional learning related to data use. First, leaders and teachers described workshops and


beginning-of-year meetings in which they received and examined campus-level and district-level

accountability reports. Second, leaders and teachers described training sessions focused on the

introduction of various district data systems. Except for rare occasions, teachers did not describe

professional learning that integrated data use with classroom practice in ways that helped

teachers view data use as an inquiry-based, collaborative activity focused on student

improvement. In fact, apart from attributions to a single district leader in Musial and a few

campus leaders in Musial who worked to show teachers how to integrate data use into team and

department meetings, this type of data-related professional learning was wholly absent. One

district leader in Gibson admitted:

We know the best model for professional development is, ―OK, we‘re going to give you a

little bit here and then you‘re going to go back and implement it and we‘re going to come

back and talk about it and you‘re going to get your own data and then we‘re going to

come back and look at it.‖ [But] that just does not happen.

In contrast, teachers wanted professional learning structures that helped them integrate

data use into everyday practice in ways that made sense to them. A Boyer teacher shared:

You need to be able to take [the process] all the way through from now to get to the data,

what does the data show us about the kid, how is that going to change instruction, and go

through all of those pieces so they see the relevance of all the data. Because we usually

just get to, ―Here‘s the data. Here‘s how to get it,‖ and we stop there.

What was done with the data retrieved? In each district, educators said they used data to

create lists of ―bubble‖ students (i.e., students on the cusp of passing or failing state exams).

They noted using data to monitor accountability scores, especially of students who comprised

subpopulations for reporting purposes, and for predicting outcomes on accountability exams.


Teachers and administrators in each district noted that data were used for grouping and

regrouping of students—sometimes for advanced instruction, but more frequently for purposes of

remediation. Educators reported using data to identify student needs in terms of particular

programs or legal requirements (e.g., Response-to-Intervention, Bilingual/ESL planning,

construction of Individualized Education Plans under special education program requirements).

Some teachers in Gibson and Musial also noted using data to drill down to the level of student

expectations by using item analyses on benchmarks and common assessments. The types of tasks

and issues addressed by ―data-related professional learning‖ seemed to be overwhelmingly

oriented toward the types of work elevated by accountability concerns.

We do note that there were a few exceptions to this messaging trend in that a few persons

who were passionate about the value of and importance of data use shared that their role as data

use advocates was shaped by interactions with specific data-able leaders. A Gibson teacher told

us:

I was not a data-driven decision maker until I worked for a principal that was very much

about making our decisions based on data. And I think under her guidance […] I really

learned a whole way to rethink about kids and faculties and campuses and staff based on

data, but I think it was through her leadership that I got there. It wasn‘t going to any

particular training. We have been to so many data [driven] decision making trainings in

my career. But it was really [through] that principal‘s leadership that I got it.

A leader in Boyer similarly shared:

[We didn‘t use data] until NCLB came along, and then our campus, back in 2000, was

required to look at the data. That was seen as a very negative thing at that time. It was not

good. But, it wasn‘t until we did actually start looking at the data and seeing those kids


through a different dimension, you could really see a lot more and guide instruction for

that. […] And that really won us over. Whereas at the beginning, it was like taking the art

of teaching away, and we felt like it wasn‘t anything we wanted anything to do with. But

once you do it, then you see it in a different way, because you can see that it makes you

more efficient and that it guides your instruction more effectively.

Thus, we did encounter ―conversion‖ stories that suggested that positive messaging from one

leader to another, or from a principal to a teacher, had the effect of not only improving data use

capacity in others, but in winning new advocates for data use to the schools

Apart from the few positive narratives, our overall findings suggest that there was a stark

absence of a systemic rationale for data use, though in this absence, the common uses of data

(which were typically oriented toward TAKS) were understood as the de facto rationale for data

use. Additionally, a few educators in each district articulated passionate and complex rationales

for why data use is not only important, but essential to what good teachers do day in and day out.

However, we found no evidence that professional learning constructs or those who planned

professional learning made efforts to ―rebrand‖ data use as about informed, ethical decision

making rather than about accountability and compliance. In fact, because of the types of issues

addressed in data-related professional learning events, teachers seemed to receive messages that

reinforced an accountability/compliance-oriented rationale for data use.

