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Does data-driven decision making matter for African American students?

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Does data-driven decision making matter for African American students?

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DOES DATA-DRIVEN DECISION MAKING MATTER FOR AFRICAN AMERICAN

STUDENTS?

by

Virginia Ward-Roberts

________________________________________________________________________

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 Virginia Ward-Roberts

ii

Dedication

I would like to dedicate this dissertation to four people that have inspired and
impacted my life tremendously: my parents Annie and William E. Ward, Jr., my son,
Aaron D. Roberts, and my maternal grandmother, Rosie L. Plair. I am especially
thankful to my parents who taught me early-on in life that success comes with hard work
and sacrifice. Your love and support have sustained me over the years. I am forever
grateful to my number one cheerleader, my mother, who has encouraged and supported
me through life challenges and successes. Her unconditional love is amazing and has
enabled me to become the woman I am. Although my father is no longer present
physically, his spirit lives on.
To my son Aaron, I dedicate this dissertation to you because I am forever grateful
for your assistance during a hectic time in my life. I appreciate your patience and
understanding, particularly when I missed or could not attend school functions and/or
golf tournaments because of my studies. I thank you for taking on additional
responsibilities and all the prepared dinners – You are an awesome cook! From mother-
to-son, enjoy life and embrace the many opportunities and possibilities you are afforded.
Once again, thank you for being a wonderful and supportive son.
Last, I am fortunate to have had the love of my maternal grandmother, Rosie L.
Plair, who yield extraordinary support, but died unexpectedly in the middle of the
doctoral program. You are forever missed and remains in my thoughts.

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Acknowledgments

It is a pleasure to thank the many individuals who supported the writing of this
dissertation. First and foremost, I would like to thank my dissertation chair, Dr. Amanda
Datnow. I am forever indebted to her for having a keen eye for details while reviewing
and supervising the writing of this dissertation. She displayed such grace and
professionalism to make a somewhat grueling task manageable. I am also forever grateful
to Dr. Gisele Ragusa and Dr. Laurie Love, who graciously served on my dissertation
committee and took time out of their busy schedules to read the manuscript and provide
meaningful input.
The writing of this dissertation was supported through the numerous friendships
that I developed in the doctoral program. I am especially thankful for the friendships of
Christine H. Sanders and Phaidra Crayton. They were truly awesome and supportive
friends.
I am thankful to the staff at William J. Clinton Elementary School in the Central
Unified School District. They provided endless encouragements which have assisted in
the achievement of this lifelong goal. I am appreciative for assistance from Betty
Saucier, Muriel Brooks, KiRita Walker, Marcela Perez, Carmen Monroy, Denise Moore,
Allison Levy, and Shanda Scott. In addition, I am also grateful to those administrators in
Central Unified School District who supported the research study: Gipson Lyles, Sydney
Burnett-Richie, Caren Floyd, Ruth Dickens, and Zakiyyah McWilliams. They provided
the time and accommodations to ensure interviews and the examination of documents
materialized on their campuses and departments.

iv
Moreover, I am thankful for the constant support and encouragement I received
from personal friends, Geri Ayers, Jacquelyn Arion, Laurie Inman, and Genevieve Destot
as well as mentoring from Jacqueline Cochran, Ed. D. and her husband, Richard Cochran,
who coached when the dissertation process seemed inconceivable. I am also eternally
indebted to Donald R. Henderson, M.D., who enthusiastically served as a personal
champion and offered numerous words of comfort and encouragements when the writing
of this dissertation appeared elusive and unobtainable.
Last, I was fortunate to have the unconditional support of my four brothers;
Alonzo, Wilbert, Regenial, and William Ward, who cheered along the way. They have
always endorsed my professional growth as an individual and as their sister. I am equally
fortunate to have the support of my ex-husband, Thomas D. Roberts, who envisioned the
writing of this dissertation many years ago and predicted that it would one day become a
reality.

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Table of Contents

Dedication ii

Acknowledgments iii

Abstract viii

CHAPTER ONE: OVERVIEW OF THE STUDY

Introduction 1
Background of the Problem 3
Individuals with Disabilities Education Act 4
Overrepresentation of African American Students 5
Use of Data-Driven Decision Making 6
Research Questions 11
Significance of Study 12
Limitations of the Study 12

CHAPTER TWO: LITERATURE REVIEW

Introduction 14
Overrepresentation of African American students
in special education 14
What federal laws govern special education teachers’ use of data 18
No Child Left Behind 18
Individuals with Disabilities Education Act 20
IDEA Reauthorizations 21
The Special Education Pre-referral Process 23
Response to Intervention 27
How does IDEIA address the use of assessment data
to support students’ IEP’s 30
How does the individual education plan assist teachers’ use of data? 31
School leaders build the capacity for teachers to use data 32
Data models that build leadership capacity for teachers
and administrators 34
The Intended use of Data-Driven Decision Making 37
High-Stakes Assessments 40
Benchmark Assessments 41
Challenges and Unintended Consequences of Data-Driven
Decision Making 42
Conclusion 45

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CHAPTER THREE: METHODOLOGY

Introduction 48
Research Design 49
Sample and Population 50
Overview of School District 51
Overview of Participating Schools 52
Instrumentation 54
Data Collection Procedures 55
Interview Norms 56
Interview Log 57
Document Analysis 58
Data Analysis 59
Ethical Considerations 60
Limitations to Study 60

CHAPTER FOUR: DATA ANALYSIS AND INTERPRETATION OF FINDINGS

Introduction 62
Research Questions and Central Themes 63
Overview of Student Demographics 65
The Role of Data Use to Support African American Students 66
Instructional Planning 67
Differentiated Instruction 72
Student Study Team and Response to Intervention 74
Professional Development 77
Use of Assessment Data 81
California Standards Tests (CST) 82
Benchmark Assessment Data 84
Teacher-Made Assessment Data 86
Informal Assessment Data 89
General Education vs. Special Education Teachers’ use of data 91
Instructional Supports 94
Time 96
Collaboration 98
Resources 99
Effects of Data Use for the Education of African America Students 102
Intended Outcomes 103
Unintended Outcomes 105
Conclusion 109

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CHAPTER FIVE: CONCLUSION AND IMPLICATIONS OF FINDINGS

Introduction 113
Research Findings and Connection to Prior Research 115
The Role of Data Use 115
African American students 116
Classroom instruction 117
Quality of teachers 118
The Gatekeeper 119
How Various Forms of Data Were Used 121
The use of assessment data 121
Feedback 122
Challenges with data 122
How Principals Build Capacity 124
Administrators’ role 124
Time 125
Collaboration 126
The intended and unintended consequences of the use of data
for reducing special education placement 128
Benefits of using data 128
Inappropriate uses of data 130
Implications for Policy and Practice 132
Principals 132
Teachers 133
District Office Administrators 134
Teacher Education Program 134
Conclusion 134

References 137

Appendices

Appendix A: Data-Driven Decision Making and Special Education
Teacher Interview Protocol 148

Appendix B: Data-Driven Decision Making and Special Education
Administrator Interview Protocol 150

Appendix C: Data-Driven Decision Making and Special Education
District Administrator Interview Protocol 152

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Abstract
For over 30 years African American students’ have been disproportionately
placed in special education programs. Because of the heightened pressures of NCLB, data
driven decision making (DDDM) has become a promising reform drawing attention to
the academic performance of several student subgroups. In many academic settings, data
use exists, however, it is unclear how DDDM assists (or not) in the schooling of African
American students.
This study examined DDDM in three urban schools in the Central Unified School
District. In addition to general education, a strong emphasis was placed on students with
learning disabilities. This study also explored varied forms of assessment data, schools’
leadership, and the intended and unintended consequences concerning the uses of data.
Semi-structured interviews were used to facilitate the interview process and identify how
educators used data to impact classroom instruction.
The findings suggest the behaviors of African American students contributed to
mistaken referrals and inaccurate placements. In addition, to the focus on instruction,
teachers differentiated lessons, and relied on teacher-made assessment data to inform
instructional practices. While data improved the education of African American students,
overrepresentation remains a constant concern.
The examination of policies and practices addressed the uses of consistent and
reliable assessment data. Nevertheless, instructional decisions and practices relied on the
correct uses of data to change for the better the educational outcomes of all students,
especially the African American subgroup.

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CHAPTER ONE: OVERVIEW OF THE STUDY

Introduction

For the first time ever, we are looking ourselves in the mirror and holding ourselves
accountable for educating every child. That means all children, no matter their race or
income level or zip code. (U.S. Secretary of Education, Margaret Spellings)

The federal government’s No Child Left Behind (NCLB) Act of 2002 has forced
public schools across the United States to use data to measure students’ mastery of
content standards. NCLB holds states, districts, and schools accountable for improving
the achievement of all students (Ikemoto & Marsh, 2007). Two major components of
NCLB have placed an insurmountable amount of responsibility on public institutions to
perform. These components require states to annually test students in grades three
through eight in reading and mathematics and report achievement by racial, parents’
income, and other special needs’ subgroups as defined by state’s enrollment criteria
(Wong & Sunderman, 2007). The legislation also mandates data usage to measure and
inform instructional decisions regarding the academic progress of all students, including
those identified as special needs (Wong & Sunderman, 2007).
NCLB set the tone for a new era in accountability for students’ with disabilities.
NCLB legislation requires the participation of all students in state and local assessments,
including those with disabilities (Malmgren, McLaughlin, & Nolet, 2005). Students with
disabilities can no longer be excluded from district and state-wide assessments (Losen,
2002). Annual reporting of special education students’ achievement results, by
proficiency levels, must coincide with those students in general education (Malmgren et
al., 2005).

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Title I, Part A, of the Elementary and Secondary Education Act of 1965 (ESEA)
advances the inclusion of all students with disabilities in state-wide assessments (U.S.
Federal Register, 2003). The amendment of Title I by NCLB ensures that states, local
educational agencies (LEA), and schools are accountable for the achievement of students
with severe cognitive disabilities. Title I places an emphasis on three critical elements.
The implementation of academic content standards, academic achievement standards, and
assessment alignments guarantee high standards are expected of all disabled students.
Because of NCLB legislation, schools have begun to incorporate these elements and
refocus their efforts in providing students with disabilities the best education possible
because achievement results count (U.S. Federal Register, 2003).
NCLB specifically holds schools accountable for the annual progress of particular
racial subgroups of students, including African American students. No longer are
watered-down curricula and little accountability for students’ achievement acceptable
(Losen, 2002). Schools must “have high expectations for educating every child and the
soft bigotry of low expectations is no longer tolerated” (U.S. Department of Education,
2008). Although NCLB legislation specifically addresses the achievement of African
American students, special education referral rates continue to surpass those of European
counterparts (Blanchett, 2006). African American students represent the highest
percentage of minorities placed in special education (Zhang & Katsiyannis, 2002).
The use of data is becoming increasingly important in public school settings. The
No Child Left Behind legislation compels educators to monitor and measure the
performance of all classifications of students in public schools, including those identified
with disabilities (NCLB, 2002). NCLB mandates schools to use data to improve

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achievement levels of all students regardless of learning disabilities. NCLB forces
teachers to use data to improve and inform decisions relating to student achievement
(Stringfield, Wayman, & Yakimowski-Srebnick, 2005).
This study seeks to understand how educators use data-driven decision making
(DDDM) to address the performance levels of African American students placed in
classrooms settings in and outside of the general education program. However, further
background on the problem is needed before explaining the details of this study.
Background of the Problem
In many states across the country compulsory education laws were adopted to
support the schooling of America’s children (Bursztyn, 2007). In spite of such laws,
students with disabilities continued to experience hurdles in receiving a free and
appropriate education (Zhang & Katsiyannis, 2002). Educating students with disabilities
were often considered useless and a waste of educational resources (Duff, 2001). These
beliefs changed when parents of disabled students challenged mistreatments and
inequalities in court (Coutinho & Oswald, 2000).
As African American students prevailed in seeking equalities in public school
systems, federal legislations became equally supportive of minorities students and those
with disabilities. The passage of several legislations such as the Rehabilitation Act of
1973, Section 504, Public Law 94-142, and Title II of the American Disabilities Act of
1990 protects individuals with disabilities (Bursztyn, 2007). Section 504 of the
Rehabilitation Act protects individuals with disabilities from discrimination. It prohibits
any agency that receives federal assistance from engaging in discriminative practices.

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Sections 504 also require schools to devise plans to accommodate students with special
needs and ensure the learning environment is accessible (Duff, 2001).
Public Law 94-142 also known as the Individuals with Disabilities Education Act
(IDEA), was originally the Education for All Handicapped Children Act of 1975. It
protects students with multiple disabilities. All previous mentioned special education
laws guarantee appropriate educational programs are provided for students with
disabilities and ensure their participation in local and state-wide assessments (IDEA,
1997).
Individuals with Education Disabilities Act
The Education for All Handicapped Children Act (EAHCA) of 1975 is currently
known as the Individuals with Education Disabilities Act (IDEA, 1997). Since 1990, the
act mandates that all disabled students are provided a “free appropriate public education”
(FAPE) in the “least restrictive environment” (LRE) (Duff, 2001; Losen 2002). Students
identified as special needs must have an Individualized Education Program (IEP),
detailing specific educational services. IDEA requires parents’ participation in the IEP
process. Under IDEA, parents can monitor and challenge services offered to their special
needs children. IDEA addresses an array of handicapping conditions such as mental
retardation, deafness, speech or language impairments, blindness, serious emotional
disturbance, physical and health disabilities, and specific learning disabilities (Duff,
2001). The 1997 reauthorization of IDEA includes autism, traumatic brain injury, and
attention deficit disorder (ADD).
The analysis of data from benchmarks, standardized, and alternative assessments
are critical in urban public school systems where an increasing number of African

5
American students are placed in special education classes. Because African American
students continue to underperform at a much lower levels than White students and are
overrepresented in special education programs, NCLB specially addresses this subgroup
in its legislation (NCLB, 2002).
Overrepresentation of African American Students
The overrepresentation of African American students in special education has
been a debatable issue for over three decades (Losen, 2002). Court cases such as Larry
P. (Larry P. v. Wilson Riles, 1972, 1979, 1984, 1986 as cited in Coutinho, Oswald &
Best, 2002) strived to better educational opportunities for African American students.
Accordingly to Zhang & Katsiyannis (2002), when the percentage of African
American students in special education program exceeds the percentage of the nation’s
student population, it becomes disturbing. For example, African American students
represent 14.8 percent of the 6-to-21-year-old general population, but account for 20
percent of the special education population in a range of disabilities (Losen & Orfield as
cited in Blanchett, 2005). Compared to White students, they are classified 2.41 times as
having mental retardation, 1.13 times as learning disabled, and 1.68 times as emotional or
exhibiting behavioral disorders (Klingner as cited in Blanchett, 2005). Minimizing the
misdiagnoses and subjective referrals of African American students are critical. Scrutiny
of educational supports and resources in general education are paramount to students’
success in school (Blanchett, 2005; U.S. Department of Education, 2004).
Throughout the country African American students in urban schools are being
placed at alarming rates in special education classes with multiple classifications (Skiba,
Poloni-Staudinger, Gallini, Simmons, & Feggins-Azziz, 2006). To address the growing

6
number of African American students overrepresented in special education classrooms,
the implementation of data-driven decision making techniques warrants the attention of
all educators.
Use of Data-Driven Decision Making
This study focuses on the use of data-driven decision making as a possible
vehicle for improving the performance of African American students categorized as
learning disabled. Using data to advance or improve an organization’s productivity is not
new. Educators emulate industry and manufacturing concepts such as Total Quality
Management, Organizational Learning, and Continuous Improvement. These models use
data to advance organizational goals (Marsh, Pane, & Hamilton, 2006). Similar to
business, data significantly advances educational accountability goals to make certain
improvements in struggling public schools happen. Today’s educators’ have begun to
expeditiously incorporate the use of data in their instructional programs to inform
classroom decisions and perfect instructional practices (NCLB, 2002). Educators do
understand that continuous data use improves student learning (Mandinach, Honey, &
Light, 2006). For example, schools use high-stake tests to measure students’
performance and determine academic growth levels.
Over the years federal policies have drastically established tougher requirements
for measuring and monitoring the academic performance of students with disabilities
(IDEA, 1997; NCLB, 2002). The 1997 reauthorization of IDEA mandates the
participation of all special education students in state and district-wide assessments
(Turner, Baldwin, Kleinert & Farmer Kearns, 2000). IDEA stipulates that students’
Individualized Education Program (IEP) needs to provide a description of

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accommodations and ensure modifications exist during high-stake assessments. For
example, if students cannot cognitively participate in regular standardized assessments, a
State Developed Alternative Assessment (SDAA) is to be provided (Turner et al., 2000).
Thereafter, data from SDAA are to inform decisions making concerning the academic
performance of students with disabilities.
NCLB and IDEA mandates the continuous use of various forms of assessments to
measure students’ academic growth, including those identified as special needs (IDEA,
1997, NCLB, 2002). Unlike high-stake tests, districts regularly administer benchmark
assessments in school settings to measure and provide timely feedback to schools
regarding students’ performance and prospective achievement (Heritage & Yeagley,
2005). Oftentimes, districts use student data to predict outcomes on standardized
assessments (Heritage & Yeagley, 2005). At school sites, teachers and administrators use
benchmark data to make adjustments in the school’s curriculum and instructional
program. Benchmark assessments along with the appropriate tools help school educators
prescribe an instructional program that immediately addresses the academic needs of
struggling students and implement state and federal government mandates.
Continued pressures from state and federal governmental agencies have forced
administrators to rely on data-driven decision making tools (consultants and elaborate
technology systems) to assist with tracking and improving students’ performance
(Mandinach et al., 2006). Although, these tools are instrumental in reporting “system
wide or school wide” test trends, minimal support is provided to individual students (p.
2). In several instances, data-driven decision making tools become beneficial to
administrators in understanding students’ general patterns of performance. Data allow

8
administrators an opportunity to monitor and identify strengths and weaknesses of
individual classes, grade levels, and school-wide achievement results. Data use can thus
help improve schools’ instructional programs. Administrators cognizant of data are
skillful in allocating appropriate instructional resources, instituting meaningful
professional development, and engaging students in intervention activities that promote
academic growth (Mandinach et al., 2006).
Understanding how teachers and school administrators implement data in
classrooms is crucial. Numerous educational researchers have devoted their efforts to
discerning how data-driven decision making practices influence student achievement
outcomes in the general education environment (Datnow, Park, & Wohlstetter, 2007).
However, studies of data use are greatly needed in learning environments where schools
have failed African American students. DDDM, combined with early intervention, could
help to deter future entry and reclassifications of African American students in special
education programs.
After students have been placed in special education classes, administrators must
support data use and ensure the delivery of a quality educational program.
Administrators in many urban schools serve as the gatekeepers. Their roles are pivotal in
the use of data in regular educational classrooms (Datnow et al., 2007). They must
ensure appropriate educational tools, instructional practices, and strategies are
implemented in general education classrooms to support teaching and learning of African
American students.
If it results in a reduction of the number of students placed in special education
and/or improvement in instruction, data-driven decision making techniques may lead to

9
fewer African American students from dropping-out of high school, incarcerations, high
unemployment rates, and lack of preparation for the workforce. According to the Civil
Rights Project (2001), special education is perceived as a form of segregation because
African American students are excluded from general education environments. In a
number of school settings, special education students receive a poor education because of
failed infrastructures within classrooms (Losen & Orfield, 2002). Because of these
inequalities in educational programs, the use of students’ assessment data influence
decisions regarding programs and interventions aimed at improving achievement
outcomes of African American students. Using data to inform and improve classrooms
may eventually ensure that more students have access to postsecondary education
(Chamberlain, 2005). However, whether or not DDDM is being implemented effectively
in educational settings serving African American students is still unknown, much less its
potential outcomes.
In classroom settings, teachers tend to use different kinds of data such as
homework assignments, teacher-made tests, in-class performances, and anecdotal records
to inform decisions about their students (Brunner, Fasca, Heinze, Honey, Light,
Mandinach, & Wexler, 2005; Honey, Brunner, Light, Kim, McDermott, Heinze, Breiter,
& Mandinach, 2002; Light, Wexler, & Heinze, 2004). Oftentimes teachers’ DDDM
techniques are not consistent enough to affect changes in their instructional practices.
Lim (2003) says educators have to address barriers such as access issues, technical
expertise and trainings to encourage teachers’ confidence in using data. For example,
teachers lack of skills and training in knowing how to use data affects their abilities to
make sense of data in terms of long-term trajectories (Confrey & Makar, 2005).

10
Frequently, teachers fail to identify gaps in students’ learning by not skillfully diagnosing
or disaggregating data to minimize stereotyping associated with minority subgroups
(Confrey, Makar, & Kazak, 2004).
According to Bernhardt (2004), few people in both schools and districts are
trained to analyze or gather data to improve instructional practices. Continuous training
of school personnel elevates performance levels of all students. Linking and alignment of
data to appropriate classrooms assignments are essential to students’ performance.
Accurate and timely data use affects meaningful adjustments in teachers’ instructional
programs. Too often administrators and teachers are inundated with meaningless data
reports that provide minimal information regarding students’ performance or progress. In
several instances, teachers do not use appropriate resources. They also lack skills in
interpreting students’ data to inform and improve instructional decisions (Mandinach et
al., 2006). Bernhardt (2004) asserts administrators must allocate adequate time for
teachers to learn how to use data to impact students’ learning outcomes.
The achievement gap between African American and White students significantly
impacts achievement results (Thernstrom & Thernstrom, 2004). The failure in general
education programs contribute to African American students’ poor performance (Reid &
Knight, 2006). For example, inappropriate classroom interventions and strategies
exclude these students from meaningful engagement. Similar to educators in general
education, special education administrators and teachers lack skills in using DDDM
strategies. Many struggle to implement and understand African American students’
Individualized Education Programs (IEPs), high-stake assessments, and districts’
benchmark assessments. The use of different forms of students’ assessment data ensure

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the learning needs of African American students placed in special education classrooms
are addressed.
Research Questions
The proposed qualitative study will examine data-driven decision making
practices in two elementary schools and one urban middle school where a
disproportionate number of African American students are identified as needing special
education. This study seeks to investigate how DDDM is incorporated into teachers’
classroom instructional programs to improve pedagogical practices.
Specifically, this study will address the overarching question:
What role does data-driven decision making play in improving the education of
African American students who may be identified as needing special education?
The following sub-questions will be addressed in this study:
1). How do teachers (general education and special education) use
Individualized Education Program (IEPs), district benchmarks, and state
assessments data, and why?
2). How do principals build the capacity of teachers to use data to improve
instruction, and/or placement?
3). What are the intended and unintended consequences for the use of data for
reducing special education placement?
These questions will be addressed through a qualitative study of teachers in two urban
elementary schools and one middle school.

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Significance of Study
The proposed study strives to understand how teachers use data-driven decision
making practices in their classrooms. Currently, extensive research on the
overrepresentation of African American students in special education exists, however,
minimal research on the use of various kinds of data such as IEPs, high-stake tests, and
benchmark assessments are used by teachers to improve instructional practices. This
study is significant because African American students classified as learning disabled
seldom return to general education settings before leaving the public school system
(Losen, 2002). Upon completion of this study, DDDM will support all teachers’ use of
data in their classrooms. It will also help teachers’ to become knowledgeable, competent,
and well trained educators. Ultimately, understanding the powerful effects of assessment
data will decrease minorities, especially African American students, placement in special
education classrooms.
Limitations of the Study
The current research on special education comes during a time of increased
accountability in public schools for all educators. A major limitation to this study is not
being able to identify “highly-qualified” teachers who are knowledgeable in knowing
how to implement various kinds of students’ assessment data to impact instructional
changes for African American students. Teachers’ limited skills and pedagogical
practices may impede their abilities to discern data appropriateness and differentiate
instructional practices and strategies.
The behaviors of African American students, lack of benchmarks and
standardized assessment data may influence inappropriate teacher actions. These actions

13
may hinder DDDM processes aimed at accessing academic growth. Nonetheless, the goal
is to use DDDM to accelerate African American students’ re-entry in general education
and curtail inappropriate placements in special education.

