2013. Vol.4, No.10, 673-682
Published Online October 2013 in SciRes (http://www.scirp.org/journal/ce) http://dx.doi.org/10.4236/ce.2013.410096
Copyright © 2013 SciRes. 673
Concerns, Knowledge, and Efficacy: An Application of the
Teacher Change Model to Data Driven Decision-Making
Karee E. Dunn1#, Denise T. Airola2, Mickey Garrison3
1Eleanor Mann School of Nursing and Educational Statistics and Research Methods,
University of Arkansas, Fayetteville, USA
2Office of Innovation for Education, University of Arkansas, Fayetteville, USA
3Director of Data Literacy, Oregon Department of Education, Salem, USA
Received July 18th, 2013; revised August 18th, 2013; accepted August 26th, 2013
Copyright © 2013 Karee E. Dunn et al. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
The purpose of this theoretical and qualitative work was two-fold. First, the Triadic Change Model (TCM)
was presented and explained. Second, the TCM was used to develop an assessment framework in order to
evaluate teachers’ status in the change process associated with the adoption of Data Driven Decision-
Making (DDDM) in the United States. One dominant profile emerged through the use of the TCM as-
sessment framework. In this profile, teachers manifested concerns indicating they were reluctant to en-
gage in DDDM, held moderate efficacy for DDDM, experienced moderate levels of anxiety associated
with DDDM, and showed low levels of knowledge required for effective DDDM. Research-based rec-
ommendations for practice and future research are discussed for this profile.
Keywords: Efficacy; Concerns; Anxiety; Data Driven Decision-Making
The purpose of the present work was two-fold. First, the
Teacher Change Model (TCM), its theoretical foundation, and
the TCM assessment framework are presented. Then, the resul-
tant qualitative analysis was based on the TCM assessment
framework outcomes. The findings presented here were used to
drive a successful statewide data driven decision-making
(DDDM) initiative (Airola & Dunn, 2011). It is important to
note that while this study was completed in the US, the push for
the use of data to drive instruction as well as the obstacles to
achieve that goal reaches beyond the borders of the US to many
other nations (Espin, McMaster, Rose, & Wayman, 2012;
Schildkamp & Kuiper, 2010).
In the US, the federal push for Data Driven Decision-Making
(DDDM) in schools has not subsided, but teachers continue to
feel threatened by the concept and remain as unprepared to
engage in DDDM as they have been since No Child Left Be-
hind (NCLB) legislation was passed in 2001 (Samuel, 2008;
Wayman, 2005). Many teachers either do not use data at all or
only trust data to confirm their intuition, rather than using data
to shape decision-making (Ingram, Louis, Schroeder, 2004).
Wayman and Stringfield (2006) stated it well when they noted
that, “the notion of involving teachers in data use is easier said
than done” (p. 2). This issue has resulted in the development of
a paradox in the DDDM literature. More specifically, a great
deal of researches investigate the impact of DDDM on student
outcomes, but very few research investigates the change proc-
ess related to teacher adoption of DDDM (Dunn, Airola, Garri-
son, & Nickens, 2011; Mandinach, 2011). This is an unfortu-
nate gap in the literature as teachers are critical change agents
in enacting any educational reform (Beck, Czerniak, & Lumpe,
Research indicates that DDDM is effective at improving
student outcomes (Airola & Dunn, 2011; Carlson, Borman, &
Robinson, 2011; Scheurich & Skrla, 2003), but the bulk of
researches on the force that leads to classroom level data use—
teachers—primarily focus on these change agent’s lack of req-
uisite knowledge and skills for DDDM. This literature fails to
explicate how to create a data-literate faculty (Bernhardt, 2009;
Creighton, 2007; Kerr, Marsh, Ikemoto, Darilek, & Barney,
2006; Wayman, 2005). Stiggins (2001) defined data-literate
faculty as possessing two skill-sets: 1) the ability to access and
gather dependable, high-quality student data, and 2) the ability
to use data to amplify student achievement. The purpose of the
current work was to begin to better understand teacher adoption
of DDDM through the lens of the TCM and to open the re-
search conversation with regard to gap in the literature pertain-
ing to creating a data-literate faculty.
The current research grew out of work with a Pacific North-
western state invested in creating a data-literate faculty. The
authors developed an assessment framework rooted in the TCM
for interpreting the impact of DDDM professional development
(PD) on teacher adoption of DDDM practices. The qualitative
*This research was completed as a part of a project funded by the Oregon
Department of Education.
K. E. DUNN ET AL.
interpretation of the assessment framework outcomes resulted
in the identification of a profile of teachers in the early stages of
the change process associated with becoming data-literate and
adopting DDDM. Furthermore, the TCM assessment frame-
work provided a means to support the development of recom-
mendations that facilitated teacher change through professional
development. Ingram and her colleagues (2004) noted that even
teachers in their sample of schools who were deemed to be
advanced in data use were “dismissive of externally generated
achievement data” and that this “is a cultural trait that teachers
learn and pass on to other teachers as the ‘right way’ to think,
act, and feel about the use of data” (p. 1273). The TCM and
related assessment framework provide a lens through which to
assess and interpret this cultural issue and to drive change.
The TCM was influenced by a number of theories. Of pri-
mary influence was Bandura’s (1978) social learning theory. In
addition to this overarching theory, Hall, Wallace, and Dos-
sett’s (1973) Concerns-Based Adoption Model (CBAM), Aj-
zen’s Theory of Planned Behavior (Ajzen, 1991), and Ohlhau-
sen, Meyerson, and Sexton’s (1992) Efficacy-Based Change
Model (EBCM) influenced this work. The TCM represents an
integration of various components of these theories, related
research, and work with PD. The TCM incorporates three con-
structs from the various theories in a new configuration of in-
terlocking determinants—concerns, efficacy, and knowledge—
intended to guide PD and to predict behavior.
Social Learning Theory
The purpose of the TCM is to uncover the link between
training and practice—in other words, to elucidate causal in-
fluences on teacher adoption of new behaviors. Through the
lens of social learning theory, causal processes are explained in
terms of reciprocal determinism in which behavior, cognition,
and environment mutually influence one another (Bandura,
1978). Bandura incorporated a self-system within the cognitive
component of reciprocal determinism consisting of cognitive
structures and cognitive subfunctions for perception, evaluation,
and behavior regulation. This self-system serves as the founda-
tion for the TCM and each of the theories upon which the TCM
Concerns-Based Adoption Model
The CBAM is an example of a theory that addresses the self-
system described by Bandura. The CBAM is an empirically
based conceptual framework that describes, explains, and pre-
dicts probable teacher behavior based upon relevant concerns as
a teacher participates in developmental activities and imple-
ments an innovation (Hall & Hord, 2011). Within this frame-
work the Stages of Concern assesses concerns, which are an
emotional response to an educational innovation (Hall & Hord,
1987). More specifically, concerns reflect an individual’s set of
feelings, perceptions, preoccupations, thoughts, considerations,
motivations, satisfactions, and frustrations related to a target
innovation. Concerns follow a developmental pattern described
as seven Stages of Concern and measured via the Stages of
Concern Questionnaire (SoCQ) (Hall, George, & Rutherford,
1979). The first stage, Unconcerned (Stage 0), indicates how
highly the respondent prioritizes the innovation. In Stage 0, the
individual may be unaware of the innovation, or the teacher
may be aware of, but unconcerned with the innovation.
