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A school's use of data-driven decision making to affect gifted students' learning

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A school's use of data-driven decision making to affect gifted students' learning

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A SCHOOL’S USE OF DATA-DRIVEN DECISION MAKING
TO AFFECT GIFTED STUDENTS’ LEARNING

by

Matt Dalton

___________________________________________________________________

A Dissertation Presented to the
FACULTY OF THE ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

May 2009

Copyright 2009 Matt Dalton
ii
DEDICATION
Growing up with parents that dedicated their lives to public education gave
me a novel perspective on the influence that teachers possess on the lives of the
children they teach. It is because of my two heroes that I choose to be a teacher, and
continue to pursue my goal of being a lifelong learner. It is within this context that I
dedicate this manuscript.
To my parents, who have relentlessly supported and encouraged me
throughout this entire process. I appreciate their love, wisdom, and timely manner of
advice. My success is a direct reflection of the ideals and values that they instilled in
me.
To my sisters, Jennifer and Amy, who have always been my biggest fans, I
have lived my whole life admiring the amazing role models that they have been for
me. Their inspiration has always been a driving force behind my hard work and
passion for life.
To my friends, who have provided me with stability and reason throughout
this process. Your support has provided me with a strong foundation that has
allowed me to bring balance to my life. To all the above mentioned, thank you for
being a part of my life and helping to shape me as a better person.

iii
ACKNOWLEDGMENTS

This study was only possible as a result of the many people that contributed
tirelessly to the completion of this manuscript. I acknowledge that your assistance
and guidance has allowed me to gain perspective on new foundations of education.
I would like to first thank Dr. Amanda Datnow, my dissertation chair, who
provided me the expertise and motivation to design and implement this research.
Her ability to facilitate and inspire me through this program has helped me to grow
as an educator and researcher. I would also like to thank Dr. Sandra Kaplan and Dr.
Margo Pensavalle, my committee members, who early on as an undergraduate
planted the seeds of my teaching philosophies, and have continued to support and
inspire me ever since.
To my colleagues, who always provided assistance and guidance when it was
most needed, I appreciate your assistance. I would also like to thank the many
professors who helped me along the way develop a better understanding for what is
necessary to become a change agent in education. I want to acknowledge all the
willing participants in my study who allowed me to gain access to their world.
Without their insights, this process would not have been possible. It is my hope that
their participation will help contribute to furthering the cause of improving
education.
iv

TABLE OF CONTENTS
Dedication ………………………………………………………………………… ii
Acknowledgments ………………………………………………………………... iii
List of Figures ……………………………………………………………………. v
Abstract …………………………………………………………………………… vi
Chapter One: Overview of the Study …………………………………………….. 1
Chapter Two: Literature Review ……………………………………………….. 10
Chapter Three: Methodology …………………………………………………… 48
Chapter Four: Analysis of the Data and Interpretation of the Findings …………. 58
Chapter Five: Conclusion and Recommendations ……………………………… 97
References ………………………………………………………………………. 122
Appendix A: Teacher Interview Protocol ……………………………………… 130
Appendix B: Principal Interview Protocol …………………………………….. 134
Appendix C: District Administrator Interview Protocol ………………………. 138
Appendix D: Data-related Meetings Observation Protocol ……………………. 142
Appendix E: Code List …………………………………………………………. 144
v
LIST OF FIGURES
Figure 1: Ikemoto & Marsh's Framework for Simple Versus Complex DDDM … 16
Figure 2: Westgard Elementary (K-6) Enrollment by Ethnicity (2007-2008) …… 50
Figure 3: School Data Analysis Plan ……………………………………………103
vi
ABSTRACT
Public schools are under constant scrutiny and face increased accountability
measures as a result of both federal and state mandates. A rise in the use of data to
make decisions and support change at the local level has provided schools with the
much needed opportunity to respond to the needs of their population. This study will
investigate how the use of data influences the education of gifted and higher
achieving students. Specifically, it will look at how teachers of gifted students use
data, what is being done to build the capacity of teachers to use data, and what
applications and crossovers from data-informed instruction for gifted education can
be used for instruction for the general population. These issues will be examined
through a qualitative case study of one elementary school serving large numbers of
gifted students. Data will be collected through interviews with teachers,
administrators, and district personnel, in addition to school observations of data
focused meetings. All teachers interviewed displayed a general understanding of
how data helps them prepare for lesson planning, and commented that they also
share data within grade levels. While not all teachers used data the same way, all
GATE teachers shared a collaborative approach that involved students with
analyzing their own data. The GATE teachers used data as an accountability
measure not only for themselves, but for students and parents as well.

1
CHAPTER ONE
Overview of the Study
The challenge for leaders is to use data, not as a surveillance activity but in the
service of improvement. We propose that the essence of accountability is looking
forward, using data to inform judgments about current performance and formulate
plans for reasonable actions (Earl & Katz, 2002).

Background of the Problem
The accountability of the American public school system, from its initiation,
has struggled to identify with a comprehensive approach that mandates all states to
be held responsible for significant gains in student achievement. However, in 1994,
the Improving America’s Schools Act was passed, which shifted national priorities
and provided districts with a model to change the way school inputs could be
connected to student outcomes through a more defined system. The law expected
states to provide students with more appropriate learning opportunities by
establishing a viable curriculum that could be measured in annual assessments. With
the aid of the state, school districts were left with the autonomy to develop methods
suitable for achieving success.
In the late 1990s, a new compliance emerged through both state and federal
initiatives that materialized in the form of “standards-based” reform. It called for
“high standards for all students” and was oriented around challenging subject matter,
acquisition of higher-order thinking skills, and the application of abstract knowledge
to solving real-world problems (McLaughlin & Shepard, 1995). In a standards-based
system, holding schools accountable for student performance is one part of a
2
comprehensive set of interlocking and mutually reinforcing policies (Swanson,
2002). Standards-based reform was designed as a collective insight to ensure that
student learning was planned and designed around the same content specific
curriculum and guided by data from assessments. It also served as an instrument of
accountability for all schools, ensuring every student the right to an effective and
rigorous education.
In 2002, the federal government, once again, reauthorized the Elementary and
Secondary Education Act (ESEA). This supported notion of standards-based reform
mandated all states to follow federal regulations of accountability. Signed into effect
by President George W. Bush, this new version of the 1965 bill was given the name
of the No Child Left Behind (NCLB) Act. States were held in compliance to
stringent accountability procedures that enforced a continual improvement in
districts, schools, and individual student performance. Under the guidelines of
NCLB, states were directed to create assessments that aligned to standards, monitor
change in student progression towards proficiency, and penalize schools and districts
that did not meet their assigned goals or “annual yearly progress” (AYP). NCLB
specifies that states must develop AYP objectives consistent with the following
requirements in the law:
1) States must develop AYP statewide measurable objectives for improved
achievement by all students and for specific groups; 2) The objectives must be set
with the goal of having all students at the proficient level or above within 12 years;
3) AYP must be based primarily on state assessments, but must also include one
3
additional academic indicator; 4) The AYP objectives must be assessed at the school
level. Schools that have failed to meet their AYP objective for 2 consecutive years
will be identified for improvement; 5) School AYP results must be reported
separately for each group of students identified above so that it can be determined
whether each student group met the AYP objective; 6) At least 95 percent of each
group must participate in state assessments; 7) States may aggregate up to 3 years of
data in making AYP determinations (Linn, 2002). The law further extended the use
of evidence in education by mandating the use of scientifically based research in
NCLB programs, effectively mandating the use of evidence in making basic
decisions about K-12 education (Goertz, 2001).
NCLB was a dramatic step that represented a federal statement in the
government’s control of public schools and districts in the United States, and
furthered the need for standards-based reform. These reforms included specific
learning goals (academic content standards) that apply to all students; extra support
to help students and schools meet those goals; increased flexibility for local schools
in order for them to do so; and greater accountability for the results, particularly as
measured by student performance on standardized tests (EdSource, 2004).

Need for the Use of Data in Schools
Particularly since the advent of standards-based reform and NCLB, the use of
data to make decisions involving student achievement has become a critical part of
school and district improvement plans. This is not surprising, as the U.S.
4
government’s reform is spearheaded by a focus on results, with demands for
evidence firmly embedded (Fullan, 2000). In addition to holding districts and schools
accountable, NCLB has brought with it an unprecedented amount of data produced
from a multitude of assessments. In response to NCLB and for their own purposes,
states have created and implemented accountability measures, collection of data on
student performance scores, and management of background and demographic data.
As a result, this increase in data has created opportunities for improved information,
but also caused problems at the state, district, and school level, causing educators to
“drown” in too much data (Celio & Harvey, 2005). Most states have tried to meet
these challenges head on, but, more often than not, demands have outstripped
capacity as data needs continue to evolve and grow more complex (Paliach, 2004).
School districts have implemented large-scale assessment and information
management systems, established indicators of effectiveness, set targets, created
inspection or review programs, tied rewards and sanctions to results and many
combinations of the above (Whitty, Powers, & Halpin, 1998). Thus, school and
district administrators can no longer make curricular decisions based off of
unfounded assumptions. They need “assessment literacy”—the collective capacity
of teachers and leaders in schools to examine data, make critical sense of it, develop
action plans based on the data, take action and monitor progress along the way
(Fullan, 2001). Schools must therefore become responsible for identifying learning
gaps in student populations, and react to overwhelming data that show both
achievement rates of groups and information comparing students to each other. The
5
data should be an instrumental step in the process of showing progress towards
overall school goals.
NCLB requires that educators know how to analyze, interpret, and use data
so that they can make informed decisions in all areas of education, ranging from
professional development to student learning (Datnow, 2007). Therefore, schools
have started to implement strategies for developing the way in which data is
analyzed. Marsh et al. (2006) defines data-driven decision making (DDDM) in
education as teachers, principals, and administrators systematically collecting and
analyzing various types of data, including input, process, outcome and satisfaction
data, to guide a range of decisions to help improve the success of students and
schools. Decisions must be based on the data to improve administrative and
instructional systems to continually promote student achievement (Doyle, 2003). In
order to accomplish this, goals must be set and held accountable towards their
progress. Organizations should concentrate on learning gaps, and evaluate the needs
of the students and teachers. Schools must reallocate resources in order to improve
outcomes in a process that involves data to inform decisions (Marsh et al., 2006).
However, it is unclear how the outcomes of different groups of students, particularly
those that are high achieving, are impacted in the process.

Gifted Education Concerns
The role of data-driven decision making in areas that concern gifted students
and gifted programs is an area of interest that needs further research. The design and
6
implementation of standards-based reform aims to help all students reach high
standards of achievement. One of the major concerns however, is the ability of
educators to achieve these goals for all students, especially those who are very low
performing or very high performing. As a result, two areas of concern include the
use of data for identification methods and the use of data to inform curriculum and
instruction.
The first area of concern with gifted education is the discrepancy in the
qualification standards for gifted and talented education (GATE). Local agencies are
left with the responsibility of interpreting their own version of accommodating gifted
students (Hoge, 1988). A variety of formal testing is used as the criteria for
qualifying, often leaving a gap in what constitutes GATE requirements. The testing
methods of professionals also vary in the level of expertise and assessment. Testing
requires presenting test items according to very specific pre-set directions and
following an exact verbal script. The results are limited and misdiagnoses may
occur. Psychologist, Julia Osborn (1998) suggests that assessing gifted students is
highly dependent upon training, theoretical orientation, personal experience, research
knowledge and clinical experience. In order to better address the needs of gifted
students, a more centralized approach on GATE must take place (Vantassel-Baska &
Brown, 2007). This includes looking at how data influences who is considered
gifted.
The next problem lies with the inconsistency and difficulties of a
standardized definition for gifted students. The federal definition of gifted and
7
talented under NCLB, when used with respect to children, are those who give
evidence of high achievement capability in areas such as intellectual, creative,
artistic, or leadership capacity, or in specific academic fields, and who need services
or activities not ordinarily provided by the school in order to fully develop those
capabilities (Title IX, Part A, Section 9101(22)). The qualifications and procedures
to be called gifted vary throughout the United States. States and districts are not
required to use the federal definition, and the percentage of students identified varies
from state to state due to differences in state laws and local practices.
Finally, another major concern is that lack of attention and differentiated
teaching that gifted students are receiving due to the pressures of NCLB on teachers.
With the main focus of the act addressing the needs of underachieving students,
oftentimes schools are left with no response in how they are ensuring the highest
level of curriculum for gifted students. This sometimes leads to underperforming
gifted and talented education (GATE) students, who become restless and bored with
the level of instruction (Delisio, 2006; Lubinski, 2004). It appears that data may not
be used effectively to identify and properly assess individualized instructional plans
for gifted students.

Research Questions
This qualitative study will examine how data-driven decision making is used
in educating gifted students and high achieving in a K-6 setting. The analysis will
examine the methods associated with teachers and administration of a GATE magnet
8
elementary school to further connect best practices to the use of data in improving
achievement.
In particular, this study will address the following overarching question:
How is data-driven decision making (DDDM) influencing the education of gifted and
higher achieving students? In conducting the case study, the following
sub-questions will also be addressed:
a) How do teachers of gifted students use data to inform curriculum and
why?
b) What is being done to build the capacity of teachers of gifted education
students to use data?
c) What applications/crossovers are there from data-informed instruction for
gifted education to instruction for the general population?

Significance of the Study
Data-driven decision making is an absolute necessity for schools and districts
to adopt as the main rationale for how and why actions are taken. With regards to
GATE, or high achieving students, schools must act on data to determine the
appropriate steps to maintain the ever-changing pace of these students.
Differentiating instruction must be a strategy for all teachers to use, regardless of the
skill set and/or ability level of their students. While more schools are using this
method to increase the capacity of both teachers and students, there is still not
enough research on how teachers effectively use data and why. This case study will
9
not only add to the growing body of literature, but also serve as a framework for
schools to follow who are in need of implementing school improvement plans.

Limitations of the Study
This case study will present itself with certain limitations due to the sample
size of the research. The study will be limited to only one school which will restrict
the ability to generalize results across the population. Also, information will be
obtained through qualitative measures and not by looking at achievement data.
10
CHAPTER TWO
Literature Review
The broad implementation of standards-based accountability under the federal No
Child Left Behind Act (NCLB) has presented new opportunities and incentives for
data use in education by providing schools and districts with additional data for
analysis, as well as increasing the pressure on them to improve student test scores
(Massell, 2001).

Introduction
Never before in American public schools, has so much of an emphasis been
placed on the accountability of school outputs, student achievement, and the efforts
made to measure them. Greater consequences for schools who fail to meet these
goals have led to immediate actions in how to address the learning needs of all
students and the issue of equity in academic opportunity. “With increased
accountability, American schools and the people who work in them are being asked
to do something new—to engage in systematic, continuous improvement in the
quality of the educational experience of students and to subject themselves to the
discipline of measuring their success by the metric of students’ academic
performance” (Elmore, 2002, p.3).
With the increased demand for the use of data to improve student
achievement and the benefits that schools receive as a result of consistent data use,
data-driven decision making is becoming more widespread. The purpose of the
following review of literature will be to observe factors related to the development,
11
regulation, and implementation of data use and its affects on gifted education in
public schools. Two key areas will be discussed:
1. An examination of the advent of data use in the American public school
system and its role and challenges in analyzing school and student
achievement.
2. An examination of gifted education, with an emphasis on learning needs,
identification, and challenges, as well as a look at the current lack of data
being used to address the needs of gifted students.
To adequately address these issues, I will first discuss the mounting concerns related
to school accountability and the roles that it has played in changing the ways districts
and schools operate.

Accountability of Public Schools
While “accountability” has become the current trend in standards-based
reform, its definition is neither evident nor agreed upon. To better understand the
context of its meaning, accountability should be looked at from the social lens of the
school organization. All schools have the responsibility for ensuring the learning
and educational opportunities of all their students. Accountability has been mandated
on a federal, state, and local level and also regulated by the stakeholders throughout
the school’s community. In every state, education is the single biggest budget item,
usually consuming 40 percent of the state’s expenditures. This large investment is
12
assessed through the means of summative and formative measures, state tests, and
performance-based assessments of student learning.
Accountability has been brought to the forefront of public education in a
greater effort to centralize the decision-making process regarding where public
monies should be spent in the most cost effective way. The demand for an increase
in student productivity by influential constituents has forced lawmakers to take
drastic measures to deal with the weak system of accountability and measurement
that was already in place. The decline in the allocation of fiscal support for
education has even further influenced how states and school districts deal with
competing demands from the public. Well-defined standards can play a central role
in accountability. By using them, school progress can be measured according to the
percentage of students that attain various levels of proficiency for various subjects
and grade levels (Walberg, 2002).
This important paradigm shift into “standards-based education,” created a
sense of urgency to transform the ways schools were being held responsible for both
the operation and rationale behind student achievement. As a result, four basic
tenets, by means of the National Education Summit (1989), were pieced together to
address the demand for national reform in public schools:
1. Use a public process—involving educators, parents, community
members, and potential employers—to establish common and transparent
expectations, known formally as standards, for what students should
13
know and be able to do upon graduation and at certain key earlier grade
levels.
2. Develop assessments geared to standards that students could prepare for
and that could provide clear targets for teachers’ instructional work with
students.
3. To preserve local control, encourage districts and schools to enact
instructional programs explicitly geared to the standards and to organize
continuing professional development around those programs. Pre-service
teacher training, too, was to be organized around the standards.
4. Create accountability systems that are based on whether students are
meeting the publicly set and assessed standards.
This call to action initiated a standardized system of accountability that
benefited from a combination of federal power and local control. It explicitly sought
to define educational outcomes, measure those outcomes, and use the resulting data
to influence instruction, either simply by providing authoritative feedback or by
linking results to rewards and punishments (Firestone, 2001). This standards-based
structure included content standards that established the knowledge or skills students
are expected to learn and tests or assessments aligned to those standards. While the
standards and assessments would be set by states, school districts and schools would
still have power over curriculum, teaching, and professional development. While
ideal in thought, this ambitious reform left states autonomous to develop assessments
and address the needs created by this new accountability system. Consequently, the
14
use of data to make decisions has emerged as a framework for schools to plan how
content should be taught to students.