Discussion

The findings of this study demonstrate that while the vast majority of educators engage in

data use, some do so with decidedly more fervor and commitment. When we examine these

findings alongside research that evidences the potential of certain forms of data use for

improving student achievement (e.g., Black & Wiliam, 1998; Anderson et al., 2010), and in


conjunction with Guskey‘s work (1989) which asserts that teachers will adopt changes in

practice if they find those changes result in improved outcomes, we ask why more teachers fail

to buy into data-informed practice. We also ask how districts might systemically increase teacher

engagement in data-informed practice, and what district leaders may be doing to preclude such

engagement. In this section, we return to our theoretical lens to consider these questions in light

of our findings.

What “Attractors” Pull Teachers Toward a Particular Orientation for Data Use?

Across districts, we encountered teachers who were passionate about data use as well as

those who engaged in what they termed ―data use‖ grudgingly. The narratives provided by these

teachers suggest that the teachers who were passionate and who willingly dedicated time and

effort to the collection and analysis of data tended to frame data use in terms of improving

practice in the service of the students in their respective classrooms. Teachers who defended the

practice of data use spoke about doing due diligence to meeting student needs, making ethical

and evidence-based decisions rather than acting on ―hunches,‖ and about the importance of

reflective practice. The narratives provided by these teachers were intensely personal and values-

laden: Data use ―fit‖ with their identities as teachers because the process helped them identify

and meet student needs to the best of their abilities.

In contrast, teachers who seemed hostile to data use tended to frame data use in terms

related to TAKS, the accountability system, and political ―games.‖ These teachers still engaged

in forms of data use (e.g., using grades and quizzes to inform practice, and participating in

campus-required methods of systemic data use), but seemed less willing to devote time, energy,

and attention to data use. For these teachers, data use seemed less an integrated part of what they

did as teachers, and more an ―add-on‖ to what they considered ―teaching‖ to be.


Because of the way teachers talked about their professional learning experiences related

to data use (both formal and informal), we think it likely that to some extent the ―attractors‖ that

pull teachers toward one frame or the other (or that encourage maintenance of a frame), exist

within district professional learning structures. When asked about previous professional learning

for data use, it was not unusual for someone who had spoken negatively about data use to relate

narratives about sitting in large meetings, reviewing accountability reports and discussing ―how

to get to Exemplary.‖ Teachers who talked more positively about data use tended to share those

stories as well, but also talked about experiencing data use in a modeling relationship with an

instructional coach, a peer, or a data-able principal. Often, these informal ―data mentors‖ were

credited with showing the teacher how to apply data use in ways that benefited the students in

the teacher‘s classroom.

Can “Mental Models” of Data Use be Shifted?

We think this pattern of narratives suggests that the language and activities used within

data-related professional learning structures matter, but this is only so if we also think that

existing ―mental models‖ of data use can be shifted. Here, the theoretical base suggests that

mental models can be shifted. Morgan‘s (2006) discussion of complexity theory notes that

attractors (here, the language and activities used in data-related professional learning) can either

reinforce system patterns or jolt a system out of one pattern towards another. Senge‘s (2006)

discussion of mental models suggests that these models can change with attention to new

information, experiences, and a willingness on the part of the organizational member to grow.

This theoretical base suggests that by working to identify current system attractors and by

attending to the creation and support of new mental models, district leaders could reframe or

―rebrand‖ data use in ways that garner greater teacher buy-in and commitment to data-informed


practice. In practicality, this means that district leaders and planners of professional development

should work to identify current attractors that may reinforce or reshape mental models of data

use by asking questions like, ―How are we currently framing data use through what we do in

professional learning?‖ It also means that district leaders work toward agreement on a clear,

systemic rationale for data use (e.g., ―What is the ‗end game‘ for data use in our district, and why

do we think data-informed practice is important?‖). Finally, those tasked with increasing data use

capacity via professional learning should consider how the language and activities included in

professional learning might encourage various mental models. For these leaders, a key question

is, ―How will we talk about data use and work with data in our professional learning?‖

In each district, a few teachers noted that they had gone through personal conversions

from being resistant or resentful of data use to embracing data use as essential to best teaching

practice. We consider these first-person accounts of shifting mental models for data use. What‘s

more, these accounts of shifting mental models were seemingly precipitated by relationships

with other educators who advocated for data use and who demonstrated an ability make data use

highly relevant to helping students in the context of individual classrooms. Given these

examples, we think it within the realm of possibility that district can ―press reset‖ on data use.

Through careful planning for professional learning, leaders could re-brand data use as about

improvement for individual students and as a catalyst for reflective practice, rather than as about

a ratings chase in an accountability context.