Three categories of concerns encapsulate the remaining six
stages: Self, Task, and Impact concerns. Self Concerns include
two stages: Informational (Stage 1), and Personal (Stage 2). In
Stage 1, the individual expresses general awareness of and in-
terest in the innovation, but requires more information about the
innovation. In Stage 2, the individual expresses concerns about
how the innovation will affect him or her and manifests con-
cerns related to personal ability, adequacy, demands, and role.
Task concerns include stage of concern, Management (Stage
3), in which the individual expresses concerns about issues
such as logistics and efficient resource allocation. The more
mature or Impact Concerns include last the three stages: Con-
sequence (Stage 4), Collaboration (Stage 5), and Refocusing
(Stage 6). In Stage 4, the individual expresses concerns related
to student outcomes. In Stage 5, the individual expresses con-
cerns related to working others to increase the impact innova-
tion. In Stage 6, the individual expresses concerns about mod-
ifying the innovation. Concerns profiles can be created with the
use of the SoCQ. One may interpret where a group or individu-
al is situated in the change process, predict the likelihood of
engagement in relevant behaviors, or diagnose impediments to
training in order to plan for implementation facilitation inter-
ventions (George, Hall, & Stieglebauer, 2006).
Research indicates that teachers’ concerns are malleable and
affect the likelihood that teachers will adopt reform-related
beliefs that later encourage reform-related behaviors (Sztajn,
2003). Thus, assessing an individual’s intensity of concerns
may be useful when evaluating the extent of implementation of
an innovation such as a new educational practice. By identify-
ing teachers’ concerns regarding the adoption of new innova-
tions, those charged with developing and delivering teacher PD
may better achieve these goals. Therefore, concerns were in-
corporated into the TCM.
Theory of Planned Behavior
The TPB attempts to predict behavior based upon one’s in-
tention to perform, which is the product of attitudes, subjective
norms, and perceived behavioral control (Ajzen, 1991). Similar
to the TPB, the TCM examines what Ajzen (1991) describes as
attitudes, subjective norms, and perceived behavioral control.
However, the TCM does so in a unique manner by assessing
teacher affect, motivation, and cognition.
With regard to teacher affect, the TCM examines teacher
concerns. Because concerns include a teacher’s or a group of
teachers’ feelings and attitudes toward an innovation or a beha-
vior, teacher concerns mirror components of Ajzen’s (1991)
model (i.e., attitudes and subjective norms). Ajzen’s (1991) also
asserted that perceived behavioral control is equitable to effi-
cacy, teacher perception of innovation-specific efficacy is as-
sumed to assess this variable in the TCM. Further, the TCM
incorporates knowledge, which is only tangentially and mini-
mally included in the TPB. Teacher knowledge also influences
teachers’ perception of their control over a given behavior. If a
teacher lacks the requisite knowledge or skills to engage in a
behavior, they are less likely to engage in that behavior. This
heuristic holds true for teacher DDDM as well (Dembosky,
Pane, Barney, & Christina, 2005).
Copyright © 2013 SciRes.
K. E. DUNN ET AL.
Efficacy-Based Change Model
The EBCM draws on three areas of research: concerns, self-
beliefs (efficacy, outcome expectancy, and outcome value), and
attributional beliefs (Ohlhausen et al., 1992). Similar to the
CBAM, the EBCM model assumes that the change process is
idiosyncratic while also following a general developmental
pattern. The EBCM views the change process, in part, as the
product of the influence of efficacy beliefs (general and teacher
efficacy) and attributional beliefs on individual’s concerns.
Although the EBCM does include two components examined
by the TCM, motivation via efficacy and attributions as well as
affect via concerns, it fails to directly measure individuals’
knowledge. While one may argue that the model indirectly
measures individuals’ perception of their knowledge, measures
of perceived knowledge may greatly vary from actual know-
ledge (Cunningham, Zibulsky, & Callahan, 2009). The TCM
incorporates the role of motivation and affect in the change
process and extends this model to include actual knowledge.
A further distinction between the TCM and the EBCM is that
the TCM does not directly include one’s attributions because
the authors viewed efficacy as an indirect measure of attribu-
tions. This decision is supported by findings from multiple lines
of research that indicate individuals’ with a high sense of effi-
cacy are more likely to attribute failures to things such as low
levels of effort or unfavorable circumstances while those with
low efficacy attribute failures to low levels of ability (e.g.,
Bandura, 1997; Silver, Mitchel & Gist, 1995; Schunk & Zim-
merman, 1994). Extensive research findings also support the
contention that efficacy is one of the strongest predictors of
future action (Bandura, 1986; Cousins & Walker, 2000; Pajares
& Schunk, 2002), whereas research clearly indicates that per-
ceived knowledge may drastically deviate from actual know-
ledge (Cunningham et al., 2009). Therefore, the authors consi-
dered it more critical to examine one’s knowledge and efficacy
than to study one’s attributions and efficacy.
The TCM is also unique from the EBCM and the other mod-
els because it examines efficacy at the behavior-specific level.