Advent and Promise of Data-Driven Decision Making
The fundamental ideologies behind DDDM, in combination with the growing
dissatisfaction in the education system, started off as a way to address the public’s
demand for immediate reform. The notion of using data to influence behaviors was
taken from successful models found in business and industry. It eventually
translated into education, when in the 1970s and 1980s states mandated the use of
outcome data as input for school improvement plans and site-based decision making
(Massell, 2001); measurement-driven instruction was implemented in the 1980s
(Popham, Cruse, Rankin, Sandifer, & Williams, 1985); and strategic planning in the
1980s and 1990s (Schmoker, 2004). However, a review of standardized tests
continued to show that American students were not making sufficient gains to
compete in a global market, and that the achievement gap within the United States
was widening at an alarming rate.
As a result, high-stakes accountability policies were drafted and the twenty-
first century created NCLB to address the needs of underperforming schools and
students. This new legislation required districts and schools to use data to measure
progress toward standards and hold them accountable for improving student
achievement. Schools were put under the microscope and accountability increased
as a result of the large amounts of data that were expected to be analyzed. One
15
assumption underlying these policies was that data use would enhance decisions
about how to allocate resources and improve teaching and learning (Ikemoto &
Marsh, 2007). However, technologies increased the amount of data available to
schools, producing a learning gap in the ways in which educators were expected to
analyze it. The response to this problem represents the latest form of DDDM.
Data-driven decision making in education refers to teachers, principals, and
administrators systematically collecting and analyzing data to guide a range of
decisions to help improve the success of students and schools (Ikemoto & Marsh,
2007). It is a continuous process used by schools to determine relevant outcomes
based on accurate analyses of given information. DDDM gives schools the
opportunity to use data as a tool to guide and inform leaders in finding solutions to
various problems that schools face. Data has the potential for improving school
effectiveness, solving educational challenges and positively affecting the people
involved in the process (Wayman, Stringfield, Yakimowski, 2004).
Ikemoto & Marsh (2007) established a framework after careful analysis of
multiple case studies that shows a correlation amongst the type of data used and the
nature of data analysis and decision making. They state that different levels of
DDDM can fall within four separate quadrants: basic (quadrant I), analysis-focused
(quadrant II), data-focused (quadrant III), or inquiry-focused (quadrant IV). Basic
DDDM involves the use of simple data and simple analysis procedures, while
inquiry-focused DDDM involves using complex data and complex analyses (2007).
16

Figure 1: Ikemoto & Marsh’s Framework for Simple Versus Complex DDDM
(p.111)

Basic DDDM is the simplest form of data usage. It involves activities such as
looking at test scores, choosing professional development areas to address areas of
weakness, and adapting schedules to address areas of need.
Analysis-focused DDDM may also examine state tests for data, but involves
school leadership groups that interpret multiple variations of data. This form of
DDDM usually does not take advantage of expert knowledge, evidence, or conduct
analysis techniques to interpret and explain the data. Activities may include
17
differentiating services for low-performing students or using data and expertise to
adopt new curriculums.
Data-focused DDDM uses complex forms of data, while exposing large
groups to data. However, the examination of data most likely took place at only one
point in time and did not draw on expert knowledge or empirical evidence. Actions
taken may include deciding to allocate resources to improve student achievement and
using surveys to inform resource allocation decisions.
Inquiry-focused DDDM utilizes a significant investment in time and
resources to search for a particular problem of practice. This usually takes place at
formal meetings or during professional development time and serves as the main
focus. Evidence may include making decisions to improve the capacity and support
of English language learners (ELL) or deciding how to improve the structures of a
school.
Marsh et al. (2006) constructed another framework using DDDM as a way to
recognize the multiple types of data used to make informed decisions. The four
categorical areas of data include: input data (i.e. expenses), process data (i.e. quality
of instruction), outcome data (i.e. rates and scores), and satisfaction data (i.e.
opinions). The framework encourages schools to organize data by means of
understanding the context of the data, then analyzing and summarizing the outputs
into information. However, the use of information alone does not drive decisions.
Schools must transfer information into “actionable knowledge” by scrutinizing the
information through careful judgment, and generating possible solutions. The
18
framework shows how actionable knowledge affects various decision types that
typically fall into two groupings: decisions that use data to inform, indentify, or
clarify, and decisions that use data to act. If a consensus is reached, then new data is
collected and the cycle repeats itself. Lastly, Marsh et al. explain how DDDM
changes as the content of the educational levels change, influencing the entire
decision-making process. If data is not disaggregated into high-quality knowledge,
and supported by ways of technical assistance, then it is likely to become
misinformation or lead to invalid inferences (Marsh et al., 2006).
Implicit in these policies and others is a belief that data are important sources
of information to guide improvement at all levels of the education system and to hold
individuals and groups accountable (Marsh, Pane & Hamilton, 2006). DDDM
proponents believe that every student can learn and that it is the duty of the schools
to find the best way possible to ensure success. These expectations reflect what
Datnow et al. (2007) call effective strategies for performance-driven school systems.
They include building a foundation for data-driven decision making; establishing a
culture of data use and continuous improvement; investing in an information
management system; selecting the right data; building school capacity for data-
driven decision making; and analyzing and acting on data to improve performance.
Schools under this type of DDDM system have a set culture in place that focuses
appropriate resources in a way that every problem poses a possible solution, as the
use of data acts as a mechanism to better understand potential options. It is therefore
necessary to gather enough data to know where the problems exist and how to solve
19
them. Using data to support inquiry and inform the instructional mission of schools
requires coordinated changes in school processes, data collection, data management,
the use of analytical tools, and the analytical capacity of school personnel (Mason,
2002). The role of DDDM has changed organizations, and has forced school
personnel to transform the way in which decisions are made. Data has transcended
how schools monitor progress, forcing teachers to share strategies, goals, and set
more accurate student plans for an increased probability of school improvement.

Facilitators of DDDM
The use of data in decision making is contingent upon the schools’ desire for
change, and the availability of support provided by the district. DDDM is not meant
to increase the amount of work for schools, but to change the culture and focus of the
schools’ vision and to increase educators’ capacity for using data (Ikemoto & Marsh,
2007; Marsh et al., 2006). Successful districts using DDDM offer teachers on-site
support, electronic reporting and analysis tools, and planning teams. It is a
progressive change that requires districts to provide support, as well as invest in time
and personnel to support professional development.
Administrative Support. Using data for school improvement often requires
that administrators take on new roles, ones in which they are not familiar. Datnow &
Castellano (2001) found that leadership positions and new responsibilities for
principals are sometimes not clearly defined, which can lead to miscommunication
and a possible lack of support. Research shows, however, that effective leadership
20
and support are conditions that all successful data-driven school systems possess
(Feldman & Tung, 2001). Their research (Feldman & Tung, 2001) determined that
administrative leadership must provide, “a vision for the school, an expectation that
all teachers would participate, and support for the process.” This support is critical to
the development of the schools’ progress. Earl (2005) compares this type of
administrative support to that of an artist, where data is constantly being gathered
and used in different perspectives: observation, investigation, and reaction.
“Educators need to use data in many different contexts – to establish their current
state, to determine improvement plans, to chart effectiveness of their initiatives and
to monitor their progress towards their goals” (Earl, 2005, p.8). School
administrators must learn to adapt to a data-rich environment where support for
teachers is necessary. In the Datnow et al. study (2008) successful administrators
found a balance between top-down support and bottom-up innovation when it came
to data use.
According to Earl & Katz (2002) leaders acquire three capacities to better
support the use of data at their school site. Principals should develop an inquiring
habit of mind, become data literate, and create a culture of inquiry in their school
community. Earl (2005) further explains that school support should follow this
framework by using inquiry and reflection to better determine and assess school
goals, use consistent methods to choose appropriate data for various situations, and
improve teacher knowledge and skills through professional development.
21
Professional Development. To effectively address the needs of gifted
students, teachers must meet the high demands of improved professional growth.
The district must take an active role in providing professional development
opportunities in order for schools and teachers to build the capacity and need for a
data-driven system. “Sustaining these attitudes, roles and practices in classrooms
requires both internal and external support” (Darling-Hammond, McLaughlin, &
Milbrey, 1995, p.2). DDDM can be a resource to improve professional development
practices and strategies and help students improve their performance. As discussed in
the above section on “administrative support,” schools must be willing to undergo
organizational change to bring about improvement. Elmore (2002) refers to this as a
fundamental and simultaneous practice of improvement involving three specific
items:
1. the values and beliefs of people in schools about what is worth doing and
what it is possible to do;
2. the structural conditions under which the work is done; and,
3. the ways in which people learn to do the work (p.30).
This framework provides a focus for any concerns a school may face working
towards their improvement plan. Elmore (2002) believes that professional
development “should be harnessed to the goals of the system for the improvement of
student achievement, rather than driven by the preferences of individuals who work
in schools” (p.32).
22
Darling-Hammond et al. (1995) view professional development as a process
that has developed over time, but has adapted to new strategies as a result of more
reforms and higher accountability. They believe that “teachers need to rethink their
own practice and teach in ways they have never contemplated before” (p. 2). This
requires a process of intense professional development, focusing on acquisition of
new knowledge and skills that follow the subsequent guidelines:
o engage teachers in practical tasks and provide opportunities to observe,
assess and reflect on the new practices;
o be participant driven and grounded in inquiry, reflection and
experimentation;
o be collaborative and involve the sharing of knowledge;
o directly connect to the work of teachers and their students;
o be sustained, on-going and intensive;
o provide support through modeling, coaching and the collective solving of
problems; and
o be connected to other aspects of school change (Darling-Hammond et al.,
1995).
The authors encourage a change in professional development services from in-
service training to opportunities for knowledge sharing based in real situations.
“Teachers need opportunities to share what they know, discuss what they want to
learn, and connect new concepts and strategies to their own unique contexts”
(Darling-Hammond et al., 1995, p. 1).
23
Joyce and Showers (2003) describe another approach towards professional
development that focuses on encouraging teachers to transfer knowledge and skills
into the classroom. Often times, they state, that teachers must receive instruction on
how to become better learners by identifying training outcomes that will lead to
teacher success. Appropriate analysis of data at the school site can lead to possible
solutions that link learning to targeted outcomes. Joyce and Showers identify four
critical goals of staff development that impacts student achievement. These steps
include gaining knowledge or awareness of new content, creating a change in self
attitude over different facets of the curriculum, the acquisition of skills and
knowledge, and the transfer of new learning into the classroom (Joyce & Showers,
2003).
The authors (Joyce & Showers, 2003) provide a rationale for how teachers
can develop an improved capacity for learning through six research-based strategies
of professional development. This framework provides a direct crossover and
transferability to understanding how to learn to teach gifted students. First is the
ability for learners to remain persistent, regardless of comfort level by continually
practicing what they learn in the context of the classroom. Next is the learners’
ability to understand the difference between the transfer of training and the
acquisition of knowledge or skills. Learning a new area of content does not ensure
its application in the classroom. Teachers must also be able to teach new behaviors
to students even though they may already be successful in their current methods.
Change is sometimes an uncomfortable feeling that requires risk and is oftentimes
24
not attempted as a result. Next, learners understand why theories exist and use
behaviors to simulate comparable results in the classroom to improve student
learning. Another area of improved capacity shows that the willingness to
collaborate with colleagues leads to higher success rates for transferring new skills to
the classroom. Finally, Joyce and Showers (2003) believe that flexibility is an
important trait that allows teachers the freedom to experiment and take risks giving
students the opportunity to learn at a higher level.
Additional Facilitators. In order to increase data use and foster a culture of
collaborative work, schools must prioritize their allocation of resources by providing
teachers additional time in the school day to meet and plan. Collaborative time for
teachers to undertake and then sustain school improvement may be more important
than equipment and facilities (Fullan and Miles, 1992). By working in a
collaborative environment, and analyzing data, teachers can build capacity for data
use. However, this process takes a long time, and requires support and consistency.
This time for collaboration is essential, especially when data use is linked to
differentiated instruction. Lambert (1998) believes that capacity building in schools
requires the collaboration of teachers engaging in professionals learning together
through discussion. Time constraints require teachers to develop professional
relationships with colleagues in order to best meet the needs of students.

25
Challenges in Data Usage
Though numerous schools and districts have facilitated DDDM through
strong leadership and professional development, using data to make decisions has
proven to be a complex challenge for schools. These challenges must both be
addressed initially and attended to continuously if a school is to make successful and
effective use of its data. Research shows that four main barriers exist in the struggle
to implement DDDM in schools: limited access to data, a lack of data knowledge,
resistance to change, and a lack of time (Thorn, 2002; Ikemoto & Marsh, 2007;
Datnow et al., 2007; Ormrod, 2002).
Limited Assessable Data. Most data collected by schools are often processed
within district information systems and are quite limited in capacity. School data
include information such as attendance, discipline, and student demographics, and
often times, are only available on systems supported by centralized computing
services. While schools are required to make efforts to remain accountable for
student achievement, the data collected are usually “inadequate for making midcourse
or interim instructional decisions within a single grade or marking period” (Thorn,
2002, p .3). Thorn (2002) states that districts often keep detailed information
regarding human resources and other business related items, but fail to collect data
other than grades and centrally administered tests to assist in helping teachers and
schools make effective decisions.
26
Lack of Knowledge. While many schools claim to make decisions based on
student data, research shows that many schools vary in the way they conduct and
examine data (Ikemoto & Marsh, 2007). An inconsistent understanding among
educators exists as to what DDDM actually means, as well as a deficiency in
terminology for various processes and activities in which data is involved. Often
times, data collected are not tangible sources of information (human characteristics,
learning, achievement, etc), and the interpretive value for decision making is
inconsistent (Earl, 2002). Schools struggle with the concept of data collection and
analysis as a means to foster student achievement. The statistical data is often too
complex and leads towards uncertainty in making decisions about school
improvement. Research shows that school personnel still lack training for how to use
data, lack an understanding in what to do with the data, and struggle to have
“interoperability” for the use of data (Grunwald, 2004). Additional knowledge and
skill barriers exist when analyzing data for improved student performance. Schools
often struggle to adapt to data usage due to their large student population size and
complex structures. This confusion often is a predominant obstacle for schools to
overcome. “Educators have not seen statistics as a useful addition to their tool kit for
decision-making. Instead, statistics are either imbued with a magical quality of
numerical ‘truth’, or they are mistrusted as blatant attempts to distort or to
manipulate an audience” (Earl, 2002, p.14).
Resistance. Even when data is made accessible to schools, another barrier
facing the implementation of a successful data-driven school system involves the
27
motivation of teachers. Datnow et al. (2007) found that challenges may also arise in
areas of teacher resistance and staff “buy-in” as well as how districts deal with those
who do not accept change. In the process of identifying resistance factors, Clark and
Estes’ (2002) framework provides indicators to assess potential problems that
include active choice, persistence, and mental effort. Active choice involves making
a conscience attempt in pursuing a goal, by identifying and selecting improvements
and changes to be made. Clark and Estes believe that, “even if [a teacher] did not
select the goal themselves, if they are actively working towards it, they can be
considered to have chosen that goal” (p.80). This can, however, lead to problems.
When unmotivated teachers are asked to incorporate new strategies into their
teaching practices, in which they did not actively choose to become involved,
resistance becomes a factor. Clark and Estes classify procrastination, avoidance,
argument, and/or postponement (p. 80) as signs of potential problems with
unmotivated people. A certain level of challenge is needed for continued personal
and professional development; however, too much pressure, and settings that are too
controlling, can lead a person to drop below their optimal level of challenge in order
to ensure a level of competence.
Time. Time management and prioritization are major issues that teachers and
schools face when dealing with the challenges of integrating data use into decision
making. Being able to focus on and address all goals usually forces teachers to
divide their attention between what needs to be done and what has to be done.
Problems occur “when [teachers] become distracted too often or for too long by less
28
important (but perhaps more attractive) work goals” (Clark and Estes, 2002, p.81).
Since current school reform has inundated schools with copious amounts of
additional work, teachers cite a lack of time as an excuse for not using data to drive
the curriculum. Literature shows that it is important for teachers to have the greatest
amount of persistence invested in the most important work goals (p. 81). Ormrod
(2000) describes this challenge of time management as the ability to execute certain
behaviors or to reach certain goals. She explains that time can be a contributing
factor and major challenge that affects the type of activities teachers choose to
enhance their teaching, how much effort and persistence they will exhibit when faced
with difficulties, and to what degree they will achieve.
In sum, the mandated influence of accountability for public schools has
drawn attention towards the use of data for potential solutions to increase student
achievement. While new literature has surfaced to provide schools with frameworks
for implementation, many schools still struggle to understand how to interpret data in
order to effectively make decisions. Moreover, schools are facing alarming concerns
in the ability to properly educate all students, including the gifted, as discussed in
more detail below.

Educating Gifted and Talented Students
Introduction
Federal mandates and local initiatives have ensured that low achieving
students benefit from resources, but often times the brightest and most applicable
29
students are left to fend for themselves. It appeared that schools who have
successfully adapted to face these challenges have increased data use and provided
teachers with opportunities to influence appropriate content specific curriculum for
gifted students. This section will address the following main issues:
1. How data affects defining the concept of “giftedness” and the current
state of Gifted and Talented Education (GATE);
2. How data affects the range of learning needs that vary among gifted and
high achieving students;
3. How data affects the process of assessment with gifted students and gifted
programs.

Defining Roles
The term “giftedness” is used in many different contexts in an attempt to
define an objective level of intelligence and/or ability. Schools have methodically
defined the term “gifted and talented” as a way to identify and track students who
possess an exceptional ability, or display high levels of aptitude in various fields.
Clark (1997), however states that giftedness is:
… a biologically rooted concept that serves as a label for a high level of
intelligence and indicates an advanced and accelerated development of
functions within the brain. Such development may express itself in high
levels of cognitive, affective, physical sensing, and/or intuitive abilities, such
as academic aptitude, insight and innovation, creative behavior, leadership,
personal and/or interpersonal skill, or visual and performing arts (p.26).

30
Furthermore, the United States Department of Education (D.O.E.) defines giftedness
as, “students, children, or youth who give evidence of high achievement capability in
areas such as intellectual, creative, artistic, or leadership capacity, or in specific
academic fields, and who need services and activities not ordinarily provided by the
school in order to fully develop those capabilities” (Elementary and Secondary
Education Act). These differing yet comprehensive classifications of students are
broadly accepted as ways for school districts to create differentiation in the form of
gifted and talented education (GATE).
The foundation of GATE programs are conditional to each state’s department
of education requirements. Although there are no standardized practices, procedures
or theories, districts are expected to provide individualized differentiation to all
students who qualify. There is a variation of opportunities for gifted education
programs which can include: separate classes, self pacing, acceleration, pull-out, or
enrichment classes.
Gifted and talented students, as recognized by the federal government, are
classified as having special educational needs; however, the presence and budget
provided for gifted education is negligible. The federal government does not
sanction additional services nor fund local GATE programs. It does however, in an
attempt to support GATE research, fund the Jacob Javits Gifted and Talented
Students Education Act (Javits). Passed by Congress in 1988 as part of the
Elementary and Secondary Education Act (ESEA), the Department of Education
(2008) states it “orchestrates a coordinated program of scientifically based research,
31
demonstration projects, innovative strategies, and similar activities that build and
enhance the ability of elementary and secondary schools to meet the special
educational needs of gifted and talented students” (2008, ¶ 1). The Javits Act has
three main components: the research of effective methods of testing, identification,
and programming (National Research Center on the Gifted and Talented); the
awarding of grants to colleges, states, and districts that focus on underrepresented
populations of gifted students; and grants awarded to state and districts for program
implementation (D.O.E., 2008).
With the passing of NCLB, the federal reform focus has veered even farther
from the needs of gifted students, shifting the majority of resources on closing the
gap of low performing students and schools. While the act does promote proficiency
of all students, it does not address any achievement standards for high functioning
students. This focus precludes gifted children, who generally work well beyond their
curricular needs. Gifted education is not mandated consistently at the state level
either, and the extent in which students receive services varies depending on fiscal
demands and resources of the districts. It is assumed that given other high
demanding federal policies, schools have little incentive to ensure proper allocations
of resources for GATE programs.
Because education is a state’s right, and is left to the discretion of districts to
interpret gifted programs, it becomes difficult to collect consistent data for decision-
making purposes. Schools and districts are left with the autonomy to make decisions
for what is perceived as the best interest of all children, specifically gifted.
32
Therefore, it would seem logical that schools should seek answers to questions
through derived methods of data use to influence the gifted curriculum and behavior
of teachers and students. That action, however, would require many schools to
change the ways that current decision making is being achieved. It is within the
individual school that change becomes a more realistic concept because it is “the
appropriate level at which to address school improvement and that effective and
lasting, change can occur only when it is initiated, nurtured, and monitored from
with the school itself” (Renzulli, 1998, ¶ 33). By using apposite data to influence
decision making, schools and districts will be better equipped with means of
providing and allocating supportive resources for high achieving students. It is noted
that program improvement at the school site will not work, “unless data is fed back
into the decision-making process to allow for the teacher or administrator to do the
most effective instructional and program planning” (Callahan, 2009, p. 253).