Key issues in shifting mental models. Leaders convey messages about data use, for

better or worse. We think three key issues at play in an effort to re-brand data use include the

language of data use, the content and format of professional learning for data use, and time.


Language. Leaders and professional learning facilitators can talk about data use in terms

of supporting reflective practice and in terms of helping teachers make good decisions for

individual learners, or they can adopt the language of accountability, peppering talks about data

use with terms like ―Exemplary,‖ ―bubble kids,‖ ―TAKS,‖ or ―ratings.‖ But they should

consider how the language they use unnecessarily entangles ―data use‖ with ―accountability.‖

Several studies suggest that when educators become consumed with meeting the pressures of

accountability requirements, creative problem-solving suffers (Booher-Jennings, 2005; Daly,

2009; Holme, 2008; Olsen & Sexton, 2009; Valli & Buese, 2007). We thus think it important

that accountability be a concern for administrators and teachers rather than the concern. But if

the language of leaders conveys that accountability is the concern—the rationale for data use in a

district—leaders risk alienating teachers from data use. A framework that uses language to focus

on collaborative decision-making, the ethical responsibility of teachers to identify and meet

individual student needs, and how reflective practice can grow teachers as well as students, may

result in increased teacher commitment to using data in the service of teaching and learning.

Content & format of professional learning for data use. We know from an extensive

body of professional learning research that particular formats work well in helping teachers

integrate new concepts into their practice. For example, teachers crave professional learning that

is directly relevant to their classrooms (e.g., Borko, 2004; Desimone, Porter, Garet, Yoon, &

Birman, 2002; Ingvarson, Meyers, & Beavis, 2005) and that allows for collaboration and social

learning (Desimone et al, 2002; Wei, Darling-Hammond, Andree, Richardson, & Orphanos,

2009; Yates, 2007). We also know that one-shot professional learning is rarely effective, but that

teachers need professional learning that spans time and affords opportunity for feedback and

incremental change (Garet et al., 2001; Yoon, Duncan, Lee, Scarloss, & Shapley, 2007; Wei et


al., 2009). Yet, when it comes to data use, teachers in these districts typically described few

structures that aligned with this research. They mainly described one-shot workshops focused on

data systems or pulling reports and meetings that reviewed district- or campus- accountability

data. Thus, the main ways in which professional learning framed data use were oriented toward

accountability and compliance concerns. Teachers rarely reported data use as integrated into

content-area focused professional learning sessions. If these are the main ways teachers see data

use portrayed in professional learning structures, leaders may find a shift toward improvement-

oriented models for data use difficult.

Time. An old adage suggests that to know someone‘s priorities, look at the person‘s

checkbook. Similarly, to know what a district prioritizes, we think it important to examine how

time is used, and time for professional learning in particular. If the only time a teacher

encounters opportunities to learn about data use is during one-shot workshops in the summer or

at the beginning of a school year—and those focus on reports and accountability ratings—then

the message received is that data use is not something to be fully integrated into practice, but

something made important because of accountability pressures. If, however, district and campus

leaders look for ways to build professional learning into the regular school day via teacher teams,

and infuse that learning with data-informed inquiry, educators receive a clear message that data

use is not ―other,‖ but is essential to the art and science of teaching.

Conclusion

As we noted at the beginning, in some ways teachers have always used data, but now

they are being asked to use more data and to use data in more complex ways—with higher

stakes—than ever before. In this study of data use in three Texas districts, we found that

educators tended to articulate a rationale for data use that was either oriented toward


accountability and compliance concerns, or toward student-focused improvement efforts. What‘s

more, a good number of teachers were willing to commit significant time and effort to data use;

these teachers articulated belief that engaging in data use helped them develop as reflective,

ethical practitioners to the benefit of the students in their respective classrooms. In contrast,

teachers who described engaging in data use only grudgingly tended to frame data use in

accountability-oriented terms. Thus, if systemic improvement that benefits every learner is a goal

for policymakers and educational leaders, we think branding data use as about accountability—

rather than improvement—is contrary to that goal.

Teachers want to use multiple forms of information (i.e., data) to help them know their

students better. To help them reflect on their practice. To help them be better at a profession to

which many of them feel called. To make ethical, responsible decisions for the children in their

classrooms. Yet is seems as if these ―better angels‖ of data use are largely absent in how district

leaders talk about and address teacher capacity for data use. Data use can be an activity not

incompatible with the heart and soul of teaching, but only if the language of leaders and the

structures that support improved capacity for data use are intentional in framing it as such.