Bandura (1997) recommended that efficacy be studied at the
most specific level possible as an individual’s efficacy may
differ between tasks. With regard to teacher motivation, the
TCM examines teachers’ perceptions of innovation-specific
efficacy. Generally, teacher efficacy is the self-reflective judg-
ment of one’s ability to influence or bring about valued student
outcomes, regardless of student or environmental attributes
(Tschannen-Moran, Hoy, & Hoy, 1998, 2001). Examining effi-
cacy at this general level is not revealing enough with regard to
adopting a specific innovation. For example, a teacher may
have high global or teacher efficacy, but report low efficacy for
DDDM. Therefore, the TCM considers innovation-specific ef-
Triadic Change Model
The TCM assesses the likelihood of an individual adopting a
new behavior by examining underlying obstacles to change and
known predictors of future behaviors: knowledge about the
innovation, innovation-specific efficacy, and concerns regard-
ing the innovation. While the TCM is intended to apply to the
adoption of new innovations in the context of organizational
change, it is important to note that the current research focused
on the application of the TCM to in-service teacher adoption of
DDDM practices. The information gleaned from the TCM was
used to evaluate and drive PD and facilitate the development of
a data-literate faculty prepared to engage in classroom-level
DDDM. The following discussion and research reflects this
Knowledge is a key component of teacher adoption of
DDDM practices and the TCM. Unfortunately, K-12 educators
are often entrenched in a system in which decisions are primar-
ily based on intuition and hindsight rather than on empirical
evidence such as student data (Cromey, Van der Ploeg, & Ma-
sini, 2000; Schildkamp & Kuiper, 2010). This instructional
paradigm is difficult to surmount as many teachers also lack the
requisite training to understand, analyze, and connect data to
classroom practice (Creighton, 2007; Kerr et al., 2006). For
example, in schools identified as innovative data users, only 19
percent of teachers and school leaders felt they had the requisite
knowledge and abilities to manipulate data in meaningful ways
(Supovitz & Klein, 2003). Thus, it is important to better under-
stand teacher knowledge levels in order to carefully craft PD to
meet and develop teachers’ DDDM-related needs and skills.
An additional and related component of the TCM is teacher
concerns. With regard to the current work, concerns were de-
fined as a set of feelings, perceptions, preoccupations, consid-
erations, satisfactions, and frustrations related to adopting dis-
trict desired DDDM practices (George et al., 2006). Concerns
were selected for inclusion in the TCM and the current work
based upon evidence from thirty years of research that indicates
teacher concerns are predictive of future practice and a power-
ful tool for facilitating change (George et al., 2006).
For the purposes of this study, the authors also examined
teacher efficacy specifically related to DDDM efficacy. DDDM
efficacy reflects teachers’ beliefs in their ability to successfully
engage in DDDM (Airola, Dunn, & Garrison, 2011). Similar to
concerns, research indicates that efficacy is both a powerful
predictor of future teacher action and a trainable teacher char-
acteristic (Cousins & Walker, 2000; Pajares & Schunk, 2002).
Thus, teacher efficacy serves as a cornerstone in teacher re-
search and the TCM. There is a limited body of research that
examines the role of efficacy in DDDM, but initial findings do
indicate that teachers’ lack confidence in their DDDM abilities
(Dunn et al., 2011; Mason, 2002; Supovitz & Klein, 2003).
One proposition of the TCM is that knowledge, concerns,
and efficacy are interlocking determinants of one another, ab-
iding in the context of mutual influence (See Figure 1). Re-
search supports the relationship of the variables to one another,
but no existing research was found that explored the collective
influence or the mutual relationship of all three constructs.
However, a great deal of research supports the assertion that
knowledge is predictive of teacher efficacy (e.g., Raudenbush,
Bhumirat, & Kamali, 1992; Sarikaya, Cakiroglu, & Tekkaya,
2005). Research also indicates that as teachers’ efficacy levels
increase, they are more likely to report higher level concerns,
Representation of TCM interlocking determinants.
Copyright © 2013 SciRes. 675
K. E. DUNN ET AL.
which are indicative of engagement in target behaviors (Dunn,
2008; Dunn & Rakes, 2011; McKinney, Sexton, & Meyerson,
Additionally, the TCM is unique in that, in lieu of a stage-
like theory consisting of qualitatively different stages of devel-
opment, it was designed to examine change in terms of gradual,
quantitative changes, allowing for the natural ebb and flow of
each of the three components within the proposed self-system
while still acknowledging the developmental and progressive
nature of the change process. Based on what one learns by ex-
amining teacher characteristics through the lens of this model,
one may predict behavior and appropriately adjust PD practices.
Therefore, this model served as the theoretical foundation for
the development of the assessment framework use in this study.
The purpose of the qualitative portion of this research was to
evaluate teacher adoption of DDDM using a TCM-based as-
sessment framework in order to identify a profile that described
teachers in the early stages of the change process associated
with DDDM. The concept of an assessment framework was
adopted from the literature in cognitive psychology and other
science-based fields in which multiple quantifiable factors or
measure outcomes are utilized to identify those at high, moder-
ate, and low risk for various conditions or issues (Aumann,
2011). Instead of identifying risk, the TCM assessment frame-
work was intended to aid in the identification of teacher status
in the change process associated with adopting new innova-
Participants in this study were teachers in a northwestern
state in which statewide DDDM PD had been implemented for
one academic year. PD included multi-day training seminars as
well as job embedded training. The total number of participants
was 2,582; of these, 1,579 respondents were included in the
evaluation of the Reluctant-Avoidant Profile. The Reluctant-
Avoidant Profile sample was predominantly female (n = 1,074,
68%). The profile consisted of 216 participants age 20-29
(19%), 281 age 30 - 39 (25%), 281 age 40 - 49 (25%), 301 age
50 - 59 (26%), and 60 age 60 or older (5%). Due to union regu-
lations designed to protect teachers, no other demographic data
could be collected.
The TCM framework assessment included three measure-
ment instruments: SoCQ, the DDDM Efficacy (3D-ME) survey,
and a DDDM knowledge test. Teachers were contacted by
email and responded electronically to the three instruments.
Teacher responses were aggregated by district. Sixty district
profiles were represented graphically. The researchers evalu-
ated the 60 district profiles. Of the 60 district profiles, 25 dis-
tricts were identified as sharing commonalities across all three
constructs. The responses of the teachers in those 25 districts
were aggregated to produce the Reluctant-Avoidant Profile.
A knowledge test that assessed teachers’ knowledge levels
related to analyzing and interpreting data as well as the connec-
tion of data to classroom decision-making was used. The test
was designed specifically to assess teacher knowledge garnered
from their DDDM PD. Three content experts examined the
knowledge test and reached consensus on the content validity
of the items. The knowledge assessed included general DDDM
knowledge and skills within the context of this state’s PD cur-
riculum. The test consisted of two portions, each of which re-
flected the two skill sets of a data-literate faculty (Stiggins,
2001). Part I of the knowledge test assessed teachers’ ability to
interpret and evaluate data. Part II assessed teachers’ ability
make instructional decisions using data. Examples of items are
presented in Table 1.
Two surveys were also used for this research: the SoCQ and
the 3D-ME. The SoCQ assessed the intensity of teacher con-
cerns and consisted of 35-items and a seven-point Likert scale.