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CHAPTER 2: LITERATURE REVIEW
The task of education is to make changes in human beings. For mastery in this task, we
need definite and exact knowledge of what changes are made and what ought to be made.
(Edward Thorndike, 1922)
Introduction
To address the overarching research question regarding the role of data-driven
decision making in dissuading the number of African American participants in special
education necessitate an analyses of the literature in the following areas that correspond
with the research questions guiding this study:
• Overrepresentation of African American students in special education
• Federal policies that impact special education
• The pre-referral process and students’ individual education program (IEP)
• Principal support for the use of data
• The intended and unintended consequences for the incorporation of data in
classrooms.
A data-driven decision making process provides educators meaningful data to
inform and improve instructional practices in classrooms. Using data effectively is
perceived as a necessary tool that will hold schools, teachers, and administrators
accountable for educating and minimizing large numbers of African American students
referred and placed in special education environments.
Overrepresentation of African American students in special education
Several studies have documented the overrepresentation of African American
students in special education (Dunn, 1968; Patton, 1998; Valles, 1998; Coutinho &
Oswald, 2000). The study of race/ethnicity disproportionality is influenced by the work

15
of Dunn (1968) who identified a discrepancy in the percentage (60% to 80%) of low
socioeconomic minority children taught in mild mental retardation classes. Dunn’s report
reveals racial/ethnic minority students were overrepresented while others were
underrepresented (Heller, Holtzman & Messick, 1982; Losen & Orfield, 2002; National
Research Council, 2002).
Bollmer, Bethel, Garrison-Mogren, and Brauen (2007) use three measures to
assess racial/ethnic disproportionality in special education at the school-district level.
These three measures are: composition, risk, and risk ratio. For example, composition
addresses the percentage of African American students with disabilities who are
compared to the number of students in total enrollment; risk examines the probability that
students of certain racial/ethnic group will be identified as having a disability and
compares them to a comparison group which establishes a risk ratio. The risk ratio
compares the racial/ethnicity of students in special education to the comparison group
(Bollmer et al., 2007). Researchers preferably use White students as the comparison
group because they are the dominant racial/ethnic group (Coutinho & Oswald, 2000).
For example, the risk ratio could be defined as African American students labeled as
mental retarded (MR) who are compared to students in the comparison group who are
MR.
A team of educational researchers also addresses the issue of overrepresentation
of African American students in specific disabilities in more or less restrictive learning
environments (Skiba, Poloni-Staudinger, Gallini, Simmons, & Feggins-Assiz, 2006).
When compared to European American students, African Americans are overrepresented
in disabilities categories such as MR, emotional disturbance (ED), and multiple

16
disabilities; American Indian/Alaskan Native are overrepresented in learning disabilities
(LD) (National Research Council, NRC, 2002; Oswald, Coutinho, Best, Singh, 1999;
Parrish, 2002). African Americans and Asian/Pacific Islanders have a higher autism
representation. Similarly, Parrish (2002) found that African Americans are
overrepresented in special education across the country in MR and ED. Compared to
European Americans, African Americans are 2.88 times represented in MR and 1.92
times in ED.
Furthermore, Skiba et al.’s (2006) Indiana Disproportionality Project coincides
with previous research regarding the overrepresentation of African American students in
special education (Fierros & Conroy, 2002; OSEP, 2003). These researchers state
African American students with disabilities are significantly “underrepresented” in
general education classrooms and “overrepresented” in more separate environments (p.
419). Specifically, African American students represent 13% of Indiana’s special
education population. These percentages closely resemble the number of students in the
state’s overall population. Data indicate that there are 8.4% of African American
students being educated in general education settings. Of this population, 27% of
students are placed in separate classrooms. Skiba (2006) contends African American
students with disabilities are .71 times as likely to be educated in general education.
They are three times more likely to be taught in classrooms outside of the general
education population 60% or more of the instructional day.
Finally, poverty and race are considered to be key components in the
overrepresentation of African American students in special education (Skiba et al., 2006).
Although poverty influences the disproportionality, other multiple variables such as

17
“non-data-based processes,” eligibility determination, referral process, personnel’s
impressions of the students’ families, and “external pressures for identification and
placement” contribute more to racial disparities in special education (p. 421). However,
findings from the Indiana study are not representative of African American students’
disparities in special education in other states.
Other researchers, Skiba, Poloni-Staudinger, Simmons, Feggins-Azziz, & Chung
(2005) investigate the race-poverty relationship as a channel for understanding special
education disparities among African American students. These authors claim minority
students are exposed to socio-demographic stressors related to poverty such as
impoverished living conditions, school’s readiness, behavioral outcomes, and low
achievement. These stressors are believed to influence special education placement.
Nevertheless, inconsistencies in the race-poverty hypothesis do not substantiate
disproportionality in special education for African American students (Skiba et al., 2005).
Although, other studies state poverty influences specific disability referrals to special
education (Coutinho et al., 2002), many researchers do not agree with the theory that says
“as levels of poverty decrease, minority students are at greater risk for referrals as LD”
(Zang & Katsiyannis, 2002), MR (Oswald, Coutinho, Best, & Nyuyen, 2001), and ED
(Oswald et al., 1999).
In sum, findings suggest there are inconsistencies in what is specifically affecting
the overrepresentation of African American students in special education (Bollmer et al.,
2007). But, prior research has shown that composition, risk, and risk ratio can determine
disproportionality. Additional studies have also shown that African American students
are overrepresented in certain disabilities such as MR and ED (Skiba et al., 2006).

18
This issue of disproportionality can be detrimental to minority students. Schools
lack of success in educating students with disabilities, particularly African American
students have warranted protection from both state and federal laws (IDEA, 1997; NCLB,
2002). These laws specially detail the benefits of using data and ensure achievement for
all students.
What federal laws govern special education teachers’ use of data?
To fully understand data importance in public schools, it is vital to be cognizant
of federal mandates imposed by the No Child Left Behind Act (NCLB) of 2002 and the
Individuals with Disabilities Education Act (IDEA) of 1997 regarding students with
disabilities. NCLB and IDEA compel states and local education agencies (LEA) to
annually measure the performance of students with disabilities along with their peers in
general education. Not only have these two legislations addressed educational concerns
relating to students with disabilities, but they provide explicit guidelines to protect
inappropriate practices associated with educating children of color, specifically, African
American students (IDEA, 1997; NCLB, 2002).
No Child Left Behind
The No Child Left Behind (NCLB) Act of 2002 reauthorized the Elementary and
Secondary Education (ESEA) Act of 1965. Under ESEA, Title I funds support schools
considered economically disadvantaged. Prior to NCLB, ESEA involvement impacted
only those schools receiving Title I funds. The passage of NCLB increased
accountability mandates for schools nationwide. NCLB expects high standards for
students’ performance and teacher qualifications to be set by local and state agencies
(O’Neill, 2004).

19
NCLB stipulates that states must administer annual assessments in reading and
mathematics in grades 2-8. Students’ scores are to be published in school and district
report cards (O’Neill, 2004). The annual assessments and re-examination of fourth and
eighth grade students’ using the National Assessment of Education Progress (NAEP) in
reading and mathematics serve as a follow-up to ensure states’ compliance with NCLB
law.
Schools who fail to make adequate yearly progress (AYP) within two consecutive
years are given support. After a two year-period, if a school continues to underperform,
parents are given an option to transfer their children to higher-performing schools, along
with Title I funds. Three consecutive years of failure require schools to use 20 percent of
Title I funds for Supplemental Educational Services (NCLB, 2002). In addition to
supplemental services, NCLB focuses on using instructional practices that are scientific
and research-based. NCLB advocates building parent-school partnerships. It supports
flexibility in addressing schools’ poverty threshold of 40 percent. NCLB indicates that
50 percent of schools’ Title I funds can be transferred among programs (Bejoian & Reid,
2005).
NCLB complicates the disaggregation of data for special needs students because
specific categories of disabilities are not defined (Bejoian & Reid, 2005). NCLB is
negligent in classifying special education sub-groups. For example, students’
ethnic/racial categories or economic status are not addressed in NCLB legislation. The
legislation does not delineate the “special” classification as it does with students in
general education subgroups (p. 224). Students with disabilities are often grouped in a
broader category.

20
Although the passage of NCLB is highly regarded by legislators and educators, it
is not without criticisms. Paul (2004) asserts numerous researchers express
disappointments in the National Reading Panel’s (NRP) flaws in its Reading First
research (Allington (2002b), Coles (2002), & Garan (2002). Critics scrutinize sampling,
methodology, and findings of NRP as it relates to African American and Latino students
(Paul, 2004). Researchers claim numerous missed-opportunities exist for NCLB’s
policymakers to eradicate the achievement gap that exists “between African American
and Latino students and their White counterparts” (p. 848).
As disclosed in the Civil Rights Project (2001) conducted at Harvard University,
approximately 2.3 million public schools students attend “apartheid” schools
(Frankenburg, Lee, & Orfield, 2003 as cited in Paul, 2004). Attendees are racially
minority students who seldom receive a quality education. Oftentimes, African
Americans and Latinos receive “dis-education” which is “disproportionate
underachievement in comparison to their White counterparts” (Carruthers, 1994, p. 45 as
cited in Paul, 2004). To ensure African Americans as well as students identified with
disabilities receive free schooling, several state and federal laws have been established to
guarantee educational opportunities (NCLB, 2002).
Individuals with Disabilities Education Act
Prior to the passage of Public Law 94-142 (known as the Education for All
Handicapped Children Act) (EAHCA) in 1975, all individuals with disabilities were
protected by Section 504 of the Rehabilitation Act of 1973. This act prohibited any
institution receiving federal funds from discriminating against individuals with
disabilities (Palmaffy, 2001).

21
Numerous lawsuits similar to the Larry P. v. Wilson Riles, (1972) continue to
protect African American students in public school systems. African American students’
liberties and access to educational programs are guaranteed by Section 504 and IDEA.
Although Section 504 has influence, IDEA institutes policy regarding students with
disabilities (Palmaffy, 2001).
The passage of EAHCA (renamed IDEA in 1990) mandates the following
services for all students with disabilities:
• Free appropriate public education (FAPE) in least restrictive environments
(LRE)
• Students with disabilities must have an individualized education program
(IEP) detailing the range of services to be provided and the location
• Mainstreaming of disabled students whenever possible
• Mandates districts to establish and determine procedures to ensure
parental and due process involvement in the development and creation of
IEP and the ability to challenge content of IEP.
IDEA Reauthorizations
Prior to the 1997 IDEA reauthorization, minimal legislations focused on
measuring the performance growth of disabled students. Most addendums to the law
expanded the “population of eligible children or range of services in which they were
entitled, either by extending coverage to younger ages or by adding named disabilities
such as “autism, traumatic brain injury, and attention deficit disorder” (p. 17).
Over the years IDEA reauthorizations (IDEA: 1997, 2000, & 2004) influenced the
nature of services provided to students with disabilities (Smith, 2005). For example, the

22
general education curriculum became accessible to students with IEPs (IDEA, 2004).
IDEA requires states to design their own standards and assessments to address learning
needs of disabled students. For those students exhibiting severe disabilities, states are to
provide alternate assessments. The federal government requires that “states set
performance goals for disabled children and include all students in their testing
programs” (Palmaffy, 2001, p. 18). Educators are to use assessments as a process for
collecting data to make decisions pertaining to students’ academic performance (Salvia,
Ysseldyke, & Bolt, 2007).
Although previous reauthorizations focus on improving services to students with
disabilities, researchers contend major problems continue to exist with assessments
(Reder, 2007). Assessment concerns stem from the federal government not identifying
explicitly what consequences will assist states in monitoring their compliance with the
law. Oftentimes states approach to developing alternate assessments is non-existent
(Reder, 2007). States express concerns with not having the expertise and knowledge-
based to devise and implement alternate assessments. In many instances, states are not
prepared to address accommodations and modifications issues associated with
administering statewide assessments for students with disabilities. Reder (2007) alleges
the invalidation of test scores for special education students sometimes occur because of
educators’ inability to properly use data.
The Individuals with Disabilities Education Act (IDEA) was renamed the
Individual with Disabilities Education Improvement Act (IDEIA) of 2004 (Smith, 2005).
The federal government’s rationale for renaming this act was to ensure students with
disabilities achieve at higher standards by promoting accountability for performance

23
results (Pierangelo & Giuliani, 2007). This new legislation places a high emphasis on
assessments and the processes teachers and administrators use with special education
students (Yell & Drasgow, 2007). IDEIA maintains teachers and administrators “need to
monitor developments in their state’s laws, regulations, and guidelines regarding students
with disabilities” (p. 194).
The Special Education Pre-referral Process
In addition to knowing the laws governing special education, understanding the
pre-referral process and how it works in schools is significant (Carter & Sugai, 1989).
Time and again, African American students are disproportionately referred for special
education services because of ineffective pre-referral procedures. However, to reduce
referrals, awareness of various stages of the eligibility process is crucial (Hosp &
Reschly, 2003).
The pre-referral intervention (PIT) is an un-mandated product of the Individuals
with Disabilities Education Act (IDEA) Amendments of 1997 (Buck, Polloway, Smith-
Thomas, & Cook, 2003). IDEA focuses on improving student outcomes and ensures
preventive programs are provided to deter referrals to special education (Telzrow, 1999).
Buck et al. (2003) asserts pre-referral interventions drew a considerable amount of
attention in the educational arena in the mid-1980s. Although many states mandated
districts to implement, Carter and Sugai (1989) found policies that only required or
recommended the use of pre-referral intervention programs. Their research found
numerous inconsistencies across states and districts. In many instances, concerns
regarding the referral, evaluation, and placement of students in special education were
questionable. For example, researchers indicate that 92% of special education referrals

24
resulted in formal testing. Of the students tested, nearly three-quarters are ultimately
placed in special education settings (Carter & Sugai, 1989).
Lee-Tarver (2006) contends many states, districts, and student intervention teams
use various names such as Student Study Team, Building-Based Student Support Team,
Multidisciplinary Team, and Student Support Team. According to Bursztyn (2007),
states or school districts determine the specific term. Generally, these teams are referred
to as the Child Study Team, Student Success Committee, Teacher Assistance Team,
Instructional Support team, and School-Based Intervention Assistance Team.
Bursztyn (2007) asserts the pre-referral intervention team is a group of
professionals whose goal is to assist the general education teacher with initiating and
implementing research-based strategies and interventions to students who are exhibiting
academic and/or behavior difficulties. The goal is to improve students’ success in the
general education environment and reduce special education referrals.
PIT uses a preventative, problem-solving approach that is team-based, action-
research oriented, and a process that enhances both students and teachers in general
education (Buck et al., 2003). Buck et al.’s (2003) replication of Carter and Sugai
(1989) research addresses several factors. These factors determine variability in PIT
status within states or districts. For example, states can mandate, encourage, or use their
own discretion in implementing pre-referral intervention teams. However, the inclusion
of general education teacher, counselor, and social worker are encouraged. The size of
the team and the involvement of team members can affect the implementation of this
problem-solving approach.

25
Specially, Buck et al. (2003) uses a six-item survey to explore states’
implementation of the pre-referral process. A survey was mailed to 50 state educational
agencies and the District of Columbia to understand the process. Similar to Carter and
Sugai’s research, this study indicates that half of the states (43%) require a PIT process,
while 29% only recommend. These findings parallel Carter & Sugai’s (1989) research.
They indicate 45% of states mandated and 22% recommended PIT. Thirteen states
adopted pre-referral intervention team, four states changed status from required to
recommended, and five states moved from required to mandated. According to Buck et
al. (2003), at the conclusion of study twenty-five states remained unchanged in requiring
a mandated intervention process.
Data indicate progressive changes have transpired since Carter and Sugai’s (1989)
research. For example, many changes include the participation of the school counselor in
pre-referral process, PIT recommendations related to “instructional modifications” rather
than behavior management procedures, and involvement of state-level administrators (p.
358). Implications from this study ensure that at-risk students receive services in general
education before being recommended for special education placement, professional
collaboration among staff members are an ongoing process, and improved efforts to
reduce inappropriate referrals to special education. However, little information is known
about specific types of interventions or services aimed at alleviating the referral process.
The pre-referral intervention process spearheaded the President’s Commission on
Excellence in Special Education (PCESE) Truscott, Cohen, Sams, Sanborn, & Frank
(2005). The commission maintains referrals to special education need to be based on
“responsiveness to intervention rather than psycho-educational test results” (p. 130). The

26
literature says PIT provides support to teachers by collecting information, identifying
concerns, developing and evaluating interventions rather than making recommendations
that allow teachers time to implement and evaluate without outside interventions (e.g.,
Fuchs, Fuchs, & Bahr, 1990; Graden, Casey, & Christenson, 1985; Rosenfield &
Gravois, 1996 as cited in Truscott et al., 2005). Although PIT is available in a number of
school districts, many researchers express concerns with their effectiveness (e.g., Burns
& Symington, 2002; Graden et al., 1985); Gutkin, Henning-Stout, & Piersel, 1988;
McDougal, Clonan, & Martens, 2000; Pugach & Johnson, 1989; Sindelar, Griffin, Smith,
& Watanabe, 1992 as cited in Truscott et al., 2005).
The aforementioned research surveys confirm multiple problems continue to exist
with PIT. For example, remedial teachers non-existence on committee/study teams
complicates or hinders services to students experiencing difficulties in school, limited
team representation fail to address issues relating to home and community, and limited
school-based consensus on PIT goals. Although demands are challenging, only 21% of
teams considered PIT as a process for “decreasing the number of inappropriate testing
and special education referrals” (p. 138). Approximately 10% of teams consider PIT as a
general education function supporting classroom learning and interventions. Researchers
indicate that 74% of respondent consider PIT a student-centered function, rather than
instructional modifications, which results in minimal classroom alterations from teacher.
Interventions are not teacher-implemented. Many classroom interventions require little
pedagogical input from teacher. For example, interventions such as “change child’s seat
(32%) or decrease amount of work (22%)” are regular strategies that have proven to be
unsuccessful in improving achievement (p. 138). Minimal information addressing the

27
effectiveness of PIT using curriculum-based assessments and graphs to monitor students’
behavior exists in literature. Interventions tend to not relate to the multiplicity of referral
problems that impact special education referrals for disadvantage students. Schools lack
research-implemented models that provide specific direction and support to deter
referrals to special education.
IDEA does not establish definite guidelines regarding state department of
educations’ implementation of PIT teams, although, local educational agencies require
schools to develop PIT to prevent the placement of students in special education settings.
Similar to PIT is response to intervention (RTI). The literature specifically identifies RTI
as a problem-solving approach to address students’ difficulties in reading. The next
section focuses on RTI and its purpose.
Response to Intervention
The Response to Intervention (RTI) process is a component of the reauthorization
of the Individuals with Disabilities Education Improvement Act (IDEIA, 2004). RTI is a
scientific research-based alternate intervention method. Educators use reading
achievement data early-on to detect at- risk students in danger of failing in school. RTI is
used as a substitute for the IQ-achievement discrepancy assessments which identifies
students with learning disabilities (Fuchs & Fuchs, 2006).
The Response to Intervention (RTI) model is a constructive safety-net used to
prevent students’ failure in school. RTI uses students’ reading data to support educators
in making informed decisions pertaining to instructional practices and the way students
learn in classrooms. RTI provides teachers strategies that will avert considerable

28
numbers of special education referrals for minority students, especially African
Americans.
Fuchs and Fuchs (2006) assert the first month of school teachers need to use
classroom reading data to identify at-risk students. Normally, in urban schools students
of color experience difficulties on the class’ initial reading assessments (Fuchs, Fuchs, &
Compton, 2004). Teachers use these assessments to compare students’ performance to
that of school or national normative estimates and criterion-referenced performance
measures. Teachers use data to provide ongoing monitoring of students’ performance.
RTI is also designed to improve the teacher’s weekly instructional program. In many
instances, African American students who are non-responsive to traditional instructional
approaches or have demonstrated difficulties on high-stake standardized assessments
need moving to the next tier (Fuchs & Fuchs, 2006). Because RTI permits schools to
establish their own performance benchmarks (criterion-referenced measures) to pinpoint
reading deficits; several different versions exist. Fuchs and Fuchs (2006) also found
several misrepresentations of data results which have resulted in the implementation of
harsher approaches (e.g., special education).
Generally, RTI is comprised of two or four tiers of instruction. However, various
data provide teachers tools to monitor academic challenges African American students
experience in learning to reading (Fuchs, Mock, Morgan, & Young, 2003). However,
leading educational researchers dispute RTI approaches. Some charge that RTI does not
address minority students’ cultural and linguistic differences. Klingner and Edwards
(2006) assert the use of cultural RTI methods tackle growing concerns associated with
educating African American and Latino students. RTI is considered problematic in

29
secondary schools, because of the changing roles of teachers and diagnosticians. Too
often, these individuals lack skills in executing effective reading strategies and techniques
to impact student learning (Mastropieri & Scruggs, 2005).
The caveat to RTI is the implementation process. Districts and schools must
ensure all classroom teachers are trained in knowing how to implement appropriate
strategies and techniques. Although IDEIA allows districts to use approximately 15
percent of their special education budgets to fund early intervention activities for students
demonstrating difficulties in reading, in many instances interventions are not delivered in
a timely manner. Oftentimes, these students (e.g., African Americans) are later classified
as learning disabled (LD) because of their inabilities to master basic fundamental
concepts of reading (Lyon, 1995 as cited in Fuchs & Fuchs, 2006).
RTI assists in determining African American students’ eligibility and special
education interventions. RTI ensures that “close integration of assessment and
intervention” exist prior to the referral process (Bursztyn, 2007). For example, after the
Tier 1 and Tier 2 processes have been exhausted, referrals to special education occur at
Tier 3. The eligibility process ensures that students’ learning difficulties result from a
disability rather than “limited exposure to curriculum; linguistic or cultural differences;
or problems that are primarily the result of visual, hearing, or motor disabilities, mental
retardation, emotional disturbance, or environmental, cultural, or economic
disadvantages” (p. 105) Fuchs and Fuchs (1998) argues the RTI process is more
responsive to learning needs of individual special education students than general
education. It is unclear what role RTI will play in the schools in this study of data driven
decision making. However, RTI merits attention, as teachers use RTI data to monitor

30
African American students’ progress and “make adjustments to ensure individualization”
addresses specific academic deficits (Vaughn & Linan-Thompson, 2003).
How does IDEIA address the use of assessment data to support students’ IEPs?
As noted above, IDEIA governs the policies and procedures regarding the use of
assessment data with students with disabilities (IDEIA, 2006). IDEIA states assessment
data needs to be specific to ensure baseline so that teachers can monitor students’
progress in his or her educational program (Yell, 2006). In classroom settings teachers
use data to monitor all students’ academic progress using formative assessments. The
incorporation of formative assessments such as teacher-made tests, district benchmarks,
and end-of-unit assessments warrant modifications in order for teachers to use data from
these assessments to address academic needs of students placed in special education. The
U.S. Department of Education, (2006) regulations stressed the following:
One of the most important aspects of good teaching is the ability to
determine when a child is learning and then to tailor instruction to meet
the child’s individual needs. Effective teachers use data to make informed
decisions about the effectiveness of a particular instructional strategy or
program. A critical hallmark of appropriate instruction is that data
documenting a child’s progress are systematically collected and analyzed
and that parents are kept informed of the child’s progress. Assessments of
a child’s progress are not bureaucratic, but an essential component of good
instruction (U.S. Department of Education, 2006, p. 684 comments).