We conclude with an important caveat: When it comes to improving teacher capacity for

data use, actions belie words. If leaders and adopt improvement-oriented rationales in an attempt

to ―rebrand‖ data use, but then use data in ways that have been associated with accountability

and compliance orientations (e.g., ―naming and shaming‖ or promoting unnecessary competition

among teachers within a school), efforts at rebranding will likely be undercut. For those

interested in increasing educators‘ ability to use data in the service of teaching and learning, the

findings of this study suggest that leaders must carefully build improvement-oriented rationales


for data use and determine how language and professional learning structures can support the

creation of positive mental models for data use among educators.

References

Anderson, S., Leithwood, K., & Strauss, T. (2010). Leading data use in schools: Organizational

conditions and practices at the school and district levels. Leadership and Policy in Schools,

9(3), 292-327, DOI: 10.1080/15700761003731492/

Beadie, N. (2004). Moral errors and strategic mistakes: Lessons from the history of student

accountability. In K. A. Sirotnik (Ed.), Holding accountability accountable: What ought to

matter in public education (pp. 35-50). New York: Teachers College.

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education:

Principles, Policy & Practice 5(1), 7-74.

Booher-Jennings, J. (2005). Below the bubble: ―Educational triage‖ and the Texas accountability

system. American Educational Research Journal 42(2), 231-68.

Borko, H. (2004). Professional development and teacher learning: Mapping the terrain.

Educational Researcher 33(8), 3-15.

Bryk, A.S., Sebring, P.B., Allensworth, E., Luppescu, S., Easton, J.Q. (2009). Organizing

schools for improvement: Lessons from Chicago. Chicago: University of Chicago Press.

Carnoy, M. & Loeb. S. (2002). Does external accountability affect student outcomes? A cross-

state analysis. Educational Evaluation and Policy Analysis, 24(4), 305-331.

Coburn, C.E. & Turner, E.O. (2012). The practice of data use: An introduction. American

Journal of Education 118(2), 99-111.

Coburn, C.E., Honig, M.I., and Stein, M.K. (2009). What‘s the evidence on districts‘ use of

evidence? In J. D. Bransford, D.J. Stipek, N.J. Vye, L.M. Gomez, and D. Lam (Eds.), The

role of research in educational improvement (67-87). Cambridge: Harvard Education Press

Copland, M., Knapp, M. and Swinnerton, J. (2008). Data informed leadership and school

improvement. In T. J. Kowalski & T. J. Lasley, T. J. (Eds.). Handbook on data-based

decision making in education. New York: Routledge.

Daly, A. (2009). Rigid response in an age of accountability: The potential of leadership and trust.

Educational Administration Quarterly 45(2), 168-216.

Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How high-performing

school systems use data to improve instruction for elementary students. Los Angeles:

University of Southern California, Rossier School of Education, Center on Educational

Governance.

Desimone, L.M., Porter, A.C., Garet, M.S., Yoon, K.S., and Birman, B.F. (2002). Effects of

professional development on teachers‘ instruction: Results from a three-year longitudinal

study. Educational Evaluation and Policy Analysis 24(2), 81-112.

DuFour, R. & Marzano, R. (2011). Leaders of learning: How district, school, and classroom

leaders improve student achievement. Bloomington, IN: Solution Tree.

Earl, L. & Fullan, M. (2003). Using data in leadership for learning. Cambridge Journal of

Education, 33(3), 383-394.

Elmore, R.F. (2009). The problem of capacity in the (re)design of educational accountability

systems. In M.A. Rebell and J.R. Wolff (Eds.) NCLB at the crossroads: Reexamining the


federal effort to close the achievement gap (pp. 230-261). New York: Teachers College

Press.

Ferrance, E. (2000). Action Research [Themes in Education Series]. Northeast and Islands

Regional Educational Laboratory. Providence, RI: Brown University. Accessed 12/1/2011 at

www.alliance.brown.edu/pubs/themes_ed/act_research.pdf

Firestone, W. A., & González, R. A. (2007). Culture and processes affecting data use in schools.

In P. A. Moss (Ed.), Evidence and decision making (pp. 132-154).Malden, MA: Blackwell.

Gallagher, L., Means, B. & Padilla, C. (2008). Teachers’ use of student data systems to improve

instruction: 2005 to 2007. Washington, D.C.: United States Department of Education Office

of Planning, Evaluation, and Policy Development. Accessed 10/12/2009 at

www.ed.gov/about/offices/list/opepd/ppss/reports.html.