The seven scales assessed the seven Stages of Concern (George
et al., 2006; Hall & Hord, 1987). Internal consistencies were
deemed acceptable; Chronbach’s alpha for each scale was as
follows: Unconcerned (α = .60), Informational (α = .76), Per-
sonal (α = .80), Management (α = .64), Consequence (α = .60),
Collaboration (α = .80), and Refocusing (α = .71). Chronbach’s
alpha was used to assess the internal consistency, and Nun-
nally’s (1967) cutoff criterion of .60 was utilized. For this study,
internal consistency was acceptable for each of the scales (α
= .60, .66, .80, .76, .61, .80, .71, scales 0 - 6 respectively). Ex-
amples of items designed to assess each stage are presented in
The 3D-ME was developed and validated by the authors to
assess teachers’ DDDM efficacy (Airola et al., 2011; Dunn,
Airola, Lo, & Garrison, 2013a, 2013b). The measure included
22-items on a five-point Likert scale and consists of four sub-
scales: 1) Efficacy for data access and report selection, 2) Effi-
cacy for use of data tools and technology, 3) Efficacy for data
interpretation, evaluation and application, and 4) DDDM anxi-
ety. In the original validation study, internal consistencies were
deemed acceptable, α = .83, .91, .93, and .89, respectively (Ai-
rola et al., 2011). Internal consistency scores were similar for
this study and were deemed acceptable for the four scales (α
= .84, .91, .92, .89, respectively).
The first scale, assessed teachers’ confidence in the critical
skill Stiggins (2001) described as the data-literate ability to
access and gather dependable, high-quality student data (exam-
ple item: I am confident in my ability to access state assessment
results for my students. The second scale, efficacy for use of
data tools and technology, is a related but separate ability.
States now have complex data technology resources for teach-
ers, and this scale assesses teachers’ confidence in their ability
to successfully utilize those technology resources (example
item: I am confident I can use the tools provided by my dis-
trict’s data technology system to retrieve charts, tables, or
graphs for analysis). The third scale, efficacy for data interpre-
tation, evaluation, and application, assessed teachers’ confi-
dence in the second component Stiggins (2001) described as
necessary to data-literacy—the ability to effectively use data to
amplify student achievement (example item: I am confident that
I can use data to identify students with special learning needs).
Finally, the fourth scale, DDDM anxiety, is an inverse indicator
of general DDDM efficacy (example item: I am intimidated by
the process of connecting data analysis to my instructional
practice). Several studies support the inverse relationship of
efficacy and anxiety (e.g., Aydin, Uzuntiryaki, & Demirdogen,
2011; Gresham, 2009).
Copyright © 2013 SciRes.
K. E. DUNN ET AL.
Copyright © 2013 SciRes. 677
Knowledge test item examples.
Measure Item Answer
When evaluating classroom
assessments, one of the most
important considerations is validity,
because you can be confident that:
A) The test scores give consistent information every time the test is administered.
B) The test was designed to measure what you want to measure.*
C) The test matches your grade level.
D) The test is long enough to give you good information
Using tests to clarify the
curriculum is different from
teaching to the test when:
A) Teachers use items from the test to determine their instructional curriculum.
B) Teachers use the results of assessment to understand the cognitive demand of the grade level standards.*
C) Teachers use the test results to build identical items for teacher-made tests.
D) Teachers use test preparation materials to provide students with a variety of questions just like the test.
Note: *Correct answer.
SoCQ item examples.
Stage of Concern Item
Unconcerned I spend little time thinking about the innovation.
Informational Concerns I would like to discuss the possibility of using data driven classroom decision-making.
Personal Concerns I would like to know how my teaching or administration is supposed to change.
Management Concerns I am concerned about my inability to manage all that data driven classroom decision-making requires.
Consequence Concerns I am concerned about how data driven classroom decision-making affects students.
Collaboration Concerns I would like to help other faculty in their use of data driven classroom decision-making.
Refocusing Concerns I know of some other approaches that might work better.
The following methodology was used in this study. First, the
authors created teacher profiles by analyzing the results of the
knowledge test, SoCQ, and 3D-ME. Using descriptive statistics,
the results were depicted in graphic form for the 60 school dis-
tricts and evaluated for commonalities. The authors independ-
ently identified one common pattern shared by 25 districts. This
shared profile was identified as the Reluctant-Avoidant Profile
an interpreted in alignment with the TCM. Descriptive statistics
were used to develop the profiles from the TCM assessment
framework, and then qualitative techniques were utilized to
identify commonalities in the profiles. The analyses for each
measure are described below.
Knowledge measure scores were calculated by determining
the number of items answered correctly. An initial representa-
tive sample was used to establish norms for raw score to a per-
centile rank conversion table. Raw scores for participants were
converted to percentile ranks and reported numerically and
graphically and then analyzed based upon the following criteria.
Scores were interpreted as high if they exceeded 80th percentile
rank, moderate if they ranged from 50th to the 79th percentile
rank, or low if they were at or below the 49th percentile rank.
The mean score for the group was computed and converted to a
To score the SoCQ, the sums of the five responses that cor-
respond to each of the seven subscales on the SoCQ were cal-
culated to provide a raw score for each subscale. Mean scores
for each item were converted to percentile rank to illustrate the
relative intensity of each stage of concern for the entire sample.
The percentile ranks were reported numerically and graphically
and analyzed based on guidelines outlined by George and his
In order to categorize 3D-ME subscale scores as low, moder-
ate, and high efficacy, the authors used the following criteria. A
score was considered low if the scale mean was less than the
mean and less than one standard deviation from the norming
group mean, moderate if the scale mean was equal to or within
one standard deviation of the norming group mean, or high if
the scale mean was more than one standard deviation above the
norming group mean (Airola et al., 2011).
For respondents in the Reluctant-Avoidant Profile group,
teacher knowledge was identified as low for Part I and Part II,
25th percentile rank and 30th percentile rank respectively (See
Figure 2). Means and standard deviations for both the total
sample and the Reluctant-Avoidant Profile are presented in
To analyze participants’ responses to the SoCQ, raw scores
were converted to percentile rank and graphically represented
following the procedures outlined by George et al. (2006). The
means and standard deviations of the total sample and the
K. E. DUNN ET AL.
Means and standard deviations for the knowledge measure.
Measure M SD Percentile M SD Percentile
Part I 4.31 1.53 25 4.19 1.53 37
Part II 10.20 4.18 25 9.87 4.15 30
Reluctant-Avoidant Profile: knowledge measure results.
Reluctant-Avoidant Profile group are presented in Table 4.
Figure 3 provides a graphic representation of the Reluctant-
Avoidant SoCQ Profile.