IDEIA suggests that continuous collection of meaningful data during the course
of instruction helps teachers ascertain whether African American students are achieving
or meeting the goals defined in IEPs. Teachers can use data to make informed decisions
pertaining to the effectiveness of the students’ instructional program (Yell & Drasgow,
2007). An ongoing collection of students’ assessment data need to drive teachers’

31
instructional programs. Data help teachers monitor African American students’ academic
progress and ensure informed decisions are geared toward improving learning outcomes.
Progress reports are most important in monitoring African American students’
progress in classrooms. IDEIA states progress reports are to be sent every nine weeks,
indicating whether students have advanced forward in meeting academic goals outlined
in their IEP (IDEIA 2006 Regulations). Progress reports build home-school partnership,
promote parental support for students in their homes, and keep parents abreast of
strategies being used at school to “improve and monitor their children’s education”
(IDEIA 2006 Regulations, p. 689).
How does the individualized education plan assist teachers’ use of data?
Yell and Drasgow (2007) argue assessment of students’ learning abilities rely on
three significant steps in the special education process: a knowledgeable team of
individuals dedicated to examining eligibility criteria, goals based on students assessment
data, and continuous assessment and monitoring of students performance to determine
whether learning is occurring.
IDEIA establishes IEP guidelines for schools and teachers to use. Although
IDEIA addresses eligibility procedures, its primary purpose is to ensure individualized
education plans are developed to meet academic challenges of special education students
(Yell & Drasgow, 2007). The IEP requires that educational institutions provide all
students a free appropriate public education (FAPE, Yell & Drasgow, 2000).
After the IEP team has determined students’ eligibility, teachers use assessment
data to plan a program for individual students. Individual student’s assessment data are
used to develop the Present Level of Academic Achievement and Functional

32
Performance (PLAAFP) (Yell & Drasgow, 2007). Teachers use data throughout the
school year to measure and monitor the progress of African American students’ placed in
special education. The use of data plays a major role in supporting teachers’ in
modifying and altering classroom lessons to reinforce learning.
The school leadership ensures data gathered from students IEPs are used
effectively to improve teaching and learning practices. As explained below, the school’s
leadership can support and cultivate a data-rich learning environment where achievement
becomes the ultimate goal.
School leaders build the capacity for teachers to use data
Years ago the use of data played no part in a school leader’s decisions (Earl &
Katz, 2002). School leaders “relied on their tacit knowledge to formulate and execute
plans” (p. 3). However, in the past several years NCLB (2002) has averted leaders from
relying on tacit knowledge to implement instructional changes. Too often leaders are
“caught in the nexus of accountability” (Earl & Fullan, 2003, p. 383). Yet, many leaders
are forced to use data to alleviate embarrassing sanctions, but fail to build a data-driven
culture that improves teaching and learning practices in classrooms (Earl & Katz, 2002).
Because of external pressures, data must not be used as a “surveillance activity,” but as a
mechanism to provide continuous improvements in schools. (Earl, 2000 as cited in Earl
& Katz, 2002, p. 9). Data enable administrators to engage in processes that supports
ongoing analysis, insights, and incorporation of new information and changes in
classroom practices.
Earl and Katz (2002) affirm that if data are to become the fabric of school
improvement, school leaders need to actively engage in data-rich environments that

33
support achievement efforts. Similar to an artist palette, data can serve as a “proxy for
ideas or concepts like learning, or achievement, or satisfaction, or implementation
strategies” (p. 13). Data require leaders to engage in “inquiry habit of mind, become data
literate, and create a culture of inquiry” (p. 13). Leaders do not need to operate as
“technicians organizing and manipulating data” but as artists following a “paint-by-
number picture” to guide and develop an inquiry habit of mind (p. 14). Leaders’ inquiry
operates in an iterative system using feedback loops to aid in making informed decisions
regarding students’ performance. Using multiple lenses, school leaders validate
understanding, investigate ideas, and ask the right questions pertaining to data to improve
instruction even when an explanation seem misleading (Earl & Katz, 2002). However, a
data literate leader uses an array of data to determine what they want to know about a
“phenomenon they are trying to understand” (Earl & Katz, 2002, p. 17). According to
Stiggins’ (1995) most individuals are “statistically illiterate” because they misuse and
misunderstand statistically information.
Earl and Katz (2002) found that school leaders are knowledgeable individuals
who use data to enhance and make informed decisions. Data assist in assessing multiple
educational concepts and programs. For example, Popham (1999) uses standardized test
scores as an example to explain Earl and Katz’s (2002) views regarding how data can
lead to negative and inappropriate assumptions. These researchers posit that high
standardized test scores imply an effective staff, whereas low scores parallel to an
ineffective staff. This may not always be true because schools’ data may not accurately
represent true practices and engagement of staff in improving student achievement.

34
Data allow school leaders’ to present phenomenon through multiple lenses to
stimulate questions and conversations as well as enrich the decision-making process.
Leaders set the tone and build the capacity by changing the mindset of individuals and
creating conditions and climates to engage school in a culture of inquiry. School leaders
provide teachers opportunities to become “inquiry-minded and data literate” (p. 23). In
essence, they facilitate the use of data and engage teachers in thinking about it purposes.
According to Fullan (1999), data provide leaders a framework for incorporating various
practices in schools. Data persuade leaders to investigate their own “tacit knowledge
through various data lenses to refine and even transform tacit knowledge, as it is
converted into explicit knowledge for use in making institutional decisions” (p. 23).
However, to embrace data excursion in their schools, leaders need to allocate beneficial
resources such as time, planning opportunities, supportive and collaborative learning
environments to engage inquiry (Earl & Katz, 2002).
Data models that builds leadership capacity for teachers and administrators
To make certain implementation occur, leaders require knowledge and skills in
knowing how to interpret data to inform decisions. Educational researchers such as City
and Murnane (2006) and Jandris (2001) have created plans to support principals in
collecting, interpreting, and devising strategies to effectively execute the use of data in
learning institutions.
City and Murnane (2006) devised a data plan that supports principals and
teachers. This plan builds their “confidence and skill levels in using data” (p. 4). Using
information from copious urban school studies, these researchers created the Data Wise
Improvement Process. Although the plan is useful, it also warrants buy-in from school

35
leaders. School leaders can use this data plan to expand teachers’ implementation of
data. The authors identify three broad categories and actionable next steps. These
processes are recognized for fostering data use in schools.
The Data Wise Improvement Process is: Prepare -- 1) organize for collaborative
work, 2) build assessment literacy; Inquire -- 3) create data overview; 4) dig into student
data, examine instruction; Act -- 6) develop action plan, 7) plan to assess progress, and 8)
act and assess. These processes set the foundation for data inquiry and encourage
teachers “to learn from student assessment results” (City & Murnane, 2006, p. 4).
On the other hand, Jandris (2001) uses a comprehensive data model to assist
school leaders in building capacity. For example, four approaches explain how data
validate teaching and learning practices. To ensure the use of data in classrooms, leaders
have to provide teachers’ time to practice their craft; facilitate data trainings where
information is usable; allocate resources to support new acquired data knowledge, and
build and foster a school culture that uses quality assessments data. These approaches
support data rich learning environments and help teachers improve their skills.
According to Jandris (2001), four data strategies support implementation. They
are: 1) principals provide a supportive school climate, 2) allocate time for teachers to
analyze assessments, 3) engage teachers in ongoing data trainings, and 4) offer
opportunities to communicate clear expectations regarding student learning.
A supportive school climate exists when a school leader shares a “vision of
excellence” regarding the use of data decisions geared toward improving and monitoring
students’ performance (Jandris, 2001, p. 18). Principals afford teachers opportunities to
work and cultivate their beliefs pertaining to learning. Teachers learn to use student

36
assessment data to scrutinize and perfect their own instructional skills. They require time
to practice and apply knowledge using various assessment tools and approaches. When
sufficient time is not given for teachers to examine and learn from student assessment
data, a “data-driven school culture will not develop” (Peterson, 1995; Bamburg, 1994 as
cited Jandris, 2001).
Engaging teachers and administrators in data discussions focus accurate
compilation, interpretation, and sharing of ongoing assessment data (e.g., test scores,
benchmarks, grades) to the school community. It becomes a common practice. For
example, Jandris (2001) describes The Inquiry Cycle. This reflective and actionable
cycle focuses on interchangeable processes aimed at improving teaching and learning
techniques (Jandris, 2001).
Jandris’ (2001) second strategy focuses on the allocation of time for teachers to
work together, share solutions, conduct discussions and collaborate on common learning
themes, review student work, understand their students’ assessment results and practice
planning better learning opportunities. Data becomes an integral part of the instructional
day and lesson plan. Coupled with time are functional technical systems that provide
immediate and accurate data reports. Principals must provide appropriate and “easy-to-
use” data delivery systems that can provide teachers with necessary information to
influence instruction practices and classroom techniques (Rabinowitz & Ananda, 2001, p.
21).
Building confidence is Jandris’ (2001) third strategy. Engaging teachers in
meaningful training supports the development of data literacy. Teachers’ involvement in
staff developments using students’ assessment data, advance their knowledge and skill

37
levels. Teachers acquire skills in knowing how to analyze and interpret test data “to
make informed decisions” (p. 24). According to Jandris (2001), when teachers better
their data skills and understand how to analyze reports, they are more confident in
aligning classroom content standards to assessments. Providing teachers continuous
trainings and mentoring opportunities support data-driven decision making by informing
instruction and putting into action practices that will enhances “curriculum and
instructional methods” (p. 24).
Jandris’ (2001) fourth strategy examines the principal’s role. The school leader
clarifies expectations regarding students’ performance. He or she identifies which
standards and benchmark assessments are crucial before implementing a data system. The
school’s leader defines how data is collected, analyzed, and disaggregated in classrooms.
He or she is responsible for articulating data information to staff and school community
to ensure everyone understands how standards, instructional objectives, and assessments
correlate to classroom practices.
In sum, leadership is important in combating the “low staff buy-in and culture
barriers” that often exist with data use (Kerr, Marsh, Darilek, & Barney, 2006, p. 496).
School leaders must provide sufficient time for teachers to analyze, disaggregate, and use
data. They must allocate resources and implement professional development to ensure
teachers acquire the necessary data skills to support student achievement efforts at school
site. The following section addresses the intended use of data in school settings.
The Intended use of Data-Driven Decision Making
Effectively using data allow schools to make better decisions regarding students’
educational needs (Bernhardt, 2004). The gathering, disaggregation, and analyses of data

38
permit teachers to engage in school reform efforts that support student learning. Teachers
use data to close the achievement gaps and assess new instructional approaches. Data
prevent schools and teachers from using “random acts of improvement” (p. 28). In
essence, students’ data are used as a tool to eliminate non-important instructional
strategies and techniques, evaluate and eliminate using ineffective educational programs,
and focus on serving specific sub-groups of student learners (Bernhardt, 2004).
Mandinach et al. (2006) contends both school administrators and teachers use
data to understand general patterns of weaknesses associated with students’ performance
in classrooms to ensure adequate allocation of resources. These researchers argue that
students’ assessment data provide a road-map that allows for the planning of meaningful
professional development and interventions targeted at improving student achievement.
Teachers implementation of multiple sources of data (e.g., homework assignments,
teacher-made tests, anecdotal observations) facilitates the learning process and help them
understand students’ thinking (Brunner, Fasca, Heinze, Honey, Light, Mandinach, &
Wexler, 2005 as cited in Mandinach et al., 2006).
Heritage and Yeagley (2005) maintain data exhibit specific characteristics that
enhance teachers’ decision making processes in classrooms. For example, a data culture
is created when teachers and administrators can engage in activities aimed at developing
pedagogical strategies. Essentially, data need to be “aligned, valid and reliable, and
sensitive to students’ differences” (p. 322). For example, it is important that alignment
occurs between what is expected of students and the actual assessment instrument
(Herman, Webb, & Zuniga, 2003; Webb, 1997 as cited in Heritage & Yeagley, 2005).

39
Misalignment in assessments and content standards prevent teachers from accurately
focusing on instructional goals pertinent to students’ learning.
The use of appropriate processes to analyze data support sound instructional
decisions (Heritage & Yeagley, 2005). For example, validity and reliability must be
valued by all teachers to evaluate learning. Validity requires teachers to determine
whether or not their classroom assessments measured the intended academic skills. On
the other hand, reliability focuses on the “consistency of students test scores” (p. 323).
Test scores provide teachers meaningful information that will assist in setting goals,
planning future instructional lessons, and contributing to the advancement of students’
learning outcomes. Teachers and administrators recognize students’ differences and use
data to inform decisions in classrooms. For teachers to successfully use data, accuracy in
students’ performance data are central. Teachers must equip themselves with assessment
skills to gather and interpret results to maximize student achievement (Heritage &
Yeagley, 2005).
Researchers claim DDDM modifies curriculum and instruction concerns that arise
in classrooms (Marsh et al., 2006). Using data becomes an action-oriented process for
many teachers. In the Southwestern Pennsylvania (SWPA) study, researchers found that
data positively supported teachers’ efforts by equipping them with the tools to
disaggregate results and tailor instruction for whole class; provide differentiated
instruction for small groups of students; and customize instruction for individual students
learning needs (Marsh et al., 2006). This study reveals how data improved teachers’
instructional practices and helped them enhance their own content knowledge in curricula
materials.

40
Commonly, schools’ assessment data initiate conversations amongst teachers
about how they can improve teaching and learning strategies in their respective
classrooms (Little, 2006). Data encourages teachers’ to participate in meetings where
meaningful “evidence-based decision making” focuses on improving student
achievement (Little, 2006). DDDM accounts for changes in the workplace discourse and
serves as a mechanism to encourage ongoing learning (Drew & Heritage, 1992;
Engestrom & Middleton as cited in Little, 2006). Although there is value in using data, a
number of challenges hamper use in schools and classrooms. Over the years, high-stake
assessments and benchmarks provide ongoing assessment data to assess ongoing
achievement of all students, particularly those with disabilities.
High-Stakes Assessments
Accountability rests a great deal on assessments and the rationale related to
testing students’ knowledge. In essence, if students are not assessed, there is no way of
knowing whether they have learned content standards established for them (McLaughlin
& Thurlow, 2003).
The reauthorization of Title I of the Elementary and Secondary Education Act
(No Child Left Behind, 2002) supports the earlier passage of the Individual with
Disabilities Education Act. Both legislations prohibit districts and schools from relying
solely on disabled students IEP goals and compliance procedures to account for student
achievement. In spite of changes, “special education reforms have fallen short of
universally improving the achievement outcomes for all students with disabilities”
(Defur, 2002, p. 204). IDEA contends educational progress lagged for students with
disabilities because of low expectations that avert access to the general education

41
curriculum (IDEA, 1997). IDEA asserts disabled students participation in “state
accountability assessment systems” will increase expectations and ensure access to
general education curriculum (p. 204).
Defur’s (2002) two primary assumptions identify the intended consequences for
educational reform efforts and students with disabilities participation in high-stakes
assessments. The first assumption confirms that including student with disabilities in
state educational reform movements institute high expectations, increase access to
general education curriculum, increase participation in state assessment system, and
ultimately enhance student achievement levels (Defur, 2002). The second assumption
asserts assessment data will better individual instructional decisions. In essence, data-
driven decisions will “influence teaching methods and curriculum” (p. 204). The use of
data will enhance educational opportunities and experiences, improve academic and non-
academic success, and the ultimate goal is to translate to post-school achievement
outcomes for students with disabilities (Defur, 2002).
Benchmark Assessments
Unlike high-stake tests, benchmark assessments provide teachers and
administrators’ timely data to improve student achievement (Heritage & Yeagley, 2005).
Assessments are aligned with content standards and administered frequently throughout
the school year. Teachers and administrators use assessment data to modify and guide
instructional practices in classrooms (Heritage & Yeagley, 2005). Assessment data can
“provide practitioners with feedback about student performance and prospective
achievement, together with some guidance to adjust curriculum and instruction” (p. 325).

42
According to Jandris (2001), benchmark assessments are powerful tools that
guide instruction. In 2000, schools in the North Carolina Department of Public
Instruction were profiled. The study revealed that educators’ used data to help close the
achievement gap for disadvantage students (Jandris, 2001). Students were assessed
“periodically for diagnostic purposes and to disaggregate the data” (p. 15). Immediate
and explicit feedback of district-wide tests and state’s end-of-grade test prove to be a
powerful tool in improving student achievement. Although benchmark data are used to
improve and support continuous learning for student, teachers, and principals (Cross,
1998 as cited in Jandris, 2001), numerous consequences and challenges slow the data-
driven decision making process.
Challenges and Unintended Consequences of Data-Driven Decision Making
The unintended consequences and challenges associated with data-driven
decisions making are complex. IDEIA and NCLB continue to require states, districts,
and schools to use data, although many challenges arise in implementation processes and
various categories of learners (IDEIA, 2004; NCLB, 2002). Regardless of students’
disabilities, assessment results count on standardized assessments (NCLB, 2002).
In many situations, teachers focus less time on improving their instructional
practices to accommodate students because of accountability pressures associated with
NCLB legislation. Teachers spend more time devising plans to comply with NCLB
mandates of high stakes tests while curricular goals and classroom activities diminish
(Earl & Katz, 2002). Teachers’ teaching methods become “test-like” and other
meaningful curricular contents are neglected (p. 6). In many instances, schools’ and
teachers’ test scores do not contribute to students’ acceleration in learning, because

43
inappropriate instructional practices are implemented in learning environments.
Normally, teachers replace good teaching with meaningless test preparation that produces
non-sustainable results.
Multiple challenges and issues discourage teachers’ use of data in classrooms
(Wayman, Stringfield, & Yakimowski, 2004). Wayman (2005) argues that schools have
been “data rich” for years, but “information poor” in accessing data to affect instructional
decisions in the classroom. (p. 296). Advances in technological supports have improved,
but remains a constant challenge. Teachers’ unwillingness to use data, in their
classrooms, occur because inconsistencies in receiving or having immediate accessibility
to student outcomes. When data are accessible, teachers struggle with knowing how to
use information to inform instructional decisions. Often data do not guarantee direct
benefits to students (Wayman, 2005). Teachers yearn for ongoing data support. They
want to know how to differentiate strategies to attend to students’ academic difficulties.
As data become prevalent in classrooms, teachers will require time to turn data into
information that will benefit and promote student achievement (Massell, 2001).
Lachat and Smith (2005) assert that districts and schools collect data in three
categories relating to “student demographics, schools, and education programs” (p. 335).
Nonetheless, data are seldom used to impact learning efforts in the classroom (Wayman
& Stringfield, 2003). Frequently, too much data exist which means inadequate
investigation and examination of students’ work (Marzano, 2003). To facilitate and
support teachers’ use of data, professional development plays a significant role although
researchers contend data-driven professional trainings are nonexistent (Newmann, King,
& Young, 2000). However, when offered most data professional developments are

44
conducted on large scale formats that prevent teachers from becoming active learners in
understanding and knowing how to analyze data. Data trainings necessitate a small scale
environment where teachers receive personal and individualized attention to cultivate
data skills (Schmoker, 2004).
Bernhardt (2004) found several barriers affecting middle schools’
implementation of DDDM. For example, copious limitations inhibited teachers’ data use:
• Teachers’ resistance to data because they lack understanding in knowing what
to do or what it says about students in their classrooms
• Teachers inability to differentiate and use data effectively – they struggle with
individualizing instructional strategies
• Teachers and administrators lack training and knowledge in knowing how to
collect, disaggregate, and analyze data to inform instructional practices in
classrooms
• Ineffective instructional tools or report devises (e. g., computers and data
report systems) to provide meaningful feedback
• Teachers beliefs and negative perceptions regarding student data prevent
implementation
• Teachers claim data takes away from instruction and is a waste of time
To ensure teachers analyze, disaggregate, and integrate data effectively in their
classrooms, the aforementioned challenges must be addressed by administrators and
district personnel. These challenges reinforce the need for progressive development in
how to use data to inform instruction practices. Understanding data and its purposes
ensure DDDM becomes a reality in all schools throughout the nation.

45
Conclusion
The success of students with disabilities, particularly African American students
rest solely on educators in public school systems. Several federal legislations such as the
Individuals with Disabilities Education Improvement Act (IDEIA) and the No Child Left
Behind Act have been established to ensure disabled and disadvantaged students receive
a free and appropriate education. Specifically, IDEIA and NCLB require educators to
use data to monitor and assess the performance of students with disabilities. In many
instances, African American students have not fully benefited from these laws because
ineffective instructional processes exist in their learning environments.
Over the past 30 years, African American children have been disproportionately
placed in special education programs. Because of overrepresentation issues, researchers
have used the composition, risk, and risk ratios measurements to determine
disproportionality. In addition to these measurements, limited general education access
and poverty-race issues contribute to African American students’ lack of achievement in
schools.
The Pre-referral Process and Response to Intervention process are designed to
serve as safety nets. Both ensure the incorporation of techniques to improve the
achievement levels of minority students prior to referrals to special education. RTI has
not always benefited African American students because of districts’ slowness in
implementing a model. However, academic supports for students in culturally and
linguistic classrooms deserve much attention. Assistance for students in secondary
schools warrants even more considerations. RTI has not always impacted learning efforts

46
of students because teachers lack pedagogical knowledge in reading to affect positive
classroom changes.
The use of systematic data-driven decision making is central to educators working
in urban schools. To address the learning needs of African American students, including
those with disabilities, educational researchers propose using data-driven decision
making processes to inform instructional practices and programs. Effectively using data
help teachers address discrepancies in students’ learning. Data ensure relevant and
appropriate instructional decisions are put in place in classrooms. However, to ensure
usability, assessment data need to be valid, reliable, and address differences in students’
achievement levels. Data have to be meaningful for teachers. Teachers’ success in
analyzing and disaggregating will ultimately lead to better individualized instructional
programs aimed at addressing various learners’ deficiencies.
Multiple barriers such as having time, ineffective database systems, limited
knowledge and skills to collect, disaggregate, and interpret results, confusion relating to
various kinds of data and what to do with it, inadequate data trainings, and teachers’
negative perceptions and beliefs contribute to non-compliance in schools and classrooms.
Nevertheless, effective school leadership ensures data foster and support learning.
Leaders can use approaches that promote a data-driven school culture. They can involve
school personnel in school improvement efforts to inform instructional reform
movements to aid minority students, particularly African Americans.
This study is dedicated to investigating what data-driven decision making
processes teachers’ use with African American students possibly identified as needing
special education. The goal is to understand how various assessment data are used to

47
influence African American students learning outcomes. Although African American
students identified as learning disabled are the quickest group of students to remediate,
once placed in special education, these students seldom return to the general education
environment. Because African American students represent the highest population of
minority students placed in special education, this study focuses specifically on
investigating best practices in data use to enhance and promote academic achievement.

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CHAPTER THREE: METHODOLOGY

Introduction

This chapter describes the research design, sample, instrumentation, data
collection, and data analysis processes used in this study. The rationale for conducting
this study was to understand the role of data-driven decision making in special and
general education settings. This study examined how teachers used DDDM to inform
instructional practices in classes heavily populated with African American students.
Previous research literature explored DDDM practices in general education classes, but
not enough research existed to explain the effect of DDDM in general and special
education settings that focused specifically on the instructional needs of African
American students. This study highlighted strategies and techniques educators’ employed
to improve achievement outcomes for the African American subgroup.
The study attempted to answer the overarching research question and following
sub-questions:
What role does data-driven decision making play in improving the education of
African American students who may be identified as needing special education?
1). How do teachers (general education and special education) use Individualized
Education Program (IEPs), district benchmarks, and state assessment data, and why?
2). How do principals build the capacity of teachers to use data to improve
instruction, and/or placement?
3). What are the intended and unintended consequences for the use of data for
reducing special education placement?

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Research Design
An intensive, descriptive-analytic qualitative study investigated how data was
used in general and special education environments. The unit of analysis for this study
was school personnel at two elementary schools (second and third grade teachers) and the
staff at one middle school (e.g., principal, RSP, Student Success Team (SST), and SDC
teachers). Using purposeful sampling ensured the selection of “information-rich” cases
(i.e., teachers and schools where they were employed) and explained and elaborated on
the research questions. The primary sources used to gather data were interviews and
document analysis.
According to Merriam (1998), in qualitative studies such as this one, the
researcher was the primary instrument used to gather and analyze data to generate an
understanding about the particular phenomenon that existed. The researcher maximized
opportunities that secured and produced meaningful data. This qualitative study provided
“rich” and “thick” descriptions of the phenomenon studied (p. 29). By using qualitative
methods, this research permitted multiple inquiries and interpretations of teachers’
knowledge pertaining to DDDM and its role in educational settings.
As the researcher, essential skills such as tolerance of ambiguity were used
because no specific procedures or protocols were identified by past researchers regarding
DDDM practices in relation to special education. The interviewer was respectful and
sensitive to participants as data was gathered. The use of good communication skills
facilitated the research process.