Goertz, M. E., Olah, L. N., Riggan, M. (2010) From testing to teaching: The use of interim

assessments in classroom instruction. Philadelphia PA: The Consortium for Policy Research

in Education.

Guskey, T.R. (1989). Attitude and perceptual change in teachers. International Journal of

Educational Research, 13, 439-453.

Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J. A., Wayman, J. C.

(2009). Using student achievement data to support instructional decision making.

Washington D.C.: Institute of Education Sciences and the National Center for Education

Evaluation.

Holme, J.J. (2008). High stakes diplomas: Organizational responses to California‘s high school

exit exam. In B. Fuller, E. Hannum, & M. Henne (Eds). Strong states, weak schools: The

benefits and dilemmas of centralized accountability. Research in Sociology of Education,

Volume 16 (pp. 157-188). United Kingdom: Emerald.

Holme, J.J., Richards, M.P., Jimerson, J.B., & Cohen, R.W. (2010). Assessing the effects of high

school exit examinations. Review of Educational Research 80(4), 476-526.

Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the "data-driven" mantra: Different

conceptions of data-driven decision making. In P. A. Moss (Ed.), Evidence and decision

making (pp. 105-131). Malden, MA: Blackwell.

Ingram, D., Louis, K.S., and Schroeder, R.G. (2004). Accountability policies and teacher

decision making: Barriers to the use of data to improve practice. Teachers College Record

106(6), 1258-1287.

Ingvarson, L., Meiers, M., & Beavis, A. (2005). Factors affecting the impact of professional

development programs on teachers‘ knowledge, practice, student outcomes & efficacy.

Education Policy Analysis Archives 13(10), 1-28.

Jimerson, J.B. & Wayman, J.C. (2011). Data-Related Professional Learning in Three Districts:

What Teachers Need vs. What Teachers Get. Paper presented at the 2011 Annual Meeting of

the University Council for Educational Administration, Pittsburgh, PA.

Kerr, K.I., Marsh, J.A., Ikemoto, G.S., Darilek, H., & Barney, H. (2006). Strategies to promote

data use for instructional improvement: Actions, outcomes, and lessons from three urban

districts. American Journal of Education 112(August), 496-520.

Knapp, M. S., Copland, M. A., & Swinnerton, J. A. (2007). Understanding the promise and

dynamics of data-informed leadership. In P. A. Moss (Ed.), Evidence and decision making

(pp. 74-104). Malden, MA: Blackwell.

http://www.alliance.brown.edu/pubs/themes_ed/act_research.pdf

http://www.ed.gov/about/offices/list/opepd/ppss/reports.html


Langley, G.J., Moen, R.D., Nolan, K.M., Nolan, T.W., Norman, C.L., & Provost, L.P. (2009).

The improvement guide: A practical approach to enhancing organizational performance

(2nd

ed.). San Francisco, CA: Jossey-Bass.

Loeb, H., Knapp, M.S., & Elfers, A.M. (2008). Teachers' response to standards-based reform:

Probing reform assumptions in Washington State. Education Policy Analysis Archives 16(8),

1-32.

Louis, K. S. (2007). Trust and improvement in schools. Journal of Educational Change, 8(1), 1-

24.

Louis, K.S., Leithwood, K., Wahlstrom, K.L., & Anderson, S.E. (2010, July). Learning from

Leadership: Investigating the Links to Improved Student Learning (Final Report of Research

Findings). University of Minnesota and University of Toronto. Accessed July 2011 from

http://www.wallacefoundation.org/knowledge-center/school-leadership/key-

research/Pages/Investigating-the-Links-to-Improved-Student-Learning.aspx

Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision-

making in schools—Teacher access, supports and use. Washington, D.C.: U.S. Department

of Education Office of Planning, Evaluation and Policy Development.

www.ed.gov/about/offices/list/opepd/ppss/reports.html.

Miles, M.B., & Huberman, A.M. (1994). Qualitative data analysis (2nd

ed.). Thousand Oaks,

CA: Sage.

Mills, G.E. (2007). Action research: A guide for the teacher researcher (3rd

edition). Upper

Saddle River, NJ: Pearson Education.

Morgan, G. (2006). Images of organization. Thousand Oaks, CA: Sage Publications.

National Center on Response to Intervention (2010, March). Essential Components of RTI – A

Closer Look at Response to Intervention. Washington, DC: U.S. Department of Education,

Office of Special Education Programs, National Center on Response to Intervention.