The Stages of Concern profile in Figure 3 represented a de-
scription of the aggregate Reluctant-Avoidant Profile identified
by the researchers’ evaluation of the results of the overall as-
sessment framework. In this Stages of Concern profile, teachers
manifested a peak at Unconcerned Stage 0 and Refocusing
Stage 6. The profile also presented a tailing up at Refocusing
Stage 6 and a negative one-two split in which Personal Stage 2
concerns were higher than Information Stage 1 concerns. This
profile supported a technical user profile (peak at Stage 0 and
Stage 6) in which teachers are resistant to the implementation
of DDDM practices (tailing up at Stage 6 and negative one-two
split), were possibly unconcerned with DDDM or more con-
cerned about other issues (peak at Stage 0), and are merely
going through the motions of engaging in DDDM practices.
Additionally, the teachers’ profile also manifests a lesser but
still important rise at Consequence (Stage 4), indicating that
they were concerned about how their engagement in DDDM
may impact students (George et al., 2006).
The results of the 3D-ME were also analyzed and depicted
graphically. After reviewing the means and standard deviations
of the four scales and comparing them to the norming group,
Reluctant-Avoidant Profile DDDM efficacy was determined to
be moderate for all four of the 3D-ME scales. The means and
standard deviations of the total sample and the Reluctant-
Avoidant Profile are presented in Table 5 and Figure 4 respec-
After profiles consisting of knowledge, concerns, and efficacy
Means, standard deviations, and percentiles for the SoCQ.
Concern M SDPercentile M SDPercentile
1) Awareness 16.173.9741 16.63 3.9763
2) Informational17.014.0742 16.44 4.0726
3) Personal 17.514.3439 16.93 4.2132
4) Management16.704.0750 16.71 3.9650
5) Consequence16.613.9848 16.80 4.0555
6) Collaboration16.974.3144 16.66 4.1744
7) Refocusing 16.024.4849 16.43 4.5560
Reluctant-Avoidant Profile: stages of concern results.
Means and standard for the 3D-ME.
M SD Percentile M SD Percentile
1 11.112.5834 11.10 2.53 34
2 9.74 3.0148 9.64 2.99 48
3 37.636.7444 37.68 6.57 44
4 18.214.3742 18.19 4.34 42
Reluctant-Avoidant Profile: DDDM efficacy results.
Copyright © 2013 SciRes.
K. E. DUNN ET AL.
were created for each of the 60 school districts, the authors
independently examined the profiles to identify commonalities
across profiles. Of the 60 profiles examined, 25 were inde-
pendently and unanimously grouped together. The authors in-
terpreted this profile as one that described those who were re-
sistant to DDDM and reluctant to engage in DDDM, and thus
labeled the Reluctant-Avoidant Profile. The shared characteris-
tics of the Reluctant-Avoidant Profile identified by the authors
are discussed below.
The Reluctant-Avoidant Profile’s SoCQ results reflected a
concerns profile in which the teachers were going through the
motions of the district-desired practices as per the peaks at
Stage 0 and Stage 6. They were exploring alternative courses of
action, and may have believed they knew of more effective
practices (George et al., 2006). In part, this profile was defined
as reluctant and avoidant because of two concerns characteris-
tics—a negative one-two split (Stage 2 higher than Stage 1) and
the tailing-up (Stage 6 higher than Stage 5). The negative
one-two split and the tailing-up (Stage 6 higher than Stage 5)
indicated that these teachers may have doubted the effective-
ness of the innovation and they were resistant to adopting the
innovation, in fact, this extreme tailing-up “should be heeded as
an alarm” (George et al., 2006: p. 42). The peak at Stage 0 fur-
ther supported the reluctant-avoidant interpretation.
Additionally, the Reluctant-Avoidant Profile included mod-
erate levels of efficacy indicating these teachers were fairly
confident in their ability to engage in DDDM practices. These
teachers also reported moderate levels of DDDM anxiety fur-
ther supporting the interpretation of this group of teachers as
reluctant and avoidant with regard to DDDM. Finally, the re-
spondents who fell into the Reluctant-Avoidant Profile reported
low levels of knowledge related to DDDM. Thus, Reluctant-
Avoidant teachers were reported greater confidence in their
abilities for DDDM than their actual knowledge supported.
The remaining profiles shared some of the qualities of what
the authors titled the Reluctant-Avoidant Profile, but lacked a
complete match with the Reluctant-Avoidant Profile and did
not share sufficient unique characteristics with other profiles to
warrant categorization into a separate profile. For example, nine
profiles reflected moderate efficacy, but reported knowledge
levels that ranged from low to moderate and a concerns profile
that indicated they were primarily concerned about how the
new innovation will impact them. Four of the aforementioned
nine profiles presented a tailing-up in their SoCQ profile, which
indicates resistance to the target innovation (George et al.,
2006). Six other profiles reported moderate to low efficacy, low
knowledge, but failed to manifest an interpretable SoCQ profile,
which may result when a great deal of diversity in responses
occurs, masking results. The remaining profiles share too few
similarities with one another to be considered for grouping.
The findings of this study, viewed through the interpretative
lens of the TCM, align with Ingram and her colleagues (2004)
finding that teachers do not yet fully trust or use data to drive
instructional decision-making. In the Reluctant-Avoidant Pro-
file, teacher responses to the SoCQ supported what is techni-
cally a User Profile; however, they are more or less going
through the motions as indicated by the spike at Stage 0 and
Stage 6 (George et al., 2006). The peak at Stage 0, Uncon-
cerned, indicated that these teachers believed other things are
more important than DDDM. The peak at Stage 6, Refocusing
Concerns, indicated that while these teachers may have been
going through the motions of DDDM to satisfy school leaders,
they were exploring alternative courses of action they believed
had more value than current DDDM practices promoted by the
state’s department of education. Moreover, the negative-one
two split and the tailing-up indicated that these teachers may
have doubted the effectiveness of the innovation and they were
resistant to adopting the innovation.
Additionally, teachers’ concerns steadily increased from Per-
sonal Concerns to Consequence Concerns. Thus, teachers held
some concerns about the impact DDDM may have on them, but
were more concerned about the impact DDDM will have on
students. When considered in conjunction with the peaks at
Stage 0 and Stage 6, it may be that concerns for how DDDM
will impact students influenced reluctance to DDDM and seri-
ous consideration of other practices.
As a result, it will be important to discover what teachers
view as more viable alternatives to DDDM in order to create
persuasive, appropriate, and effective training. By applying the
metaphor of teaching as persuasion (Murphy, 2001) to PD ef-
forts, trainers can unearth misconceptions teachers may hold
about DDDM or about the practices they perceive as inherently
more valuable than DDDM. Using this approach, trainers will
provide teachers with opportunities to discuss what other
courses of action they view as having more value than DDDM,
and subsequently, trainers address any misconceptions or mis-
understandings (Murphy, 2001).