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Sample and Population
This research focused specifically on how administrators and teachers used data
to inform instructional decisions and educate African American students. The study was
conducted at Kingview and Longhorn Elementary Schools and Wright Middle School.
The categories of teachers’ interviewed were: Resource Specialist Teachers (RSP),
Special Day Class (SDC), and general education teachers. At the elementary school sites,
second and third grade teachers as well as the schools’ counselors were interviewed.
Oftentimes, counselors served as the school’s SST chairperson. However, at Wright
Middle School, one SDC teacher and one Resource Specialist Teacher were interviewed.
In the study a total of 31 individuals were interviewed ranging from teachers,
administrators, and staff from the district’s special education department. Purposeful
sampling assisted in the selection process of participants. However, at Wright Middle
School teachers’ were selected based on their assignments to special education or their
contact with a large number of African American students. At Kingview and Longhorn
Elementary Schools, teachers in grades two and three volunteered to participate in study
without outside influences from researcher or site administrators. These teacher
volunteers articulated how they used data in their classrooms to impact the academic
performance of African American students.
In addition to teachers in general and special education, three elementary
principals were interviewed. The two elementary school principals at Kingview and
Longhorn were interviewed. I also interviewed Longhorn’s former principal because of
her tenured at the site. She was responsible for the school’s established infrastructure and
viable data-culture. Likewise, the principal at Wright Middle School was interviewed

51
along with other staff members. Additionally, staff from the special education department
such as two school psychologists, three program specialists, special education
administrator, and one senior director were interviewed. Many of these individuals
provided direct services to the aforementioned schools. During the interview process, one
school psychologist declined an interview because she felt CUSD did not provide
consistent DDDM support. Nonetheless, other personnel spoke candidly about how data
was used in the special education department. Personnel from the special education
department provided meaningful insight regarding DDDM and how it was used in their
schools and classroom environments.
Overview of School District
The teachers who participated in this study taught at Kingview and Longhorn
Elementary Schools and Wright Middle School. These teachers and schools were located
in the Central Unified School District (CUSD), in Southern California. The research
study was conducted at Central Unified, a Title I public school system. It provided
educational services for 28,101 students in the following ethnic categories: Hispanics –
74.0, African Americans -- 24.3%, and others – 2%. The majority of these students were
from low socioeconomic backgrounds (CBEDS, 2007). The district participates in a
consolidated school-wide free or reduced lunch program in 24 elementary schools, 8
middle schools, and three comprehensive high schools. The district also has one
continuation high school, two community day schools, an adult school and a Regional
Occupation Program (ROP) (SARC, 2007). In 2008, CUSD entered its third year as a
program improvement district. Yet, elementary students at numerous sites continue to
outperform middle and high school students. For instance, two of CUSD elementary

52
schools were recognized as California Distinguished Schools by the governor’s
educational panel.
In CUSD, African American students continue to be overrepresented in special
education classes. They outnumbered students from the Hispanic ethnic subgroup. The
district’s special education department identified approximately 2,133 students as special
needs in CUSD. However, of this number, 38% of African American students were
categorized as special needs under the Learning Disabled (LD) classification. The
Learning Disabled classification represented the highest disability category for the
enrollment of African American students in special education programs.
Overview of Participating Schools
The student population for CUSD elementary schools range in size from 300 to
1,200 whereas middle schools educate from 600 to 1,400 students. All CUSD middle
schools were identified as year 5 or 6 program improvement, while elementary schools
varied in their program improvement status. CUSD has 12 program improvement
elementary schools and 12 non-program improvement schools. However, two of the
three research schools served African American students in same community. The two
elementary schools fed into Wright Middle School.
These three aforementioned schools were instrumental in the incorporation of
instructional and strategic plans that addressed African American students’ learning
deficits, yet, they all differed vastly in student achievement outcomes. For example, at
Longhorn Elementary School, the student population dropped drastically because of
declined enrollment. The school was program improvement, Year 3. It had an API score
of 612. The past five years the school struggled with the achievement outcomes of its

53
African American students. The school failed to meet its API and AYP because of the
underperformance of various subgroups, especially that of African American students.
Serving students in a community similar to Longhorn was Kingview Elementary
School. Kingview served a student population of 850. It was a non-program
improvement elementary school. Its API score was 706. Over the years, Kingview
managed to meet projected academic goals for all subgroups, including African American
students.
Similar to the elementary schools, the Academic Performance Index (API) score
for Wright Middle School was 642. It API score was well below California’s standard
for achievement. Wright Middle School was program improvement, Year 5. At this site,
school officials struggled endlessly to improve the academic performance of it students,
especially the African American subgroup. Many African American students performed
at a much lower academic level than Hispanic and Samoans peers.
However, at both elementary and middle school settings, African American
students AYP scores in reading and mathematics continued to fall below the Annual
Measurable Objective (AMO), which was 24.4 (Reading) and 26.5 (Mathematics) (CDE,
2008). As the AMO criteria for students’ proficiency levels increased, African American
students’ academic scores in reading and mathematics decreased. Although CUSD’s
Hispanic population increased at both the elementary and middle schools, African
American students continued to underperform on standardized assessments. Similar to
the selection of teachers, purposeful sampling played a major role in the selection of the
elementary and middle school sites. Kingview and Longhorn Elementary sites were
selected based on their high percentage of African American students’. Wright Middle

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School was also selected because of the high percentage of African American students in
special education. Also, the continued tracking of African American students from the
elementary schools to the middle school was a major consideration.
Instrumentation

According to Patton (2002), interviews help interviewers discover information
that cannot be observed. The interviewer comprehended and realized the perspective of
the study’s participants through the utilization of interviews. A negotiation between the
interviewer and interviewee were established to advance the study. The negotiation
ensured respondents’ responses were articulated “comfortably, accurately, and honestly”
(p. 341). The goal was to create a relationship between the interviewer and interviewee,
so that interviewer embraced the interviewees’ world.
Prior to the interviews, the interviewer conducted a prepared series of interview
protocols (Appendices A, B, C). They were field tested by the interviewer to provide
interviewees a platform to tell their stories regarding how they used data to inform
instructional practices and decisions in their classroom environments where African
American students were educated.
The prepared interview questions guided the interview process with principals,
teachers, and district/special education personnel. However, ecological validity played a
significant part in ensuring protocol questions addressed what this study attempted to
measure. For example, interviewees’ specific interactions and behaviors drove the course
of study regarding teachers’ data use with African American students.

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Data Collection Procedures
At Kingview and Longhorn Elementary Schools and Wright Middle School data
were collected from an array of general and special education teachers. Semi-structured
interviews were conducted with all participants. The open-ended questions used by the
interviewer allowed for additional probing. A total of 6 to 8 interviews were conducted at
the aforementioned school sites during a three month period. Initially, two days were
allocated for the interviewer to interview participants at the noted schools. Nonetheless,
time constraints and the availability of the interviewer and interview participants resulted
in revised schedules being created. Subsequently, interviews were conducted over a two
week period at each school.
The purpose of the interviews were to “explore, probe, and ask questions that
elucidated and illuminated” information regarding teachers and district staff’s perceptions
and implementation of data (Patton, 2002, p. 343). An interview guide explored emerging
“worldviews” regarding general and special education teachers’ use of data-driven
decision making exercises to inform instruction (Merriam 1998, p. 74). Differentiated
interview protocols (Appendices A, B, C) were administered to teachers, administrators,
and district/special education personnel.
The interviewer disseminated information to participants through schools’ staff
meetings or grade level meetings. School principals permitted time during the regular
staff meetings for the study to be introduced. During these presentations, the interviewer
identified the purpose and rationale for conducting the study. After staff or grade level
presentations at Kingview and Longhorn Elementary Schools, teachers signed-up using a
pre-made form distributed by the interviewer. The participants’ provided their names,

56
telephone numbers, and e-mail addresses. This information was forward to the
interviewer by the secretaries at both elementary school sites. Yet, at Wright Middle,
participants were selected based on their classroom assignments and interactions with
African American students. However, in the district’s special education department,
participants were selected based on their affiliation and assignments to the three schools.
The interviewer made follow-up telephone calls and e-mails to schedule interviews with
individuals who expressed interest in joining the study.
Three weeks into the school year, the interviewer interviewed the principal of
Kingview Elementary. Interviews for other staff members occurred in the proceeding
months. Thereafter, scheduled interviews and meetings followed for Longhorn
Elementary and Wright Middle Schools participants. Interviews with district/special and
general education personnel occurred in the third month of study. The special education
personnel were interviewed either at the school site or in the district office at the close of
the work day.
Interview Norms
According to Taylor and Bogdan, (1984 as cited in Merriam, 1998), the
interviewer addressed five basic issues before proceeding with interviews. Prior to
interviews, the interviewer shared the following information: the rationale for conducting
study, the assurance of protection of participants through the pseudonyms, indicated who
had jurisdiction over the study’s content, communicate whether or not payment was
involved, and the logistics regarding appropriate time, place, and number of interviews
scheduled. The interviewer’s responsibility was to ensure that a respectful,
nonjudgmental, and nonthreatening environment existed before starting interviews. In

57
essence, the interviewer made the participants comfortable and ensured reliability and
validity of the gathered data regarding African American students were not jeopardized.
Interview Log
An interview log was kept. The interviewer recorded all respondents’ responses
throughout the study. Prior to the interviews, the interviewer prepared the protocols by
writing the participants names, dates, schools, and grade levels on documents.
Information gathered from these protocols was coded in respect to categories and themes
constructed to support study.
During the interview, the interviewer took descriptive notes and used a tape
recorder to record interviewees’ responses. These processes assisted in the compilation of
raw data. Taking notes and recording interviews ensured the preservation of gathered
information for later analysis. Each participant participated in a 30 to 45 minute
interview. However, there were several interviews that lasted well over an hour.
Throughout the interviews, the interviewer wrote additional notes pertaining to DDDM
practices. On three occasions, the interviewer was asked to turn the recorder off because
two general education teachers and one special education teacher elaborated or provided
additional insights regarding the implementation of data at their schools. These
individuals were uncomfortable with recording the information. In addition,
accommodating and allowing teachers to provide their own reflections allowed the
interviewer an opportunity to monitor and analyze the data collection process to make
certain respondents answered the research questions as dictated. Verbatim transcriptions
of recorded interviews were used for analysis (Merriam, 1998). During the interview
process, ongoing assessment of data took into consideration participants’ moods,

58
behaviors, time, and purpose for participation to ensure the accuracy of the information
communicated and gathered.
After interviews had been conducted, the interviews were transcribed. Eleven of
the thirty-one interviews were transcribed by an outside source. The interviewer
transcribed nineteen interviews. Once the interviews were completed, the interviewer
manually colored-coded transcriptions. The interviewer analyzed respondents’ data from
interviews and cut and pasted responses that addressed the research questions.
Respondents’ statements were charted and posted. The interviewer identified major over-
arching themes and several sub-themes for each research question. Responses that
continued or repeated themselves under the various categories were identified and used in
the analysis section of the study.
Document Analysis
An analysis of “material data” assisted interviewer in understanding teachers’ and
principals’ commitments regarding the use of data as a mechanism to inform instructional
decisions (Patton 2002, p. 293). Reviewing data sources such as the number of students’
IEPs goals (e.g., omission of students’ names), benchmark assessments, teacher-made
tests, unit assessments, class or school-wide assessment profiles, and standardized
assessments assisted in evaluating classrooms approaches prior to the study. Throughout
the interview process principals and teachers candidly shared their data binders,
spreadsheets, and profile sheets regarding how they recorded and used data. Of the three
school principals, the principal at Wright Middle shared a data binder that thoroughly
documented the ongoing use of students’ assessment data. Within the binder was meeting
agendas, the breakdown of students’ data, and teachers’ actions plans for implementation.

59
However, in the general and special education settings teachers produced assessment
folders, binders, or spreadsheets that documented their use of data.
Data Analysis
Data analysis was an emergent and simultaneous process that correlated with data
collection. The interviewer made sense of the collected data through an in-depth analysis
of information (Merriam, 1998). Data analysis required the frequent review, audit and
refinement of data. According to Merriam (1998), the interviewer “read, reread the data,
making notes in the margins to comment on the data” to ensure ongoing reflections or
comments regarding ideas, concepts, hunches, and themes gathered from interviews were
captured (p. 161). Daily reflections and analysis of data supported the interviewer’s next
steps. The interview processed was successful, because immediately following
interview, the interviewer checked recorder for recording purposes, checked batteries,
made appropriate copies of protocols, etc. The interviewer implemented data analysis
steps identified by Merriam (1998):
• Read and transcribed field notes or comments
• Jot down notes and made comments in margins of interview protocols
regarding findings
• Asked questions and made notes regarding various interview comments
pertaining to data and African American students
• Grouped notes and comments that related to data and African American
students
• Continued ongoing transcription of field notes and document analysis

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• Compared commonalities, classifications, recurring regularities or patterns
identified in interviews
• Name categories – reflective of DDDM terms and concepts
In addition to the above steps, data from interviews were analyzed and coded
manually by interviewer. The interview did not use HypeResearch or Atlas.ti. The
interviewer manually coded interview contents in various categories representing
respondents’ answers to research questions.
The trends, commonalities, and differences across teachers and sites were
analyzed to make a broader statement regarding teachers’ DDDM processes. A variety of
data warranted the triangulation of study findings. Charts were used to aid in
comprehending central themes and concepts.
Ethical Considerations
To ensure the ethical appropriateness of this study, obtaining permission from
participants and a CUSD’s cabinet-level administrator were required. A cabinet level
administrator granted permission to conduct and investigate DDDM in Central’s
elementary schools and one middle school.
The interviewer used appropriate steps to protect respondents’ confidentiality and
identities. However, the next step was obtained through the Institutional Review Board
(IRB) from the University of Southern California.
Limitations to Study
The willingness of teachers’ participation in this study was compounded by
multiple instructional directions from site administrator and the Special Education
Department regarding student learning discrepancies. Special education teachers’

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interactions and dialogues with the general education teachers circumvented other issues
and hindered compilation of accurate data if inclusion in the schools’ educational
program was not the norm.
The interviewer was deeply concerned with the disproportionate number of
African American children in special education programs. However, the interview
respected and validated all participants’ responses and remained professional and
nonjudgmental throughout the interview process. It was imperative that the interviewer
own personal biases did not impede the collection of data and prevent the collection of
insufficient information regarding African American students in the general and special
education setting.

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CHAPTER FOUR: DATA ANALYSIS AND INTERPRETATION OF FINDINGS
Introduction
The reauthorization of the Elementary and Secondary Education Act of 2002,
recognized as the federal government’s No Child Left Behind (NCLB) has placed a
significant amount of pressure on public schools across the nation to measure the
academic growth of all student subgroups. The NCLB legislation holds schools’
accountable for the academic performance of students regardless of ethnicity, gender, or
learning capabilities. Educators are required to use data as a tool to effectively plan an
instructional program that ensures student achievement. School officials must monitor
the academic progress of all students to make sure their mastery of grade level standards
have been accomplished. Although data use is being implemented in schools and
classrooms across the nation, the achievement gap between minority students and their
White counterparts continue to widen. Using assessment data to yield academic results
are major challenges for educators, especially for those individuals educating African
American students residing in urban communities.
This chapter is an analysis and interpretation of the role of data, how various
forms of data are used to improve the achievement levels of African American students,
the principals’ role in building the leadership capacity to ensure a data literate
environment exists, and the intended and unintended outcomes of data.
In many instances, assessment data indicate that African American students’
failure to achieve can be contributed to outside influences that impede or hinder their
success in school. This research focuses on how data advance the learning outcomes of
African American students in general and special education. The data collected in this

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qualitative study also alludes to the fact that environmental or societal factors in African
American students’ lives are known to hamper the effectiveness of data. In essence, if
African American students’ issues overwhelm the classroom environment, data become
non-essential. Too often outside factors make it difficult for teachers to use data
efficiently to deliver a comprehensive instructional program to progress the achievement
of African American students.
Research Questions and Central Themes
This chapter seeks to discern how data are used to improve the learning outcomes
of African American students, particularly those in special education, several schools in
Central Unified School District. This chapter is organized to correspond with the research
questions. The major sections and sub-themes follow:
Research Questions:
1. What role does data-driven decision making play in improving the education
of African American students who may be identified as needing special education?
The role of Data Use to support African American Students
1. Instructional Planning
2. Differentiated Instruction
3. Student Study Team and Response to Intervention
4. Professional Development
Sub Questions and Over-Arching Themes
2. How do teachers (general and special education) use Individualized Education
Program (IEPs), district benchmarks, and state assessments data and why?

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Use of Assessment Data
1. California Standards Tests Data (CST)
2. Benchmark Data
3. Teacher-made Data
4. Informal Data
5. General Education vs. Special Education Teachers use of data
3. How do principals build the capacity of teachers to use data to improve
instruction, and/or placement?
Instructional Supports
1. Time
2. Collaboration
3. Resources
4. What are the intended and unintended consequences for the use of data for
reducing special education placement?
Effects of Data Use for the Education of African American Students
1. Intended Outcomes
2. Unintended Outcomes
Prior to addressing these themes, it is important to understand CUSD’s student
demographics. A change in the African American students’ population and continued
under-achievement illuminates the significance of data. Data have become increasingly
necessary in urban learning environments where African American students are being
educated. This next section describes how CUSD’s student population has changed
considerably over the years.

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Overview of Student Demographics
In recent years the demographics of Central Unified School District has changed
significantly from African American to Hispanic students. Although the Hispanic
students represent 65% and African American students represent 30% of the population,
African American students continue to outnumber Hispanics in receiving special
education services. Of the 30% of African American students enrolled in the district,
65% are housed in special education classes classified as learning disabled. However,
African American students’ poor attendance has made it difficult for teachers to use data
to plan a prescriptive instructional plan. Many students lack the stability and foundation
in their home environments and/or the necessary parental support to help them achieve in
school. At any number of CUSD school sites, it not uncommon for 50% of the African
American student population to be wards of the court system. Frequently, scores of
African American students reside in either foster homes and/or group homes where
minimal academic support is provided by home or staff officials. The family structure as
well as the environmental and social surroundings of African American students have
deteriorated substantially from years past in the Central community. The decline in the
family structure has greatly impacted the achievement of African American students in
local schools and classrooms.
In several instances, African American families and outside environmental
factors have contributed considerably to students’ achievement outcomes. To advance the
education of African American students, the use of data has become increasingly
indispensable for teachers in urban classrooms. CUSD educators in various
administrative or classroom positions articulated that data influenced their instructional

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decisions significantly. Teachers and principals felt data not only influenced the learning
outcomes of African American students, but benefited other student subgroups such as
Hispanics, Samoans, and Caucasians.
The next section illustrates how educators use data to support the education of
African American students in the learning environment.
The Role of Data Use to Support African American Students
Data play an important role in supporting the achievement efforts of African
American students in CUSD. During the course of the interview process, numerous
educators shared so eloquently their views regarding the aforementioned themes and the
fate of the African American student. Oftentimes, these themes generated much
conversation that dealt with how to use data to improve the academic success of African
American students and reduce special education referrals.
Teachers as well as administrators in both general and special education clearly
communicated and understood the importance of using data to inform instructional
decisions. They said that data was instrumental in driving instruction in teachers’
classrooms. Data hold educators accountable for student learning, measure the academic
progress or lack of progress of students, and make predictions regarding students’
progress. Teachers also commented on being able to use data to help them recognize the
academic areas that students may need additional support whether it is offering extra
teacher assistance and/or more resources. Data are recognized as instructional tools that
assist teachers in planning goals and objectives for students who may have an
Individualized Education Plan (IEP). Ongoing assessment data assist teachers’ in
developing weekly grade level or individual lesson plans. Administrators use this

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ongoing assessment data to monitor the continuous progress of African American
students and supervise the implementation of pedagogical practices in the classrooms.
For example, one administrator stated:
Decisions are based on the data and I can see that it is coming all the way from
the board of education. Data are the vehicle being used by the superintendent to
share a new vision for the district, which is geared around data-driven instruction.
I think that is one of the reasons why we are trying to use the data to change
classrooms and change instructional practices in classrooms. I can see that as a
major focus. It seems to be the focus that is discussed at various meetings. It is
definitely discussed at the meetings among district administrators. So I would say
data-driven decision making is really at the forefront in Central, even though they
do not necessarily use those terms. However, you can tell with the information
that is distributed to everybody that they are definitely talking about data-driven
decision making.

Central’s educators continue to use data to advance the academic achievement
efforts of African American students regardless of the students’ circumstances. In
several instances, teachers, principals, and district personnel identified four major areas
where the use of data influenced the education of African American students. This
section describes the role of data in relation to instructional planning, differentiated
instruction, Student Success Team (SST), Response to Intervention (RTI), and
professional development. These instructional supports influenced the learning outcomes
of all students in CUSD, but are especially targeted at elevating the proficiency levels of
African American students who repeatedly fail to meet achievement goals.
Instructional Planning
Each year experienced principals at the two elementary schools and one middle
school conduct similar goal setting meetings using California Standards Tests (CST) data.
These initial meeting places a high emphasis on the use and analysis of students’
assessment data at the onset. During these meetings, staff analyzes and disaggregates the

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data received from standardized tests administered in the spring. The primary goal is to
compare the federal government’s Adequate Yearly Progress (AYP) and the Academic
Performance Index (API) data to that of other schools, both in the state and district. Yet,
the utmost goal is to inform instructional decisions for the upcoming school year. Along
with staff, administrators review a range of categories such as the demographics, genders,
and specific student subgroups, etc. For the first three days of the school year this is an
expected and critical planning process that ensures both principals and teachers engage in
conversations regarding CST data. In all schools, this summative CST data are reviewed
over a series of years to identify and acknowledge patterns and trends in students’
learning. These meetings generate conversations about school wide and grade level
goals/plans and how student data are to be used to promote the achievement of specific
subgroups, especially African American students. One principal said, “Teacher talks
allow grade levels to script and chart goals and their instructional plans for the new
school year.” These instructional plans are based on student data. For example, teachers
in grade three review the data from grade two to determine how well students performed
on specific standards. They then create grade level plans that take into consideration
students’ strengthens and weaknesses from the previous school year and their new plan of
action to improve student achievement.
The planning process for the middle school is similar to that of the elementary
schools in relation to African American students’ struggles in school. However, middle
school personnel review CST data, but in retrospect, they use the data to help them
understand what is going on with students who are capable of performing well in school,
but do not. The middle school principal along with his staff spends a lot more time

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addressing African American students’ personal and home situations as a precursor to the
data, although this is also addressed in the elementary schools. Oftentimes, low-income
African American students’ environmental factors make it difficult for them to achieve in
school. For example, middle school educators express the importance of addressing the
“whole child,” as said by a school counselor. Middle school educators believe that if
they do not understand what obstacles are preventing African American students from
achieving, data become non-essential. In essence, the data of each African American
student is examined in relation to the kinds of extended supports they receive outside of
school. The principal believes that goal setting is extremely critical, especially for the
African American subgroup because they lag behind their peers, the English Language
Learners or Hispanics. Although there are major hurdles, the principal continues to
establish standards and guidelines to support teachers and all students. These standards
and guidelines ensure that every adult individual on the campus understands what is
expected as it pertains to African American students. The principal stated:
The students here and the majority of the population are socially-disadvantaged.
Academically, the school is a program improvement -- Year 5. As a result, we
have determined that our primary focus is to be able to change our culture in
terms of providing a school where students feel good about coming, and where
students learn to believe in themselves, and learn to overcome any obstacles that
come before them.
Our goal this year is to move at least 10% of our students to the proficient
level and I would say more. In addition to students that already proficient, so that
is our goal this year. And, I know that will not meet the federal NCLB – Annual
Measurable Objective (AMO) or AYP, and I am well aware of that. But our
philosophy is that we have to grow incrementally and just fill in some gaps in
students learning before we even can think about reaching 45% proficient.
Presently, we are at 20% in English Language Arts and we are 15% in Math, in
terms, of AYP. So our primary goal is to grow students academically. Success to
us would be significant growth and if we can sustain that over a period of time.