Retrieved 2/20/12 from http://www.rti4success.org/pdf/rtiessentialcomponents_042710.pdf

Olsen, B. & Sexton, D. (2009). Threat rigidity, school reform, and how teachers view their work

inside current education policy contexts. American Educational Research Journal 46(1), 9-

44.

Park, V. & Datnow, A. (2009). Co-constructing distributed leadership: District and school

connetions in data-driven decision-making. School Leadership and Management 29(5), 477-

494.

Ravitch, D. (2010). The death and life of the great American school system. Philadelphia, PA:

Basic Books.

Senge, P.M. (2006). The fifth discipline: The art & practice of the learning organization. New

York: Currency Doubleday.

Supovitz, J. (2010). Knowledge-based organizational learning for instructional improvement. In

Hargreaves, A., Lieberman, A., Fullan, M., & Hopkins, D. (Eds.). Second international

handbook of educational change (pp. 707-723). New York: Springer.

Valli, L. & Buese, D. (2007). The changing roles of teachers in an era of high-stakes

accountability. American Educational Research Journal 44(3) 519-558.

Vasquez Heilig, J. & Darling-Hammond, L. (2008). Accountability Texas-style: The progress

and learning of urban minority students in a high-stakes testing context. Educational

Evaluation and Policy Analysis 30(2), 75-110.

http://www.wallacefoundation.org/knowledge-center/school-leadership/key-research/Pages/Investigating-the-Links-to-Improved-Student-Learning.aspx

http://www.wallacefoundation.org/knowledge-center/school-leadership/key-research/Pages/Investigating-the-Links-to-Improved-Student-Learning.aspx

http://www.ed.gov/about/offices/list/opepd/ppss/reports.html

http://www.rti4success.org/pdf/rtiessentialcomponents_042710.pdf


Wayman, J.C. & Cho, V. (2008) Preparing educators to effectively use student data systems. In

Kowalski, T. J., & Lasley, T. J. (Eds.), Handbook on data-based decision-making in

education (pp. 89-104). New York: Routledge.

Wayman, J.C., Cho, V., Jimerson, J.B., & Snodgrass Rangel, V.W. (2010, May). The data-

informed district: A systemic approach to educational data use. Paper presented at the 2010

Annual Meeting of the American Educational Research Association, Denver, CO.

Wayman, J.C., Cho, V., & Johnston, M. (2007). The data-informed district: A district-wide

evaluation of data use in the Natrona County School District. Austin: The University of

Texas.

Wayman, J.C., Jimerson, J.B., Cho, V.. (2010, October). District policies for the effective use of

student data. Paper presented at the 2010 Annual Meeting of the University Council for

Educational Administration, New Orleans, LA.

Wayman, J.C., Jimerson, J.B., & Cho, V. (in press). Organizational considerations in

establishing the Data-Informed District. School Effectiveness and School Improvement.

Wayman, J.C. & Stringfield, S. (2006). Technology-supported involvement of entire faculties in

examination of student data for instructional improvement. American Journal of Education,

112(August), 549-571.

Wei, R.C., Darling-Hammond, L., Andree, A., Richardson, N., and Orphanos, S. (2009).

Professional learning in the learning profession: A status report on teacher development in

the United States and abroad. Dallas, TX: National Staff Development Council.

Wells, A.S. (2009). ―Our children‘s burden‖: A history of federal education policies that ask

(now require) our public schools to solve societal inequality. In M.A. Rebell and J.R. Wolff

(Eds.) NCLB at the crossroads: Reexamining the federal effort to close the achievement gap

(pp. 1-42). New York: Teachers College Press.

Yates, S.M. (2007). Teachers‘ perceptions of their professional learning activities. International

Education Journal 8(2), 213-221.

Yoon, K. S., Duncan, T., Lee, S. W.-Y., Scarloss, B., & Shapley, K. (2007). Reviewing the

evidence on how teacher professional development affects student achievement (Issues &

Answers Report, REL 2007–No. 033). Washington, DC: U.S. Department of Education,

Institute of Education Sciences, National Center for Education Evaluation and Regional

Assistance, Regional Educational Laboratory Southwest. Retrieved from

http://ies.ed.gov/ncee/edlabs

Young, V.M. (2006) Teachers' use of data: Loose coupling, agenda setting, and team norms.

American Journal of Education 112(August), 521-548.

http://ies.ed.gov/ncee/edlabs

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