Range of Learning Needs
The ability level of a student who is identified as gifted varies within a large
range of learning characteristics. They do, however, all possess the ability to
perform at exceptional rates in one or more subjects, but not necessarily in all areas.
What works for one student, might not be sufficient for another. That is why it is
important that, “regardless of the giftedness, schools must provide opportunities for
these students to reach their full potential by providing professional development so
that teachers will know how to establish situations for gifts and talents to emerge,
33
how to observe characteristics over time, and how to observe characteristics in
groups that are typically underrepresented in programs for gifted and talented
students” (Johnsen, 2004).
Gifted children have a deep intrinsic motivation to master the domain in
which they have high ability, and are almost manic in their energy level (Winner,
1996). With that said, gifted students should, like their special education
counterparts, benefit from individualized instructional plans in all states to better
meet the needs of all who qualify. Giftedness, by nature, assumes the very essence
of achieving through means of acquired knowledge in a large range of learning
needs.
Gifted and talented students reach high levels of thought and understanding
by executing multiple approaches to solve problems using creative solutions. Gagnè
(1999) describes this natural ability as having “gifts.” However, he notes that these
“gifts” require continuing work to become “talents,” which emerge through the
systematic learning, training, and practicing “of skills characteristic of a particular
field of human activity or performance.” Often times, these talents are developed in
part by satisfying a need to thrive on “autonomy and independence” (Baird 1985). It
is, however, important not to make overgeneralizations about any learners. Gifted
children display various levels of successes in school, and greatly range in social and
emotional states as well. Some gifted students have learning disabilities, while
others show profound levels of giftedness in every aspect of life. With such a diverse
34
group of learners, there is clear evidence that gifted students come in all different
types and exhibit a large range of learning needs.
While there exists an array of gifted learners, researchers have consistently
identified common characteristics as relating to general intellectual abilities (Clark,
1997; Renzulli, Smith, White, Callahan, Hartman, & Westberg, 2002; Rogers, 2001;
Renzulli & Reis, 1981):
• Has an extensive and detailed memory, particularly in an area of interest.
• Has vocabulary advanced for age.
• Has communication skills advanced for age and is able to express ideas
and feelings.
• Shows tenacity.
• Asks intelligent questions.
• Is able to identify the important characteristics of new concepts,
problems.
• Learns information quickly.
• Uses logic in arriving at common sense answers.
• Has a broad base of knowledge.
• Understands abstract ideas and complex concepts.
• Uses analogical thinking, problem solving, or reasoning.
• Observes relationships and sees connections.
• Finds and solves difficult and unusual problems.
35
Generally, “gifted” students are able to score 130 or higher on an IQ test, meeting a
superior intelligence standard, and those who score 160 or above are characterized as
profoundly gifted. However, scoring high on a test does not reveal the level of
intelligence a student has. Terman (1926) notes, “We must guard against determining
intelligence solely in terms of ability to pass the test of a given intelligence scale” (as
cited in Thorndike, 1921, p. 131).

Historical Perspective of Gifted Identification
Inconsistent definitions for what constitutes giftedness have led to
problematic attempts to standardize the identification process, leaving intelligence
tests, historically, as the main indicator for qualification. The Stanford-Binet
Intelligence Scale, created by Terman in the early 1900s, served as the main measure
of intelligence, and functioned as a predictor of giftedness for students. The test
focused on language comprehension, eye-hand coordination, mathematical
reasoning, and memory. Gifted and talented students were able to score at the top 1
percent of the population on this test (Brown, Renzulli, Gubbins, Siegle, Zhang, &
Chen, 2005). Terman further developed the concept of mental capacity by adopting
the work of Stern to calculate an intelligence quotient, or IQ. This was done by
dividing “mental age” by chronological age and multiplying by one hundred. A
score of at least 130 classified students as highly intelligent. High scores, however,
do not qualify students as gifted unless what Tannenbaum (1991) describes as an
“investment in confidence” is linked with it. This research provides insights into
36
widely believed notions that multiple instruments should be utilized to screen and
identify gifted students (Gallagher & Gallagher, 1994; Tuttle, Becker, & Sousa,
1988), and that IQ tests alone should not be the sole means of identifying individuals
(Clark, 1997). A multiple measures test seeks to determine both ability and
achievement level through means of observing behaviorist outcomes. Coleman and
Gallagher (l992) found that “all 49 of the states which have state level policies
related to gifted education use some form of standardized IQ and achievement test in
their identification process. However, a variety of other sources are often included”
(p.7).
The qualifications for giftedness were reexamined by Renzulli (1978, 1982,
1984) once again in an attempt to establish characteristics of gifted behaviors rather
than gifted individuals. “Whether or not it is the writer's intent, such statements will
undoubtedly be used to direct identification and programming practices, and
therefore we must recognize the consequential nature of this purpose and the pivotal
role that definitions play in structuring the entire field” (Renzulli, 1998, p. 80). He
found that gifted behaviors reflect an interaction among three basic clusters of
human traits—above average ability, high levels of task commitment, and high levels
of creativity (Renzulli, 1978). He concluded that any attempt at defining giftedness
must follow specific criteria:
1. It must be based on the best available research about the characteristics of
gifted individuals rather than romanticized notions or unsupported
opinions.
37
2. It must provide guidance in the selection and/or development of
instruments and procedures that can be used to design defensible
identification systems.
3. It must give direction, and be logically related to programming practices
such as the selection of materials and instructional methods, the selection
and training of teachers and the determination of procedures whereby
programs can be evaluated.
4. It must be capable of generating research studies that will verify or fail to
verify the validity of the definition.
Prominent research has taken place, changing the identification process for
gifted students, and giving educators an insight as to how gifted learners think
(Brown et al., 2005; Bouchard, 2004; Renzulli, 1984). Bloom produced a
classification level of intellectual behaviors critical to the measure of aptitudes. It
was found that gifted students cognitively processed thoughts at a higher level, and
could be assessed based off of the specific level of comprehension. Bloom (1956)
identified six levels of cognitive domain, from the simple recall or recognition of
facts, as the lowest level, through increasingly more complex and abstract mental
levels, to the highest order which is classified as evaluation. Gardner (1983) found
that people function on a series of “multiple intelligences.” Linguistic, logical-
mathematical, spatial, musical, bodily-kinesthetic, interpersonal, intrapersonal, and
naturalist (later added) could serve as the focus of an identification procedure.
Sternberg (1985), with his conception of the “triarchic theory of intelligence,” found
38
that focusing on specific types of measurable mental abilities limited the capability
to properly identify giftedness. This theory looked at analytical, synthetic/creative,
and practical intelligences as being multiple ways to test students for giftedness.
With a greater awareness and an increase in the perception of gifted students,
schools have the ability to use data to appropriately design and instruct using a
differentiated curriculum. DDDM provides an opportunity for schools to achieve at
higher rates, and to become more accountable to high achieving students; however, it
may or may not be used these ways.

Challenges in Adequately Assessing GATE Students
Critical challenges face educators in the attempt to accurately identify
children who are gifted. Both political and emotional components create
controversies, and require more prudent identification procedures in order to
facilitate the proper instructional services for all gifted students. Schools encounter
problems with the development and procedures of identification, including an equal
distribution of money and time. With little resources, schools must allocate a
proportional amount of each into both the identification and implementation of the
GATE program. While most states require identification of gifted students, some do
not even require the provision of services (Coleman & Gallagher, 1995).
A major challenge facing the identification process historically, involved the
use of IQ test levels and teacher selection. In a study, Carroll (1940) found that only
15.7 percent of students nominated, where 6,000 teachers chose the most intelligent
39
in the class, were found to actually qualify for the gifted group. As new methods
were developed to more accurately test students, new challenges were also created.
The instrument of identification often times is impacted due to budgetary constraints,
which in turn, could impair the selection procedure and create inaccurate measures.
This dilemma creates concerns with misdiagnoses of over and under populated
groups. Coleman (2003) believes that the identification process has historically failed
to address the following issues: disproportionate representation, disregard for
theoretical knowledge of intelligence, inappropriate use of statistical formulas,
mismatch between identification and services.
Current trends in disproportionate representation face students predominately
of minority status, both racially or ethnically, in addition to students from
economically disadvantaged families and communities, those with limited English
proficiency, as well as students with disabilities. Causes are linked to biased state
tests that serve as qualifying procedures for many districts. “As the definition of
giftedness is extended beyond those abilities that are clearly reflected in tests of
intelligence, achievement, and academic aptitude, it becomes necessary to put less
emphasis on precise estimates of performance and potential and more emphasis on
the opinions of qualified judges in making decisions about admission to special
programs” (Renzulli, 1998, p. 258). Oftentimes, young students who have gifted
qualities are unlikely to be identified due to district polices, age restraints, or
fearfulness of labeling young children. Under-populated groups exist due to what
Coleman (2003) sees as, “narrow conceptions of intelligence and the resulting
40
definitions of giftedness, and the procedures and policies that guide local and state
gifted programs” (¶ 5).
Another challenge in identifying gifted students is the particular kinds of tests
that are applied to designate students. One-dimensional tests (i.e. IQ tests)
impartially disregard students whom may exhibit other areas of giftedness. With the
vast research available to school districts, single criterion tests, should no longer be
the sole impetus behind identification policies. Coleman (2003) suggests using
alternative or multiple measures for a more appropriate evaluation, for instance:
student portfolios, showing work over time; performance-based assessments; and
projects that involve collaboration with peers can all supplement standardized
testing. Many districts also use policies that involve “cut scores” or formulas to
derive a score as ways for students to qualify for GATE services. This once again,
eliminates opportunities for students, and proves the need for a broader base of
identification criteria.
Finally, challenges ensue as a result of matching services to a student’s
identification focus. The educational decisions of the student should directly match
the data collected during the identification process (Coleman, 2003). If the
identification process includes multiple measures, then students are more likely to
receive the services in which they qualify for. Proper use of data will help relate
identification assessment information directly to programs, curriculum, activities,
and evaluation.
41
The use of data as a resource for guiding the curricular guidelines of the
gifted and talented educational programs is not a widely used practice. NCLB has
shifted the attention to students who have scored less than proficient on state tests; as
a result, high achieving students in regular classrooms may receive far less attention.
An examination of how data is used in combination with current GATE programs
will follow.

Combining the Use of Data and Gifted Education
Current Knowledge
The NCLB, Title II, Part D legislation, calls for “an increased academic
achievement through strategic, effective approaches for the use of technology by
schools.” This directive pushes the need for a more standardized approach to using
data for instructional planning and decision making. DDDM aims to accomplish this
directive by using resources to help schools accomplish organizational goals.
However, very little research has taken place that documents the combination of
DDDM and gifted education. While data is collected to document IQ scores and
identification practices, there is little evidence that DDDM has been used as a main
source defining achievement among gifted and high achieving students. The current
trend in DDDM is focused primarily on issues, such as attendance and standardized
test scores regarding all students, with little emphasis placed on the performance of
gifted and high performing students. Much is to be learned with respect to
42
instruction that might arise out of DDDM that can influence the education of gifted
and higher achieving students.
DDDM is a central process that focuses on providing opportunities for
educators to make the right choice for any given situation based on analytical data.
Literature shows that it also used to successfully improve resource allocation and
instructional program decisions (Thorn, 2002). The key to using data is being able to
interpret the results in a way that benefits school goals and increases student
production. This directive is critical to understanding how DDDM can influence the
instruction of gifted education. Most research on DDDM states the obvious
assumption that data should drive instruction; however, the literature is limited in
how schools can methodically use data to affect curricular change and meet the depth
and complexity of gifted students. This section will attempt to correlate strategies in
DDDM informed instruction to gifted education.
Datnow et al. (2008) provide evidence of differentiated instruction in urban
high schools as a result of data informed instructional strategies similar to that found
in gifted research. Data analysis help “teachers decide how to pace their instruction,
align their lessons to standards, identify lessons for re-teaching, guide their flexible
grouping of students, and target students for intervention,” as well as provide
“learning goals and targeted benchmarks” (p. 7). For example, in some classrooms,
teachers received immediate student feedback through the use of “exit tickets” (p.
50). This enabled teachers to see what students were able to independently learn,
and what needed to be re-taught in a more appropriate way. Another school used
43
“instructional snapshots” to highlight the level and depth of instructional practices
across the system. Teacher lessons were assessed on levels of depth and complexity
through observations that looked for (1) learning objectives, (2) the level of Bloom’s
taxonomy, (3) types of instructional strategies, (4) learner engagement, and (5) a
survey of the learning environment (p. 41). Many teachers also used teacher-made
benchmark tests to determine the extent of proficiency for his students. For example,
after analyzing benchmark data, one teacher determined that a majority of his second
language learners did not truly understand the concept due to a lack of scaffolding
and visual aids. The teacher collaborated for additional ideas to improve his lesson,
and re-taught it with more appropriate differentiated strategies.
“Many teachers noted that having greater knowledge of their students’
performance levels led them to think about experimenting with new instructional
strategies” (Datnow et al., 2008, p.57). This finding directly connects with the
guiding principles of The National Association for Gifted Children (NAGC)
standards, stating, “Regular classroom curricula and instruction must be adapted,
modified, or replaced to meet the unique needs of gifted learners” (Burke, Cox,
DeWaard, Hansford, Hays, Montjoy, Reid, & Slanina, 2000, p. 8). DDDM offers
teachers the opportunity to differentiate curriculum regardless of ability level, in a
variety of ways. Data influence teachers to modify instruction, strategically improve
the curriculum, and adjust to the various needs of students’ ability levels (Datnow et
al. 2008). This could dovetail well with the goals of gifted education; however, the
influence of DDDM on gifted education remains unexamined.
44
Issues for the Future
Existing and emerging technology will continue to make data use both more
prevalent and more effective for decision making and improvement. This in turn
will also create more complex and time consuming practices for school personnel.
In order to encourage schools and teachers to use data, future issues must be
addressed. Ikemoto & Marsh (2007) suggest the following four issues:
1. Acknowledging that DDDM is not a straightforward process;
2. Improving the availability, timeliness, and comprehensiveness of data;
3. Providing professional development aimed at building educators’
capacity to examine data and conduct research and act on these findings;
and
4. Helping educators access external partners, expertise, and tools.
DDDM is a challenging reform that requires schools to be patient, as it is not a
program, but a change in culture. By incorporating this process into the GATE
program, the use of data to make decisions could further extend the duties of what
schools and teachers already are expected to do. This barrier is a major challenge
that schools will continue to face as accountability measures rise for public schools.
Literature in the field of DDDM and gifted education is limited at best. It is difficult
to identify best practices and establish professional development to increase
instructional capacity as a result. It is the intent of this study to gain more
information about the use of data and its influence over the curriculum taught for
45
gifted students, in addition to the strategies involved in maximizing data-driven
decision making.

Summary of Literature Review
The American public schools system has undergone a major accountability
movement that has created a compelling sense of urgency to improve student
performance and raise test scores. Federal and state mandates requiring schools to
document progress, has pushed districts to redirect both their focus and emphasis on
educational outcomes and accountability (Goertz 2001). As a result, districts have
started to use school data to inform and create school improvement plans. The
strategy of DDDM has provided districts with effective results, and has created a
new area of growing research to provide frameworks for participating schools.
Current literature on DDDM ranges from its origin in business to the spawn
of standards-based reform. With an increase in available data, schools have relied on
various measures of data to assist in making decisions about how students should
learn, and why. This process has evolved into practical frameworks that give schools
the opportunity to increase student performance by understanding what kind of data
is available and how it is organized. Furthermore, literature also describes processes
for understanding data-based strategies and provides schools the opportunities to
guide improvement from informed decisions. There is emerging research on DDDM
and it affects on school site management, as well as information about the
procedures that facilitates this process. Additional literature discusses the challenges
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that schools face in the successful implementation of DDDM. This reform initiative
is designed to bring about change and develop a more complex culture of data use
that is applicable towards students of various ability levels.
In addition, vast amount of literature exist explaining the history of giftedness
and the services provided to students as a result of their identification. Research is
clearly documented on the ideals of what makes kids learn, and the strategies for
measuring them. However, gifted definitions and inconsistent practices of GATE
curriculum across states leave much to be learned. GATE programs remain
autonomous and therefore vary according to the budget and priorities of local
districts. The services provided for gifted students fluctuate as well as the
intellectual and ability range for those who qualify. Research shows that students
who are classified as gifted, do so as a result of high IQ results or exceptional test
scores. This creates a difficult challenge in assessing gifted students as ability and
interest levels vary among them.
There is a growing body of literature that exists about the impact of DDDM
on schools, in addition to extensive research on gifted education. Some studies have
been conducted on the methods used to analyze student data, the impact it has on
instruction, and the successes of those schools. However, when studying the
interaction between DDDM and its implications on gifted students, limited literature
is available. It remains a stretch to imply how data provides differentiated
curriculums as a result of extensive data analysis and collaborative decision making.
This remains a new area of study for education, and has created a gap of knowledge
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for researchers. This study will add to the literature about data use and its affect on
gifted education by examining the roles that data makes in the processing,
identification, and implementation of effective curricular decisions for increased
achievement of gifted students. This study will elaborate the process and structures
put into place for data-driven decision making in a gifted education context. An
analysis of significant factors, including the culture, leadership, and support of data
use will further contribute to the literature available on data in a more individualized
approach. This study will allow schools interested in how DDDM can better address
the needs of gifted students and support teachers in making more appropriate
curricular decisions to follow the steps and procedures for improved student
achievement.
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CHAPTER THREE
Methodology
Introduction
This chapter will address how the design, sample, unit of analysis,
instrumentation, data collection, and data analysis will be determined. The analysis
will seek to answer the following research question and supplementary sub-
questions: How is DDDM influencing the education of gifted and higher achieving
students?
1. How do teachers of gifted students use data to inform curriculum and
why?
2. What is being done to build the capacity of teachers of gifted education
students to use data?
3. What applications/crossovers are there from data-informed instruction for
gifted education to instruction for the general population?