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To continue to support instruction and improve the learning efforts of all students
as well as African American students in the all classrooms, it is important for principals
to monitor instruction daily. Principals said they conduct daily classroom observations for
at least two hours a day to support and supervise teaching and learning along with the use
of data. Two of the three principals organized ongoing data conferences with teachers to
encourage data use and strengthen their schools’ instructional programs. One principal
explained:
I meet with teachers in their data conferences where at the beginning of each
month we discuss grade level content area and design common assessments based
on the expected standards that should be covered throughout the school year.
They discuss the pacing of those standards – and that is done on a monthly basis.
At the end of the month, students are assessed and we have a conversation about
how students are doing based on the data. That is the check-point regarding
students who are not learning and we will have a conversation about what are our
next steps to better their progress. And we have various goals for each subgroup
of students based on data results.

As teachers and administrators become data literate, the focus on instruction and
the use of data to inform instructional decisions have grown to be necessary in the
classroom environment. In the elementary schools data use is an ongoing process in
improving instruction. However, it is not without challenges. At both elementary sites,
principals have sustained a permanent teaching force that continues to perfect their skills
in using and analyzing data to enhance instruction. This does not hold true for the middle
school. For example, the special education teachers interviewed were knowledgeable in
using African American students’ assessment data to inform and transform teaching
practices, but lacked stability in their department. Sustaining a permanent teaching staff
in special education is not always consistent at the middle school. Prior to the interviews,
the school lost two teachers who had been trained in using data in the classroom with

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students. Because of NCLB highly qualified requirements, these teachers were relieved
of their duties. This has resulted in African American students being without a permanent
and capable teacher. The secondary principal said, “African American special education
students classified as ‘learning disabled’ who have the potential to ‘catch-up with their
peers’ if they had a permanent teacher, are now educated by long-term substitute
teachers.” These teachers lacked the pedagogical skills to affect instructional changes or
the knowledge to use data skillfully to transform instructional practices. This presents a
challenge for both teachers and students because students lack consistent instruction. As
noted by the secondary principal, “Maintaining a capable teaching staff is problematic
because teachers do not always meet NCLB highly qualified requirements that now apply
to special education teachers.” The principal also exclaimed:
At times it is difficult to impact the education of African American students that
have been placed in special education, especially those classified as learning
disabled, because of the school’s inability to retain teachers. Once we get special
education teachers on board and they become trained in interpreting and
understanding how to use data, we lose them because they are not considered
highly qualified. Oftentimes, they are pulled by human resources and we are sent
a substitute to deliver instruction. Again, this puts us further behind in improving
students’ education. A substitute teacher cannot do the job of a trained teacher!
In the past few years, I have lost several good teachers who were data literate and
really made a difference with students, but because of NCLB, I lost them.

Regardless of the shortage of highly qualified special education teachers in the
middle schools, teachers in all settings are expected to use data and differentiate
classroom lessons. The following section describes how data are used to differentiate
instructional lessons to progress the achievement levels of African American students
who are destined to fail if instruction is not meaningful.

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Differentiated Instruction
Principals and teachers used CST assessment data to design and support
instruction. Several classes use differentiated instruction to address and accommodate
the multiple academic levels that exist among African American students. For example,
during English Language Development (ELD) rotation for English Language Learners
(ELLs) in the elementary schools, Reading/Language Arts classes for African American
children are grouped. Within the heterogeneous groupings, African American students
are again re-grouped homogenously based on their proficient levels in English Language
Arts such as Far Below Basic (FBB), Below Basic (BB), Basic (B), Proficient (P), or
Advanced (A). In any one class, four-to-five groupings may exist. These groups of
African American students work with the intervention teacher on a daily basis to learn
skills that hinders their achievement. The intervention teacher differentiates African
American students’ lessons based on individual diagnostic assessment data. The
intervention teacher may also compile and gather data from assessments administered by
the students’ regular classroom teacher. The teacher may use data from Open Court
reading assessments, reading fluency, Fry Oral, or other assessment instruments to
develop students academically. In many instances, the intervention teacher (who is an
experienced reading teacher) may administer his/her own assessments. The intervention
teacher uses data to re-teach or modify lessons for African American students.
On numerous occasions, data use and differentiated instruction are topics that
engage discussion at every site. These topics keep the data dialogue fresh and allow for
ongoing interaction among teachers and principals regarding appropriate instructional
strategies as well as modifications and adjustments of lessons that focus on African

73
American students’ academic needs. A month or two within the school year, the teacher
and/or intervention teachers at the elementary sites use students’ assessment data and
other forms of data to make instructional changes or decisions for African American
students. The intervention teacher or regular classroom teachers are provided
opportunities to design an instructional program that addresses African American
learning deficiencies. Oftentimes, African American students progress and do better in
school when teachers use data as an instructional tool to provide small group support.
Classroom documents revealed that students perform at grade level or close to when
differentiated classroom lessons focused on their individual academic challenges. In
these small groupings, African American students have opportunities to reflect on
challenging standards and/or reading difficulties. They learn strategies and skills that
help them perform better in the classroom. Many times the intervention teacher uses
data from various assessments to individualize students’ reading program. A variety of
instructional resources or supplementary materials exist in these classrooms to address
the range of reading and learning levels. According to one teacher:
We use the California Standards Tests results to initially help place students in
these groups. Teachers also give their own diagnostic assessment to the students
to further inform what is going to happen within their classrooms. They use
fluency assessments from Open Court and from teacher-made assessments in the
classroom. We use this information consistently. We make judgments based on
students’ abilities so they can be placed in the appropriate reading groups during
the language arts period.

To improve instruction in the general education environment, it is important that
principals help teachers learn and understand how to interpret data. At the end of the
year, principals have used African American students’ assessment data and teachers’
results to reorganize and restructure in order to strengthen various grade levels and

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differentiate their schools’ instructional program. Sometimes this means a movement or
reassignment of teachers from one grade level to another. This is problematic for
teachers. At one elementary school site this process occurred and teachers did not
understand why the changes were being implemented. The changes or transfers from one
grade level to another was perceived as a form of punishment and reprimand because of
poor test scores. Grade level changes were not perceived as a means to improve student
achievement. An elementary teacher said:
Yes, she made grade level changes. She saw that on one grade level teachers
connected. They seemed to have gotten the data information, and seemed to
know how to differentiate lessons. So she was grouping together or moving
people to build and strengthen so expertise would be spaced out on every grade
level. That way, those teachers who were weak with data or weak with
differentiating instruction would have a strong teacher on the grade level to pull
the students and improve test results.

In addition to the instructional changes or differentiated instruction processes,
numerous African American students continue to struggle academically. To rectify and
address the multiple learning difficulties African American students demonstrate, many
schools use the SST process to talk about challenges that arise in the classroom. The next
section will address how data works with the Student Study Team (SST) and Response to
Intervention (RTI).
Student Study Team and Response to Intervention

The Student Study Team process is a vital instructional support component. It
supports classroom teachers and addresses the learning needs of students who encounter
academic difficulties in the classroom. The SST also addresses students excelling in
class and who requires advance academic support. However, a special education
program specialist disclosed:

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The SST process could be better. In the schools where they are working, there is
good record keeping of SST meetings and there is a chair that coordinates all the
meetings. Regular meetings are held to discuss treatment plans to document what
progresses are being made and what is working and not working. It is not only
one person making recommendations, it is a team that uses research-based
approaches that are working and those that are not. The parents are involved so
they know what is going on. However, at schools where the SST is not working,
the lack of follow-through, in terms, of referral to the psychologist are not
consistent. Too often, teachers attend SST meetings and continually see the same
non-effective process. Normally, they go back to their classrooms where nothing
is different for the student. We need to have school administrators have
documentations (data) to prove that various methods are being used to support
students and limit the referrals of African American students.

Although the SST process is in place in all district schools and data are used to
make referrals, there appears to be inconsistencies in the implementation at different
sites. One program specialist said, “The SST varies from site-to-site because part of the
problem is that there is no set procedure or universal manual. The effectiveness of the
SST depends on team members and how much emphasis they place on supporting
classroom teachers when they make a referral.” As previously mentioned, in some
schools the SST works very well and in some schools it is “almost non-existent or does
not function at all,” as reverberated by a school psychologist.
However, a special education administrator stated, “The process is there but the
interim period between the first and the second meeting is where the break down
happens.” In some cases, the SST does not work because principals and teachers want to
expedite the process. For example, if there is an African American student they want out
of their school and/or class because of behavioral problems, meetings and paperwork are
pushed much quicker. Often the SST process is not given an opportunity to work
because teachers fail to implement appropriate strategies, modifications, and

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interventions that were discussed in the meetings. However, a school psychologist
comments on the SST process at the elementary sites. He said:
The SST process itself is okay but once the decision is made, which should be
made to give the student support and interventions. That does not happen. And if
given, it is not given the amount of time that is needed for the interventions to
kind of kick-in. It needs to be at least 10 to 12 weeks. I am telling you that
students go back to the SST within six weeks. That was not enough time for
anything to happen. You have attendance, you have school days off, and they
(principals and teachers) are ready to take them back to SST and have them
placed in special education.

According to a district psychologist, “When engaging in the SST process,
teachers use CST data, benchmark assessments, and teacher-made data to make
referrals.” At times African American students’ assessment data are distorted and used
inappropriately. For example, “Students do not come to the SST process unless the team
has already decided they are going to track for special education,” as communicated by
one school psychologist. Frequently, major behavioral challenges demonstrated by
African American students in the classroom result in special education referrals. A lot of
times a learning problem may not exist, but because social and environmental factors are
rampant and are evident in classrooms, African American students’ behaviors prevent
them from succeeding in the classroom environment, “the use of data becomes null and
void,” according to a school psychologist. However, there are teachers who use African
American students’ assessment data in SST and IEP meetings. They share how well
students are progressing in the classroom environment. But, a lot more teachers in
general education use the assessment data of African American students to refer them for
special education services because CUSD does not have a viable Response to
Intervention (RTI) program established in its schools or other interventions are slow in

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coming. As noted by several psychologists, but stated by one, “Because teachers are
frustrated and lack classroom supports, the ‘quick fix’ is to use data inappropriately to get
students out of their classrooms.” The non-use or the inappropriate use of data frequently
results in poor instructional practices and non-delivery of effective lessons for African
American students. However, a special education administrator exclaimed:
Truly looking at RTI is significant. If RTI is practiced in its authenticity, I think
we will see authentic referrals based truly on student needs, not on the mindset
that exists regarding, ‘I can’t control this child, so I’m going to get rid of this
child.’ Response to Intervention is going to make a bigger impact than the SST
process. It should move the SST process off the table because the SST process
has become a track for special education instead of a true response to intervention.

To ensure the efficient use of data and to provide for continuous monitoring of
African American students, instructional leaders are engaging teachers in ongoing
professional development to support instruction in the classroom. The following section
discusses how the use of data and professional development serves as a mechanism to
improve the delivery of instruction in the classroom.
Professional Development
Professional development is provided throughout the school year for general and
special education teachers, administrators, and counselors to improve instructional
practices and develop pedagogical skills. These ongoing trainings equip adult individuals
with instructional tools to help African American students excel in school. For
administrators, trainings such as AB 75 and AB 430 are offered throughout the school
year in a seven-week series at the Los Angeles County Office of Education (LACOE) to
aid them in becoming better school leaders. One practicum, in particular, is dedicated to
training administrators to learn skillfully how to use students’ assessment data to inform

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instructional changes in the classroom. Administrators are taught how to access and
disseminate information to the school’s staff and community. They also learn how to use
data to affect the achievement of all students’ subgroups at their school sites. In addition
to professional development from LACOE, several CUSD district administrators and
principals traveled to Harvard University and participated in UCLA Principals’ Institutes.
These institutes promoted school leaders’ data literacy. They also allowed for immediate
transformation and implementation of data in their schools.
Elementary teachers participated in AB 472 trainings. These trainings place a
strong emphasis on using data and the teaching of reading. However, math trainings are
also ongoing and teachers learn to interpret and analyze data that result in re-teaching, re-
adjusting, and modifying of instructional practices to sustain learning. Central’s
implementation of a new math program is a lot more data focused than the current
reading program. The math professional developments aid teachers in knowing how to
use students’ assessment data to conduct an item-analysis and identify specific math
standards that will progress student learning. These ongoing professional development
activities help teachers learn how to write better lesson plans and build their content
knowledge. Teachers learn how to “pin-point students” academic deficits in mathematics
which are known to affect learning outcomes,” as stated by an elementary teacher. One
second grade teacher stated, “In several trainings, we learn how to use data to develop a
prescriptive plan of action to support learning in our respective classrooms and inform
instruction.”
The Curriculum and Instructional Improvement Department provides ongoing
professional development in mathematics that focus on data. The professional

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development ensures that teachers are data literate. They assist teachers in being able to
use data effectively to facilitate teacher results as well as advance the learning outcomes
of students. One second grade teacher stated, “Math professional development appears to
be a lot more ‘data-focus’ than reading. They are ‘teacher-friendly’ and place a strong
emphasis on teachers learning how to interpret and use students’ data to inform
classroom lessons.”
Despite the fact that the district’s curriculum department provides teachers with
ongoing professional development, these trainings may not be data-related or aligned to
the core curricula or classroom standards. For example, special education teachers have
begun to participate in professional development that perfect their skills in writing IEPs,
establishing IEPs goals and objectives as well as determining whether or not appropriate
measures are being implemented within their classrooms. However, in one such training,
special education teachers learned how to analyze CST and the California Modified
Assessment (CMA) data. Ongoing professional development is new for special education
teachers because they were not truly held accountable for student learning. Special
education teachers are now receiving trainings that focus on the incorporation and
development of classroom strategies and techniques that meet the multiplicity of student
needs for a diverse group of African American students. One site administrator explains
the only caveat to professional development is:
Training to get information from data is important. The question is: Are we
trained to know what we are looking for? What is the data saying? However, the
next steps that I plan on using as an administrator are to continue to monitor the
use of data, provide more trainings and time to analyze the data, and seek
understanding in knowing what it is that teachers need to implement to impact the
data and students’ assessments results.

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Principals do what they can to ensure that teachers are inundated with
opportunities to learn how to analyze data. Many set aside and budget monies to send
teachers to conferences that revolves around the use of data. In addition to conferences
and in-house professional development, heaps of teachers receive trainings from their
graduate programs at the local universities. Because an insurmountable of responsibility
is placed on educators to use data, universities have begun to require newly credential
teachers to become data literate and skillful in knowing how to interpret and analyze data
to effectively affect instructional changes in today’s urban classrooms as well as use the
data to provide assistance during intervention and/or tutoring. One principal stated,
“Teachers knowledge in knowing how to use data successfully aids and increases African
American students’ performance in their classrooms, because data significantly shows
you where the child is and some of the troubled areas that need to be targeted to address
the child’s specific needs.”
In sum, data play a central role in the alignment of curriculum and instructional to
meet the academic needs of African American students in the classroom setting. Not
only does data support teaching and learning in the classroom, but it is used by teachers
and administrators to ensure academic services or interventions are provided to students
who have not mastered grade level content. Professional development supports ongoing
training for educators who commit themselves to improving the educational outcomes of
African American students. They also assist teachers in being able to use various forms
of data to inform instructional practices.

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The next section examines research question two on how general and special
education teachers use an array of assessment data to inform instructional decisions that
may have an effect on the learning outcomes of African American students.
Use of Assessment Data
Using assessment data have become an integral part of the urban classroom,
because many minority students continue to under-achieve. States’ and the federal
government have firmly enacted legislations holding educational institutes accountable
for the ongoing use of data. These legislations have made the use of data extremely
beneficial to educators in urban school settings. State and federal legislation require
relentless monitoring of student learning to demonstrate academic growth. In several
urban school settings, data use has become common practice for both teachers and
administrators. During the interview process one administrator stated:
Data serve as a tool in assisting teachers in making adjustments in their teaching.
Data have benefited, in terms, of identifying African American students academic
needs. Teachers are able to look at students’ deficits and instruct using different
ways of teaching (strategies). There are more opportunities for teachers to share
with each other. They collaborate and look at ways of processing and changing
the curriculum that matches the characteristics of the African American students.
For example, teachers may use a lot more debates or hands-on activities to get
students to learn the material rather than teaching them in a lecturer format. Data
have helped teachers to understand the different ways African American students
learn and engage them in the learning process. These students may find it
difficult to sit in a class for fifty minutes without interactive engagement. Being in
tuned to African American students’ different ways of learning has helped
teachers to modify lessons and instructional practices. Understanding African
American students by embracing the data received from monthly assessments
have supported teaching and learning.

Data use has changed the way African American students are perceived. In
numerous instances, educators must know and be able to understand what this specific
subgroup can achieve. They must familiarize themselves with the individual data for each

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African American student and use various forms of data to make appropriate instructional
decisions that will benefit all. Teachers need to know precisely how to analyze and
interpret data to impact learning in the classroom. Being cognizant of multiple data and
their significance allows for the advancement of teachers’ pedagogical skills in the
classroom. Teachers and administrators articulated that multiple forms of assessment
data exist across the district. In some cases, the implementation of data, at school sites,
become problematic when minimal district supports are apparent.
As noted earlier, teachers and principals’ use multiple assessment data to make
academic decisions pertaining to the education of African American students.
Consistently, CUSD educators use different kinds of assessment data to elevate or sustain
student achievement. Data derived from assessments such as CST, benchmark
assessments, teacher-made, and informal data significantly influences how principal-
teacher, teacher-teacher, and teacher-student interact in relation to learning. Granting all
of this, the use of assessment data vary from general and special education teachers.
African American students learning and instructional needs may dictates how different
assessment data are used to re-adjust, re-teach, or modify a classroom lesson.
In addition to data derived from the state’s CST, teachers in all CUSD’s schools
use a variety of assessment data to inform instructional practices for African American
students.
California Standards Tests (CST)
The assessment results from the California Standards Tests are disseminated to all
schools’ every year, normally at the end of July. Once principals have reviewed and
analyzed data for the entire school population, they share the data with the school’s staff,

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various parent councils, and parent population. The CST data inform everyone how
students are performing academically. The CST data also provides individual student
data, grade level data, ethnicity, gender, and special education data, etc.
School educators use CST assessment data to establish school-wide and grade
level goals. The data serve as a guide to initiate conversation between administrators and
teachers. This articulation includes staff participating in horizontal and vertical
articulation sessions and talking about student data. School leaders and teachers use CST
data to determine whether students have mastered content standards. For example,
teachers on different grade levels examine what worked the previous school year and
what did not work? They can use data to calculate their grade level API (Academic
Performance Index) by dividing the total number of students that achieved proficient or
advanced by the total number of students tested. CST data help teachers and
administrators plan and make decisions about instructional improvements that need to
occur for the upcoming school year. Data assist in implementing or deleting resources,
programs, or interventions that may have impacted or hindered learning in the classroom.
Through-out the school year, educators’ uses this summative data to channel the
development and execution of interventions, strategies, or techniques that influences
African American students’ success in the classroom. For example, one administrator
stated:
While teachers find standardized test scores (CST) relevant and helpful during the
beginning of the school year. However, they use this data as baseline data. This
data are also obsolete by the time students actually begin instruction in the
teacher’s classroom. Teachers find the data that they retrieve from their own
assessments, compiled during their grade level collaboration sessions, to be
useful. They are able to quickly analyze the data and realign instruction in the
upcoming weeks to determine if students have mastered the targeted standards

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and objectives. Along with the grade level weekly or bi-monthly assessments that
they make, teachers also use data from their chapter and unit tests, teacher-made
tests, performance tasks, and teacher observations.

As one principal stated, “CST data assist teachers in reviewing each student’s
strengths and weaknesses.”
The data put a “face” on each student, as stated by one principal. Data support
students and allow them to participate in goal setting and ensures their achievement in
their respective classrooms. For example, if a student is performing below 350 on the
English/Language Arts section of the CST, the data help teachers inform decisions and
identifies specific areas of weaknesses or strengthens. Teachers review specific content
cluster data on the English/Language Arts profile. This area of the CST profile indicates
where each student is having difficulty. Many times teachers say they provide additional
classroom support by grouping them with other students or providing individuals
tutoring. One teacher communicated, “For those students who are under-achieving, “we
know that they have a lot of work to do and we know what type of interventions to put in
place in class or offer during tutoring.”
Benchmark Assessment Data

Benchmark assessments are perceived as being important learning tools that
support instruction. The benchmark assessments are given to all students in CUSD
schools. Every school site participates in testing regardless of students’ ability or
knowledge to handle academic material. These quarterly benchmark assessments are
administered four times during the school year. The premise behind administering the
benchmark assessments are to use data to determine whether students have mastered

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content in the core curricula (English/Language Arts, Mathematics, Social Studies, and
Science) and make predictions about the achievement potential of district schools.
Unfortunately, there appears to be copious challenges in the validity of the
benchmark tests. Too often there are inconsistencies and inaccuracies in the test contents.
For example, test items may not always coincide or be aligned with what is actually
taught in the classroom. One site principal stated, “Inappropriate answer choices are
‘mix-matched’ and do not correspond to the test questions.” In essence, when the test
data are returned to schools, they are not highly respected because many teachers do not
believe they measured the performance of students with accuracy and reliability.
Teachers and principals complain about the turn-around-time of the actual data to
schools. The district is slow in returning data to schools. Tests administered may take up
to two-to-three weeks for schools to receive the actual data. Schools grumble about their
inabilities to effectively analyze or interpret data in a timely manner to inform
instructional decisions. Too often teachers do not have time to modify or re-teach lessons
because student data are slow to return back to their classrooms. Oftentimes, teachers
have moved on to other instructional lessons or content standards. Again, benchmark
assessment data are not always effective in measuring and informing instruction.
According to an elementary principal, “The data are null and voided, as well as
irrelevant.” Echoing the sentiments of others, one teacher said, “The data from the
benchmark assessments are not accurate because it is prepared by the district – it is not
always what we taught. Grade level tests reflect what we teach. You look at them and
everyday is an assessment.”

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Not only are district benchmark assessments not taken seriously by teachers, but
school administrators participating in the study also complained about CUSD’s
assessment process. They reiterated that benchmark assessment data do not actually
support or inform instructional practices in their schools. One principal explained:
District benchmarks data are reviewed as reports become available. Reports are
made available at the school site level, but there are obstacles at the current time.
However, when the data are received teachers are able to analyze the data and
begin to realign instruction. Oftentimes, there is not a direct correlation between
the standards that teachers are targeting for the district benchmark tests and their
weekly assessments. During grade level collaboration, they must focus on using
the data which provides information regarding student performance on standards,
to identify target standards for upcoming instruction. Using this data, teachers
must be able to connect the district curriculum guides, pacing schedules, and
instructional materials.

Even though benchmark assessment data are considered important by district
officials, school sites do not rely on data to inform best practices. Both teachers and
principals depend on data results from their own teacher-created assessments to inform
instructional decisions in their classrooms and schools.
Teacher-Made Assessment Data

Teacher-made assessment data are derived from tests teachers create to address
specific content standards that are being taught in their classrooms. Teachers use paper
and pencil quizzes, chapter tests or unit tests to monitor learning. Teachers also use grade
level release questions. These questions are prepared by the California Department of
Education and can be accessed by schools from the CDE website. They assist teachers in
preparing assessments in the core curricula that addresses essential and/or power
standards. Teachers produce assessments that resemble the actual CST tests. They focus
on specific academic standards and skills they think students should know. Once

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assessments have been administered, teachers immediately use students’ data to re-adjust,
modify, and re-teach lessons to improve instruction for all students, especially for African
American students.
Countless teachers rely on their weekly or bi-weekly assessment data. These
assessments are teacher-made. They are standards-based and focus specifically on
essential skills they want students to learn. It is not uncommon for a teacher to
communicate the following, as one teacher stated:
We look at the data to see whether the kids have met the standards. If they do not
meet the standards, we may go back and revisit the standards and find a different
way to teach the standards the next year.