An example of a common misconception teachers hold is
that two different scales are equal or comparable. For example,
teachers may compare English Language Arts (ELA) scores
and Math scores from a state-mandated test without under-
standing the parameters of the scaled score. By relying on intui-
tion, teachers may assume that a large increase in ELA scores
from a previous year indicates that students had greater gains
when compared to a small increase in Math scores. However, if
the statistical characteristics of the scale were better understood,
teachers may find that the seemingly small increase on Math
scores was more meaningful than the large gains in ELA scores.
Teachers who make this mistake may lose faith in DDDM, as it
does not align with what they see in their students, or they may
make incorrect decisions on what is most important to address
in their classrooms. Once these types of misconceptions are
understood, PD efforts may correct misunderstandings and
integrate evidence that supports the effectiveness of DDDM.
It may also be the case that these teachers held beliefs that
may complement and work in tandem with DDDM. For exam-
ple, many teachers believe that the phrase DDDM only alludes
to state-mandated summative assessments, and that formative
assessments are superior to DDDM (Anderson, Leithwood, &
Strauss, 2010). In this case, PD would need to explain how both
formative assessment and summative assessment data may be
used to improve student outcomes through DDDM. Whether
teachers hold misunderstandings about DDDM or simply know
of another practice they prefer to DDDM, PD efforts may be
tailored to fit the results of an inquiry about what teachers be-
lieve DDDM is and what practices teachers believe hold more
value than DDDM. Through targeted PD efforts, teachers may
be persuaded to engage in more DDDM.
In addition to considering alternatives to DDDM, the Reluc-
tant-Avoidant Profile teachers reported moderate levels on all
four 3D-ME scales. It is important to address these teachers’
Copyright © 2013 SciRes. 679
K. E. DUNN ET AL.
DDME efficacy as efficacy indicates the likelihood of engage-
ment in targeted practices (Pajares & Schunk, 2002; Zimmer-
man, 2000). It is also important to note that these teachers
scored moderately on DDDM anxiety. While anxiety is an in-
verse indicator of efficacy (Aydin et al., 2011; Gresham, 2009),
a moderate level of anxiety may impede engagement in innova-
tions (Learner & Timberlake, 1995). Thus, teacher anxiety is a
contributing factor to teacher resistance to DDDM and may be
an impediment to these teachers engagement in DDDM.
PD activities should be designed to address and ameliorate
teachers’ DDDM anxiety. Research findings provide support
for a variety of strategies that may reduce anxiety related to
statistics. These strategies include instructor encouragement
(Wilson & Onwuegbuzie, 2001), the provision of coping strate-
gies to students (Pan & Tang, 2004), sensitivity and attentive-
ness to learners’ needs (Pang & Tang, 2005), the use of real
world examples (Pang & Tang, 2005), and the use of a humor-
ous instructional style (Schacht & Stewart, 1990; Wilson, 1998).
As statistics are a critical component of DDDM, the application
of these strategies to DDDM PD may reduce DDDM anxiety.
Future research should investigate the influence of these prac-
tices on teachers’ DDDM anxiety.
The Reluctant-Avoidant Profile included low scores on both
components of the knowledge measure. This indicated that
teachers did not possess the requisite knowledge to effectively
or accurately engage in DDDM. Thus, it will be important to
build, expand, and deepen the knowledge base of those who
reported a Reluctant-Avoidant Profile. This will serve to
strengthen teacher efficacy (Scherer & Bruce, 2001) and facili-
tate higher-level concerns (Zielinski & Bernardo, 1989) as well
as more successful DDDM.
Moreover, the disparity between efficacy and knowledge is
of great importance when considered with the technical user
concerns profile. Essentially, these teachers are more confident
in their abilities and knowledge related to DDDM than their
skills and knowledge support. Thus, if they are engaging in
DDDM it is likely ineffective (Raudenbush et al., 1992). Fur-
thermore, teachers are making judgments about practices they
believe are superior to DDDM without fully understanding how
to engage in DDDM. The application of the teaching as persua-
sion metaphor in PD may also amend this situation by revealing
and addressing inadequate understanding.
The synergistic evaluation of the TCM assessment frame-
work revealed that teachers who fit the Reluctant-Avoidant
Profile are only engaging in DDDM practices at a superficial
level. The teachers lack the necessary knowledge base and con-
fidence needed to engage in effective DDDM as promoted by
the state and the district. By increasing teacher knowledge and
efficacy, it is likely that a true user concerns profile may mani-
fest (Boz & Boz, 2010; Charalambos, Philippou, & Kyriakides,
Direct instruction related to DDDM practices will concur-
rently build the needed knowledge base and increase related
forms of efficacy, as research indicates a link between motiva-
tional beliefs and knowledge acquisition (Bruning, Schraw, &
Ronning, 1999; Chuang, Liao, and Tai, 2005). Peer modeling is
another powerful tool that serves to increase both knowledge
and confidence (Bandura, 1997; Latham, Millman, & Miedema,
1998). Direct knowledge instruction and peer modeling may
both serve to increase knowledge and efficacy (Bruning et al.,
1999; Chuang et al., 2005), and subsequently encourage teach-
ers to higher level concerns (Dunn & Rakes, 2011) and en-
gagement in classroom-level DDDM. Additionally, if teachers
are provided the opportunity to apply what they learn in PD
training at a level where they will experience success and im-
mediate reinforcement (however not so low a level as to seem
patronizing) (Locke & Latham, 1990), teacher DDDM efficacy
should increase and likely lead to an increase in classroom
DDDM. Future research should examine this chain of infer-
It is critical that no one component of the TCM assessment
framework be examined without taking into consideration the
characteristics and influence of the other variables. For example
in this profile, teacher knowledge related to DDDM was low. If
one were to simply address low knowledge in order to facilitate
adoption of DDDM, these teachers would likely continue to be
resistant as indicated by the concerns profile. In the case of the
Reluctant-Resistant Profile, trainers should first address teacher
Refocusing and Personal Concerns as well as DDDM anxiety.
Subsequently, teachers will likely be more open to learning
about DDDM. Without an understanding of all the components
of the TCM assessment framework as well as their shared in-
fluence, haphazardly addressing one of these variables without
consideration of the others my cause teachers to become more
entrenched in their reluctance and avoidance related to adopting
DDDM. Thus, the strength of the TCM assessment framework
lies in the gestalt of the mutual influence of the three compo-
The current study had a number of limitations. First, while
the sample was large, generalizability was limited because a
convenience sample from only one state in the US was used.
Thus, future research should explore the outcome of the TCM
assessment framework with teachers in the early stages of
adoption of DDDM in other states and in other countries who
also encourage evidence-based instruction. This study was also
limited because teacher data could not be linked to student data.