Case Study Methodology
Qualitative case study methods were used to examine how data-driven
decision making influenced the education of gifted and higher achieving students.
Patton (2002) considers qualitative data gathering methods to include: in-depth,
open-ended interviews; direct observations; and written documents. Therefore, these
techniques were used as a means to determine the extent in which decisions about
the gifted are made on the basis of data.
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This qualitative analysis focused on “understanding the phenomenon of
interest from the participants’ perspectives” (Merriam, 1998, p.6) or identifying the
emic behavior of those who are being studied. By following the structure of what
Merriam describes as a descriptive case study, an intended body of research was
conducted in an attempt to establish a “database for future comparison and theory
building” (p.38). The focus of this case study was to provide a detailed account of
the way data influences behaviors and decisions at a school with specific attention
towards teaching gifted students. By studying one school, recognized for using data
to develop successful student achievement, new findings brought light to the views
that the stakeholders have regarding the impact of data use at the school site.
Merriam (1998) describes this as an opportunity to gain an in-depth understanding of
the situation and meaning for the study, by focusing on the process, context, and
discovery of the results. The goal was to “help understand and explain the meaning
of social phenomena with as little disruption of the natural setting as possible”
(Merriam, 1998, p.5).

Sample and Population
Through the use of purposeful sampling, the unit of analysis is Westgard
Elementary, a K-6 gifted magnet school located within the Sunset Unified School
District (SUSD). This district was of special interest in conducting research as it is
one of the original districts to provide gifted education for students in California. It
is within this history that the district’s data analysis originated, as a means for
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providing statistics to champion the cause for gifted education. At the school site,
the population is diverse in socioeconomics, serving 28.7% English Learners and
31% percent of the students receive free or reduced price meals. The school has a
large and growing gifted population (approximately 70%), and a Special Education
program that includes full inclusion students. All data are analyzed and compiled as
one school. The following graph helps to identify the enrollment and demographics.

Figure 2: Westgard Elementary (K-6) Enrollment by Ethnicity (2007-2008)

Note: From School Accountability Report Card (SARC)

The following criteria were used to select the study site. The school has
shown steady gains in an already high achieving district scoring high APIs and
meeting all subgroups for AYP scores. The site was also chosen due to the analysis
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of individualized and holistic data used to modify instruction, as well as the
reputation and recommendations received through professional networking. Another
contributing factor was large numbers of gifted students being served at the school,
and the diverse population of learners. The last criterion used was the district’s
gifted education program and the organization of grouped homogeneous gifted
students together in classrooms. This type of sampling is what Merriam (1998)
classifies as “unique sampling” or atypical in attributes and occurrences. Data use
with gifted students can very well be considered a rare phenomenon in a typical
public elementary school.
In determining what purpose the information served in selecting appropriate
subjects to interview, Patton’s (2002) criterion sampling worked as the most logical
for the specific study purpose and resources associated with gifted education. I
chose participants to interview that fit specific criterion including, but not limited to:
successful application of student data use in teaching strategies; working with gifted
students; and experience in using data to inform the curriculum and articulation of
gifted and/or GATE practices. Therefore, all of the teachers interviewed met these
conditions. I also interviewed the principal, and the director of GATE education to
gain additional insight to the various ways data are used.

Data Collection Procedures
In order to effectively collect purposeful data, a focused semi-structured
interview process following Patton’s (2002) interview guide was used. This allowed
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the respondents the time and scope to talk about particular opinions on the given
subject, as well as provided individual points of view. By contributing to what
Patton (2002) calls qualitative “raw data,” I gained access to the respondent’s
perspective, and thus limited the generalizations about behavior. To capture these
critical viewpoints, I began with the school’s principal and focused on the culture of
successful data use at the site. The interview lasted approximately 50 minutes and
was digitally recorded in an attempt to capture patterns and interpret meaning. Data
were gathered from 12 additional interviews consisting of two general education
teachers and 10 homogeneously grouped gifted students lasting approximately 30 to
45 minutes in length, focusing on how data has contributed to the progression of
curriculum and decision making for gifted students. A final interview, lasting
approximately 45 minutes, with the director of gifted education responsible for the
implementation of GATE curriculum was conducted to determine the focus and
strategies used to effectively use data to differentiate instruction and provide students
with appropriate levels of education. The interviews were conducted with the direct
purpose of answering the over-arching research question as well as the
supplementary sub-questions. Each interview took place in a mutually agreed upon
location, allowing participants to feel comfortable and lowering the affective filter
that might limit actual responses. The framework behind the questions allowed
responses to be both authentic and deep in emotion. Asking open ending questions
with back-up prompts additionally provided opportunities for participants to engage
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in accurate and thorough descriptions of real life experiences. The protocols for all
interviews are included in Appendices A, B, and C.
Additional data were collected in the form of observations. Patton (2002)
believes that “participant observation permits the evaluation researcher to understand
a program or treatment to an extent not entirely possible using only the insights of
others obtained through interviews” (p.22). In order to provide depth and detail,
teachers and administrators were also observed during a meeting where data were
being used to drive instruction. In order to study participants in as natural as an
environment as possible, I researched strictly as an observer, and not a participant,
while taking field notes to incorporate with interview data. An observation protocol
was used and is included in Appendix D. Actual uses of data by teachers were
determined based on these observations and triangulated with collected interview
data.
The third type of data collected was a review and analysis of applicable
documents related to data usage. Patton (2002) considers this “material culture” as a
rich source of information that “proves valuable not only because of what can be
learned, but also as stimulus for paths of inquiry that can be pursued” (p.294). The
documents collected for analysis included: state assessment reports, district or school
generated reports, teacher created reports, lesson plans, and other formal and
informal documents used by teachers and administrators. Merriam (1998) suggests
that all documents should be assessed for their authenticity and nature, as well as
coded and cataloged for easy access in the analysis and interpretation stage. Each
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document was therefore coded in order to describe what is being presented in each
subjects’ interview.

Data Analysis Procedures
To carefully analyze the interview data, a precise transcribed account of
participants’ responses recorded a literal account of the answers provided resulting in
approximately 150 pages of data. In addition to transcribed interviews, remaining
data consisting of observations, documents, and field notes underwent a procedure of
coding.
The in-depth coding of the interviews and field notes was first initiated by
identifying labels expected to cover a broad spectrum of participants’ replies.
Initially, the data was coded in response to the general research question, and then
the data were categorized into themes following this study’s conceptual framework.
Data were also managed and analyzed in a mixture of manual and computer aided
methods, as well as organized into tables and charts to better interpret the
significance. More detailed codes emerged as additional review of the transcripts
took place. By adding the existing codes to the already reviewed data, new codes
become apparent which led to more themes.
After the preliminary coding was finished, a careful examination of each
code helped to organize the data into sections specific to the emerging themes. This
was repeated until all the themes and codes were identified. Next, a comparison of
the materials within categories took place to find variations in the meanings. The
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data was then compared across the categories and connections were made between
the themes. The data was then printed out and compared back to the research
questions. An outline was then created and used to help arrange a response for the
results of the analysis. As the outline was completed, an additional look back at the
transcriptions ensured that the information collected and organized into codes would
provide the necessary information to write a qualitative narrative of the findings.
During this process, quotations were used, as well as summarized versions of the
coded information with references back to the participants in order to support the
conclusions made. Through this process, the patterns, trends, and themes that had
emerged were crosschecked and triangulated from the various sources of data
collected to validate and ensure accuracy in the results. Lastly, the results have been
used to report the findings through a rich, thick description of the subjects’
responses.

Ethical Considerations
To create a study that is ethically sound, specific steps have been taken to
protect the anonymity of individuals and institutions reviewed. The first step was
receiving approval from the Institutional Review Board (IRB) for the University of
Southern California. A careful evaluation took place to ensure that all research
subjects were protected by scientific, ethical, and regulatory guidelines. I also
obtained informed consent from participants prior to conducting any interviews
and/or observations. All subjects understood their rights as voluntarily participants
56
in the research study, as well as understood that they could have withdrawn from the
study at any time. The nature of the study and the methods for collecting data were
reviewed, and subjects were aware of the possible benefits that might come as a
result of the study. I minimized the risks to anonymity by keeping the names of all
participants secure from the other participants. Data gathered during research, such
as taped interviews, interview transcripts, field notes, and other documents, have
been kept confidential and secure throughout the research process, and proper
protocol has been followed as required by university and school district guidelines.

Limitations of the Study
Specific limitations in this study are inherent through the design, duration,
and selection of the sample size. The research took place at one school in a selected
district for the duration of a couple months. The school was chosen from a
combination of measures including professional networking and from specific
selection criterion. Outside issues may interfere with the ability to transfer the
results and recommendations about the findings to other school sites, therefore
limiting the ability to generalize results across the population. The selection was
purposeful, however, with the intent that the data found would be relevant and
broaden the sampling begun in this study and continue this investigation.

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Researcher’s Subjectivity
My interest in gifted education and the role that data-driven decision making
plays in establishing better opportunities for students presents a possible bias in
which my own subjectivity may become a deterrent. With that said, careful analysis
and interpretation of participants’ responses have been done so with impartiality. I
have been involved in gifted education as both a student and a teacher, and see the
imbalance of resources allocated to provide adequate and appropriate content level
for students. While many gifted students still thrive and achieve at high levels, not
enough gifted students are being pushed to maximum potential with vigorous
curriculum at an early age. While this too poses certain biases, my overall purpose
in pursuing this area is to establish means for better understanding methods of
developing gifted students through the use of all available and applicable data. My
intent is to offer solutions for other educators interested in making change and
contributing to this area of study.
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CHAPTER FOUR
Analysis of the Data and Interpretation of the Findings
Introduction
This study looks at one school’s attempt in using data to change the mentality
behind teacher perceptions and gifted student outcomes. This chapter will present
the results of the analysis of the qualitative data gathered in this study. The
organization and focus of this chapter is to categorize the collected qualitative data
into thematic sub groups that correspond to the study’s research questions. The
overarching question is “How is DDDM influencing the education of gifted and
higher achieving students?”
Sub Questions
a) How do teachers of gifted students use data to inform curriculum and why?
b) What is being done to build the capacity of teachers of gifted education
students to use data?
c) What applications/crossovers are there from data-informed instruction for
gifted education to instruction for the general population?
Before delving into the research questions, I will first provide a brief overview of the
school and how it began its work in data driven decision making.

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Overview of the School
Leadership
Westgard Elementary, a K-6 gifted magnet school located within the Sunset
Unified School District, is led by one site administrator who has been the sole
principal for the past five years. Much change has occurred within that time period,
but the API has continued to show steady growth, with a rise of 57 points in the past
three years (SARC, 2008). Westgard’s National Blue Ribbon and California
Distinguished School status has created a positive, but demanding accountability
pressure on the school’s already high achieving climate. In fact, Westgard has
undergone a complete reform in and of itself within the past several years. With a
new leadership style and directive, the principal saw an opportunity to create an
environment that celebrated data analysis as a way to improve teacher performance
and student achievement. When asked what role the principal felt she had in data
collection at the school, she responded,
I have to create an environment for teachers to analyze their data so they can
adjust their instruction. I have to create an environment for all students,
especially gifted to be able to analyze their own data, so they know the truth
and the realities. They can take personal responsibility in their own growth
so they’re not just sitting there being fed. I really believe in empowering kids
to take on responsibility, and data helps us to develop a relationship with
reality.

Before the current principal arrived, “teachers did not even look at data at all.” Now
consequently Westgard has inevitably changed their approach towards data use. The
principal believes that, “the whole model that we have right now has been a result of,
not just me, but creating a positive environment that fosters support for others.” Of
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the district personnel, staff, and teachers I spoke with, all have recognized that
leadership, as well as other factors, have played a significant role in creating a
consistent growth in student performance. As a former instructional technology
specialist, the principal has made it a priority to assist teachers on how to integrate
technology into their curriculum, as well as provide state of the art equipment for
classroom instruction. This has benefited the data-driven cause, as it has encouraged
teachers to use and become more comfortable with technology as a means of
producing measurable outcomes. As a result, all teachers are capable of interacting
with equipment that allows students the opportunities to participate in more
interactive lesson models, which in turn has supplied teachers with additional ways
to assess students.

Teaching Staff
Westgard has undergone change in the teaching staff as well since the
beginning of the current principal’s tenure. This can be a difficult issue, as changes
in staffing can affect a school’s climate. Even though all 28 teachers are fully
credentialed, only 96.4 percent of classes in core academic subjects are taught by
what No Child Left Behind (NCLB) classifies as “compliant” (SARC, 2008).
However, all teachers that teach in a GATE classroom have met NCLB standards.
Since Westgard is unique in the high composition of gifted students enrolled, only
qualified GATE teachers are allowed to teach those specific classes, making it a
school composed of a majority of teachers that are willing to undergo additional
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professional development and time preparing curriculum for the classroom. This
however, has benefited the school, as the principal has been given opportunities to
bring in and retaining a dedicated staff who as one teacher put it, “you just have to
really love what you do to teach here.” School leadership also extends farther than
the principal’s office. Every grade-level chair is responsible for creating a system of
accountability within their core group of teachers, whether it is a gifted classroom or
a regular general education classroom. Westgard is fortunate in its structural
makeup, as all grade level chairs are high performing teachers who serve the best
need of the school. In alignment with the data-driven culture, each department,
under the leadership of grade level chairs, has implemented the Thinking Maps
commercial curriculum in a unique manner, supporting the school’s best practice, to
record and analyze student data. These Thinking Maps have been a consistent
source of data collection for the school throughout the year and are kept in a
centralized student created portfolio. Grade levels use this method in an ongoing
attempt to centralize data collection within the school, and to give students
experiences with looking at individual data in a practical approach. Grade level
chairs are considered the “experts” to whom colleagues go to for help and support in
this process. Teachers have taken a bigger leadership role, as they are using data to
hold students accountable for their own learning, as well as guide what needs to be
done in the classroom. As one grade-level chair and GATE teacher stated in her
interview,
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It benefits the kids and it made it easier for me so I know exactly what we
need to work on. Students can highlight missed answers, and then show their
parents at home. They explain how they got their score on any given test and
are put in a situation where they must reflect on possible solutions to improve
next time. They have to take responsibility for it because they have to parent
signatures for every test that they take.

Leadership at Westgard has taken unprecedented steps to improve a process
that has involved change for everyone at the school. From the administration to
teachers, and even students, the effects of data-driven decision making has
empowered everyone to take a bigger role in the implementation of a standards based
differentiated curriculum. The rationale for how teachers prepare for lessons and to
what depth the students are learning are now based off of data and not assumption.
The next section will explore the process that Westgard has taken to reach their
current state of data analysis with gifted students. In addition, a thorough account of
the best practices used to improve student achievement and the organizational
structure of the school will be identified.

Data Use for Teachers of Gifted Students
“Data helps us to develop a relationship with reality.”
(Interview Participant, Westgard Elementary, 2008)

This section will help to elucidate the findings by differentiating the range of
practices used in a school that changed its culture as a result of using data to drive
instruction. The analysis of the data collected focuses on the organization of the
school, the methods employed by teachers of gifted students who use data, and the
support structures used at the site. This section will pay particular attention to the
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first sub question: How do teachers of gifted students use data and why? The
following describes the behaviors and expectations of data use at Westgard.
Sunset Unified School District (SUSD) prides itself in advocating the use of
data to drive all decision-making processes at each local school site. Principals
therefore, are indirectly expected to implement effective ways for teachers to
properly understand what data is available to them. As a result, Westgard Elementary
has begun to recognize the necessity for appropriate and constructive student data to
help inform the degree and pace for how the curriculum must be taught. Even
though the school is surrounded with data of multiple types, including compound
levels of disaggregated information, the responsibility is nevertheless left to the
teachers to construe and interpret the results. While all teachers interviewed suggest
that data is somehow incorporated into their classroom, not all conceptual
understanding of data was consistent. When asked what data were available for
teacher use at the school, responses varied from state tests, to data collection
software (Data Director), to reports printed up by the principal, to informal
observational data. While all are important variations of data, it seems that Westgard,
like many other public schools, struggled in the initial stages of identifying what
constitutes the most effective way to use data. Every teacher interviewed suggested
different possible uses for how they individually used the data. Some teachers were
extremely knowledgeable about using data and instituted their own methods for
students to become familiar with their own data, while others considered themselves
at the beginning stages.
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The one consistent aspect identified was that the school was cognizant about
data use. All teachers interviewed showed examples of student data and explained
that they use various forms of it to help them better understand the efficiency of
lessons taught. In addition, teachers showed a willingness to experiment and pilot
new methods to achieve better results.

Data Collection Team
One such experiment resulted in the formation of a data collection team. Still
in the developmental stages, Westgard implemented a piloted beginning process for
regulating how data should be collected and analyzed as a school. As part of a
Master’s project, a former teacher who has since moved on to become a principal,
helped organize a three year cohort, distinct only to Westgard, that has strategically
combined Thinking Maps with data collection to create organizational tools for
teachers to analyze student achievement. By combining expertise of graphic
organizers with learning opportunities for students, the committee was able to create
tools that enabled students to reflect about their learning.
The committee developed teacher ready tools that required very little lesson
preparation and were accessible digitally through the internet. The tools included
manipulated versions of Thinking Maps that engaged students in higher level
processing skills, which developed a culture of reflection and goal setting. It is
expected that all teachers incorporate these graphic organizers into their curriculum.
The Thinking Maps are used as a data processing tool for students. They work to
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directly link specific thought processes in a consistent manner, allowing the student
to connect learned behavior with comprehension. Regardless of the curriculum
taught or model of instruction, this data tool is an effective method for teachers to
convey visual thoughts to students to demonstrate higher level cognizant awareness.
It is a regular part of everyday classroom instruction as well as a tool for student
assessment that begins as early as Kindergarten. By the time students reach sixth
grade, they are projected to understand how to access the highest level thinking skills
and be able to retain more information as a result of the visual component of
Thinking Maps. One teacher replied, “We use it across all grade levels so the kids
know how to use it at a higher level for the next year. Our goal is to align
curriculum within and across all content areas.” Several GATE teachers reported
constructive feedback documenting noticeable growth in students’ ability to
maneuver through Bloom’s hierarchical levels of thinking, and differentiate between
analysis, synthesis, and evaluation of their own results.
Not only are gifted students at Westgard expected to understand and apply
these high levels of metacognition through Thinking Maps, but now teachers also are
given an opportunity to, as one interviewed participant noted, “Practice what we
preach.” It has served as an internal accountability system for the entire school and
grade levels have become responsible for modifying it to fit their particular needs.
After the completion of year one, some teachers have individualized different ways
to become more comfortable with this new system of accountability that is teacher
directed and student run. Each grade level has handled the conception of this data
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informed culture differently, making it age and developmentally appropriate for their
students. The future benefits are noted by one teacher who reflected that, “it changes
the way you look at your lessons, and how effective you are as a teacher in preparing
students for desired outcomes. All the work makes it worthwhile when you witness
your students achieving their full potential.” The thought behind the initial intense
work was what one teacher on the committee described as, “a lot of time and effort,
but worth it in the end so that there is complete teacher buy in.” Several teachers
have reported that the process has begun to run itself as students have become more
comfortable understanding expectations and procedures.