In preparation for teacher-made assessments, teachers use Data-Driven
Classroom, Reading First OARS, Pearson-Prosper system, and Grade Pro. All these
systems or instructional supports provide teachers with immediate feedback so that they
can affect learning in their classrooms. At all the schools participating in study, teachers
use Pearson-Prosper. The Grade Pro system is used at one elementary school. However,
the Pearson-Prosper and Grade Pro systems generate data reports instantaneously. They
produce an analysis of specific items or questions students have answered correctly
and/or incorrectly. Pearson-Prosper and Grade Prop identify standards that are difficult
for students along with creating individualized grade level and student reports. Teachers
interpret students’ assessment data and use to inform their classrooms lessons. One
second grade teacher exclaimed, “These systems break down the data according to what
skills students missed or gaps in their learning.” Teachers use data to determine the
academic road map they will take with students. Once decided, they use data to make

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immediate instructional decisions regarding whether students have mastered grade level
content standards. For example, a teacher explained:
The results of data are instruction-driven and that we form different subgroups
from testing results. For example, the program we use is Grade Pro. We test and
input results and we will sort the students according to African American or
English Language Learners. One year we did African American male students
specifically. From the data, we mapped children where they are compared to
other students and grade levels. At that time we started to design curriculum that
was specific for the subgroup – African Americans. The data identified specific
areas for each child. Grade Pro sorts and helps group students so specific
objectives and data are used. It provides immediate information at the end of
lessons. It can be used across the curriculum. For example, low scores,
behaviors, special education and especially African American students – there
needs to be a movement to address different learning styles of African American
boys. As a result, I never before really grouped African American students
because did not have a problem, but now I group. I can focus on them. There is a
learning range. In previous years, low scores and no one did anything about it.
Low schools, behavior problems, move to special education, especially African
American boys.

In certain situations, teachers use district-wide assessments to perfect their skills
in being able to analyze data. Teachers may use assessment data as a mechanism to gain
knowledge and learn how to apply it in their classrooms. One teacher divulged, “Using
assessment data to re-adjust or modify lessons may not always work for African
American students if there are outside influences.” Oftentimes, CUSD teachers’ convey
that they must develop their own skills in knowing how to examine data and use it to
effectively address the academic needs of African American students. Sometimes, this
data may not always be presented in a traditional manner.
The section that follows explains how teachers in urban school settings use
informal data to inform instructional decisions pertaining to African American students’
academic deficits.

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Informal Assessment Data

Informal assessment data are crucial in the classroom learning environment.
Teachers and administrators shared that they used informal data such as observations,
reflections, and anecdotal notes to supervise the progression of African American
students’. According to one teacher, “Teacher notes are used by several teachers at the
elementary school site.” Teachers’ notes are defined as informal data collected on
students as the teacher observes their interaction with peers. For example, one teacher
explains a combination of teacher observations and notes are significant. She stated:
I use a lot of teacher observations, teacher notes, and student observations.
Students do observe themselves. I put them in groups and they journal what they
have learned and observed from class. Students do observe themselves and
critique themselves. We have show and tell.

Teachers also engage in ongoing dialogue with African American students as a
form of informal data to observe their performance in relation to their peers and how they
engage in classroom assignments. In many instances, data from African American
students do not always coincide with their actual abilities or performance. For example,
African American students may not be able to pass multiple choice tests or standardized
assessments, but can pass assessments when the teacher verbally talks to them about
classroom content. From this observation, teachers’ use informal data and classroom
observations to help them modify instruction and determine what exact practices or
techniques are required to aid African American students in test-taking and learning.
One third grade teacher shared how surprised she was to learn that an African
American male student had failed her bi-weekly assessment in mathematics. The data
indicated that he had not learned any of the concepts. But as the teacher reviewed the

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math problems with the class, the student was over-heard explaining the answer to
another student. When the teacher approached the student and questioned him about his
performance, using the data, he informed her, “I did not feel like taking the test, so I did
not do it,” as stated by a third grade teacher. The teacher concluded that data do not
always measure the actual performance of African American students and do not account
for students’ lack of motivation or interest in school. After learning this information and
witnessing the classroom scenario, the teacher realized the importance of looking at data
as well as observing the actual interactions of African American students. She also
examined other instructional methods that she felt would motivate and encourage African
American students to engage in learning activities in the classroom. She realized that
informal data played a major role in informing instructional practices and decisions.
Assessment data have a profound impact on instruction in both general and
special education classrooms. Nonetheless, as teachers use student data to inform best
practices in their classrooms, data use changes as the learning capabilities of African
American students’ changes. For example, an elementary general education teacher may
use bi-weekly assessment data to monitor and inform instruction for students performing
below grade level and make monumental growth. However, for teachers working with
African American students in the middle school who continue to under-perform, data
become a lot more difficult because the students are multiple grade levels behind their
peers and teachers may have to adjust his/her teaching practices considerably.
The following section explores the differences between how general and special
education teachers’ use African American students’ assessment data to inform
instruction.

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General Education vs. Special Education Teachers use of data
Both general and special education teachers are expected to use the same
assessment data such as CST, benchmarks, teacher-made, and informal data to inform
instructional practices for African American students. However, for special education
teachers, in additional to the above assessments, data from other instruments such as the
Woodcock-Johnson-III,¹ WRAT-4,² and the Brigance³ are used to monitor and measure
African American students learning. In an ideal situation, data from one assessment
probably works, but that does not happen in CUSD because multiple assessment data are
the norm in special education.
For African American students in elementary schools, data are used
constructively to influence instructional changes. In some occasions, data have been used
inappropriately to place African American students’ in special education. Oftentimes,
data play a minimal role because African American students’ behavioral problems and
environmental or social factors greatly persuade referrals and placements.

¹Richard Woodcock, Nancy Mather, and Kevin McGrew use instrument to
measure the intellectual abilities and academic achievement in reading and determine
student’s need for special services.

²Gary Wilkinson, Ph.D., in collaboration with Gary Robertson, Ph.D. define the
Wide Range Achievement Test 4 (WRAT-4) as an assessment instrument that measures
the basic academic skills of reading as well as sentence comprehension, spelling, and
mathematical computation.

³See Albert Brigance (1999). Brigance is an extensive comprehensive criter ion-
reference test that measures the basic academic skills in readiness, speech, word
recognition, oral reading, comprehension, etc.

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While on the other hand, various forms of data may not benefit African American
students being placed in special education, especially if they are academically two or
more years behind their peers and data was used inappropriately.
African American students placed in special education classes are held to the
same academic standards as students in general education. However, special education
teachers are mandated to adhere to students’ Individual Educational Program (IEP) plans.
The data outlining students’ instructional and performance levels along with established
goals and objectives are followed according to what has been prescribed by the IEP team
and the school psychologist’s report. In addition to data from other assessments, there
are minimal opportunities for general and special education teachers to deviate from
students’ IEPs. Oftentimes, special education teachers complain about the fairness and
validity of tests. For example, one special education teacher stated:
I use CST, IEPs, and benchmark assessments. I keep that data. But, it is not
fundamentally used in my grading process. Teacher-made assessment data are
used on a daily basis because they are modified. The Standards Plus and Fast
Forward Math, I use those diagnostics as formative and summative assessments.
Because the data-driven information is based on testing at grade level standards
and it is written for this age group, I do not think that it is fair. For example,
seven out of 15 of my kids are reading at a middle-to-below first grade level. The
other eight students are reading above the fourth grade level. So is that a fair
assessment of what they know if you are testing them at an eighth grade level?
Eighth graders are being tested at an eighth grade level, but they are actually
performing much lower. What it does tell me – the data – is that it is very
fundamental for me to understand the skills that I need to review or re-teach.

A special education teacher stated, “The incorporation of ‘do nows’ or ‘sponge
activities’ into my daily instructional program are critical.” The teacher uses sponge
activities, a result of data, to monitor African American students’ progress in class. For

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example, teaching numeric expressions using order of operations are difficult for many
African American students. A special education teacher disclosed:
I have them do four problems that use all the skills in order of operations and I
have my instructional assistant correct those four problems and get them back
before the end of the period. They are not graded on those problems. However,
they see the skills were there to allow for struggle. So they get immediate
feedback that same day. This type of formative assessment allows me to know
what I am teaching correctly and what I am not teaching correctly. It helps me to
see what skills they are struggling with and what they getting and not getting.
And, I look back at that data and say okay – five out of six of my kids missed
question three and that lets me know that that is a skill, it is a topic, or vocabulary
term that I need to review. So when it comes with what do we practice the next
day or the day after, that is where data from my daily formative assessments are
used.

Special education teachers have to be a lot more skillful in modifying and
interpreting data than their general education colleagues because of the variation of
learning needs and academic levels present in their classes. However, over the years this
has changed. Collaboratively, teachers in both general and special educations have begun
to use data more effectively to reduce special education placements and to support those
students who are receiving services. Special education teachers continue to make
adjustments that allow students access to the core curriculum. Similar to teachers in the
general education environment, special education teachers complain about spending so
much time gathering and compiling data to disseminate to their site administrators. Too
often, “We are concerned with showing the proof of data, when in actuality taking time to
document takes away from valuable teaching time and lesson planning,” as conveyed by
a special education teacher.
In sum, although numerous general and special education teachers have begun the
process of using student data to reflect and inform instructional practices in their

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classrooms, it is not apparent in all settings where African American students are taught
that data are used efficiently to warrant changes. However, data use compels
administrators at school sites and in special education department to critically examine
the data to ensure students are exposed to standards-based curriculum and instruction.
To ensure the appropriateness of data use in both general and special education
settings, principals must build the capacity for teachers to be able to use data successfully
to support learning. The next section examines research question three regarding
instructional supports and addresses how principals support data-driven decision making.
Instructional Supports

To ensure student achievement happens in classrooms and in schools, it is
important that school leaders engage teachers in continuous data use. Principals are
responsible for providing the essential instructional supports to build a data culture that
benefits all student subgroups. During the course of the research, numerous challenges
were articulated by educators regardless of their job classifications or responsibilities. At
the school sites, teachers felt that their principals were “very supportive of them using
data,” as stated by one teacher. According to a special education administrator,
“Although an overall or general data culture is perceived in CUSD, ‘a real data culture is
promoted primarily by the school sites.’ However, data use various from site-to-site and
administrator-to-administrator. The schools’ determine data importance and how to use
and review it.” Some teachers felt the use of data has changed tremendously over the
years for schools. It is not uncommon to walk into a school and see data displayed in the
front office, principal’s office, and/or teachers’ classrooms. Teachers participating in the
study have data boards, binders, or graphs in their classrooms. These displays break

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down students’ assessment data in English/Language Arts and mathematics. The data are
derived from skill focus tests. These data displays let principals and district officials
know how well students are doing academically. Data also let students know where they
are and how the data drives their instructional program and drives their learning.
According to a special education administrator, “Data keep students on point with where
they are.” Generally, teachers’ lesson plans reflect how the data are being used the
following week in class with students. However, principals’ play a vital role in ensuring a
data culture materializes in schools’ learning environment. Despite their multiple
responsibilities, one administrator shared, “Principals are responsible for engaging
teachers in talk about data and how to improve. For example, the data say this, so now we
need to do x, y, and z. So there should be conversations where data are discussed and you
know that the data informs decisions based on conversations.” One special education
administrator emphasized:
The principals in CUSD, especially the elementary principals are very much
aware of how to use data and they are looking seriously at the data of their
subgroups. However, they just have to establish an ongoing data culture that
drives decisions in schools and drives the instructional programs for our African
American students. I think we have some principals that are using data
effectively, some that ignore what the data are telling them, and what the data is
saying for the African American students. Principals are so busy putting out the
day-to-day fires that they have a difficult time focusing on the ongoing use of
data, especially for African American students. But there should be a real focus
on African American students, but really for all students. However, I think that
there needs to be more supports for principals. At many school sites there is a
lack of staff to support principals and data use. Too often CUSD principals wear
multiple hats.

The most prevailing instructional supports that teachers wished principals can
provide more of to ensure a productive data culture exists are: time to analyze and use

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data, opportunities to collaborate with colleagues, and many griped about not having
enough instructional resources.
Time
Time is the biggest challenge facing teachers’ use of data. Numerous teachers are
given time by their site administrators, however, they complain that it is not enough or
inadequate. The time to gather the data, discuss, and analyze the data and think about
where to go from it does not happen with ease for most teachers. Despite time
constraints, every Wednesdays, all principals allocate one-hour for teachers to collaborate
and analyze the data. But, one teacher said, “I think the challenge is the time to learn
more about data and how to use it effectively. It takes time. We need the repetition for it
to sink in. We need to practice it. If we don’t start organizing and analyzing the data, we
will never get better.”
Several teachers spoke of coming in early and staying up late at night to analyze
student data. Teachers felt that they are given time to start the conversation about data
and analyze in scheduled grade level meetings, but adequate time is the most challenging
concern. Resonated by several teachers, as one teacher communicated, “There is little
time to analyze or no time to teach concepts.” One teacher stated, “CUSD is a test-driven
society. It provides students no time to master the taught skills, especially low students.”
Teachers concurred that meetings do occur, but agree that no time to put together needed
changes or interventions for students. An elementary teacher exclaimed, “We just go
over results with little time to talk about the delivery of instruction or best practices.”
Teachers’ at all three sites indicated that they use their own time or extra time
before or after school to analyze data. Several teachers stated that they talk in hallways,

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during recess or lunch, on the playground or cafeteria regarding students’ data. Pressure
exists to use data at schools because principals are expected to submit the school’s
monthly data assessment results to district administrators. Although data are expected,
numerous teachers wish more creative ways were devised to use time during the
instructional day subsisted. Teachers associated their lack of time to analyze data with not
having sufficient time to teach effective lessons in their classrooms. Countless, CUSD
teachers echoed the feeling of others, as one teacher articulated, “Assessment,
assessment, assessment takes away the creativity!” One teacher explained:
Data have not improved my teaching. Mainly, the problem is that there is a lack
of time to deal with issues regarding particular students needing help – no time! I
go to bed at night asking for guidance regarding problems that I am facing. Data
have not helped me improve my strategies or techniques. We are “data fact-
driven, not, imagination-driven. You catch kids with imagination and learning
happens.

One of the most refreshing moments for teachers are being given time by their
principal to conduct “data forums,” as revealed by a secondary counselor. These data
forums enable teachers’ time to track the data, discuss the data, and generate ideas about
what the data are saying and how to use it to impact student learning. Teachers found
this open data forum to be effective in strengthening their skills in learning more about
data.
To alleviate teachers’ frustration with not having enough time to use data
effectively, principals’ have begun to creatively set aside time within the instructional
day or during their staff meetings to collaborate and discuss African American students’
assessment results. Although teachers expressed being overwhelmed with not having

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sufficient time to analyze and use data, however, many support data use and strive to use
it to further the education of African American students.
Collaboration
As noted above, teachers in both the elementary schools and middle school are
given time weekly to collaborate and meet in grade level. The collaboration meetings
include both general and special education teachers. In all schools teachers in the Special
Day Teacher (SDC) and Resource Specialist Teacher (RSP) programs work closely with
general education teachers on various grade levels to support African American students.
Nonetheless, in the middle school, the SDC and RSP works collectively with grade levels
and departments to make sure students are being offered the core curricula in their
classrooms.
During the collaboration meetings, teachers look at students’ bi-weekly
assessment data and talk about those African American students that did not mastered
grade level content standards. Teachers also look at ways to modify instruction and
improve learning in their classrooms. At all sites, grade level collaboration starts as early
as the beginning of the school year when teachers discuss students’ CST test scores/data.
As the year proceeds, teachers review data from weekly skills focus tests. As a grade
level, “We go through and look academically at where students lack and where they score
the lowest – maybe this is an area the teacher needs to focus,” as expressed by one third
grade teacher. The collaboration meetings include teachers sharing instructional strategies
and techniques, team-teaching approaches, teacher methods, and how to group students
according to reading and math abilities. It is common practice for a teacher on the grade
level to share how they taught a specific standard if students performed well on bi-weekly

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skills tests. Although some teachers are comfortable with sharing data, others are not
comfortable with sharing their students’ data results if they are assigned a group of
African American students who are low achievers.
While meeting other non-data-related issues might be discussed, unless principals
set the agenda prior to the grade level or departmental meetings. Frequently, teachers
disaggregate data by students’ ethnicities, subgroups, and genders. This data are
submitted to principals weekly, but often “untimely feedback is not always readily
available” for teachers to effect change in their classrooms because of frequent
submissions, as communicated by teacher participant. Collaborations are somewhat
challenging for both teachers and principals, one principal maintained:
I provide the tools to support teachers through monthly data conferences.
Through this process, the teachers become familiar with looking at the data and
being able to see instructional issues and individuals needs of African American
students regarding specific skills. Through the data conferences, teachers
determine how to develop interventions, devise plans, and implement lessons.
They look at new data to see if students have moved academically. For struggling
teachers, I collaborate and assist them in writing their instructional plans for
student achievement. I also send them to trainings and conferences. For example,
the Thinking Maps Process is one example of a conference that my teachers have
attended.

Unlike the challenges associated with grade level and department collaborations,
instructional resources are considered plentiful at all sites to support teachers’ use of data
and foster a strong data culture. However, there are teachers who continued to complain
about not having enough resources to use or implement data in their classrooms.
Resources
As instructional leaders, principals participating in the study make available to
teachers whatever instructional resources they deem appropriate to support their use and

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analysis of data. To lessen teachers’ fears and frustrations with using data, all principals
pride themselves in purchasing instructional resources such as laptop computers, flash
drives, computer centers, extra copier papers, scantrons, etc., to encourage data use. One
principal stated, “Providing the extra resources ‘eliminates the excuses’ that teachers’
normally convey.” In essences, teachers cannot complain about not having sufficient
tools, supplies, resources, or equipment to analyze data and disseminate timely results.
For example, one elementary teacher compliments the principal for the resources she
provided. She said:
She requires bi-weekly assessments and collaboration. The supports she
provides… a lot of us use a computer technician who is data literate. He compiles
our classroom data results and creates profiles. She also sent four teachers to a
conference to learn about how to use data better. I felt this was a good use of our
school’s resources and it encourages data use. I think we all have access to Grade
Prop to look at numbers and trends. Having access to Excel and laptops are big
supports.

In additional to instructional resources, principals allocated monies in their
schools’ budgets for teachers to attend outside conferences to further advance their data
skills. Several teachers maintain that they are pleased with having the opportunity to
attend off-site conferences.
One of the most powerful instructional resources that have been beneficial and
positive for many teachers were having the chance to net-work and visit with teachers
from other district schools. During these off-site observations, teachers collaborated with
their peers from neighboring schools to understand how other teachers analyzed African
American students’ data to modify or re-teach classroom lessons. From classroom
observations, teachers shared information about students’ data and how they were able to
get students to perform on grade level assessments.

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While principals at both the elementary and middle school sites have put forth
much effort to support teachers’ use of data, this is not apparent in all CUSD
departments. For example, officials in district departments such as special education and
curriculum and instruction lack the resources to support data literacy endeavors at the
school sites. For example, a special education administration shared:
No, we haven’t used our resources to help the schools, other than indirectly using
human resources to go out there and support teachers in knowing how to read the
data. Other than the human resources… no, we haven’t directed resources
specifically at schools to help with analyzing the data.

To make certain teachers are provided the necessary resources to implement data
successfully in their classrooms, it is critical that human resources as well as monetary
resources are budgeted to support instructional improvements.
In sum, in order for principals and teachers to become highly skillful in analyzing
and using data effectively in classrooms and schools, the initial supports pertaining to
time, collaboration, and resources become a priority for schools and district departments.
The feelings communicated from the schools are that principals and teachers are left on
their own to do the work of improving student achievement. Schools’ are using data a lot
more effectively to inform instructional decisions. However, without additional supports
or resources from district office, data use becomes extremely demanding. To strengthen
and build the capacity for teachers to become comfortable with using data, teachers felt
they should not only receive instructional supports from site principals, but from central
office staff. One special education administrator exclaimed, “The thrust of school
reforms need to be data-driven.” However, many staff felt supports/resources regarding
data use need to be communicated from district office administrators.

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Effects of Data Use for the Education of African American Students
As noted above, many educators in CUSD use students’ assessment data to
inform instructional decisions and impact teaching and learning in the classroom. And
there are other teachers who believe they are “unjustly accountable because some
instructional goals designed for African America students are unrealistic,” as stated by a
special education teacher. Interviewees asserted that assessment data have not always
benefited African American students because of their inappropriate classroom behaviors.
Teachers felt that African American students’ behaviors are a direct result of “outside
factors such as unstable home environments or societal factors” that complicates their
lives and hinder their “motivation to learn,” as revealed by teacher participant.
Unwanted student behaviors are present in both general and special education.
Multiple behaviors in general education has led to an increase in special education
referrals. One principal stated, “Teachers and principals distort or used data
inappropriately to get students out of their classrooms and schools.” In truth, an increase
in African American students’ referrals or placements in special education classes or
programs lessen the “unwanted or undesirable behavioral problems,” as noted by an
elementary administrator.
In several schools and classrooms referrals for special education are a growing
phenomenon. Using assessment data with African American students have produced
intended and unintended outcomes. In many cases, the data have produced an array of
intended consequences for African American students that have resulted in both positive
and negative outcomes which are explained in the following sections in response to the
final research question guiding this study.

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Intended Outcomes
The initial adoption of California’s Academic Performance Index (API) and the
federal government’s Adequate Yearly Progress (AYP) forces schools’ and classroom
teachers to comply with requirements established in the NCLB legislation. Ongoing data
use profoundly impacts and addresses the academic deficits of all student subgroups,
especially those groups who continue to under-achieve. The formation of these state and
federal legislations places a stronger emphasis on the use of data. Schools use API and
AYP assessment data reports to enhance the education of African American students.
Annual API and AYP data reports provide teachers and administrators useful information
to inform and support instructional decisions.
While data use seems taxing, teachers use data willingly to educate all students,
especially African American students classified as under-performers. One second grade
teacher said, “Data draw attention to the subgroup that we may not have looked at before,
both male and female.” Another teacher stated, “Prior to API and AYP, African
American students’ assessment data may not have been a true representation of what they
can do. In the past, data may have been invalid or not relevant because of students’
behaviors or emotional problems.” Data are intended to improve teachers’ instructional
strategies and techniques while allowing for adjustments and realignment in their
instructional practices. NCLB mandates schools’ to consistently use data to inform
instruction for all student subgroups, especially African Americans.
Even though teachers realize the importance of using data, it has not prevented
them from complaining or voicing their concerns regarding over-use. One teacher said,
“We spend a lot of time following rules and making students follow them. In order to get

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through required materials, we are losing a lot of kids – we just push, push, push because
of data!” While this feeling is shared by other teachers, one teacher said, “We all use data
because of NCLB and API. As a teacher you have to test students and use data to make
classroom decisions.” Since African American students tend to demonstrate more
behavioral problems than other ethnicities, they also benefit when teachers use data
appropriately and consistently. One teacher said:
Teachers need to address the various learning styles of African American
students…modify instructional practices. For instance, students that cannot sit,
teachers need to let them stand to take away the focus from student not being
engaged in lesson. Teachers need to use data to modify and accommodate
learning needs.

One teacher exclaimed, “As a result of using data in my classroom, I am a better
teacher.” Many teachers said that they used data in their classrooms before NCLB.
However, NCLB’s mandates have helped teachers strengthen their data skills and provide
an in-depth and comprehensive instructional program for African American students.
Principals felt that data not only benefited teachers and students, but it aided them in
supporting their staffs in providing instructional services or interventions to encourage
African American students.
Numerous teachers felt principals, because of pressures from district office
administrators, set unrealistic goals for schools and students based on data. The principal
may set instructional goals that are not obtainable for African American students. For
example, expecting a subgroup to make substantial academic gains on AYP and API
scores may not be realistic if students in this group are expected to grow 50 to 60 points
above their instructional level or where they are actually performing academically.