Thus, future research should explore the influence of the TCM
profiles on student outcomes. Also, this study only explored the
early stages of the change process associated with teacher
adoption of DDDM. Future research should explore the full
change process from introduction to DDDM to full integration
of DDDM in the instructional process. Although the study was
limited in a variety of ways, it was an important first step to-
ward better understanding the change process associated with
teacher adoption of DDDM practices.
After a decade of investing in PD intended to facilitate
teacher engagement in DDDM in the US and beyond, the TCM
assessment framework may provide a useful tool for meeting
the goal of a data driven decision-maker in every classroom.
The results garnered from the TCM assessment framework may
help trainers in the US and abroad to interpret the impact of PD
on teachers and derive suggestions for improving PD from the
existing research base associated with teacher concerns, effi-
cacy, and knowledge. By using data to drive PD efforts, tai-
lored PD efforts may yield more desirable results—teacher
DDDM and improve student outcomes. Finally, it is important
to note that if states and districts utilize assessment frameworks
to assess teacher data and to connect that data to the develop-
ment of targeted PD efforts, they will model the actions in
Copyright © 2013 SciRes.
K. E. DUNN ET AL.
which they want their teachers to engage. The result is a simple
application of another metaphor for teaching—Practice what
Airola, D. T., & Dunn, K. E. (2011). Oregon DATA project final
evaluation report. Fayetteville, AR: Next Level Evaluation, Inc.
Airola, D., Dunn, K. E., & Garrison, M. (2011). 3D-ME: The validation
of the Data Driven Decision-Making Efficacy questionnaire. Wash-
ington, DC: the American Psychological Association Convention.
Ajzen, I. (1991). The theory of planned behavior. Organizational Be-
havior and Human Decis i o n Processes, 50, 179-211.
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, 292-327.
Aumann, C. A. (2011). Constructing model credibility in the context of
policy appraisal. Environmental Modeling and Software, 26, 258-265.
Aydin, Y. C., Uzuntiryaki, E., & Demirdogen, B. (2011). Interplay of
motivational and cognitive strategies in predicting self-efficacy and
anxiety. Educat i o nal Psychology, 31, 55-66.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York:
W. H. Freeman and Company.
Bandura, A. (1986). Social foundations of thought and action: A social
cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (1978). The self system in reciprocal determinism. Ameri-
can Psychologist, 33, 343-358.
Beck, J., Czerniak, C. M., & Lumpe, A. T. (2000). An exploratory
study of teachers’ beliefs regarding the implementation of construc-
tivism in their classrooms. Journal of Science Teacher Education, 11,
Bernhardt, V. L. (2009). Data use: Data-driven decision making takes a
big-picture view of the needs of teachers and students. Journal of
Staff Development, 30, 24-27.
Boz, Y., & Boz, N. (2010). The nature of the relationship between
teaching concerns and sense of efficacy. European Journal of Tea-
cher Education, 33, 279-291.
Bruning, R., Schraw, G., & Ronning, R. (1999). Cognitive psychology
and instruction. Upper Saddle River, NJ: Prentice Hall.
Carlson, D., Borman, G., & Robinson, M. (2011). A multistate dis-
trict-level cluster randomized trial of the impact of data-driven re-
form on reading and mathematics achievement. Educational Evalua-
tion and Policy Analysis, 33, 378-398.
Charalambos, C., Philippou, G., & Kyriakides, L. (2004). Towards a
unified model on teachers’ concerns and efficacy beliefs related to
mathematics reform. Proceedings of International Group for the
Psychology of Mathematics Education, 2, 199-206
Chuang, A., Liao, W., & Tai, W. (2005). An investigation of individual
and contextual factors influencing training. Social Behavior and
Personality, 33, 159-173.
Cousins, B., & Walker, C. (2000). Predictors of educators’ valuing of
systematic inquiry in schools. Canadian Journal of Program Evalua-
tion, Special Iss ue, 25-52.
Creighton, T. B. (2007). Schools and data: The educator’s guide for
using data to improve decision-making (2nd ed.). Thousand Oaks,
CA: Corwin Press.
Cromey, A., Van der Ploeg, A., & Masini, B. (2000). The Call for
Data-Driven Decision Making in the Midwest’s Schools: NCREL’s
Response. Oak Brooks, IL: North Central Regional Educational
Cunningham, A. E., Zibulsky, J., & Callahan, M. D. (2009). Starting
small: Building preschool teacher knowledge that supports literacy
development. Reading and Writing, 22, 487-510.
Dembosky, J. W., Pane, J. F., Barney, H., & Christina, R. (2005). Data
driving decision-making in southwestern Pennsylvania school dis-
tricts. Santa Monica, CA: RAND.
Dunn, K. E. (2008). The implementation of learner-centered principles:
An analysis of preservice teacher beliefs and concerns. Doctoral
Dissertation, ProQuest Dissertations and Theses, 3328225.
Dunn, K. E.,Airola, D., Garrison, M., & Nickens, B. (2011). Becoming
data-driven: The influence of data-driven decision making efficacy
beliefs on teachers’ consequence concerns. Washington, DC: The
American Psychological Association Convention.
Dunn, K. E., Airola, D. T., Lo, W., & Garrison, M. (2013a). Becoming
data-driven: Exploring teacher efficacy and concerns related to data-
driven decision making. Journal of Experimental Education, 81, 222-
Dunn, K. E., Airola, D. T., Lo, W. J., & Garrison, M. (2013b). What
teachers think about what they can do with data: Development and
validation of the data driven decision-making efficacy and anxiety
inventory. C o ntemporary Educatio nal Psychology, 38, 87-98.
Dunn, K. E., & Rakes, G. C. (2011). Teaching teachers: An investiga-
tion of beliefs in teacher education students. Learning Environments
Research, 14, 39-58. http://dx.doi.org/10.1007/s10984-011-9083-1
Espin, C. A., McMaster, K. L., Rose, S. & Wayman, M. M. (2012). A
measure of success: The influence of curriculum-based measurement
on education. Minneapolis, MN: University of Minnesota Press.
George, A. A., Hall, G. E., & Stiegelbauer, S. M. (2006). Measuring
implementation in schools: The stages of concern questionnaire.
Austin, TX: Southwest Educational Development Laboratory.
Gresham, G. (2009). An examination of mathematics teacher efficacy
and mathematics anxiety in elementary pre-service teachers. Journal
of Classroom Interaction, 44, 22-38.
Hall, G. E., George, A. A., & Rutherford, W. L. (1979). Measuring
Stages of Concern about the innovation: A manual for use of the SoC
Questionnaire. Austin, TX: University of Texas.
Hall, G. E., & Hord, S. M. (1987). Change in schools: Facilitating the
process. New York: State University of New York Press.