Data Wall
Posting data for all teachers to see has been empowering for the staff at
Westgard. All teachers are required, as part of their professional development at the
local school site, to post updated versions of student work and data examples.
Located in a central location, privy only to Westgard staff, various uses of student
generated work samples are displayed so others can integrate new ideas into the
classroom. In a possible vulnerable situation, Westgard has embraced this new
collaborative environment and have learned to put their guards down and allow for
professional growth to occur. Displays are changed weekly so that the various
examples can foster new lesson planning and differentiated teaching. Teachers are
held to high standards, but the data wall has helped to increase communication and
build relationships. Teachers that are more veteran to certain skills have the ability
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to showcase their expertise, as newer teachers benefit from being immersed with
numerous examples of student work. This wall has worked as an accountability
measure as well as a way to display student work and data collection samples.

Banking Time
Westgard Elementary has created a schedule that provides them with regular
time each week for grade-level meetings, whole-staff professional development, and
teacher study groups. They created this opportunity by “banking” time – adding
instructional minutes to each day, so that every Wednesday afternoon the students go
home one hour earlier. This banking time is not intended for administrative business,
but instead, teachers review student work, analyze data, plan lessons and receive
training. Professional development at Westgard now consists of professional
reading, collaboration, and analysis of student work and data, all focused on the
school goal of student achievement. The banking time allows teachers additional
opportunities to learn how to better use student performance data to improve
teaching and learning. Most meetings consist of data analysis within grade level
teams. Benchmarks and tests are examined and dialog takes place regarding what
effective use of differentiation took place in the classroom, and what steps were used
in order to try to meet difficult standards. The teachers share data with each other to
compare how students within the same grade level did on certain assessments, and
reflect on ways to help each other improve student performance. Teachers also take
notice of “band jumpers,” those students that move backwards in proficiency levels,
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and use common techniques to deal with those students. The principal recalled an
experience using data that took place during a banking time meeting,
As a grade level, teachers talked about 100 percent of students scoring
proficient or above on a common formative assessments in writing.” Why
did this happen? Teachers used the flow map for the last story to assess
understanding of the sequence of events.” Why was this successful? The
teachers framed it and then applied applications as a reflective piece done
together as a team.

Portfolios
Thinking Maps are not only used for instruction and learning, but as a form
of data analysis at Westgard. Students in the upper GATE classes have created data
portfolios that integrate learning, assessment, and goal setting. Teachers use
assessment data to help students understand the process involved in analyzing results
to improve performance. This allows teachers an opportunity to reflect and assess the
content learning and thinking processes of each student. Students are trained early in
the year to understand what data looks like and why it is essential to reflect on its
meaning. The first step involves looking at class data compiled from a benchmark
test. The students visually see what they, as a class, are good at and what they still
need additional help with. This is an important part of learning for gifted students.
They become aware of their own patterns and tendencies related to comprehension,
by identifying their own weaknesses. Next, the class uses Thinking Maps to
determine possible causes and tangible effects. This plan allows students to set goals
to improve in the identified areas. They must write down steps to achieve their
goals. Students are not allowed to make generic suggestions, for example getting
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better grades, or doing better on the test. Rather the steps must be a substantial plan
for how to improve in a specific area, like working in small groups with a teacher
and taking notes. After that, students plot and track their own specific benchmark
scores and relate the missed problems to subsequent standards assessed. They
continue to add to it every time they take an assessment. At the end of the year, the
students conduct a meeting with the parents where they go over their scores.
Students build visuals consisting of line plots to show their progress, and organize
the data into specific content clusters. This way, students are able to break down
data every quarter and construct meaning by recognizing patterns and trends of
individual assessment data. Teachers report that they believe that, “… data is
absolutely relevant to students’ success and achievement.” Data analysis helps
students and teachers to determine what standards require more or less work, which
gives the students a more individualized plan that gives more access to learn the
standards with depth and complexity.

Online Data Management
Sunset Unified, as part of their district-wide goal on data-driven decision
making, has partnered with DataDirector, an online data warehouse and assessment
management system. This program is an invaluable tool that allows schools to
integrate data from any number of sources, including student assessment data,
demographic data, as well as state assessments, and district-administered tests.
Another important function is that it allows teachers insight into a students’ history
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by providing longitudinal data. DataDirector gives teachers at Westgard an instant
way to access multiple forms of data from all computers at any given time. It creates
opportunities to analyze patterns and trends of achievement levels and create
solutions to improve student performance. Westgard is in the process of integrating
a scanner for teachers that is connected to the data warehouse. Teachers will be able
to scan tests into the system to have instant organized data for immediate analysis.
This will help reduce time spent grading tests, and instead give teachers additional
time to see what impact their lessons had on comprehension. It will also give
teachers the option to create self-made assessments that can be digitally stored and
shared with other classrooms. It provides teachers with the ability to examine
school-wide data for a variety of assessments. DataDirector organizes inputted
scores from teachers and compiles reports. Without a warehouse to store and
manage data, Westgard would not have the capacity to make important decisions
based off tangible data. This data analysis tool is imperative to support the school’s
plan of data use.
In sum, Westgard has been able to capitalize on many modes of data
collection and analysis. The school has discovered various ways to incorporate using
data in the classroom to challenge gifted students. It has also forced teachers to
realign the differentiated GATE curriculum to better meet the needs of their students.
The following section will describe the support structures that are in place in Sunset
Unified and at Westgard Elementary to help teachers use data in further ways.

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District Supported Data Use
Sunset Unified provides outlets for teachers to engage in the use of data
through outside supports. This support comes in the form of both funding and
personnel to assist schools, administrators, and teachers with the necessary guidance
to achieve the highest possible performance. SUSD’s structural framework aligns
district priorities with available resources that afford schools the opportunities to
succeed.
GATE Office
The GATE office supports teachers who work with gifted students in the use
of data as a way to strategically decide how instruction should be delivered, as well
as a measuring point for how they prepared students for end-of-year standardized
testing. At the beginning of each new school year teachers analyze the data for the
students that they taught in the previous year as well as the incoming students. The
expectation in SUSD is that all the children in GATE will score in the advanced
levels of the standardized tests. The supervisor of gifted education believes that,
“…even though advanced is categorized as comprehension of full grade level, it is
still a glass ceiling for improvement.” It is noted, that some factors do play a role in
not achieving advanced. Issues such as Limited English Proficient (LEP) and social
emotional needs are all factored into data analysis. As a result these students receive
extra support from the school and in the classroom to ensure that students that get to
the advanced band, stay there. Data also plays an important role in finding gifted
students who are falling behind. Indicators include test scores that show negative
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movement between achievement bands from advanced to proficient. These students
also receive additional support and attention and are targeted all year to return back
to the advanced level.
District Office
Sunset Unified prides itself as a data-driven district that makes all decisions
effecting student learning outcomes on quantitative data and not instincts alone. To
support schools, the district has created a position at the district level to help
principals with data analysis. The roles differ between schools, as each has their
own specific needs and wants. Often times, schools request assistance in creating
specific data reports for high stake assessments. The district personnel in charge of
data are experts in using DataDirector, and have full access to all different types,
including assessments, demographics, subgroups, and just about any other category a
principal would want to access. Part of their job also includes providing support and
professional development to principals to ensure that all possible data is being used
to improve teacher and student performance. The principal of Westgard noted, “I tell
him what I am looking for and he teaches me how to find it in the form of a report.”
Collaboration with the district also provides services to help gather data for
information pertinent to writing the school site plan (SSP) that incorporates API,
AYP and benchmark data for the school, the district, and state. This role is
extremely important if a district is serious about using data. Often times, districts
preach one thing, but fail to provide support and assistance in achieving goals.
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In summary, Sunset Unified plays an important role in providing schools
support in using data to produce improved student performances. Data-driven
decision making at the school sites takes precedence for the district and it reflects in
the outcomes. The district allots resources for teachers to collaborate and undergo
professional development. These experiences enhance teacher performance and
build confidence and knowledge in using data to differentiate the curriculum. As a
result, Westgard applies data analysis methods as part of everyday instruction for
gifted students. Evidence in the next section reinforces how teachers build
knowledge and skill levels of data use to inform curricular decision making.

Building Capacity
This section will explore the ways the district builds the efficacy of teachers,
as well as explain the roles teachers play in analyzing student work. It will also
consider the challenges faced by schools and teachers while trying to implement
data-driven decision making as a process for all student issues. This section will
focus on the second sub question: What is being done to build the capacity of
teachers of gifted education students to use data? The following stresses the
importance of putting the right people in the right place in order to maximize
fundamental change.

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Finding the Right Fit
In order to teach GATE classes within Sunset Unified School District,
teachers must first complete an application process to qualify for an interview with
the district supervisor overseeing gifted education. To pass this prescreening process,
teachers are expected to display evidence of, “a thirst for knowledge.” When
interviewed, district personnel replied that they seek teachers that “do not have
preconceived notions of gifted children.” SUSD does not use GATE certification as
a requirement to be hired, because the district believes that their teachers undergo a
much deeper training that is both ongoing and substantive. In fact, the district does
not even offer a certificate program for the very reason that, “it offers teachers a
stopping point for training.” It is noted that teachers willing to work with gifted
students must be “willing to put in extra time because the reality is, teaching GATE
requires somebody who cares and is passionate about giving that time.” Teachers of
gifted students in SUSD receive no extra pay, but instead choose to be a part of the
program because they are “natural leaders.” An interview participant noted a
significant amount of principals and administrators have been given berth through
GATE. “I think part of the reason is that the teachers have to go to so much training,
they become great teachers.”
Westgard has also seen change in the past five years that has shown a
transformation in the culture of the school. The principal has had the rare
opportunity to hire approximately 75 percent of the staff, which is an amazing
chance for an administrator to have an immediate impact on the school. Westgard
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has a relatively younger teaching staff that shows great motivation to improve in
their teaching strategies and learn from their colleagues. Many teachers on staff
have widespread experience teaching in the GATE program and have received
extensive training on how to teach students at a higher level. This has also helped
with the induction of data use as a school focus, since gifted education uses data as a
daily part of informing differentiated instruction.

Professional Development
Building the capacity of teachers is a vital part to ensuring continued success
and growth. Sunset Unified provides extensive professional development exclusive
to gifted teachers. This includes examining strategies for differentiation as well as
looking at data to change teaching methods. General education teachers also receive
training, but in a more familiar format. The next sections will describe the influence
of professional development on teachers of gifted and regular education teachers.
GATE Teachers
Because SUSD only fills 34 GATE positions to fulfill the needs of over 900
gifted students, roughly six percent of the total district population, teachers work
closely to support and help train one another as well as share expertise in many
areas. The range of experience varies from veteran teachers to rookies. First year
GATE teachers in SUSD must go through initial district training with the supervisor
of gifted education before they even open their class. Teachers are required to fulfill
two full days developing a sense of for how to teach a GATE class and the key
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components in understanding the social emotional needs of their students. The
district requires all GATE teachers to become experts in the literature of James
Webb, a psychologist who works with the social and emotional needs of gifted kids.
It is expected that teachers use it as a resource for situational knowledge as well as
sharing information from it with parents. Teachers next continue training in an
overview of both the state content and GATE standards (CDE, 2005); introduction to
universal themes, thinking tools, depth and complexity, and content imperatives.
They are also taught to work with parents and how to open a GATE class for the new
school year. The training is substantial and requires a great understanding in
interpreting large amounts of preexisting data in order to accommodate the diversity
of needs in a classroom.
The model of professional development executed by teachers of gifted
education is integrated with the overall district goal to improve education. It is
guided by a coherent long-term plan and driven by disaggregated data on student
outcomes. The goal is to increase teachers’ knowledge and skills, create
opportunities for data analysis, and change teaching practices to ensure that gifted
and high achieving students remain motivated to achieve higher learning standards.
Teachers attend monthly trainings, regardless of experience. All topics are based on
elements of differentiation (flexible grouping, targeted instruction through data
analysis, etc.), and occasionally reach issues that include social emotional needs like
attention-deficit hyperactivity disorder (ADHD), as well as autism and Asperger’s
syndrome in gifted children. The core focus of the meetings is driven off of specific
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teacher needs usually consisting of curricular issues dealing with understanding
differentiation. For example, one participant in the study responded in the following
way,
This year we adopted a new social studies and science program and realized
how ridiculously easy it is. The curriculum does not have a themed text book,
but instead consists of pull out supplements for each lesson that is written at a
very basic level. The students need additional material. This will be a key
focus this year in the GATE program.

All beginning GATE teachers remain in probationary or “rookie” status for
the first two years regardless of total number of years taught. This does not affect
employment status rather it determines the amount of required professional
development. All rookie GATE teachers must go to additional monthly “rookie
raps” where they receive additional trainings on all the previous topics that have
been presented. The meetings are quick and short with the intent of providing
assistance for enriched lesson plans without overwhelming teachers. For example,
one teacher reported that one of the described meetings was used to explain how to
conduct a “talk shop debate.” The group would work together and prepare a
differentiated lesson plan, teach it, then the following week meet again to discuss
how it worked, what worked, and what did not work. Sunset Unified also provides a
program called “Collegial Partners” where all rookie GATE teachers can pick a
veteran GATE teacher to co-teach with. This provides hands on opportunities for
new teachers to see actual lessons being taught and spend time planning with
someone who understands the process. Several different types of lessons are
modeled, including but not limited to, deductive and inductive reasoning, concept
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attainment, and advanced organizer. Together they plan and the veteran teacher
shares expertise on how to differentiate instruction. Of the small group of GATE
teachers, some also are new to the profession of teaching as well. These teachers are
treated with even more care and support, guiding them along the way to ensure that
they do not become overwhelmed or discouraged. They receive additional assistance
from their BTSA support provider to assist in the overall adaptation to teaching.
District
Professional development is also offered as part of the overall district mission
geared towards “focusing on student achievement, high standards, and opportunities
for all students to acquire the knowledge and skills necessary to live a productive
life” (SARC, 2008). The district’s program is composed of a four part research-
based strategy involving initial training, demonstrations, coaching, and second-level
training. The use of student achievement data also assists in providing clear goals
and expectations for planning inservices for teachers. Before the school year starts,
Sunset Unified requires all teachers to attend a five day consecutive training to
enhance new knowledge and skills for the upcoming school year. Professional
development is an ongoing process at SUSD and is offered after school and by
release time throughout the school year. The topics vary from training in district
curriculum, technology, research-based strategy instruction, to data analysis.

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Goal Setting
Another focus that builds capacity through data analysis is goal setting.
Through the GATE office, teachers are required to review data and set goals for
where they want to grow. To gather data, teachers then conduct self evaluations at
the end of the year that are all based on the GATE program standards. The belief
behind this practice is that it builds competence and leadership in all gifted education
teachers. That data is then carefully examined through a scope and sequence
guideline that is used to determine teacher competence and confidence. Teachers are
asked to examine their own abilities and determine if they are ready to teach a
colleague individually, teach a small group of colleagues, present at GATE trainings
to district colleagues, or even conduct trainings at a conference. Data is also used to
establish individual teacher proficiency levels. Teachers investigate different layers
of leadership and determine comfort levels by checking where they feel experienced
as well as where they need more training. The district offers opportunities to further
develop leadership and training, as well as maintain current methodologies for
veteran GATE teachers. Veteran teachers are paired with beginning teachers and are
asked to present experiences of successful differentiated lessons for others to learn.
All trainings are a direct result of feedback from the teachers. This comprehensive
system of checks and balances is used for a variety of analysis. It is a vehicle for
producing district data on how teachers are effectively learning to teach gifted
students, as well as what still needs to be done to reach all GATE students. The
supervisor of GATE had the following observation:
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The schools and the principals are the ones that really drive a lot of the data,
but I disaggregate the data for our GATE teachers. I find that sometimes
there is a sense that students are doing fine, when in fact they are not. If
students are not remaining in the advanced band and are given additional
enrichment to go even above and beyond, they are not doing fine. The real
problem and the danger with NCLB is that it gives the false perception that
the kids are fine when they are in specific bands and it does not even begin to
measure what they can do.

Reflection
An essential part of data collection at Westgard, as written into their school
site plan, is the reflection portion. Both teachers and students alike are expected to
reflect on personal growth and assessments.
Reflection is an everyday routine for teachers at Westgard. It has become
part of the school’s culture to always reflect on what is being done to improve
instruction and adjust to the learning needs of students. Teachers are no longer
allowed to just accept student failure, but instead expected to reflect on why it
happened. In grade level meetings, teachers constantly talk with each other about the
current state of student achievement in their classrooms. At the beginning of the
year, teachers carefully examine how students performed on the California Standards
Test (CST) by breaking questions down to specific strands. They find the levels of
proficiency for the different strands, and consider possible reasons for outcomes.
Teachers reflect on their own teaching by using data to examine the achievement
levels on specific assessments. The reflection process cannot happen without the use
of data analysis. From printed up results of chapter tests to benchmarks, teachers can
instantly see what strands students had trouble with. Then the reflection portion
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allows them to come up with possible scenarios that lead to appropriate solutions.
Teachers reflect on issues that lead to questions like, “Did I not teach this well? Do I
need to give extra practice for this? Can I get help from another teacher?” The walls
have become transparent at Westgard, and teachers have become more comfortable
using this reflection process to share experiences and failures with each other to
improve instruction.
Students
Being able to reflect on assessment data is an important process for students
at Westgard. Many GATE students are trained to understand the importance of
reflection to improve performance. They are expected to take ownership for the
results that they produce, and be able to analyze reasons why the data came out the
way it did. Motivation plays a part in reflecting on individual data. Teachers of
gifted students reported that oftentimes, students make a better effort to improve
when they internalize negative results in an assessment. Likewise, the motivation
remains high for students who test high and want to maintain it. Creating a sense of
reality with data allows students to have an ownership that becomes an eye opening
experience for them. The process of highlighting and calculating and seeing
mistakes that are made is an important part of reflection, and allows the student to
become invested in the process. A gifted education teacher remarked that, “It is more
tangible to my students to see the data as opposed to saying you did really badly with
this standard.” Students get a chance to highlight mistakes and analyze why they did
a certain portion of an assessment wrong. They use a peer share model in student led
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conferences to reflect on their own goals, look at the questions again and come up
with possible solutions. The reflections lead to the point where students are actually
presenting results to their parents and saying, “These are my weaknesses. These are
my strengths.” This creates a deep sense of empowerment for students, as they are
able understand what else they still need help with. “It is just so valuable when they
can look at their own scores and see where they are still lacking.” Teachers
continuously work with students to teach them the value of reflection. This ongoing
process makes students always think about what they are going to do next time to
make their own results better. One GATE teacher noted that, “It is an eye opening
experience for many of my students to realize they are not the smartest kid and that
they still have flaws that need to be worked on to get to where they need to be even
though they are gifted. So it is a reality check for them.” Students use constant
reflection as a way to improve achievement.