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Several classroom teachers revealed, but voiced by one, “Because African
American students are tested frequently in between the CST and benchmark assessments,
many are ‘burned-out’ with the whole testing process.” Data aid teachers in planning
lessons for African American students. In the past, these students may have been
“written-off because of behavioral problems because their academic concerns were not
readily addressed,” as stated by a third grade teacher. Another teacher said, “The data of
African American students’ help eliminates teachers’ own prejudices and biases
regarding students’ achievement levels.” In essence, teachers cannot use African
American students’ behaviors as an excuse to not make instructional decisions that will
impact their education. Data assist teachers and administrators in developing classroom
lessons and intervention programs that specifically addresses the educational concerns of
African American students. For example, one elementary school designed a program
entitled the Gentlemen’s Club and solicited ongoing support from city officials and the
100 Men of Central to mentor African American boys. One elementary principal said,
“The use of data assisted in identifying the African American boys’ struggling in the
classroom environment.” Ultimately, these African American students benefited from the
school’s effective use of data and the additional attention they received.
The intended outcomes necessitate positive use of data for African American
students’. However, unintended outcomes continue to have a tremendous impact on
referrals and placement rates.
Unintended Outcomes
Some school psychologists and program specialists expressed concerns with how
school officials use African American students’ assessment data, especially the boys.

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These aforementioned educators reported that when an African American student exhibits
a behavioral problem in the classroom, principals and teachers use assessment data to
rush the referral or placement process. As one teacher said, “If there is a behavior
problem, must need special education.” In a number of cases, data are not always used
appropriately because of students’ behavioral problems. In some situations, teachers and
principals have developed pre-conceived ideas regarding these students’ abilities to
perform. Many have “low expectations and the attitude that African American students
can’t perform because of their behaviors,” as disclosed by a school psychologist.
A school psychologist stated, “The behavior becomes the ‘stumbling block’ and
prevent African American students from receiving the best possible education using
assessment data, especially in elementary settings where behaviors manifest themselves
early on.” In array of classroom settings, the teacher may not challenge or use higher
order thinking skills or strategies with African American students because of data results.
For instance, if an African American student has low CST scores or benchmark data
results, a teacher may assume that the student cannot learn or handle complex academic
material. A large number of African American students are placed in special education,
as early as second or third grade without having the benefit of receiving extra academic
support. Sometimes, “Many are not challenged or given an opportunity to engage in
meaningful lessons,” as revealed by a special education administrator. Nevertheless, one
principal participating in the study stated, “It is the responsibility of the principal to serve
as the ‘gatekeeper’ during SST meetings.” They must guarantee appropriate educational
opportunities such as interventions, resources, and academic supports are provided to
African American students to ensure their success in school. Principals communicated

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their concerns about the number of referrals and the placement rates of African American
students in CUSD special education program. Principals articulated the daily monitoring
and observation of classrooms as well as providing teachers support in using data are
pivotal for African American students. Additional academic supports facilitate the
learning process for most students. They ensure students’ progress in the academic
setting and minimize special education services.
In addition to low expectations and behavioral problems, teachers say they may
not always use data to support learning in the classroom. One teacher said:
If an African American student is non-participatory, demonstrates a quiet
demeanor, and does not display inappropriate behaviors, teachers may not always
review assessment data even if there are learning challenges and academic
assistance are warranted. Sometimes African American students are inadvertently
ignored if their behaviors are not out of control in the classroom.

One teacher stated, “A student with a quiet demeanor, academic needs may go
unnoticed.” As noted by the aforesaid teacher, “Instructional decisions do not influence
what the data revealed regarding African American students’ learning discrepancies
unless they bring attention to their deficit through their behaviors.” The assessment data
may not always be used to modify instructional practices because teachers may not feel
enthusiastic or motivated to work with academically low students. According to one
teacher, “Assessment data may not be a true analysis of the potential of African
American students.” Although data are used to inform instructional decisions and
practices, one teacher asserted:
When you focus on data, I spend a lot of time feeding my children facts…but kids
want to have fun learning. Having fun is lost in our classrooms. We need to do
more stories and activities to engage student in learning and I think it will
ultimately impact our data results. For example, a teacher dressing like Martha

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Washington encourages students to learn more as oppose to give them dates.
What incentives are there for students to want to attend school?

Students’ involvement and engagement in learning is supported by a continuous
analysis of data. Effective implementation of data encourages students to participate in
class when lessons are tailored to meet their academic needs. However, teachers said that
African American students want to have fun. At times, “Fun in the classroom is lost
because so much emphasis is placed on data use, which hinders creativity and variety,” as
communicated by a second grade teacher. While data use is intended to elevate the
achievement of African American students, sometimes unintended outcomes occur and
deter the learning process for both general education and special education students.
All principals articulated that being astute and perceptive are crucial, especially
when assigning African American students their classes and teachers. Administrators
stated it is important to know what teachers are good for what students. Teachers’ skills,
knowledge, and tolerance profoundly impacts how they use data to inform instructional
decisions. All classification of teachers felt that they needed to be confident in
addressing learning challenges and social issues that arises with the African American
subgroup.
In sum, the effects of using assessment data to inform the education of African
American students have potentially hazardous outcomes, both intended and unintended.
Because students’ lack the necessary academic skills, many school educators strive to
comply with NCLB. State and federal laws require educators to use data on a consistent
basis to improve their instructional practices and make informed decisions regarding
various student subgroups. This is particularly relevant to African American students.

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Teachers’ are becoming a lot more knowledgeable in using ongoing assessment data to
reduce further remediated or special education programs. When assessment data are used
appropriately, African American students themselves take pride in learning and are
motivated to do the work. On the other hand, unintended outcomes stem from the
inappropriate use of data and students’ behaviors. Oftentimes, the unintended outcomes
result in increased special education referrals/placements and lessen instruction in the
classroom. To improve the academic fate of African American students, school leaders
stated they must take an active role in making sure data are used appropriately to
guarantee African American students success in urban settings.
Conclusion
This chapter describes the significance of using assessment data to transform and
improve classroom practices to influence educational outcomes for African American
students. During the course of the study, an array of data was gathered at two CUSD
elementary schools and one middle school site. The premise for collecting a myriad of
data was to answer the primary research question which seeks to understand how general
and special education teachers used data to inform instructional decisions as they related
to African American students. After gathering and collecting data, several themes and
over-arching themes were identified. These themes addressed the research questions
presented in this study along with the sub questions. The sub questions focused on the
use of various forms of assessments data, principals’ ability to build the leadership
capacity for teachers to use data, and the intended and unintended consequences of data.
Educators in both elementary and middle schools’ articulated that data are
essential in improving the educational outcomes of African American students in areas

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such as instructional planning, differentiated instruction, Student Study Team and
Response to Intervention, and professional development. As teachers developed their
data skills, they learned how to use the data within their classrooms and through
instructional supports provided through SST meetings to affect students’ learning
outcomes. School site administrators and teachers realized that being cognizant and
skillful in using a multiplicity of assessment data such as CST, benchmarks, teacher-
made assessments, and informal assessment data benefited African American students.
Informal assessment data was the most revealing data. Although some teachers felt the
under-performance and behaviors of African American students were special education
indicators, there were other teachers who were knowledgeable in recognizing that under-
performance in their classrooms did not constitute students’ candidacy for special
education. Data gathered from teachers’ observations does not always correlate with the
formal assessment data of African American students. These informal assessment data
required teachers to become intuitive in their attempts to vary strategies and techniques to
support African American students.
All study participants (principals, teachers, special education administrators,
psychologists, and program specialists) embraced the ongoing use of data to educate
African American students. The majority of CUSD’s teachers and principals are data
literate. School educators use data to address educational concerns relating to the African
American student, however, numerous complaints about not having enough time to
accomplish instructional-related tasks existed. Teachers’ at both the elementary and
middle schools used data in their classrooms to make better educational decisions as they
related to African American students. Collaborations among teachers are frequent.

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Teachers focused on analyzing and interpreting data to impact learning in the classroom.
Yet, some teachers shared data, but others were not comfortable with sharing data results
of their students. Although, teachers collaborate and meet weekly, time to consistently
plan and develop lessons are insufficient.
The most disturbing issues communicated were the environment and social
factors that inhibited the family stability of the African American students. Because of
the families’ instabilities, these prevailing factors impeded the learning environment and
hindered African American student’ academic performance. As a result of these factors,
middle school educators felt they needed to address the aforementioned issues before
focusing on data. Before diving into data use, the middle school staff utilized
instructional and outside services to alleviate those controllable factors that hindered
students once they arrived at school. The intended purpose of using data is to address the
learning deficits of all students. However, teachers’ at all three school sites used data
faithfully along with additional resources to facilitate learning in the classroom.
Nevertheless, the unintended outcomes have resulted in the placement of African
American students in special education because of undesirable classroom behavioral
issues that present themselves.
To prevent the overrepresentation of African American students in special
education, principals’ participation on SST and IEP teams are critical. Administrators’
attendance in meetings and serving on teams ensure that African American students are
not erroneously placed in special education without having the benefit of receiving
adequate resources and interventions. However, the ultimate goal is to make certain

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principals’ and teachers’ use data skillfully to promote the academic success for all
African American students in various learning settings and urban school communities.

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CHAPTER FIVE: CONCLUSION AND IMPLICATIONS OF FINDINGS
Introduction

The purpose of this study was to understand how educators in general and special
education environments used data to inform instructional decisions for African American
students. As previously noted, in many urban school settings, African American students
are frequently overrepresented in special education (Dunn, 1968, Patton, 1998; Valles,
1998; Coutinho & Oswald, 2000). The assumption is that the uses of data may have not
been readily utilized by teachers in general education environments to inform worthwhile
instruction practices or decisions. For example, in this study numerous teachers and
administrators indicated inconsistencies in uses of data were apparent when it came to
African American students. The theory is that improper data practices or procedures may
have played a role in the over-zealous placement of African American students in special
education (Confrey, Makar, & Kazak, 2004; Confrey & Makar, 2005). This study sought
to understand and determine how general and special educators used data in classrooms
while addressing other societal challenges African American students encountered.
The study participants servicing the two elementary schools and one middle
school shed light on the importance of data use. At each school, multiple on-site
interviews were conducted with general and special education teachers as well as
administrators. For example, special education personnel such as the school
psychologists and program specialists working with African American students in these
three locales were also interviewed. The semi-structured interviews were accomplished
over a three-month period. The goal was to discern the uses of data and its relevance to
classroom decisions and instructional practices.

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Administrators and teachers scrutinized various forms of data to determine
whether changes occurred in classroom instructional practices for the noted subgroup.
The interviewees revealed a wealth of knowledge regarding the uses of data and helped
answered the research questions. A major finding of the study was that educators in both
the general and special educators’ environments relied heavily on different types of data
to improve the educational outcomes of African American students. The findings also
revealed that the use of various forms of data was meaningful, however, classroom
administrators and teachers were more likely to depend on formative assessment data
such as benchmark assessment data, teacher-made assessments, and unit tests rather than
summative data gathered from California Standards Tests (CST) assessments. Unlike
CST data, formative assessment data helped educators expedite instructional decisions to
increase student achievement for all students (Heritage & Yeagley, 2005).
Both elementary and secondary teachers made known their concerns with not
having sufficient time to analyze data, explore educational resources, and collaborate
with their peers regarding African American students’ assessment data and their
performance in classroom. Many teachers said they depended on their site administrators
to ensure support mechanisms existed. CUSD educators used ongoing assessment data of
African American students to inform instructional decisions and enhance their
achievement efforts. The findings revealed that often in the general education
environment, data intentionally resulted in the misplacement of African American
students in special education. Oftentimes, African American students exhibited
inappropriate classroom behaviors and challenged the schools’ infrastructure. Because of

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these undesirable outbursts, many students were placed on a fast track for special
education.
This chapter will connect these findings to prior research, discuss implications for
further research, and make suggestions for policy and practice.
Research Findings and Connection to Prior Research
Prior research emphasizes the overrepresentation of African American students in
special education (Losen, 2002; Coutinho, Oswald, & Best, 2002). This study uniquely
addressed how data informs instructional decisions and practices in general education,
with the aim of preventing special education placement. The study also reviewed how
special education teachers used data to work with middle school students previously
placed in special education classes. The next section examines and compares findings
from this study to the literature in Chapter 2.
The Role of Data Use
The study revealed that administrators and teachers in all learning environments
continued to use data to improve instructional efforts that focused on educating African
American students. Yet, numerous environmental disparities such as poverty and
environmental factors existed in communities of the noted subgroup (Skiba et al., 2005).
Many times these environmental ills impeded the education of African American
students.
Previous research contended that African American and Latino students continue
to receive an inferior education than White students (Carruthers, 1994 as cited in Paul,
2004). Similar to the aforementioned research, in this study an inferior education for
minority students were contributed to their disadvantaged backgrounds such as poverty

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and the numerous societal or environmental factors that presented themselves in the
classroom setting (Skiba et al., 2006). Thus, one of the objectives of this study was to
ensure educators regardless of the backgrounds or poverty circumstances of African
American students used data to provide a comprehensive educational program.
The findings of this study corroborated previous studies which indicated
numerous missed-opportunities existed in closing the achievement gap between African
American and Latino students compared to their White counterparts (Paul, 2004). Yet,
data have not always benefited minority students because of their special education
placements. African American students are overwhelmingly represented in special
education programs and underrepresented in general education classrooms (Fierros &
Conroy, 2002; OSEP, 2003).
To address the learning difficulties associated with African American students,
school officials identified several instructional and educational supports aimed at
improving their academics outcomes. In all three schools’ studied, educational supports
such as instructional planning, differentiating instruction, Student Study Team (SST) and
Response to Intervention (RTI), and professional development were implemented to avert
special education referrals and placements. However, the findings denoted that educators’
at all three schools focused specifically on African students, classroom instruction,
teacher quality, and the role of administrators. In many instances, school principals in all
settings served as the gatekeeper. They often monitored classrooms to ensure the
achievement gap minimized for African American students.
African American students. African American students were the second largest
leading subgroup in the Central Unified School District (CUSD). Unique to this study

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were the number of African American students enrolled in CUSD’s special education
programs. These students dominated CUSD’s special education population. Unlike the
varied assessment results of the Latino population, African American students continued
to under-perform at much lower achievement levels. The mandates of NCLB legislation
encouraged general and special education educators to use data regularly to address
learning outcomes (NCLB, 2002). In compliance with NCLB mandates, all three
schools’ principals established high expectation regarding students’ performance and
teachers’ certification as discussed in earlier research studies (O’Neill, 2004). The
findings also disclosed that principal participants consistently incorporated regular uses
of data into their schools’ instructional programs to ensure the academic needs of
minority students were addressed (NCLB, 2002).
Classroom instruction. NCLB emphasized the uses of data with the African
American subgroup (NCLB, 2002). Previous studies revealed that NCLB placed a strong
emphasis on educators using data to perfect their instructional practices as well as
monitor student growth, especially for African American students (Bejoian & Reid,
2005). Similar to the requirements of NCLB, sites used data often to inform instructional
decisions. Data helped administrators and teachers plan and re-adjust classroom lessons.
Teachers’ instructional practices were apparent in all three sites studied, although
additional supports were required to promote educational opportunities for African
American students. Every site administrator said they monitored classroom instruction
daily to ensure appropriateness of data with noted subgroup. Principals’ encouraged
teachers to use students’ assessment data to differentiate classroom lessons. Paralleled to
other studies, site administrators consistently analyzed African American students’

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assessment data to ensure the implementation of classroom strategies and a culture of
inquiry existed (Earl & Katz, 2002). However, findings of this study revealed a major
caveat to classroom instruction was not having data literate or qualified teachers who
understood how to use data effectively to inform and make realistic instructional
decisions.
Quality of teachers. The NCLB legislation called for highly qualified teachers in
every school and classroom setting regardless of students’ learning classifications
(NCLB, 2002). Highly qualified teachers were not always obvious in the middle school
do to human resources inability to attract such individuals. However, findings of this
study disclosed that a steady and dependable teaching staff existed at both elementary
school sites. But, as previously noted, highly qualified special education teachers were
not always evident at the secondary site. At the middle school, the door swung like a
pendulum regarding the comings and goings of special education teachers. For example,
teachers in the special education department entered and exited regularly. The average
stay of a special education teacher was less than two years. This played a major part in
the school not being able to advance African American students’ education through the
uses of data or meet the API and AYP performance goals. Of the four teachers required
to teach special education, only two met the NCLB highly qualified requirements. The
other two worked as long-term substitutes with minimal or no experience using data.
However, the two other teachers delivered a comprehensive instructional program using
data, but lacked the collegial support to assist with the enormous academic challenges
manifested in classrooms filled with African American students. Because few highly
qualified special education teachers existed in CUSD, the principal was left with no other

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option but to depend on un-experienced substitutes to educate African American students.
Once again the lack of sufficient instruction using appropriate data contributed to the
under-performance of these students.
The gatekeeper. The principal’s role as instructional leader was significant. As
previous researchers denoted, school leaders play a pivotal role in engaging teachers in
data-rich environments, if data use was to become the fabric of school improvement (Earl
& Katz, 2002). The findings revealed that administrators in all school settings
(elementary and middle) made sure teachers’ used data to inform their instructional
programs and address the specific academic needs of African American students.
Frequently, administrators observed classrooms practices and analyzed students’ data to
support the delivery of instruction and decisions regarding student achievement. The
principals at the three school sites explained that they examined data, but their
expeditious return of data to teachers was an overwhelming task that frustrated them.
Previous studies revealed African American students’ disproportionate referrals
for special education services existed because of ineffective pre-referral procedures
(Carter & Sugai, 1998). The research indicated the site administrator roles in facilitating
reform is crucial (Datnow et al., 2007). In CUSD, principal participants served as
gatekeepers at their respective schools. They ensured African American students were not
erroneously placed in special education as a result of ineffective uses of data or
procedures. Like earlier studies, schools administrators’ deterred special education
placements for African American students because of their awareness of the various
eligibility stages and steps of the Student Study Team (SST) process (Hosp & Reschly,
2003). Studies revealed that the SST was an unmandated product of the Individuals with

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Disabilities Education Act (IDEA) that focused on improving student learning outcomes
and a preventive mechanism to deter special education (Buck, Polloway, Smith-Thomas,
& Cook, 2003). This study found that the Student Study Team was required and expected
of all CUSD schools. Yet, the effectiveness of the Student Study Team was unclear and
inconsistent in both the elementary schools and one middle school. At two of the school
sites (one elementary and the secondary), the responsibilities of the SST was handed-over
to the assistant principal and school counselor to support the education of African
American students.
In addition to the SST, previous research contended that improving the
educational outcomes of African American students were the use of research-based
strategies outlined in a Response to Intervention (RTI) model (Bursztyn, 2007).
Implementation of a RTI model was not evident in any of the three CUSD schools
participating in the study, although one elementary principal struggled to incorporate
practices without adequate district office supports.
In sum, this study was consistent with other studies where data was the primary
focus (Earl & Katz, 2002). However, unlike the aforementioned data studies, this study
focused on the specifics of using data to educate African American students. The most
persuasive finding was the role of the site administrators. All principals conveyed it was
their responsibility to support and ensure teachers used appropriate forms of data to
educate African American students. Using the SST process, school administrators made
sure instructional practices, classroom strategies and aids utilized students’ assessment
data as tools to inform instruction. Every principal disclosed that they used data to
advance the academic outcomes of African American students in the learning

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environments, although performance results did not always reflect the implementation of
data. The next section addressed how teachers and administrators used various forms of
data to support students’ achievement of African American students.
How Various Forms of Data Were Used
The heightened pressures of NCLB have placed an insurmountable of
responsibility on educators to improve the achievement outcomes of all students’
subgroups by using ongoing assessment data (NCLB, 2002). The findings of this study
revealed three major factors such as use of assessment data, feedback, and data
challenges were evident at the three school sites visited to support African American
students.
The use of assessment data. A myriad of studies divulged that meaningful
assessment data enable educators to determine whether instructional goals were
established in IEPs for African American students (Yell & Drasgow, 2007). Researchers
also stated that data helped teachers inform instructional practices regarding the
effectiveness of general education programs. Although somewhat challenged by the data-
driven decision making process, teacher participants in both elementary schools and the
one middle school depended on functional IEP teams to analyze data and identify the
learning deficits of African American students. The uses of data as well as the goals and
objectives established by schools’ IEP teams coincided with the intended outcomes
identified in earlier studies (Yell & Drasgow, 2007). Although African American
students’ lacked the necessary academic skills, however, researched disclosed that
assessment data influenced teaching methods and curriculum (Defur, 2003). Oftentimes,

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teacher participants in both the general and special education arenas used various forms
of assessment data to educate African American students with learning disabilities.
Feedback. Unlike high-stake tests, researchers emphasized the importance of
administering periodic benchmark assessment (Jandris, 2001). The goal was to
disaggregate data and provide immediate and explicit feedback to inform instructional
decisions (Jandris, 2001). Unique to this study and the opposite of the findings linked to
the literature, feedback or data derived from district benchmark assessments were not
always readily available to classroom teachers or administrators. In the three schools
examined, benchmark assessments seldom informed the data-driven decision making
process as indicated in literature (Cross, 1998 as cited in Jandris, 2001). The numerous
obstacles associated with the untimely turn-around of data discouraged teachers.
Although beneficial, CUSD teachers in the general and special education settings rarely
relied on benchmark assessment data to transform instructional practices in their
classrooms. At the three sites, teachers and administrators relied on formative
assessments such as teacher-made, end-of-unit, or grade level assessments to inform
classroom lessons. The immediate feedback from formative assessments data and
teachers’ skill-sets in knowing how to quickly transform instructional practices were
apparent in all learning environments. Teachers used teacher-made data to swiftly inform,
re-teach, and re-align their lessons, although challenges subsisted.
Challenges with data. The pressures associated with NCLB mandates discouraged
teachers’ uses of data. CUSD teacher participants complained about not having time to
teach because of their administrators’ persistent focus on data. Parallel to Earl and Katz’s
(2002) study, teachers in CUSD felt they spent less time on using data to improve

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instruction, differentiate or tailor classroom lessons to accommodate the academic
discrepancies of African American students, but gathered and compiled more data to
comply with district office administrators’ submission mandates. As validated in the
literature, teachers in all three school settings complained that compliance with NCLB
made them “test-like” which negates the implementation of other curricula material (Earl
& Katz, 2002, p. 6). Previous research indicated that schools are “data-rich,” but
“information poor” in retrieving assessment data to impact decisions in the classroom
(Wayman, 2005, p. 296). As noted earlier, educators are skillful in accessing data, but
continued to struggle with the effective implementation in classroom settings as
confirmed in the literature (Wayman, 2005). As described in several research studies,
multiple challenges discouraged teachers from wanting to use data in their classrooms
(Wayman et al., 2004). Although teacher participants grumbled, many used data
relentlessly to better student achievement efforts for African American students.
In sum, the findings are consistent with the previous research literature regarding
the implementation of multiple forms of data. The heightened pressures of NCLB
mandated the uses of data to inform instructional practices in classroom learning
environments for all students (Earl & Katz, 2002). Similarly, varied forms of data were
used, but teachers at all sites moaned about the over-use of data in their classrooms.
Many felt data hindered their ability to plan or prepare well-defined classroom lessons
that addressed specific learning difficulties of African American students. Nevertheless,
the next section addressed principals’ roles in building the capacity to support teachers
and encourage ongoing engagement of data to improve and inform instruction for African
American students.

