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Answer the question belowTo receive the maximum number of points for your initial post, it must be at least 200 words and cite either the textbook or another, outside literature source.Chapter 1 Questions1. Refer to Figure 1.3 on page 16 of the Data-Driven Leadership. In which of the four quadrants do you believe most schools fall in using data-driven decision making? Explain your answer.
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The Promise and Pitfalls of Data-Driven Decision Making
3. Instructional data, which include teachers’ use of time, patterns of course enrollment, and the quality of the curriculum
4. Perception data, which provide insights regarding values, beliefs,
and views of individuals or groups (e.g., surveys, focus groups)31
These varied forms of data are useful for a range of purposes. In
this book, we refer to data that inform teachers about their teaching
and the learning of their students and, to a lesser extent, to data that
inform school and system leaders about improvement more generally.
Data from assessments may show patterns of student achievement, but they do not tell teachers what to do differently in the
classroom.32 And large-scale assessment data may be useful for
school and system planning, but they are less useful at the teacher
or student level.33 So while the heavy emphasis on accountability
may have saturated schools with a wide array of data, educators are
still figuring out how to develop the skills to use those data in both
basic and more sophisticated ways.
When districts and schools begin to define what data or evidence means in their local settings, a more complex definition of
student learning goals emerges. Even prior to any mention of the
new Common Core Standards in the United States, we found that
districts relied on a broad range of evidence to inform decision making, including standardized assessments, placement data, benchmarks, observational data, and other sources at the system and
school levels. Some forerunner districts are gathering and analyzing
data on the extent of student engagement in order to improve student involvement in their own learning.34 These findings are particularly pertinent to the work of district and school leaders who will
be thinking about new ways to measure and track student learning.
What Is Data-Informed Decision Making?
The term data-informed (or data-driven) decision making is sufficiently vague to be a catchphrase for all things having to do with
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12
Data-Driven Leadership
data. With the presence of accountability systems that are so
closely tied to test scores, schools and districts are likely to consider themselves data informed whether or not they desire to be
so. Reports of high-performing schools and districts engaging in
data-informed decision making persuade some leaders to embrace
data use, even though the strategy itself may not be completely
defined.35 Getting clearer on what data-informed decision making
is—and is not—is essential.
Some who seek to define data use focus on information processing of data. According to Mandinach and Honey’s model, individuals collect and organize data—as raw pieces of facts.36 The raw
facts become information when individuals analyze and summarize them. In other words, information is data with meaning, and
it becomes knowledge when the information is synthesized and
prioritized. Thus, knowledge is essentially information that has
been deemed useful to guide action. Figure 1.1 depicts this model
of data use. This information model is helpful because it lays out
the stages of data use and highlights that it is not a simple process
of having and then using data. This is a critical piece of the puzzle in conceptualizing data use. Instead, data must be interpreted
and knowledge must be actively constructed in order for the data
to affect decisions. The model also highlights the fact that data
use at the classroom level is embedded in the larger context of
the school and the district. But there are some aspects of datainformed decision making that this model doesn’t capture because,
of course, no one model can capture everything. Different levels of
capacity may shape educators’ abilities to engage in the process of
transforming data into knowledge. In addition, educators’ beliefs
and assumptions likely shape their interpretations of data, and
their ability to use data may be enabled or constrained by these
factors. For these reasons, data use is not likely to be an entirely
sequential process, since it varies a great deal with the context.
Another data use model concentrates on educators’ abilities and capacities to use data. This learning model, described by
Earl and Katz, recognizes that schools and individuals may have
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District
Building
Classroom
DATA-DRIVEN
DECISION MAKING
KNOWLEDGE
decide
prioritize
Tech-Tools
INFORMATION
Wireless PDA
summarize
GROW Reporta
DATA
Data Warehouse
organize
synthesize
implement
analyze
feedback
collect
impact
Figure 1.1.
a
Sequential Model of Data Use
Reprinted by permission of Publisher. From Ellen B. Mandinach and Margaret Honey, eds., Data-Driven School Improvement: Linking Data
and Learning, New York: Teachers College Press. Copyright © 2008 by Teachers College, Columbia University. All rights reserved.37
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Novice
No practical
experience;
dependent on rules
Figure 1.2.