Challenges
All organization change comes with its own specific challenges. The same is
true for Westgard Elementary, in the school’s attempt to change the culture into one
of complete data-driven decision making. While the transition has already started, it
is an ongoing process that requires everyone associated with the school to become
invested in the performance goal’s outcome and accept responsibility for behavior
and corresponding consequences. Teachers must reevaluate how their motivation
affects the level to which their knowledge and skills influence improved student
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performance, with focus on the implementation of individual and organizational
goals. In addition, the school must provide an atmosphere that does not impede the
progress that closes performance gaps, but instead promotes the support necessary to
reach the potential of their actions.
Motivation
At Westgard, working towards the goal of creating a school that focuses on
data-driven decision making has been an ongoing transformation that is still taking
place. While it was easy for some to adapt to this new method of teaching practices,
other teachers had a hard time seeing the benefits that it could lead to. One teacher
in her interview replied, “Up until last year, I always said data was a four letter word
every time it was spoken because I did not see how it applied to me.” Teachers
sometime lack motivation to improve professional practices at an already high
achieving school. This generates problems of inconsistency among teachers’
performance, creating an uneven balance in school culture. The effect of school
culture on school improvement efforts is significant. The attitudes and beliefs of
teachers impact the learning opportunities provided for students. This realistic
attitude for some teachers can have drastic effects on the overall vision for the
school.
Buy In
Although Westgard is a data-driven school, it is only because of the
leadership style of the principal and teacher leaders. Not everybody on staff initially
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bought into the idea of using data to inform curricular decisions. Some teachers saw
this data movement as an intrusion of privacy where administrators would have
additional ways to evaluate teachers. They did not like the idea of other teachers
and/or the public knowing the scores of their students. The emotional investment
was seen as a direct reflection of how good a teacher they were. Change is hard for
people, and especially teachers, as it serves as a way for others to see potential flaws
in teaching skills.
Another factor involved the considerable amount of programs that teachers
are expected to use without ever having a say in the implementation process.
Education does not have a good track record for maintaining and supporting all
programs mandated for schools, and some teachers saw this as “just another
program” that would eventually go away. One teacher reflected on this time by
saying, “Why invest in time and effort for something that was forced on you? Many
thought that it was just another fad the district was going through.”
The principal of Westgard understood these reasonable fears and used her
teacher leaders to help alleviate the tension. Her idea was that if she could get
teachers to talk about it with each other instead of pushing it from the top down, she
would get better participation. She understood that teachers would react better to
their colleagues than the administration, so slowly she started having her teacher
leaders disseminate data, talk about it with grade level teachers, and start the wheel
moving. Soon, those who still did not buy in started to notice that they were
outnumbered. Not liking being in the minority, teachers started to try using data
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even when it was uncomfortable. The principal responded, “I just wanted them to
look at it. I did not care how they got to that point.” Once these teachers noticed the
benefits on student achievement, they started to understand that data use is not a
program, but a way of teaching. Slowly, the culture of the school started to change.
Time
Time management and prioritization are major issues that teachers face when
dealing with the challenges of integrating new practices into their curriculum. Being
able to focus on and address all goals usually forces teachers to divide their attention
between what needs to be done and what has to be done. So many issues play into
the curricular planning of a school day. Preparation for standardized tests and
completion of content for the California curriculum standards take up so much time
and energy for teachers to plan, that very little time is left over for adding additional
programs and new directives from the district. Many of the teachers interviewed
mentioned that learning how to analyze data, as well as teaching students to examine
their own data takes a lot of time to do. When teachers have to lesson plan for all
subjects, differentiate between students, create and correct tests, as well as
everything else that goes into a school day, very little time in the day remains. Time
plays an even bigger factor at Westgard since the majority of students are classified
as GATE. Teaching a GATE class requires a lot of time to plan additional
differentiated activities to meet the needs of all learners. The fast pace of a gifted
class requires a teacher to over plan with various learning centers, different models
of instruction, as well as collaborate with other GATE teachers. Finding the time to
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analyze data was at first, a difficult thing to find time to do. However, over time,
teachers have started to report that using data to inform instruction and with students
to reflect on achievement levels, has given them more time to plan for the
appropriate instruction. This reduces the times that teachers have to go back and re-
teach missed standards, allowing for more time for new material. Also, teachers
noted that students seem to spend their time more wisely after they started reflecting
on personal data as well.
Overall, building the capacity of teachers to use data is an intensive process
that involves appropriate resources being used in the most effective ways. Even
though Westgard faced certain inevitable challenges, the data-driven culture
prevailed because proper steps were taken to ensure longevity and success. Support
from the district through professional development and continual goal setting and
reflection helped to produce more efficient teachers that are comfortable using data.
The use of data at Westgard is reflected by the highly differentiated instruction that
takes places. On the whole, teaching strategies learned and used allowed teachers to
reach greater depth and complexity with students, and pushed learning to more
rigorous levels. The next section examines how these strategies can be used by any
teacher to produce more desirable outcomes. The strategies previously discussed are
looked at in hopes that a possible spillover effect might take place. This involves
possibilities for all students to benefit due to the focus and implementation of data
use for teachers to meet the needs of all.

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Crossovers
“Differentiation is not a style of teaching exclusive to GATE teachers;
it is a style of teaching used by all good teachers.”
(Interview Participant, Westgard Elementary, 2008)

The third part of this analysis examines the methods of differentiation
instruction of gifted students and its effects on student achievement. It evaluates the
level to which teachers of gifted students are trained and the potential for change for
all teachers regardless of demographics and location. This section attempts to
answer the third sub question: What applications/crossovers are there from data-
informed instruction for gifted education to instruction for the general population?
The following stresses the importance of putting the right people in the right place in
order to maximize fundamental change.

Differentiation
All instruction at Westgard is expected to be differentiated, regardless of
ability level. The principal at the school noted how teachers put the students at the
center of instruction and focus how to best meet the needs of all the students.
Westgard buses in a large percentage of students serving over three cities and
understands that each learner comes to school with a different set of learning needs.
Teachers undergo training in how to proactively choose various models of teaching
to best facilitate effective learning experiences by combining student abilities with
identifiable materials. Different approaches are used to meet various learning styles
at all levels. Flexible seating as well as a mixture of whole group and small group
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instruction is used to encourage student participation and produce appropriate
outcomes. Instruction must also be flexible since differentiation depends on the
ability of the teacher to continuously modify lesson plans based off of data analysis.
A teacher at Westgard made it a point that, “Data helps me guide instruction. It
helps me differentiate better so I know what students need and what skill they are
missing. It allows me to change something if it is not working.” It is expected that
teachers, through data analysis, alter the method of instruction to fit students’ need as
opposed to students falling behind because they do not understand the way it was
taught. Many different techniques can be used when differentiating; however, it is
important that teachers create lessons that access students’ multiple intelligences to
provide optimum learning opportunities.

Changing the Data-driven Climate
Like any new program, anything worth implementing for long term growth
will take time to develop. The principal at Westgard has made it a point to ease the
staff into progressive change. Her approach is, “to stretch out anything new and
gauge how teachers are doing and not power launch everything at once.” This is an
extremely substantial part of any induction program that has the intent of changing
the norms and cultures of an organization. Westgard’s principal was fortunate to be
surrounded with teachers that pride themselves in professional growth and a strong
commitment to student improvement. It was within these teacher leaders, and under
the leadership of the principal that data were introduced and used by the school. At
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first it was uncomfortable for some teachers because it was something new.
However, the principal was aware that in order for teachers to want to use data, they
had to be invested in the process. Grade level chairs were instrumental in this
process, linking student test data to improvement. The principal understood this
dynamic, as she wanted the teachers to be the ones to want to use data, so she
allowed the process to start slow and simple. Initially, teachers looked at CST data
as a staff to determine trends and areas of concern. It next moved to grade level,
where more careful analysis was conducted. While some still struggled with the idea
of other teachers seeing how her students scored, teacher leaders worked hard to
lower the affective filter and holistically use the data to suggest improvement
practices. Soon data became a regular part of grade level meetings, and teachers
collaborated to plan lessons and target specific groups of students. Although much
has changed in the development and transition into a data-driven culture, the
principal still wants more. The goal is for all teachers and all students to become a
part of this culture, and use data as a way to assess what has been accomplished and
what still needs to be done.
Change must take a gradual progression to warrant desired outcomes. Often
times, teachers become overwhelmed and cynical of new programs that are directed
in a top down leadership style. A veteran on staff supported this notion by stating,
“New programs and administrators come and go. I only care about effective
measures that benefit me as a teacher and do not add hours to my already busy
schedule.” This attitude is a reality that schools must confront if systemic change is
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necessary. The only plausible change comes to organizations that can provide
tangible evidence to support a worthwhile cause. Everyone associated with the
school must become invested in the performance goal’s outcome and accept
responsibility for behavior and corresponding consequences. Teachers have to
reevaluate how their motivation affects the level to which their knowledge and skills
influence improved student performance. In addition, the school must provide an
atmosphere that does not impede the progress that closes performance gaps, but
instead promote the support necessary to reach the potential of their actions.

Teacher Perceptions
There are strong misconceptions that only smart or gifted students can be
taught using higher level thinking strategies. This is a huge mistake, as students of all
learning styles and levels can benefit from multiple models of teaching. While many
GATE teachers are required to undergo training and professional development in
special differentiated lesson planning, many regular general education teachers rely
on more simple direct instruction models. Oftentimes teachers repeat the cycle of
teaching the way they were taught as students themselves. This is unfortunate, as it
proves how slow teacher preparation programs and educational practices are
progressing to keep up with the achievement gap. Schools must take a more
progressive view of the methods teachers are using to educate. When a principal
walks into any classroom, there should be consistent signs of high level questioning,
flexible grouping, and various options for students to learn. There should be talking
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and movement, and students should feel excited to be a part of something new.
However, all too often students find themselves in trouble because they are not
sitting down quietly listening. Or students are labeled as trouble makers because
teachers cannot identify the correct learning style that fits their specific need.
Many teachers at Westgard provide hands on learning experiences to best fit
the various learning styles of their students, and as a result, do not have huge
behavior problems, and continue to see improvement in student performance. GATE
teachers are trained to understand the physical dispositions of children, specifically
gifted students, and know what models of teaching are most appropriate for specific
learning situations. Students are given the opportunity to be an active part of the
lessons, both in physical activity and in how they learn, because they do not spend
too much time sitting like students in traditional classrooms. Students become part of
the learning by engaging their interests through relevant and content specific
activities and questioning. Teachers focus on how specific languages of each
discipline can encourage students to find connections and take ownership in their
learning. Traditional classrooms of rows and columns of students sitting quietly and
teachers reacting to behavior problems are a thing of the past. Active learning means
students are doing, making, and building manipulatives in an attempt to become
problem solvers and react to their own metacognition.

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A Shift Towards Better Education
While society has evolved through a series of paradigm shifts, it seems
schools have been less progressive in their evolution. As modern technologies have
advanced, school systems have remained virtually untouched. Identified processes
for gifted students, as noted in previous sections of chapter two, have been
inconsistently used, as well as the level of rigor for instruction of GATE students.
From class to class, a discrepancy exists in how differentiation is implemented, or if
any takes place at all. American public schools, as a local entity, remain unresolved
in how to build collaborative approaches that meet the needs of all students. While
gifted education has established its roots, it has failed to follow a regular pattern of
implementation between districts and throughout states.
Westgard has an ideal model to follow for schools of all kinds. Even though
it is a gifted magnet school, and students are bussed in to attend it, it still offers
collective approaches to high level differentiated teaching that schools of all levels
could emulate. Westgard gifted teachers are trained in high level cognitive models
of teaching that consist of different approaches to interacting with students to make
lessons student run and orientated. These interactive models include lessons of
concept attainment, inductive and deductive reasoning, direct instruction, simulation,
and group investigation. Each model is intended to created high level thinkers from
students that foster problem solving and logical thinking. Students all too often
receive the same model over and over and certain students have a difficult time
understanding or thinking at an appropriate level. This is how achievement gaps
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form, and it makes it very difficult for teachers of future grades to catch up. If
schools work towards helping teachers to learn more differentiated lesson models,
students will have opportunities to become more connected to their learning and take
a more proactive approach towards intrinsic motivation for success.
When schools can combine the power of true differentiated instruction with
data analysis, schools will start to understand the power that they hold in their hands.
Too often schools fabricate excuses for why students do not succeed. When schools
start following more successful and research-based models of data-driven decision
making combined with differentiated instruction, true change will start to take place.
By using data to understand what lesson to teach, the most appropriate model of
instruction, the levels of questioning, and that the pace, teachers will empower their
students to learn. Education must adapt to the high demands and issues that face
schools, by allowing data to provide evidence of what needs to be done, and how is
should be done.

Conclusion
The objective of this chapter was to discuss findings regarding the use of data
and its influences on the education of gifted and higher achieving students. Through
a collection of analyzed data from teachers of Westgard Elementary School,
subsequent questions were answered through a series of patterns and trends reveled
in the data. The three sub questions focused on the ways teachers of gifted students
use data to inform instruction, what is being done to build the capacity of those
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teachers to use data, and possible applications/crossovers from data-informed
instruction for gifted education to instruction for the general population. From the
responses, observations, and document analysis conducted, the questions were sorted
into categories that revealed significant perspectives into the use of data with the
gifted population.
Westgard Elementary uses data as a central component of instructional
decision making for all students. Its influence started with a change in
administration, but spread to teacher leaders in an attempt to disseminate the
influential effects of data analysis on student assessments. While all teachers report
that they now use data in at least one capacity or another to inform instruction,
GATE teachers have created a culture within itself, empowering students to reflect
on their levels of proficiency. Gifted students and teachers are capable of
interpreting and analyzing how data individually effects their development. The data
culture is evident, and growing with the development of a data analysis committee,
which is standardizing the way teachers use data at Westgard. It is rare for schools
to offer opportunities for teachers to learn how to use data. Often times, teachers are
expected to know how to analyze data, and what to do with it. Westgard’s attempt to
provide professional development opportunities for data use is beneficial to increase
the culture for all teachers to realize the potential it has on student performance.
Outcomes have resulted in more appropriate pace and rigor of instruction, as well as
students’ comprehension of how data can influence their learning behaviors.
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While the data showed that challenges took place that slowed the data using
culture of Westgard down, many successes come about as a result of consistent
application and reflection. It took everyone involved in the process some time to
understand the purpose and goal behind this movement, but with the help key
leadership, data started to create change. As a result the teachers started to see using
data to inform instruction as a beneficial resource towards improving instruction.
This allowed teachers an opportunity to link assessment results with individual
achievement levels, creating a way for all teachers to ensure all students were
receiving appropriate content specific instruction. Another noticeable benefit of
using data at Westgard involved the data analysis portion of learning which helped
with teacher effectiveness and student comprehension. Both groups were able to
determine necessary pacing and focused relearning by reflecting on the results of
achievement data. Students learned how to look at their data and understand
possible reasons for the results, and come up with goals and steps to learn the
missing standards. The data did prove that all it takes for schools to start changing
the culture and understanding of what data analysis can do for student achievement is
perpetual use, practice, and willingness to try.
Most teachers understood the advantage of incorporating data use into their
classrooms, and used it to inform instruction in some capacity. The results of the
study also showed that by using data, teachers became more consistent in both
creating lessons that fit the appropriate depth and complexity of the desired
outcomes, as well as generating assessments that matched instruction. What was
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being taught was more aligned with how students were learning, and adjustments
were made when the data proved otherwise. To some, using data began as just
another program, but has slowly changed into a reliable source of curricular
development and planning. Overall, data-driven decision making has impacted the
instruction and learning, creating a school culture that has become more open to
change and improving student achievement.
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CHAPTER FIVE
Conclusion and Recommendations
The purpose of the study was to examine the influence of data-driven
decision making and analysis on the teaching of gifted and high achieving students.
The practices were evaluated with an attempt to explore the structures and processes
that may allow gifted students an accelerated opportunity for higher level thinking
and metacognition. A GATE magnet school was visited to determine the effects of
data use in the context of student achievement and improved teacher practices. The
significance of this study is important to the development and implementation of
data analysis as a resource for teachers and students to enrich practices in the
classroom. By studying how a school uses data to change the curricular pacing and
instructional rigor of gifted students, the findings can contribute to school
improvement planning in various school contexts.
The research questions were initially created as a response to the limited
literature that exists about the use of data and its effects on districts, schools,
teachers, and students of gifted education. As a result, a study took place to
investigate the methods used within a district and at a Southern California public
school, Westgard Elementary, that had already implemented a culture rich in data
use.
All of the data collected was thematically coded to better understand the
overarching research question: How is data-driven decision making influencing the
education of gifted and higher achieving students? This question was created to
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better understand how teachers of gifted students were using assessment data to drive
curricular issues and make decisions that impacted student achievement.
The first of the sub questions was: How do teachers of gifted students use
data to inform curriculum and why? This was a necessary question in order to find
fundamental connections between the cause and effect of teacher decision making
and student outcomes. The question was used as a way to investigate the
consequences of schools and teachers who used data to determine potential solutions
for improved instruction with higher levels of depth and complexity for student
growth.
The next research question was: What is being done to build the capacity of
teachers of gifted education students to use data? This question was instrumental in
finding out what tools districts and schools were providing teachers for necessary
and effective professional development for data use. Teachers are generally
expected to use assessment data as a regular part of professional practices for lesson
planning. However, often times this process receives little to no direction or
professional development. The study provides possible solutions to this question
that can be used for future implementation.
The fourth and final question was: What applications/crossovers are there
from data-informed instruction for gifted education to instruction for the general
population? This question attempted to find methods for how other schools can learn
from what teachers of gifted education are successfully implementing with data in
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the classroom. Specific data analysis methodologies were examined to determine
potential crossovers into general education classrooms.