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How Principals Build Capacity
The principals in this study used their leadership abilities to promote data use at
their schools. However, administrators’ engagement and analysis of data to affect
classroom instruction did not always concur with the findings of past studies. Years ago,
data played little part in school leaders’ decisions to transform instructional practices
(Earl & Katz, 2002). Studies indicated that school leaders “relied on their tacit
knowledge to formulate and execute plans” (p. 3). Nonetheless, this has changed for
today’s school leaders and especially for those responsible for educating students in
urban communities. Consistent with Earl and Katz (2002) study, principals participating
in the study utilized “multiple lenses” to validate understanding, investigate ideas, and to
ensure that the right questions were asked to improve instruction (p. 14). Apart from
administrators understanding and knowing how to analyze data, all three principals
provided ongoing learning opportunities for teachers to discuss and analyze their
students’ data results. In addition, three constant attributes were evident at every site. To
foster data use, principals’ focused on their roles, times for teachers to analyze and
collaborate regarding African American students’ assessment data.
Administrators’ role. The accountability pressures and mandates of NCLB
(2002) have forced administrators to take an active role in promoting the uses of data in
their schools and classrooms. The findings of this study indicated that CUSD school
leaders were probably held to a much higher accountability standard regarding the
implementation of students’ assessment data because of the district’s program
improvement status and the repeated underperformance of students. Earl and Katz
(2002) indicated that school leaders were forced to use data to prevent embarrassing

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sanctions, but fail to build a data-driven culture that improved teaching and learning
practices in classrooms. This is not true with CUSD administrators. School
administrators’ at both the elementary school sites and the middle school used students’
assessment data to build a data-driven culture and advance the achievement of all
subgroups, especially African American students. Similar to Jandris’ (2001) study, all
three principals collected, analyzed, and disaggregated varied forms of data. They
ensured that teachers’ data results correlated to content standards, instructional
objectives, and linked to their classroom practices. The data used was not only important,
but assisted principals and teachers in addressing their schools’ instructional programs.
The findings emphasized that data became significantly important for two of the three
schools because of their program improvement (PI) ranking. The principals at these two
schools used African American students’ assessment data to tackle under-achievement
challenges. Nonetheless, at two of the three sites a strong emphasis was placed on the
African American subgroup because their inability to master grade level content resulted
in the schools’ PI status, which meant ongoing analysis and scrutiny of their assessment
data. However, at the non-program improvement elementary school, the principal used
data to develop achievement action plans and inform classroom lessons. On the contrary,
a great deal of attention was placed on the education and academic growth of African
American students.
Time. The use of time corresponded with findings of earlier studies regarding
teachers not having enough to improve classroom practices (Jandris, 2001; Earl & Katz,
2002). Jandris (2001) revealed that administrators should provide teachers the needed
time to practice their teaching craft. Teachers required time to scrutinize data and perfect

126
their own instructional skills. Administrators at the two elementary schools and one
middle school provided teachers opportunities to practice and apply their data knowledge.
Teacher participants communicated that time was the most grueling challenge. Not
having enough time to analyze data and plan lessons sometimes hindered their
proficiency with using data. Teachers in both the elementary and secondary settings
articulated a desire for more creative ways to use time during the instructional day.
Although administrators allotted time during weekly staffing meetings two-to-
three times a month, teachers stated it was not enough to adequately analyze data and
develop a prescriptive action plan for African American students. The findings revealed
that numerous middle school teachers used their own personal time away from their
school sites to analyze and interpret data. Likewise, elementary teacher participants
echoed the same concerns regarding having additional time during the instructional day
to analyze data and prepare lessons without having to do so outside of school. As
previous noted, researchers concurred that all classifications of teachers needed ample
time to apply and implement various data assessments and approaches (Jandris, 2001).
Similarly, these findings indicated teachers’ felt that if sufficient time was not allocated
for them to examine and learn from data, a data-driven culture does not materialize
(Peterson, 1995; Bamburg, 1994 as cited Jandris, 2001).
Collaboration. In addition to time, the findings revealed teachers at both
elementary schools and the one middle school correlated to the research literature on
collaboration (Jandris, 2001). CUSD teachers used collaboration meetings to work
together and discuss students’ performance. Many teachers reviewed and synthesized
students’ assessment data, shared common themes and concepts, and provided

127
opportunities to improve their professional practices in delivering effective classroom
lessons (Jandris, 2001). Although, collaboration meetings were expected, teachers did
not always focus on assessment data unless guidance was provided by the school’s
principal. For example, the principals’ at an elementary and middle school generated the
grade level collaborations agendas to ensure conversations centered around African
American students’ assessment data. Oftentimes, principals at these two schools
specifically requested an analysis of the data for African American students as well as
creation of teachers’ achievement action plans. Nevertheless, collaboration meetings at
another elementary school did not always spotlight data because of unplanned agendas.
At this particular site, teachers often deviated from data conversations pertaining to
African American performance and discussed non-data related topics.
In sum, the findings suggest that school leaders’ roles have become increasingly
important for building a data-driven culture. Because of the intensified pressures
stemming from NCLB, principals no longer can take a passive position in the educating
of African American students. The employment of multiple lenses ensured the
appropriate implementation of students’ assessment data in classrooms (Earl & Katz,
2002). Jandris (2001) study emphasized that principals’ build the capacity to support
teachers’ analysis and interpretation of data to improve the academic performance of all
student subgroups, specifically African American students.
The fundamental purpose for the ongoing uses of data was for educators to inform
instructional practices and decisions. In many stances, the placement of African
American students in special education programs resulted from the improper uses of data.

128
The next section examined the intended and unintended consequences that emerged at the
schools participating in the study.
The intended and unintended consequences of the use of data for reducing special
education placement.
The effective uses of students’ assessment data have tremendously benefited
schools’ and teachers. Data use allowed teachers opportunities to make better
instructional decisions regarding the learning needs of their students (Bernhardt, 2004).
Although data was instrumental in informing instruction, the findings of this study stated
harmful consequences occurred when data was used inappropriately. In the next section, I
discuss indicators that have augmented the implementation of data and inadvertently
contributed to African American students’ overrepresentation in special education.
Benefits of using data. Past research concentrated on the utilization of data as
support mechanisms to close the achievement gaps that existed among minority students
(Bernhardt, 2004). Mandinach et al. (2006) stated data provided teachers and
administrators a road map to ensure effective planning, professional development, and
interventions that ultimately lead to improved student achievement. Similar to the
aforementioned studies, teachers in all three CUSD schools gathered ongoing assessment
data of African American students. They disaggregated and engaged in data analyses to
elevate achievement in their respective classrooms which promoted the education of
African American students. All teachers benefited from learning and developing new
instructional approaches and strategies. As noted in previous research, the ongoing
analysis of data provided opportunities for teachers to rid themselves of non-effective

129
instructional strategies and techniques, educational programs, and to concentrate on the
under-performance of certain subgroups (Bernhardt, 2004).
The incorporation of data ensured CUSD’s principals and teacher participants did
not exercise “random acts of improvement” to improve African American students’
education (Bernhardt, 2004, p. 28). Parallel to preceding studies, data permitted
principals and teachers to make better decisions that were not only beneficial to African
American students, but other subgroups as well (Earl & Katz, 2002). The study findings
disclosed general and special education teachers used assessment data to pin-point
specific areas of weaknesses for African American students. Teachers in both the
elementary and secondary settings used data to remediate and provide
tutorial/intervention supports to improve student achievement efforts.
Unique to this study is the finding that data not only helped middle school
teachers understand African American students’ performance, but assisted them in
providing additional supports to minimize societal ills that transferred to their classrooms.
Essentially, middle school educators used data in a positive manner to target those
African American students not in special education. However, similar to findings in
earlier studies, the secondary special education teachers used ongoing assessment data of
African American students to inform instructional decisions and prevent expansion of
services (Salvia, Ysseldyke, & Bolt, 2007). The middle school principal always
questioned what the data said as well as the validity of the data. A conscious effort was
always being made by the principal to ensure proper implementation of data. However,
the principal conveyed teachers’ non-attentiveness to data results influenced improper
uses of data and yield a lack of achievement for African American students. On the other

130
hand, in the next section the unintended uses of assessment data for African American
students will be discussed.
Inappropriate uses of data. Although data supported the educating of African
American students, numerous environmental factors such as poverty and societal ills
contributed to their under-performance in school. Coinciding with previous studies,
teachers at the three school sites did not always understand the academic gaps or the
frustrations of African American students stemmed from stereotypes associated with their
subgroup (Confrey, Makar, & Kazak, 2004). Oftentimes, stereotypes and the
inappropriate uses of data in various general education settings contributed to African
American students’ overrepresentation in special education. Similar to Reid and Knight’s
(2006) study, teachers in the general education setting used inappropriate interventions
and strategies in the classroom as a result of data. Frequently, African American students
were excluded from meaningful engagement with peers and prohibited from participating
in lessons because of their undesirable classroom behaviors. Likewise, teachers in both
the general and special education environments sometimes lacked data-driven decision
making strategies or instructional skills to affective positive changes in their classroom
(Reid & Knight, 2006).
This section is related exclusively to teachers in the general education setting at
the two elementary schools. The study findings revealed that second and third grade
teachers regardless of their racial backgrounds or ethnicities generated numerous special
education referrals based on the behaviors of African American students and not the data.
In this study, African American students’ behaviors dominated the conversations between
interviewer and interviewees, especially in relations to boys. Many general education

131
teachers expressed they lacked experience in knowing how to handle forceful African
American youngsters. In addition to behaviors, elementary teachers used African
American students’ lack of achievement to make special education referrals. This
practice was evident when students’ data from various classroom assessments fail below
that of their peers or grade level standards. General education teachers at the two
elementary schools were more inclined to write referrals for African American boys than
for girls. In several situations, the findings indicated that general education teachers could
not substantiate how they used African American students’ assessment data to improve
classroom performance prior to the referral process. If an African student misbehaved,
the uses of data were non-existent for academics. Moreover, the under-performance,
behaviors, and gender of African American students prompted general education teachers
in the two CUSD elementary schools to use data inappropriately to exodus students from
their classes when a surge of environmental factors presented themselves in their
classrooms.
In sum, the intended and unintended consequences of data uses have impacted
African American students considerably in urban school settings. The intentional uses of
data were to enhance teachers’ classroom strategies and instructional practices (Heritage
& Yeagley, 2005). Classroom data influenced educational decisions pertaining to
interventions and various academic programs. The rationale for the uses of data
benefited classroom teachers and their work with African American students. However,
on numerous occasions, teachers’ and administrators’ inappropriate data uses have
resulted in unwarranted referrals and placements of African American students in special
education programs. Findings from this study revealed disadvantaged students such as

132
African Americans were placed at a much higher rate in special education programs than
other subgroups because of classroom behaviors and poverty circumstances. To alleviate
such practices, further studies on the uses of data with African American students will
require extensive investigations to inform schools’ instructional policies and practices.
Implications for Policy and Practice
While further research is no doubt important, the urgency of the problem of the
overrepresentation of African American students in special education requires immediate
action in educational arenas. To support student achievement in today’s urban
classrooms, it is important for educators to employ policies and practices that guarantee
all students’ access to a comprehensive and rigorous educational program. The
incorporation of data requires teachers to assess the academic performance of various
subgroups as well as African American students. Using data frequently to assess
academic progress minimizes special education referrals for the noted subgroup. The
intensification of data in schools has vastly influenced educational policies and practices.
However, the next section identifies numerous implications associated with the following
educators:
Principals
1. It is essential that principals develop and strengthen their leadership
knowledge and skills in interpreting and using data appropriately to transform schools’
instructional practices and decisions (Earl & Katz, 2002).
2. Principals have to use their data lenses to ensure data-driven cultures exist to
explore different kinds of assessment data (Earl & Katz, 2002).

133
3. The ongoing uses of data require principals to provide general and special
education teachers with additional supports such as time for analysis, ongoing
collaboration, instructional resources, and opportunities to design lessons addressing the
academic challenges associated with African American students.
4. The establishment of school-wide data teams to support the implementation of
school-wide data efforts (City & Murnane, 2006)
5. The incorporation of frequent and small scale professional development
trainings focusing specifically on the analysis and uses of data for all student subgroups
(Newmann, King, & Young, 2000).
Teachers
1. The accountability pressures of NCLB legislation requires that teachers are
cognizance of the purposes of data and use to inform, modify, and alter instruction to
inform instructional decisions in their classrooms (NCLB, 2002; Bejoian & Reid, 2005).
2. Teachers have to use current assessment data to plan effective and meaningful
instructional lessons addressing academic challenges that may arise in their classrooms.
3. The employment of data during the Student Study Team and implementation of
a school-wide Response to Intervention program encompasses the use of appropriate
interventions and classroom strategies to support the education of African American
students are critical.
4. To facilitate instructional improvements, teachers’ continuous awareness and
use of various forms of assessment data such as CST, benchmark assessments, teacher-
made assessments, unit tests, and informal assessments will promote student achievement
for African American students’ (Heritage & Yeagley, 2005).

134
District Office Administrators
1. District office administrators’ must engage in continuous review and
knowledge of NCLB to ensure compliance with mandates. Understanding the full extent
of NCLB in regards to the uses of data will ensure appropriate implementation at the
district and school levels (NCLB, 2002).
2. To provide ongoing uses of data, district office administrators have to provide
the instructional resources, funding, and technological tools to support schools’
implementation of students’ assessment data for disadvantaged youngsters.
Teacher Education Programs
1. In conjunction with the California Teaching Commission, colleges and
universities must establish certification procedures to ensure teachers understand how to
use different forms of assessment data to address the strengthens and weaknesses of a
range of classroom learners. The uses of data may need to be a part of the credentialing
process for newer teachers. However, additional ideas for further research warrant
attention.
Conclusion
The rationale for conducting this study was to focus on the uses of data in school
environments populated with African American students. The objective of this study was
to ascertain whether data influenced instructional practices to minimize the
overrepresentation of African American students in special education programs. A
thorough examination and analysis of the uses of data in the general education
environment shed light on the viewpoints and practices that resulted in African American
students being referred and placed in special education placements. To prevent further

135
referrals, educators need to ensure a range of instructional supports such as instructional
planning, differentiating lessons, Student Study Team/Response to Intervention, and
professional development are evident in schools to improve the education of African
American students.
In addition to the above supports for the uses of data, varied forms of assessment
data are required in classrooms to inform instructional decisions for African American
students. Administrators as well as teachers must be savvy in interpreting and
implementing data to inform classroom practices. To progress the achievement of
African American students, the role of principals are to build the leadership capacity to
support teachers’ analysis of data as well as providing them time and opportunities to
collaborate.
The uses of data have intended and unintended outcomes. There are many
instructional benefits for teachers’ to use data. Yet, data are not always used properly
because African American students’ demonstrate undesirable behaviors and are
bombarded with an influx of environmental and societal factors that impedes their
performance in class. The use of students’ assessment data was crucial in these three
urban schools. Although challenges persist, teachers and principals learned how to use
and understand data when African American students presented non-related factors in
their classrooms and schools.
In both the general and special education environments educators have to work
relentlessly to improve the educational outcomes of African American students. If
African American students are to compete successfully with their peers and strive in an
academic environment, the ongoing uses of assessment data are imperative. Ultimately, a

136
better education will provide African American students endless educational possibilities
in a forever changing world.

137
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148
APPENDIX A

Data-Driven Decision Making and Special Education
Teacher Interview Protocol

Participant’s Name
(Optional)________________________________________Date:__________________

Position:________________________________________________________________

[Introduction: The purpose of this interview is to understand how general and special
education teachers use data to inform instructional decisions. During the interview, I will
take notes as well as tape record your responses. Please note: all information shared
during the interview is strictly confidential. However, if there is something that you
would like to say off the record, the tape recorder will be turned off and this information
will be omitted from my notes. The interview will take approximately 30 to 45 minutes.
Do you have any specific questions before we begin?]

1. Please tell me about your teaching experience? How long have you taught
general or special education? How long have you taught at this particular school
site (background)?
2. What role does data play in helping African American students at your school
master grade level performance goals? How do you know whether or not
performance goals have been met? (Probe: indicators, IEPs, CST, district
benchmarks, etc.) (research question 1)
3. What kinds of data are used in your classroom (e.g., standardized/district
assessments, classroom, school, etc.) (research question 1)
4. How does the pre-referral process influence data-driven decision making for
African American students who may be identified for special education? (research
question 1)
5. As a teacher, have you been trained in how to use various forms of data in your
classroom? If so, how often are follow-up sessions provided? (research question
1)
6. Does your school administrator support a culture of data-driven decision making?
If yes, what kind of support does your administrator provide to encourage the use
of data? (research question 2)
7. Does your school administrator provide teachers opportunities to analyze data?
(research question 2) If yes, please answer the following questions. If no time is
provided, please explain why you think this is so.

149
a) Are there scheduled meetings where data are the primary focus? (i.e., staff
meetings, grade level, early release time, etc.)
b) How often do you meet to discuss data?
c) Do you submit meeting documents to your administrator regarding data?
d) Are you provided enough time to analyze your students’ data?
e) Do you collaborate with other teachers regarding your students’ data? If
yes, what does that collaboration look like?
8. Has your school administrator made any significant changes and/or decisions in
the past school year as a result of student data? (research question 2)
9. Do you feel the use of data have enhanced or hindered the achievement of African
American students in your school? (research questions 1 and 2)
10. Do you believe the over-use or lack of data use have contributed to special
education referrals for African American students? (research questions 1 and 4)
11. In your opinion, how has federal or state legislation influenced your use of data?
Please explain? (research questions 1 and 4)
12. Has using data helped you become a better teacher? If yes, explain how it has
perfected your skill in knowing which specific strategies and techniques to use
with your students? If no, what problems have you encountered using data?
(research question 4)
[Concluding Remarks/Questions: Is there anything else you would like to share? Thank
participant for cooperation and his/her time. Inform participant that the final report will
be shared once the research has been concluded.]

The following documents are requested:
• Bi-weekly or weekly classroom assessments, district benchmark assessments,
grade level assessment profiles
• School-wide professional development calendar
• Grade level agendas

150
APPENDIX B

Data-Driven Decision Making and Special Education
Administrator Interview Protocol

Participant’s Name (Optional)__________________________________ Date:________

Position:________________________________________________________________

[Introduction: The purpose of this interview is to understand how general and special
education teachers use data-driven decision making to inform instructional practices. This
interview will also discern what supports regarding data are provided. During the
interview, I will take notes as well as tape record your responses. Please note: all
information shared during this interview is strictly confidential. However, if there is
something that you would like to say off the record, the tape recorder will be turned off
and this information will be omitted from my notes. The interview will take
approximately 30 to 45 minutes. Do you have any specific questions before we begin?]

1. Please provide a little information about the students and the community that you
serve? How would you describe the culture of your school/district? (background)
2. What is your position? How long have you served in this position? What is your
prior experience and training? (background)
3. What pre-referral processes influence the use of data with African American
students? (research question 1)
4. Does your school/district have performance goals? Do teachers establish goals for
students in their classrooms? If so, how are performance goals established? How
do you know whether or not goals have been met? How often are students’ goals
revisited? (research questions 1 and 2)
5. Can you explain how your district has adhered to major state/federal government
legislations (NCLB and IDEA) in the past five years? (research questions 1 and 2)
6. What types of data do teachers find useful when making decisions about
instruction and curriculum? Please explain. (research questions 1 and 2)

7. What kind of data do teachers have access to? How do they collect and examine
data? How often is data reviewed? (e.g., state/district benchmark assessments,
school, classroom, IEPs, teacher-made, etc.) (research questions 1 and 2)

8. How often is professional development tailored to address different kind of
student data? What additional professional development will help teachers
become data literate? (research questions 1 and 2)
9. How do you use data as a school leader? (research question 3)

151

10. How do you support teachers’ use of data? What kinds of supports are provided
for teachers struggling or proficient in using data? (e.g., resources, data systems,
computers) (research question 3)

11. Do you provide time to analyze data? Do teachers in general and special
education classrooms have time to collaborate? If yes, please explain how
teachers use time in meetings (e.g., grade level meetings or early release days) to
collaborate. (research question 3)

12. What have been the negative effects of teachers’ over-use of student assessment
data? (research question 4)

13. Do you think teachers have low expectations and misinterpret data when working
with African American students? Please explain (research questions 2 and 4)

14. Can you describe an instance where data has benefited or hindered African
American students in performance at your site? (research question 4)

15. As a school leader, what do you believe are challenges that may prevent teachers
from successfully using data? What do you think are your next steps to ensure the
continuous use of data? (research question 2)

[Concluding Remarks/Questions: Do you feel that there is anything else that will support
the data-driven decision making process at your school? Thank participant for
cooperation and his/her time. Inform participant that the final report will be shared once
the research has been concluded.]

The following documents are requested:
• Bi-weekly or weekly classroom assessments, district benchmark assessments,
grade level assessment profiles
• School-wide professional development calendar
• Grade level agendas
• Meeting minutes
• District assessment reports

152
APPENDIX C

Data-Driven Decision Making and Special Education
District Administrator Interview Protocol

Participant’s Name
(Optional)___________________________________________________Date:_______

Position:________________________________________________________________

[Introduction: The purpose of this interview is to understand how district/special
education personnel support data-driven decision making practices in elementary and
middle school classrooms. During the interview, I will take notes as well as tape record
your responses. Please note: all information shared during interview is strictly
confidential. However, if there is something that you would like to say off the record, the
tape recorder will be turned off and this information will be omitted from my notes. The
interview will take approximately 30 to 45 minutes. Do you have any specific questions
before we begin?]

1. Briefly describe the history of your community, district, schools, and students
focusing on the last five years (reform initiatives, major structural changes,
relationships with external providers) (background).
2. Please describe your position in the district/special education department. How
long have you been in this position? What is your prior experience and training
(background)?
3. Is data-driven decision making a priority (e.g., school board trustees, district’s
leadership (cabinet), special education department, and schools? Why or Why
Not? (research question 1)

4. What is the district’s purpose for collecting and using data? Is there a specific
model/plan used to collect, analyze, and facilitate use of data in the special
education program? Please describe process. (research question 1)

5. Please describe the types of data that the district collects (e.g., teacher-made
assessments, state/federal standardized assessments, district’s benchmark
assessments, African American students’ IEPs)? How often is this data collected?
(research question 1)

6. Please describe supports the district and/or special education department provide
to teachers and principals, in relation to the pre-referral process and their use of
data. (research questions 1 and 3)
7. How is performance data for African American students used to make decisions
regarding instructional practices, professional development, budgeting concerns,

153
and staffing? Please provide examples where the district has used African
American students’ performance data to make changes or adjustments in
instructional practices. (research question 2)
8. What data would you expect teachers to use to improve the academic performance
of African American students? (research question 2)
9. What does the district and/or special education department do to encourage the
culture of data use in schools? How would you know such a culture if you saw it?
What does it imply for principals, teachers, and students? What have been the
challenges? (research questions 2 and 3)
10. To what extent do principals have decision-making authority over professional
development, curriculum and instructional practices, staffing, and budgeting?
Can you think of an instance in which a principal used their decision-making
authority to make a change based on African American students’ performance
data? (research question 3)
11. Has your district and/or special education department facilitated professional
development for schools that focus on using data to make decisions? In what
specific areas is professional development provided? Is professional development
voluntary or mandatory? (research questions 2 and 3)
12. What difficulties have schools experienced in using data to inform data-driven
decision making? Please explain. (research question 4)
13. Reflecting upon your data-driven decision making practices at the district and/or
in special education department, what works, what has not, what do you wish you
knew more about data-driven decision making, and what do you wish you were
able to accomplish? (research question 4)
14. How will you continue to support and sustain reforms in the data-driven decision
making process? (research question 4)
[Concluding Remarks/Questions: Do you feel that there is anything else that will support
the data-driven decision making process in the district and/or special education
department? Thank participant for cooperation and his/her time. Inform participant that
the final report will be shared once the research has been concluded.]

The following documents are requested:
• District benchmark assessments and state standardized assessments result
• District-wide professional development calendar

154
• District and Special Education Department agendas/meeting minutes (e. g., data
focus)
• District data assessment reports

Abstract (if available)

Abstract For over 30 years African American students' have been disproportionately placed in special education programs. Because of the heightened pressures of NCLB, data driven decision making (DDDM) has become a promising reform drawing attention to the academic performance of several student subgroups. In many academic settings, data use exists, however, it is unclear how DDDM assists (or not) in the schooling of African American students.

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Asset Metadata

Creator Ward-Roberts, Virginia (author)

Core Title Does data-driven decision making matter for African American students?

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 04/27/2009

Defense Date 03/11/2009

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

Tag African American students and data use,data use and teachers' instructional practices,data use in general and special education classrooms with African American students,data-driven decision making,OAI-PMH Harvest,the uses of various forms of assessment data with African American students (e.g., benchmarks, standardized, teacher-made, bi-weekly, informal)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Datnow, Amanda (committee chair), Love, Laurie (committee member), Ragusa, Gisele (committee member)

Creator Email vyvetteward@yahoo.com,wardrobe@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2132

Unique identifier UC1255649

Identifier etd-WardRoberts-2829 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-229259 (legacy record id),usctheses-m2132 (legacy record id)

Legacy Identifier etd-WardRoberts-2829.pdf

Dmrecord 229259

Document Type Dissertation

Rights Ward-Roberts, Virginia

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