Hall, G., & Hord, S. (2011). Implementing change: Patterns, p r inciples,
and potholes (3rd ed.). Needham Heights, MA: Allyn and Bacon.
Hall, G. E., Wallace, R. C., & Dossett, W. A. (1973). A developmental
conceptualization of the adoption process within educational institu-
tions. Austin, TX: University of Texas.
Ingram, D., Louis, K. S., & Schroeder, R. G. (2004). Accountability
policies and teacher decision making: Barrier to the use of data to
improve practice. Teachers College Record, 106, 1258-1287.
Kerr, K. A., 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, 496-520.
Learner, D. K., & Timberlake, L. M. (1995). Teachers with limited
computer knowledge: Variables affecting use and hints to increase
Latham, G. P., Millman, Z., & Miedema, H. (1998). Theoretical, prac-
tical and organizational issues affecting training. In C. J. De Wolff, P.
J. D. Drenth, & H. Thierry (Eds.), A handbook of work and organiza-
tional psychology: Personnel psychology, Vol. 3 (pp. 185-207). East
Sussex: Psychology Press, Ltd.
Locke, E. A., & Latham, G. P. (1990) A theory of goal setting and task
performance. Englewood Cliffs, NJ: Prentice-Hall.
Mandinach, E. (2011). Recent developments in how the field of educa-
tional psychology has contributed to educational policy debate and
implementation. Washington, DC: The American Psychological As-
Copyright © 2013 SciRes. 681
K. E. DUNN ET AL.
Copyright © 2013 SciRes.
Mason, S. (2002). Turning data into knowledge: Lessons from six Mil-
waukee public schools. Madison, WI: Wisconsin Center for Educa-
McKinney, M., Sexton, T., & Meyerson, M. J. (1999). Validating the
efficacy-based change model. Teaching and Teacher Education, 15,
Murphy, P. K. (2001). Teaching as persuasion: A new metaphor for a
new decade. Theory into Practice, 40, 224-277.
Nunnally, J. C. (1967). Psychometric theory. New York: McGraw-Hill.
Ohlhausen, M. M., Meyerson, M. J., & Sexton, T. (1992). Viewing
innovations through the Efficacy-Based Change Model: A whole
language application. Journal of Reading, 35, 536-541.
Pajares, E., & Schunk, D. H. (2002). Self and self-belief in psychology
and education: A historical perspective. In J. Aronson (Ed.), Improv-
ing academic achievement impact of psychological factors on educa-
tion (pp. 3-21). Amsterdam: Academic Press.
Pan, W., & Tang, M. (2004). Examining the effectiveness of innovative
instructional methods on reducing statistics anxiety for graduate stu-
dents in the social sciences. Journal of Instructional Psychology, 31,
Pan, W., & Tang, M. (2005). Students’ perceptions on factors of statis-
tics anxiety and instructional strategies. Journal of Instructional
Psychology, 32, 205-214.
Raudenbush, S. W., Bhumirat, C., & Kamali, M. (1992). Predictors and
consequences of primary teachers’ sense of efficacy and students’
perceptions of teaching quality in Thailand. International Journal of
Educational Resear c h , 17, 165-177.
Samuel, M. (2008). Accountability to whom? For what? Teacher iden-
tity and the force field model of teacher development. Perspectives in
Education, 26, 3-16.
Sarikaya, H., Cakiroglu, J., & Tekkaya, C. (2005). Self-efficacy, atti-
tude and science knowledge. Academic Exchange Quarterly, 9, 38-
Schacht, S., &Stewart, B. J. (1990). What’s funny about statistics? A
technique for reducing student anxiety. Teaching Sociology, 18, 52-
Scherer, Y. K., & Bruce, S. (2001). Knowledge, attitudes, and self-
efficacy and compliance with medical regimen, number of emer-
gency department visits, and hospitalizations in adults with asthma.
Heart Lung, 30, 250-257. http://dx.doi.org/10.1067/mhl.2001.116013
Scheurich, J. J., & Skrla, L. (2003). Leadership for equity and excel-
lence: Creating high-achievement classrooms, schools, and districts.
Thousands Oaks, CA: Corwin Press.
Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum re-
form: Which data, what purposes, and promoting and hindering fac-
tors. Teaching and Teacher Educat io n , 26, 482-496.
Schunk, D. H., & Zimmerman, B. J. (1994). Self-regulation of learning
and performance: Issues and educational applications. Hillsdale, NJ:
Silver, W. S., Mitchel, T. R., & Gist, M. E. (1995). Responses to suc-
cessful and unsuccessful performances: The moderating effect of
self-efficacy on the relationship between performance and attribu-
tions. Organizational Behavior and Human Decision Processes, 62,
Stiggins, R. J. (2001). Student-involved classroom assessment (3rd ed.).
Upper Saddle River, NJ: Prentice Hall.
Supovitz, J. A., & Klein, V. (2003). Mapping a course for improved
student learning: How innovative schools systematically use student
performance data to guide improvement. Report, Philadelphia, PA:
Consortium for Policy Research in Education.
Sztajn, P. (2003). Adapting reform ideas in different mathematics
classrooms: Beliefs beyond mathematics. Journal of Mathematics
Teacher Education, 6, 53-75.
Tschannen-Moran, M., Hoy, A. W., & Hoy, W. K. (1998). Teacher
efficacy: Its meaning and measure. Review of Educational Research,
68, 202-248. http://dx.doi.org/10.3102/00346543068002202
Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Cap-
turing an elusive construct. Teaching and Teacher Education, 17,
Wayman, J. C. (2005). Involving teachers in data-driven decision-
making: Using computer data systems to support teacher inquiry and
reflection. Journal of Education for St ud e nt s Placed At Risk, 10, 295-
Wayman, J. C., & Stringfield, S. (2006). Technology-supported in-
volvement of entire faculties in examination of student data for in-
structional improvement. American Journal of Education, 112, 549-
Wilson, V. A. (November 1998). A study of reduction of anxiety in
graduate students in an introductory educational research course.
New Orleans, LA: The Annual Meeting of the Mid-South Educa-
tional Research Association.
Wilson, V. A., & Onwuegbuzie, A. J. (2001). Increasing and decreas-
ing anxiety: A study of doctoral students in educational research
courses. Little Rock, AR: The Annual Meeting of the Mid-South
Educational Research Association.
Zielinski, E. J., & Bernardo, J. A. (1989). The effects of a summer in-
service program on secondary science teachers’ Stages of Concern,
attitudes, and knowledge of selected STS concepts and its impact on
students’ knowledge. San Francisco, CA: The National Association
for Research in Science Teaching.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn.
Contemporary Education a l Psychology, 25, 82-91.