Summary of Research Findings
Data analysis at the local site is fundamental to changing the way students
learn. Research shows that goals for school change must be realistic and focus on
aspects of learning and development over which schools have the most influence
(Renzulli, 1998). Responses showed that all participating teachers at Westgard
displayed a general understanding of how data helps them prepare for lesson
planning, and commented that they also share data within grade levels. While not all
teachers used data the same way, all GATE teachers shared a collaborative approach
that involved students with analyzing their own data. The GATE teachers used data
as an accountability measure not only for themselves, but for students and parents as
well. Teachers made data available to students and engaged them as active learners
so they would understand how their actions and effort resulted in given outcomes.
Students became responsible for goal setting, conferencing with teachers and parents
to check progress, and monitoring behavior. The philosophy behind this was that
everyone had to take more responsibility for improved performances. Data analysis
forced teachers to regulate their lessons in terms of content, rigor and pace. Students
became an integrated part of learning, as they had to create plans for achievement.
Parents were notified of every assessment result, and were asked to involve
themselves in the goal setting process of their child.
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Teachers collectively agreed that they changed the way they used assessment
data to plan a lesson. Oftentimes, teachers would meet together with colleagues to
plan better ways meet the needs of all their students. Teachers shared ideas, and
visited other classrooms to improve their practices. Discussed items included
flexible grouping, independent practice, differentiation, various lesson models, and
pacing. Grade level meetings consisted of a data analysis portion to ensure that all
students received a consistent education across the board. The school also used a
data analysis committee to help teachers better identify special needs students that
needed additional resources to meet or excel grade level standards. This supports the
notion made by Wayman (2005), that it is necessary for schools to establish
structures for collaborative data use and to preserve these data tools as main
ingredients of collaboration.
Administrators also played an important part in using data to improve teacher
and student practices. As Price (2004) reinforced, school leaders must possess the
ability to analyze and use data to determine areas in need of improvement. The
principal at the study site influenced the way the school uses data, and implied that
the culture has completely changed as a result of more teachers accepting data as a
resource for teaching as opposed to another district mandate. The principal was
highly involved in promoting the use of data with both teachers and students. While
treating teachers like professionals, she also provided support to those who she feels
can benefit from it. The principal considered data use vital to the improvement
efforts of any school, and made data available in various forms, and through assorted
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measures. Her philosophy was “inspect what you expect,” meaning if she demanded
her teachers to use data, that it became her duty to make sure they did it correctly and
received the support necessary to be successful. She continued to grow in her own
expertise in data analysis, and she knew where to find help when needed. The
district supported the local sites by providing assessment results, technical help with
inputting data, and compiling any number of requested reports. The principal has
given the teachers the means to use data to change teaching and learning, and
expected it continue to develop.
Teachers received support in using data at the school site. As a product of
the data analysis committee, classroom teachers were expected to relay assessment
data at grade level meetings and in the classroom. Teachers worked together to help
interpret data and compare results to lessons taught to determine effectiveness. Still
a work in progress, this team has already developed what is working as data
collection measures and communication pieces for the school. The committee
expects teachers to collect assessment data and share with students. Graphic
organizers have been used to help students conceptualize the bigger picture of each
assessment. Teachers also had access to DataDirector, a computer-based online
program that organizes data for immediate analysis. The school is working towards
including a scanner that uploads and organizes data into the program for teacher
made tests. This is part of the goal for teachers to be able to compare common
formative assessments between grade levels and compare scores at any given time.
In addition to the regular support services offered at the school for teachers, all
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GATE teachers received added professional development in the classroom that is
based on teacher data. The GATE office supplemented teacher learning with data
based lessons that are chosen by teachers. Most GATE support came as a result of
teacher input and needs.
The additional training for GATE teachers led to a change in the way
students learned at Westgard. Teachers practiced new methods for differentiated
teaching, used collaboration time to examine data, and modified instructional
planning to change curriculum. Because of the special training these teachers
received, a spillover effect took place with the regular education teachers as well.
Teachers of general education classrooms experienced a change in their own
instruction because of their collaboration with GATE teachers. Of the teachers
interviewed, both reported that they have observed differentiated classrooms and
have attempted using traditionally used GATE teaching methods with their own
classrooms. All teachers interviewed noted that they feel that they are better teachers
as a result of the additional GATE training that they have undergone. In sum, the
standards of teaching at Westgard have risen due to a large part of differentiated
training and implementation of GATE strategies, and can be used as a model for all
teachers to follow. Therefore, the approach used to develop teachers of GATE
should not be unique to only gifted students, as they act as effective methods for
improved student and teacher performances overall.
Based on an analysis of these findings, the following chart was created to
show the obvious and consistent patterns that linked strategic steps of data use with
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gifted students. The cycle of improvement and implementation proved to be the core
component in the continued reform efforts conducted by the school.

Figure 3: School Data Analysis Plan

In following these sequential steps, Westgard was able to establish a
framework for continued school progress. These steps help provide additional
research that is essential to better understanding how consistent data use at a school
can improve student achievement. It should first be noted that without a complete
buy in from the entire staff, this process will never reach full potential. Building a
data-driven school requires all stakeholders to understand and promote the rationale
for analyzing various forms of student data. Support from district and administration
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must work to build roots from the ground up. If it is pushed too hard too fast, the
teachers will never accept it for themselves, rather they will reject the movement and
it will never become a part of the culture. Opportunities for teachers to look at
student data and benefit from its results must originate from the principal, and occur
during work hours. Once teachers experience or witness growth as a result of using
data, the culture will start to develop and grow from there. Students must also be
involved in the process, as it helps them to understand and mature from their own
experiences while motivating them to do better. Once students see their own growth,
they too will understand the influence of setting goals and working towards
achieving them. Parents help to complete the final part of incorporating data into the
school’s culture. Their involvement opens a door of communication between school
and home, and gives parents an active role in helping their children to succeed.
Along with building a data-driven school culture, schools must also provide students
the opportunities to drive their own instruction. At a school like Westgard, where
majority of the students are gifted and high achieving, teachers must implement
effective ways to build the capacity of these students. This can only be done by
using data efficiently and appropriately. If students already understand a concept,
teachers must be able to provide additional support through learning centers, and
individualized differentiated learning opportunities. Boring gifted students by
forcing them to sit through lessons they already have mastered, will only discourage
and create unmotivated and often times disruptive students. By identifying what
students know before it is taught, teachers can modify and enhance what needs to be
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taught and to which level of rigor it is presented at. Data use at any school will
provide teachers and students a ground to stand on.

Connections to Prior Research
This case study makes some instructive connections with the existing
literature, while building on a new body of knowledge specific to data use and its
influence on GATE instruction. As discussed in chapter two, research in the area of
data-driven decision making and its effects on gifted education remain virtually
uncovered. There is however, a connectedness between the separate fields in the
areas of: accountability, support structures, and culture. In the following section I
will draw conclusions between these areas of research and the results from my study.

Accountability
The Accountability of Public Schools subsection of the literature review
framed four areas of concern as noted by the conclusions made at the National
Education Summit (1989). The research conducted created an agenda for national
reform and established four tenets to ensure appropriate standards based education
for all students. The development of expectations, creating the appropriate
assessments, maintaining local control, and revising accountability systems became
the important focus for accountability. It is through these areas of concern that
schools became more liable for their actions and subsequently started to rely on data
to produce a more tangible decision making process. The application of data-driven
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decision making at the local school site has served as a solution for schools to
maintain accountability, while at the same time provide students with the appropriate
rigor of instruction. The research holds true to the finding of this study.
At Westgard, the accountability to continually improve gifted students
performance is evident in the structures that have been set in place. The expectations
for GATE teachers are rigid, as they are expected to prepare more for gifted students
in order to increase overall performance and create higher level thinkers. GATE
teachers undergo an intense introduction to GATE methodologies, and continually
collaborate with other teachers to ensure that expectations are met. This study
provides legitimate claims that the accountability of GATE teachers is complex and
requires a multifaceted approach to be successful.
One of these methods is using data with gifted students to make them more
aware of their own progress. At Westgard, the accountability of students is just as
important as that of the teachers. Students analyze individualized assessment data
and determine suitable objectives for optimal improvement. Teachers and students
look at assessments as a way to adjust educational rigor and curricular pacing issues
in order to continue to learn at higher levels of instruction. Teachers, in turn, create
assessments that align with classroom instruction, and ensure that depth and
complexity is measured at an appropriate level for each grade level. This change in
culture is only possible when, “using data becomes synonymous with organizational
improvement, the processes and practices surrounding the use of data are likely to be
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sustained and refined and become intrinsically entwined with educational decisions”
(Earl, 2001, p.8).
Another advantage for Westgard is the local control it possesses over the
large population of gifted students it serves. Sunset Unified has designated the site
as a GATE magnet, and busses in a diverse population of students from around the
district who qualify for the gifted program. This is an important finding in this study
that provides additional research for the education of gifted students. By creating a
system for all GATE students to achieve through consistent methods, Sunset Unified
has ensured that all gifted students will receive the same high level of instruction that
is required for all students qualifying for special education.
In holding true to the last tenet described in the literature review, Westgard
has remained accountable as a result of the system that it has created for GATE. In
order to teach gifted students, teachers must be chosen from a list of highly
recommended teachers, receive in-house training, and intrinsically determine if they
are capable of maintaining high standards and long work hours. Teachers are held
accountable to follow both the California content standards, as well as the National
Association for Gifted Children (NAGC) standards. The system in place assures that
gifted students will receive the highest possible instruction and schools will remain
accountable.

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Support Structures
In the section that analyzed the purposes of DDDM and the facilitators that
support it, the literature review sustains the rationale behind its implementation and
offers analogous data to that collected by interviewed participants of this study.
Ikemoto & Marsh (2007) established a framework in their research of DDDM and
determined that schools use data in the following ways: basic, analysis-focused, data-
focused, or inquiry-focused. These four quadrants parallel the ways in which data
were implemented and used at Westgard when creating a system dependent on data
analysis to guide teacher and student improvement. This section will look at Ikemoto
and Marsh’s (2007) four categories for the purpose of analyzing data, as well as the
support structures in place to provide evidence of additional research through the
interviews conducted in this study.

Basic
Ikemoto and Marsh (2007) describe this form of data as the simplest analysis
procedure, where schools look at test scores, choose professional development
activities to address gaps, and change schedules to address the identified needs. The
data collected showed that Westgard far surpasses this stage, but identified this step
as an important first stage of implementation for schools. Teachers acknowledged
that before the current principal was hired, many decisions were made without
assessing gaps in student CST assessment data. Now, a basic understanding of data
is commonplace and expected for all teachers. However, this process takes time and
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careful planning of implementation strategies. It is noted that without mastery of
basic data knowledge, any other use of data should not be expected of teachers.

Analysis-focused
Data shows that Westgard also goes beyond the basic one time analysis-
focused stage the Ikemoto and Marsh (2007) describe as complex analysis and
decision making with the use of simple data. In fact, one of the most important
themes identified in the collection of data for Westgard, was the ability to
standardize the way data were supposed to be analyzed by teachers. The data
resulted in the creation of a teacher run team that met regularly to create data
analysis forms and interpret complex data in various ways. It is noted that Westgard
did not consent the expertise of researchers to help analyze data, but still displayed
complex strategies for examining data. A contributing factor of success was found
through this step of DDDM, as teachers became more familiar with all types of
available data. The committee also spread the use of data to other teachers, and
enabled them to analyze classroom data in a regular time frame. One area of
research that was discovered came in the form of analysis-focused student teams.
The teachers used analyzed data to communicate with gifted learners in
unconventional methods that placed the responsibility for learning back on the
students. Data showed that students responded highly to being a part of analyzing
their own data. It empowered students to make more appropriate decisions and
organize their thoughts during instruction. Gifted students benefited from higher
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levels of integrated thinking and analysis, and produced gifted level work as a result.
This also opened the door of communication for students and teachers, as it placed
less emphasis on the structured teacher-student relationship which, in turn lowered
the affective filters of gifted students.

Data-focused & Inquiry-focused
Westgard finds itself on a balance between different quadrants. Often times,
the analysis and decision making process lacks complexity but the data collection
methods are involved, placing the DDDM methods closer to data-focused spectrum.
However, data also shows that since Westgard is so closely aligned with the district
office as a result of being a gifted magnet, often times the analysis and decision
making processes are enhanced with expert advice and reliable qualitative data. This
shifts the school closer to the Inquiry-focused quadrant which Ikemoto and Marsh
(2007) describe as “a significant investment in time and resources to search for a
particular problem of practice” (p 117). GATE teachers at Westgard experience a
more coherent approach to DDDM, than teachers of general education students.
Curricular decision making and what strategies are used in the classroom are a direct
result of consulting expert researchers during monthly GATE meetings. Ideas are
formulated and lessons are planned based off of research from books and articles. In
addition, teachers make decisions based of data collected through observation and
collaboration with groups of other GATE teachers, and from “master” GATE
teachers who were expected to demonstrate high levels of differentiated instruction
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across the district. As a result, Westgard performs high levels of DDDM because of
the GATE system that supports the school.

Professional Development
Another component that involves the support structure of the district and
school is professional development. As noted in chapter two, numerous studies
identify the key components to effective professional development (Elmore, 2002;
Darling-Hammond et al., 1995; Joyce & Showers, 2003). In the case of Westgard,
Elmore’s research provides the most applicable connection to results collected. He
states that three items must be present in order for any kind of professional
development to be successful: the values and beliefs of people in schools about what
is worth doing and what it is possible to do; the structural conditions under which the
work is done; and the ways in which people learn to do the work (2002). In Sunset
Unified, a dichotomy exists for teachers of gifted education. While they receive
“normal” professional development with the rest of the teachers, they are also
required to undergo GATE professional development. It is within these results the
most insight for gifted education take place.
To begin with, GATE teachers at Westgard are considered to be not only
teacher leaders, but also some of the most competent teachers at the school as noted
by both the principal and the director in charge of GATE. It is for those reasons that
professional development must be examined. As stated earlier, only the best
applicants are chosen to become part of the GATE community. These teachers
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become important parts to the change process at the school site, as they help to form
the culture and beliefs. “If data-driven decision making is to be integrated into the
administrative fabric of the school, then ongoing training will be needed to keep
skills honed and to allow entry to the process by teachers” (Picciano, 2008, p.132).
GATE teachers receive differentiated training in an attempt to create high level
atmospheres for gifted students to achieve. Teachers report that through their
professional development, they can acknowledge that their teaching strategies and
ideal for what students can do change. They realize that teaching a GATE classroom
is as one teacher reported, “like teaching in a classroom with a glass ceiling.”
Teachers no longer rely on text books to drive instruction, but instead trust in
differentiated models of instruction and data analysis to advance learning.
Another key part to the research rests in the makeup of the professional
development itself. It follows what Darling-Hammond et al. (1995) recognize as
necessary steps for continued improvement: engaging, participant driven,
collaborative, connected to classroom, and sustained. But more than that, the
professional development for GATE teachers in Sunset Unified works in
collaboration with world renown researchers and has adopted and implemented
strategies proven to engage gifted students in higher level thinking skills. Teachers
note that these strategies are so effective with students, that regular education
teachers are also starting to use them as well.
The ways the GATE teachers learn to do the work is an important part of the
research collected as well. Teachers of gifted students learn how to learn before they
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teach students how to learn. Regardless of experience, all first time GATE teachers
are classified as rookies, and undergo rigorous training in what gifted means, and
how to teach students who are gifted in diverse areas. Teachers are given research to
read and learn, go to conferences, and then receive hands on guidance in learning
how to implement differentiated strategies. They rely on each other for support, as
well as observe classroom instruction to learn to do the work necessary to teach
gifted students. It is a very complex system, but has proven to be successful as a
result of using data to drive professional development.

Culture
It was noted that schools that follow DDDM processes, are more likely to
improve school effectiveness, and ultimately create positive experiences for all
involved (Wayman, Stringfield, Yakimowski, 2004). Through the data collection
process at Westgard, it became clear that all participants interviewed felt a better
sense of self efficacy as a result of using data to make decisions, as well as have a
better perception for individualizing learning for gifted students. The data enabled
teachers to identify specific learning patterns, and differentiate curriculum to meet
the various range of learning needs. Research also shows that schools that use data
to inform curricular decision making become more accountable and show greater
student growth (Marsh, Pane & Hamilton, 2006). As a high achieving gifted
magnet, great efforts have been made to continue to grow in student achievement
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and API scores. The accountability for the district, school, and students continue to
increase; consequently, using data has facilitated this process.
The culture change and continued success at Westgard has been attributed to
the augmented use of all student data. This changing culture is something that the
principal credits to both administrative and teacher leadership. Earl & Katz (2002)
support this change in culture at Westgard with evidence of teacher and student
inquiry, on top of a new sense of data literacy by administration, teachers and
students. The school’s culture of data literacy is a result of empowering teachers and
students to experiment and collaborate with data results. Westgard has established a
central focus for the entire school that is surrounded by data use as a way to promote
growth for teachers and students. Ikemoto & Marsh (2007) and Marsh et al. (2006)
support that changes like this occur at a school because of an aligned focus and
vision that leads to increased capacity for using data.
The data collected from this study were fairly consistent with certain aspects
of the information obtained from the literature review. However, it should be noted
that much was learned as a result of studying the impact of data-driven decision
making with gifted education. As described in more detail in chapter four, Westgard
implemented a system of ongoing change that required both time and commitment
from all stakeholders. There are additional connections that have resulted in
implication for policy and practice. For that reason, the next section will discuss
possible connotations that can lead to future practices.

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Implications for Policy and Practice
The study of data-driven decision making revealed numerous implications for
policy and practice. The following section will discuss this further.
1. All school decisions that affect student outcomes must be based on a
careful analysis of multiple forms of data.
Schools can no longer assume that decisions based off of instinct alone will
be justified. Educators must involve themselves in using data as a means to align
decision making with facts. Ikemoto and Marsh (2007) noted that data use enhances
decisions about how to allocate resources and improve teaching and learning. My
study reaffirms this notion as participants recognized data as a main reason for
continued success and advanced student achievement levels.
2. School districts must mandate and support data analysis teams at all
schools sites to ensure a data rich culture.
It is not enough to ask schools to use data to inform decision making
practices. Districts must go one step further by establishing regulated data analysis
teams as part of their recommended school site plan. These committees must
involve all possible stakeholders to ensure credibility and maintain efficacy. The
data team should meet consistently and provide relevant feedback to the rest of the
staff, as well as create measuring tools to make gathering data more uniform. If
there is a structure in place that supports the use of data as well as provides methods
for teachers to more conveniently incorporate it into normal practice, then teachers
will be more likely to accept it. Using data to support inquiry and inform the
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instructional mission of schools requires coordinated changes in school processes,
data collection, data management, the use of analytical tools, and the analytical
capacity of school personnel (Mason, 2002).
3. Districts must provide specific and continuous professional
development in how to use data.
Districts must understand that teachers, although often asked to use data as
part of their assessment practices, have usually never been officially trained in how
to analyze data and what kinds of data are readily available. Joyce and Showers
(2003) note that teachers must receive instruction on how to become better learners
by identifying training outcomes that will lead to success. If data use is an important
requirement of the school, then it must be directly linked to the vision, and properly
supported at the school site and through district professional development. However,
district support must become systematically embedded in school plans or teachers
will not accept it as valid or significant.
4. Schools must provide mandatory time for collaboration amongst
teachers.
In order to fully implement change Fullan and Miles (1992) found that
collaborative time for teachers was necessary to undertake and sustain school
improvement. This proved to be true for Westgard as it was incorporated as a core
component into weekly meetings as part of banking time minutes. Grade levels met
weekly and data were disseminated to colleagues as an accountability measure.
Curricular issues were discussed and agendas were followed to guarantee that
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collaboration was taking place. Schools must provide this necessary time and guide
teachers with support, but at the same time allow them to run their own meetings to
focus on the most critical issues facing their classrooms.
5. Differentiated methods taught to GATE teachers should be applied to
all general education classrooms.
GATE teachers in Sunset Unified receive exclusive and ongoing training to
learn how to teach gifted students using a high assortment of differentiated measures
and lesson planning. This training is supported by the district and extends
throughout the year. Each meeting, teachers learn a new method of teaching,
collaborate, and share ideas for future lessons. Teachers talk about data analysis,
read books about differentiation, and depend on other GATE teachers for additional
support inside and outside the classroom. The training to be a gifted education
teacher requires much more extensive work, as Sunset Unified believes that it
“requires a special person willing to put in extra time.” This is a unique situation, as
most districts do not provide any kind of gifted training or support to teachers with
GATE students. Teachers of general education classrooms receive different
professional development for other issues that often times are dictated by district or
administration necessities and do not always help increase teacher efficacy. All
teachers should engage their students in higher level thinking skills, provide
opportunities to excel, and hold high expectations through goal setting and reflection.
These commonly used GATE methods should be looked at as good teaching
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strategies and be incorporated into every classroom. This will not happen until
schools demand support and districts respond with necessary resources.
6. Teacher preparatory institutions must teach multiple models of
instruction to ensure that differentiation is a normal practice in all classrooms.
Beginning teachers should be well equipped with a versatile arrangement of
multiple models of instruction to better meet the needs of all learners. All too often
teachers rely on one method to provide instruction to students. This obsolete method
does not provide students with enough options to reach all learning styles and keep
students involved with lessons of depth and complexity. From intervention classes
to advanced gifted classes, students must be given learning opportunities to use
various senses and synthesize learning. Only when teachers start to explore
differentiated methods of instruction will students be able to learn at a different level
and change achievement levels. In order for this to happen, communication between
schools, districts, and universities must take place. It must be clear that teacher
preparation classes are not consistent across the board in what they are offering
teachers to develop skill and knowledge for teaching methodologies.
7. Some methods used to support DDDM are not taking place in the
gifted education context.
While it is clear that data use is becoming a more aligned practice for school
improvement, the concept of DDDM still remains a process fueled largely by
achievement levels on state standards, and not towards gifted education standards.
This study showed that data was used regularly to monitor and check grade level
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concepts as prescribed by the state and district standards; however, it failed to
scrutinize the levels of differentiation, depth and complexity, or rigor mandated by
the national standards for gifted education. As a result, the data collected shows that
teachers were improving practices and strategies within their classrooms to inform
general education curriculum, but still have not created a data system that checks for
gifted instruction.