Limited experience;
still dependent on
rules; expects
definitive answers;
some recognition of
patterns
Analytical; locates
and considers
possible patterns;
internalizes key
dimensions so that
it’s automatic
Uses analysis and
synthesis; sees the
whole rather than
aspects; looks for
links and patterns;
adjusts to adapt to
the context
Expert
Understands
context; considers
alternatives in
iterative way and
integrates ideas into
efficient solutions;
solves problems and
makes ongoing
adaptations
automatically
Stages in Growth from Novice to Expert in Data Use
Source: Adapted from L. Earl and S. Katz, Leading Schools in a Data-Rich World: Harnessing Data for School Improvement (Thousand Oaks,
CA: Corwin Press, 2006), 102.
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The Promise and Pitfalls of Data-Driven Decision Making
15
different levels of expertise when it comes to thoughtfully using
data. Because of the developmental process, educators require
opportunities to learn and apply their skills. This conceptualization underlines how data use is not just a sequential process
of finding and using information, but one of skill and learning.
Figure 1.2 lists the stages of the model, which together highlight
the developmental nature of using data.
This model helps us understand the continuum of skills in
learning to use data, which is also part of
This model helps us
the puzzle. However, it does not address
understand the continuum
how the development of skills is supported
of skills in learning to use
or hindered by conditions in local educadata, which is also part of
tional settings, an issue we take up explicthe puzzle.
itly in this book.
A third model, presented by Gina Ikemoto and Julie Marsh,
blends aspects of these first two models, focusing on the capacity
of schools to engage in data use and the range of related processes
that educators may undertake. This model presents two overlapping continuums having to do with the relative complexity of data
types and of data analysis and decision making. Data complexity
has to do with a range of factors, including the time frame of the
data, the type of data, the data source, and the level of detail of
the data. The relative complexity of data analysis and decision
making relates to how the data are interpreted. In other words:
r Are analyses based on assumptions or empirical evidence?
r Are they rooted in basic or expert knowledge?
r Are analysis techniques straightforward or sophisticated?
r Are decisions made individually or collectively?
r Are they rooted in single or iterative processing?38
Depending on the balance of the complexity of the data and
the complexity of data analysis and decision making, a school
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DATA
Simple
I
II
ANANLYSIS AND
DECISION MAKING
Simple
III
Figure 1.3.
ANANLYSIS AND
DECISION MAKING
Complex
DATA
Complex
IV
Framework for Simple versus Complex Data-Driven Decision Making
Source: G. S. Ikemoto and J. A. Marsh, “Cutting through the ‘Data Driven’ Mantra: Different Conceptions of Data-Driven Decision Making,”
Yearbook of the National Society for the Study of Education, 106 (2007): 105–131. Reprinted with permission from Gina Ikemoto and Julie Marsh.
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The Promise and Pitfalls of Data-Driven Decision Making
may be considered basic, analysis focused, data focused, or inquiry
focused. Figure 1.3 depicts this model.
Schools that fell in the basic category tended to use simple
data and engage in simple analysis. For instance, a principal in
one school noticed students didn’t perform well on the state test
in mathematics and scheduled professional development. In other
words, only one person at just one point in time used only one
source of data. Analysis-focused schools also focused on the collection of simple data, but they undertook complex analysis and
decision making collectively. Data-focused schools collected complex data but engaged in simple analysis. For example, one school
brought a group together to look at a range of data on student
learning, but they didn’t draw on empirical or expert knowledge
to analyze the data. Inquiry-focused schools collected complex
data and employed complex analysis and decision making. These
schools drew on multiple sources of data and examined evidence
collectively over a period of time in order to address a particular problem of practice. They also integrated the knowledge of
experts. Although the majority of schools that took part in the
study that informed this model described their practices as falling
in the basic category, in reality they covered the full range, from
basic to inquiry-focused models of data use.
It is useful to consider this entire range of data use models
because it underscores the importance of examining how different contextual factors may influence how schools use data.
Ultimately the model makes clear that data use is not a straightforward process and no single model is the ideal type; rather,
different models may be useful for different purposes and in different places.
Our own research and the work of other scholars suggest that
all three of these models have relevance. In other words, we need
a clear understanding of how data are conceptualized and used in
specific contexts because individual schools are at different stages
of implementation and have different models of data collection
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