Recommendations for Future Research
The research conducted is this study was done so on a very small scale. It
should be known that in order to produce more comprehensive results, a larger
sample size composing of various schools in opposing school districts should be
selected. To better determine the affects of data-driven decision making on gifted
students, a bigger population would be advisable to determine additional
implications within the study.
This study represents new literature for DDDM in collaboration with gifted
education. The hope of this research is that additional studies are conducted and
contribute to a deeper understanding of how these systems can work together to
create more efficient learning outcomes. A larger and more in-depth analysis would
help to reduce the amount of error that a small sample size may have created.
Additional methods of data collection could also be used, including a survey of
teachers, focus groups with students, and in depth classroom observation. This could
120
allow for further development of data use for GATE teachers for a more diverse
population of schools.

Conclusion
The results of this study suggest that data-driven decision making with
teachers of gifted students is a unique process that allows both teachers and students
an opportunity to reflect on performance measures. It was found that in order to best
facilitate an entire school, the data culture had to be set and tools for measuring data
must be consistent and available. That included the willingness of all stakeholders to
engage and maintain active strategies that would not slow down the flow of
instruction, but enhance the opportunities to assess and analyze student data.
Research supports this notion showing that in order to initiate a data-driven culture,
the process must involve all teachers using data in their day-to-day function
(Wayman & Stringfield, 2006).
The results also proved that data can be used in a variety of ways to motivate
and improve teacher and student behavior and affect achievement. However,
leadership was necessary both with teacher leaders and in the administration to
initiate this process. These leaders distributed critical information and helped
establish norms within the school. While strong leadership is important to a data-
driven district, leadership should not be dependent on one core person or group of
people. This study illustrated how critical the principal was in establishing data use,
but without her passion and drive, the school may not have flourished in the data
121
capacity it has shown. The data culture of the school must become strong enough to
withstand administrative and teacher change, and be able to maintain such practices
regardless of leadership. Research has shown that data initiatives are unsustainable
when they depend on the unusual effort of one or more individuals (Stringfield,
Reynolds, & Schaffer, 2001).
In addition, support was provided for teachers to collaborate with each other
regarding the use of data. A committee was formed that helped to regulate this
process and start to define how data analysis impacted student performance.
Offering time for data use has been found to be a critical support, and collaboration
time has shown to be an effective method for developing data use (Datnow et al.,
2007). However, this study also shows that it is imperative to provide time for
teachers and schedule time into the working day, providing for a structured and
consistent work time to use data.
Change comes slowly, and those who are in charge of overseeing its progress
must be patient and accommodating. The use of data proved to be a major factor in
creating a more productive environment for teachers and students of gifted
education. As Renzulli (1998) alluded in his “rising tides lift all ships” reference to
reform, significant changes are only possible by introducing more effective practices
into existing school structures.
122
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APPENDIX A
Teacher Interview Protocol
Participant’s Name: _____________________________ Date: ______________

Position: ___________________________________________________________

Research Question to Keep in Mind:
How is Data-Driven Decision Making influencing the education of gifted and higher
achieving students?

Introduction:
- Introduce yourself and the purpose of the study.
- Explain that the interview will be taped, but will remain confidential.
- Let them know that if they would like to make a comment off tape, to let you
know.
- Let them know the approximate length of the interview and ask if there are
any questions before beginning.

I. Background

1. Please tell me about your teaching experience at this school, including grades
you have taught and how long you have been a teacher here.

II. Data-Driven Decision Making Processes

Research Sub-Question 1:
How do teachers of gifted students use data to inform curriculum and why?

Guiding Questions:

1. What kinds of student data do teachers have access to and use?

2. How often do you receive this data?

3. What strategies do you use to interpret data?

4. Do you ever use data that is related to gifted students?
o If so, what kind?
o What do you use it for?
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5. Do you ever change your lesson plans to account for specific differentiation
of gifted students as a result of student data you have received?
o If so, how?
o Examples?

7. Do you discuss data with your students/teachers?
o How do you discuss data ?

8. How has your instruction, specifically with GATE students, changed as a
result of using data?

Research Sub-Question 2:
What is being done to build the capacity of teachers of gifted education students to
use data?

Guiding Questions:

1. Have you attended any training for GATE students?
o In what specific areas?
o Is the professional development voluntary or mandatory?

2. Have you ever received training on data-driven decision making?
o In what specific areas?
o Is the professional development voluntary or mandatory?

3. What is your school doing to support you to use data in your classroom?

4. Do you differentiate your instruction as a result of student data?

5. Do you have a person on staff to support you in the use of data?
o If so, what type of support is provided to you?

6. Is there a person or team of people in your district that supports teachers in
their attempts to use data at the school site level?
o Can you elaborate on the type of support provided?
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Research Sub-Question 3:
What applications/crossovers are there from data-informed instruction for gifted
education to instruction for the general population?

Guiding Questions:

1. How do you differentiate instruction?

2. Do you feel like you spend more time planning and teaching your
underperforming students, or your high achieving students?
o Does data play a role?

3. What kind of strategies do you use to meet the needs of all your learners?
o What about your gifted students?

4. Is time allotted for collaboration among teachers, with respect to analyzing
data and developing curriculum?
o How often?

5. Do you think there is any connection between teaching high achieving
students and low achieving students?
o Probe:
o Data?
o Teaching strategies

III. Results and Outstanding Needs (Research Questions #1 and #2)

1. What problems have you had in the classroom in trying to use data to inform
the curriculum you plan?
o How has the school tried to help you?

2. Does your school have a strong community of gifted learners?
o What kind of extracurricular activities does your school provide?

3. Reflect upon the data-driven decision making processes at the school.
o What works?
o What doesn’t work and why?
o What do you wish you were able to do?

4. What do you consider to be your top 3 major accomplishments in teaching
gifted students in the area of data use?

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5. What do you see as the next steps in your personal use of data when it comes
to meeting the needs of your gifted students?

Conclusion:
- Is there anything else I should know?
- Thank them for their cooperation and time.
- Let them know that you may need to contact them for follow-ups.
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APPENDIX B
Principal Interview Protocol
Participant’s Name: _____________________________ Date: ______________

Position: ___________________________________________________________

Research Question to Keep in Mind:
How is Data-Driven Decision Making influencing the education of gifted and higher
achieving students?

1. How do teachers of gifted students use data to inform curriculum and why?
2. What is being done to build the capacity of teachers of gifted education
students to use data?
3. What applications/crossovers are there from data-informed instruction for
gifted education to instruction for the general population?

Introduction:
- Introduce yourself and the purpose of the study.
- Explain that the interview will be taped, but will remain confidential.
- Let them know that if they would like to make a comment off tape, to let you
know.
- Let them know the approximate length of the interview and ask if there are
any questions before beginning.

I. Background
1. Could you tell me a bit about the history of your school, focusing on the last
five years (reforms, strong partnerships with external groups, major structural
changes)?
2. Could you tell me a little about the students and the community you serve?
3. How long have you been at this school? What is your prior experience and
training?
4. As principal, what are your roles and responsibilities around data (accessing,
summarizing, interpreting, planning, monitoring)?
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II. Data-Driven Decision Making Processes

A. Types of Data (Research Question #1)
1) How do teachers of gifted students use data and why?

1. What kinds of student achievement data does the school collect (standardized
tests, benchmarks, school or district developed)? Are they mandated or
elected to administer?

2. What kinds of programs does your school offer for gifted students?
o Are they mandatory or optional?
o Do they cost or are they free?
o What percentage of students participate?

3. Which kinds of student data do your teachers have access to?
o Is any of the data specifically designed to monitor GATE students?

4. What do you expect to see as you walk into a classroom if you were
observing the use of differentiated instruction.

5. How do teachers at your school plan to meet the needs of ALL their learners?
o Examples?

B. Structural Support and Capacity (Research Question #2)
2) What is being done to build the capacity of teachers of gifted education students
to use data?

1. What are the district’s expectations for gifted students?
o What are your expectations for how teachers should use data to create
lesson plans?

2. Can you provide an example of when your school used gifted student
performance data to make decisions about instructional programs?
o Professional development?
o School organization and staffing?
o School budget?

3. Do principals have the authority to make teachers use data as part of their
curriculum planning?
o Are there any examples that you can think of?

136
4. What kind of opportunities has your school had to learn more about
differentiated instruction?

5. Have you attended any in-services on gifted education?

6. Is there a teacher who you have observed to be an effective user of gifted
student performance data? Could you describe this teacher and his/her
approach to data to enhance student learning and classroom instruction?

7. Has the district provided professional development for principals that focus
on data-driven decision making?
o In what specific areas?
o Was the professional development voluntary or mandatory?

8. Is professional development on data-driven decision making offered to
teachers?
o In what specific areas?
o Was the professional development voluntary or mandatory?

9. Do you have a person on staff to support teachers in the use of data for gifted
students?
o If so, what type of support is provided for them?

10. Is there a person or team of people in your district that supports you and/or
teachers in their attempts to use data at the school site level? Can you
elaborate on the type of support provided?

D. Applications/Crossover (Research Question #3)
3) What applications/crossovers are there from data-informed instruction for gifted
education to instruction for the general population?

1. How does preparing to teach gifted students help with the planning of all
students?

2. Do you think most teachers at your school are adequately preparing all
students regardless of ability level?

3. Is student data shared between teachers?

4. Is time allotted for collaboration among teachers, with respect to analyzing
data?
o How often?

137
5. Is time allotted for additional planning and/or collaboration?

III. Results and Outstanding Needs

1. What problems have you had in your school in trying to use data for decision
making?
o Has it had an impact on GATE students?
o If so, can you give me examples?

2. Reflect upon the data-driven decision making processes at the school.
o What works?
o What doesn’t work and why?
o What do you wish you knew that you cannot find out today?
o What do you wish you were able to do?

3. What do you consider to be your top 3 major accomplishments in the area of
data use?

4. What do you see as the next steps for the district to support schools use of
data?
o What are the next steps for you in your role as principal in supporting
teachers?

Conclusion:
- Is there anything else I should know?
- Thank them for their cooperation and time.
- Let them know that you may need to contact them for follow-ups.

Document Request:
- School schedule
- Meeting minutes
- Professional development agenda and calendar
- Data-driven decision making meeting calendar
- Work samples to demonstrate data use
138
APPENDIX C
District Administrator Interview Protocol
Participant’s Name: ______________________________ Date: ______________

Position: ____________________________________________________________

Research Question to Keep in Mind:
How is Data-Driven Decision Making influencing the education of gifted and higher
achieving students?

1. How do teachers of gifted students use data to inform curriculum and why?
2. What is being done to build the capacity of teachers of gifted education
students to use data?
3. What applications/crossovers are there from data-informed instruction for
gifted education to instruction for the general population?

[Introduction: Begin with a few minutes of explaining the study, who you are, and
the purpose of the study. Explain that while the interview will be taped, their
responses are strictly confidential. Let them know if there is something they would
like to say off tape, they can inform you and the recorder will be shut off for their
comment. Also, let them know the approximate length of the interview and ask if
they have any specific questions before beginning.]

I. Background- Laying the Foundation
Before I ask you specific questions about your role and district practices, I would
like to start by asking you some general questions about your district and its
surrounding neighborhood in order to gain a broader understanding of the context in
which you work within.

1. To get a sense of your district, community, schools, and students, could you
tell me a bit about the history of your district, focusing on the last five years
(e.g., particular reform initiatives, strong partnerships with external groups,
major structural changes, etc.)

2. Can you tell me a little about the school that is in my study?

3. Please tell me your title and your role in the district, particularly with respect
to gifted education.

139
4. How long have you been in this role? What is your prior experience and
training?

5. Describe the district’s leadership group (e.g. cabinet)? Could you tell me
about this group (e.g., members, function, organizational structure)?

6. Is GATE a priority of the school board?

II. Data-Driven Decision-Making Processes

A. Goals (Research Question #1)
1) How do teachers of gifted students use data and why?

1. What is your theory of action for collecting and using data to analyze gifted
students?
o Is there an overall model/plan that your district relies on to collect,
analyze, and use data?
o Could you describe the process? (Probing for organizational model
for how DDDM process should flow).

2. What are the student performance goals of gifted students for this
district/system and the schools within it?
o How were these goals established?
o How do you know when the goals have been met? Probe:
benchmarks, indicators.

3. To what extent have NCLB and state accountability systems influenced your
performance goals for gifted students?
o Has anything else influenced your goals for students?

4. What are the expectations of the district about how schools instruct gifted
students?
o Is there any accountability for teachers to use data for gifted students?

III. Structural Support and Capacity (Research Question #2)
What is being done to build the capacity of teachers of gifted education students to
use data?

1. To what extent is the idea of continuous improvement discussed at the district
level? Can you share with us an example of an area identified through data as
in need of improvement?
140

2. Has your district sponsored professional development for schools that focus
on gifted education?
a. In what specific areas?
b. Is the professional development offered voluntary or mandatory?

3. At the district level, is there a person or team of people who support schools
in using performance data to improve decision-making?

4. Does the district support or provide for school-level staff who assist teachers
in using data?

5. Does your district work with an external partner in the area of data-driven
decision-making?
o What has the external partner contributed (i.e., tests, surveys,
software, professional development)?

6. To what extent do principals have decision-making authority over
professional development?
c. Over curriculum and instruction?
d. Over staffing?
e. Over budget?
f. Can you think of an instance in which a principal used their decision-
making authority to make a change based on student performance
data?

IV. Applications/Crossover (Research Question #3)
3) What applications/crossovers are there from data-informed instruction for gifted
education to instruction for the general population?

1. How does the district encourage schools to share successful use of data with
teachers?

2. What kind of strategies are teachers expected to use to appropriately meet the
needs of gifted students?

3. Do you think all teachers are qualified to teach GATE students?
o If so, can you tell me what the district has done to ensure this?
o If not, is there something you think the district can do to help?
141

4. Do you think it is more difficult to teach very high achieving students, or
very low achieving students?
o Why?
o Examples?

[Concluding Remarks/Questions: Is there anything else we should know? Thank
them for their cooperation and time. Inform them I will share my report with them
once it is done and that I might need to contact them for follow-ups]
142
APPENDIX D
Data-related Meetings Observation Protocol
I. Meeting Overview

Date:

Time:

Type of data used for discussion:

Location:

Topic:

Purpose/Objective:

Materials used/handed out (Describe):
Formal Agenda(Y/N):

Participants:

Format of data presented (Describe):
143

DESCRIPTION MEMO/COMMENTS

144
APPENDIX E
Code List
The following is a list of the codes used to analyze the data gathered through the
interview process:

o Access to data
o Aptitude levels of data use
o Attitudes towards the use of data
o Availability of data
o Challenges using data
o Collaboration
o Communication
o Consistency of data use
o Data analysis culture
o Effectiveness of data with gifted students
o Evidence of data use
o Leadership
o Professional development
o Sharing data
o Teacher efficacy
o Use of data to change pacing
o Use of data to differentiate curriculum
o Use of data with students
o Use of technology to access data
o Various types of data

Abstract (if available)

Abstract Public schools are under constant scrutiny and face increased accountability measures as a result of both federal and state mandates. A rise in the use of data to make decisions and support change at the local level has provided schools with the much needed opportunity to respond to the needs of their population. This study will investigate how the use of data influences the education of gifted and higher achieving students. Specifically, it will look at how teachers of gifted students use data, what is being done to build the capacity of teachers to use data, and what applications and crossovers from data-informed instruction for gifted education can be used for instruction for the general population. These issues will be examined through a qualitative case study of one elementary school serving large numbers of gifted students. Data will be collected through interviews with teachers, administrators, and district personnel, in addition to school observations of data focused meetings. All teachers interviewed displayed a general understanding of how data helps them prepare for lesson planning, and commented that they also share data within grade levels. While not all teachers used data the same way, all GATE teachers shared a collaborative approach that involved students with analyzing their own data. The GATE teachers used data as an accountability measure not only for themselves, but for students and parents as well.

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Asset Metadata

Creator Dalton, Matt (author)

Core Title A school's use of data-driven decision making to affect gifted students' learning

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 04/08/2009

Defense Date 03/02/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag accountability,analyzing school data,data-driven decision making,DDDM,Gifted Education,gifted students' learning,OAI-PMH Harvest,school improvement

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Datnow, Amanda (committee chair), Kaplan, Sandra (committee member), Pensavalle, Margo T. (committee member)

Creator Email mddalton@usc.edu,mr_dalton16@hotmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2061

Unique identifier UC1290930

Identifier etd-Dalton-2767 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-207202 (legacy record id),usctheses-m2061 (legacy record id)

Legacy Identifier etd-Dalton-2767.pdf

Dmrecord 207202

Document Type Dissertation

Rights Dalton, Matt

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu