University of Southern California Dissertations and Theses

Educational resource allocation at the middle school level: a case study of six middle schools in one California district

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Educational resource allocation at the middle school level: a case study of six middle schools in one California district

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Content Running Head: EDUCATIONAL RESOURCE ALLOCATION 1

EDUCATIONAL RESOURCE ALLOCATION AT THE MIDDLE SCHOOL LEVEL:
A CASE STUDY OF SIX MIDDLE SCHOOLS IN ONE CALIFORNIA DISTRICT

by

Sandra Garcia

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

August 2013

Copyright 2013 Sandra Garcia
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Dedication
This dissertation is dedicated to my parents, Salvador and Monica Garcia, and to
my two lovely sisters, for encouraging and supporting me throughout my journey in
education. It is also dedicated to all of my students, whom I love and respect dearly, may
my doctoral achievement serve as an example of my commitment to you and education.

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Acknowledgements
I would like to thank Dr. Lawrence Picus for accepting me into his thematic group
and for patiently waiting for my chapter submissions and revisions. Thank you, Dr.
Picus, for your authentic feedback and optimism.
I would also like to thank my family, especially my sisters, for respecting my
choices and allowing me to skip family events and outings. I love you!
Lastly, I would like to thank my two extended families; I could not have survived
the program without their support and encouragement. To the “OC-8” (Melissa Sais,
Sarah Ragusa, Amber Lane, Steve Behar, Larry Hausner, Tamra Rowcliffe, and Jonathan
Swanson) and to the LA cohort members (Dipali Potnis and Dominic Nguyen), thank you
for your infinite words of encouragement, text messages, and on-going support. This
program is what it is because of you, I love and thank you all.

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Table of Contents
Dedication 2

Acknowledgements 3

List of Tables 6

List of Figures 7

Abstract 8

Chapter 1: Overview of the Study 10
Brief History of School Finance 12
Statement of the Problem 14
Purpose of the Study 15
Importance of the Study 16
Methodology 17
Limitations 18
Delimitations 18
Assumptions 18
Definitions 18

Chapter 2: Literature Review 22
School Improvement Strategies 22
Allocation of Human Resources 32
Limited Resources/Fiscal Constraints 42
Gap Analysis 47

Chapter 3: Methodology 49
Overview of Methodology 49
Research Questions 50
Sample and Population 50
Instrumentation and Data Collection 54
Data Analysis 55
Summary 57

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Chapter 4: Study Results 58
Overview of the District 58
Findings for Research Question One 63
Summary and Recommendations from Research Question One 72
Findings for Research Question Two 72
Summary of Findings from Research Question Two 83
Findings for Research Question Three 84
Summary of Findings for Research Question Three 87
Findings for Research Question Four 88
Chapter Summary 90

Chapter 5: Conclusions 92
Purpose of the Study 92
Importance of Study 93
Methodology 93
Sample and Population 94
Summary of the Findings 95
Limitations 99
Implications for Practice 100
Recommendations for Future Research 101
Conclusion 102

References 103

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List of Tables
Table 1: School Improvement Strategies According to Research Studies 28

Table 2: Recommendations for Adequate Resources for Prototypical Elementary,
Middle and High Schools 40

Table 3: Class Size Reduction Relief, 2008-2009 to 2011-2012 46

Table 4: Student Total Enrollment 52

Table 5: Student Enrollment by Group 53

Table 6: Demographics for all Six Middle Schools in Sample District 60

Table 7; Student Achievement Data for Sample Middle Schools 61

Table 8: Subgroup Achievement Data – Three Years Growth 61

Table 9: Leadership – Superintendent’s Expectations 64

Table 10: Leadership – Four Areas of Focus 65

Table 11: Professional Development 71

Table 12: Certificated Teaching Staff 75

Table 13: Certificated Staff 78

Table 14: Pupil Support Staff 80

Table 15: Classified Staff 82

Table 16: Office Staff 83

Table 17: Human Resource Allocation Gaps for Six Middle Schools 85

Table 18: Recommended Number of Instructional Coaches per Site 90

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List of Figures
Figure 1. Total Revenues Per Pupil: 1970-2009 33

Figure 2. Current per-pupil expenditures in the United States: 2008–09 33

Figure 3. The Evidence-Based Model. Borrowed from Odden and Picus, 2009 39

Figure 4. California’s Education Revenue System 43

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Abstract
The purpose of this study was to analyze the human resource allocation of one
urban school district located in Southern California in comparison to the
recommendations of the Evidence-Based Model. Prior to collecting data from the
district, the researcher identified four strategies for increasing student achievement.
These strategies were utilized as a benchmark when analyzing the strategies used by the
district. A mixed-methods approach was used in this study. Qualitative data in the form
of anecdotal notes was used to comprehend the district’s decision-making when
implementing specific strategies and practices. Quantitative data on position counts was
input into a simulation database model developed by Picus and Knight (2012) to identify
gaps among the district’s desired allocation, its current human resource allocation, and
that recommended by the Evidence-Based Model.
Overall, the study provides the district with responses and findings to the
following research questions: 1) What research based human resource allocation
strategies improve student achievement? 2) How are human resources allocated across
the district of study and its schools? 3) Is there a gap between current human resource
allocation practices and what the research suggests is most effective? 4) How can human
resources be strategically re-allocated to align with strategies that improve student
achievement?
Findings of this study serve as a tool for the district of study, educational leaders,
and policy makers. The study takes into account California’s fiscal situation at the time
of this study in addition to the difficulties of meeting state and federal demands with
limited funds. The district’s human resource allocation is displayed, demonstrating its
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effective use of resources in some areas and its lack of personnel in others. This study
provides the district with recommendations regarding human resource allocation trade-
offs that meet its current funding budget and benefit student achievement. Results of the
study demonstrate that, although recommendations are applicable, they are not significant
in closing the gap between the district’s current human resource allocation and the
Evidence-Based Model. Therefore, this study may be used as a sample for policy makers
to examine the great efforts of one district and the barriers imposed due to lack of
adequate funding.
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Chapter 1: Overview of the Study
America’s public education system invests a large amount of funding in human
resources; therefore, it is critical to examine human resource allocation strategies that
help improve student achievement in order to maximize limited funding. In a time of
budget cuts, expenditures for personnel continue to be the largest and most valuable
district expense; approximately 80% to 85% of a district’s budget goes directly to
personnel expenditures (Alvy & Robbins, 1998). In defining human resources, these
expenses include all of the salaries of the staff employed in public schools, such as
district administrators, instructional staff, site administrators, teachers, teacher aides,
counselors, librarians, and support staff (Odden & Picus, 2008). These positions assist in
ensuring that the organization functions with the prime objective of preparing all students
to become problem solvers, thinkers, and participants in the fields of higher education,
workforce, and global economy. However, the task of providing an adequate education
to tomorrow’s generation becomes more challenging as budget cuts continue to affect
school finance.
Among all financial uncertainties, California also faces the pressure of ensuring
that all students are 100% proficient in English Language Arts and Mathematics by 2014
as mandated by the Federal No Child Left Behind Act (NCLB) of 2002. NCLB initiated
the two-pronged accountability system that facilitated the scrutiny of public education, as
the progress of schools began to be measured through the federal Adequate Yearly
Progress (AYP) and state Academic Performance Index (API). Never before had schools
felt the pressure of being measured, both individually and in comparison to others. As a
consequence of failure to meet minimum performance levels, schools and districts were
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identified as “At Risk of Entering Program Improvement,” “Program Improvement”, and
“Persistently Lowest-Achieving Schools” (California Department of Education, 2012).
Underperforming schools also faced the possible sanction of being taken over by the state
(EdSource, 2005).
In addition to financial restraints and accountability, California experiences a
unique situation with the responsibility of educating such a large and diverse population.
California educates more students than any other state (EdSource, 2008). California also
has the highest proportion of households with parents who have not obtained a high
school diploma (EdSource, 2011). Furthermore, California educates a large proportion of
students who live near or below the nation’s poverty line, some 24% of California’s
students are English Learners (EL), and 44% of our state’s students speak a language
other than English at home (EdSource, 2011).
Hence, the purpose of this chapter is to examine California’s school finance while
taking into consideration the challenges and, most importantly, the solutions that can be
implemented through the thoughtful allocation of human resources. This chapter presents
a brief history of California’s school finance, outlining the landmarks that have
contributed to the current funding system. Equity and adequacy are presented as
theoretical models for funding schools. The statement of the problem is followed by the
purpose and importance of the study. Lastly, a brief summary methodology is included
along with the limitations, delimitation, and assumptions. This section concludes with a
list of key terms and definitions used throughout the study.

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Brief History of School Finance
California’s funding system is primarily a product of two 1970 court rulings.
First, the California Supreme Court decision known as Serrano v. Priest (1971) found the
state’s school financing system to be unconstitutional under the Fourteenth Amendment
(Weston, 2010). Widespread financial discrepancies existed among school districts.
Picus (1991) reports that, in 1970 and 1971, the disparity in average daily attendance
(ADA) amounts ranged from $420 to $3,447. As a result, Proposition 13 (1978) initiated
changes by lowering the amount of local property tax revenue and implementing a one
percent rate cap. Prior to Proposition 13, school districts determined the amount of local
property tax, which constituted the majority of the districts’ school funding. This was
found inequitable since communities of higher socioeconomic status raised more revenue
for property taxes in comparison to lower-wealth communities. As a result, the control of
school funding shifted from local to State. In a continuous effort to stabilize and equalize
school funding, Proposition 98 was passed, dictating, through a complex formula, the
minimum amount that the state must spend in K-12 and community colleges (EdSource
Resource Cards, 2011; Loeb et al., 2008). Hence, the funding of public education relies
on the growth of state revenue.
Proposition 98, as noted in Picus (1991), contains three major provisions: a
minimum funding guarantee for schools, allocation of funds when state revenues exceed
the Gann spending limit, and school accountability report cards. Minimum spending
level under Proposition 98 is determined by one of three tests: Test 1 – Percentage of
General Fund Revenues, Test 2 – Adjustments Based on Statewide Personal Income, and
Test 3 - Adjustments Based on Available Revenues (Ed Source Policy Brief, 2009).
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Another option to the three tests is suspension. Proposition 98 can be suspended for a
year, and suspension occurred once during the 2004-2005 term. Every district has a base
revenue limit, which determines a dollar amount per pupil, funded through local property
taxes. When property taxes cannot fund the base amount, the state is obligated to close
the gap.
California receives funds from several sources; these include the state’s General
Fund, property taxes, federal government, local miscellaneous sources, and the state
lottery (EdSource, 2011). The two main sources are the state’s General Fund and local
property taxes. Funds are allocated to districts as either restricted or unrestricted;
unrestricted funds account for approximately 70% of the average district’s funding. For
the most part, restricted funding comes mainly through categorical programs.
Equity to adequacy. As a direct response to inequitable funding, cases such as
Brown v. Board of Education (1954) and Serrano v. Priest (1971) led the public to
question and policymakers to reanalyze the funding and services offered to all students.
Equity models were developed in an attempt to ensure equal access to funding regardless
of the revenues available due to property taxes. However, the equity model did not take
into account other inequitable factors embedded in the system, such as unequal access to
qualified teachers, unequal access to high-quality curriculum, and students’
socioeconomic status (Darling-Hammond, 2007). Therefore, to equalize education,
funding needs to be allocated according to student needs (Darling-Hammond, 2007;
Odden 2003). To design a system based on adequacy, Odden (2003) suggests the
following: identify the funding level for the typical student, identify the funding level for
students with special needs, and lastly manage those resources to produce desired results.
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Overall, the main objective is not to equalize funding, but to provide sufficient funding to
educate all students to high levels of achievement.
Odden (2003) introduces four methods that have been used to determine adequate
funding: 1) the successful district approach, 2) the cost function approach, 3) the
professional judgment approach, and 4) the evidence-based model. The approach that
will be used in this study is the Evidence-Based model. This model, developed by Odden
and Picus (2008) relies on strategies that have proven to be successful in raising student
achievement. To begin, the model identifies and assigns a cost to each item, followed by
a total cost. This provides sites and school districts the opportunity to assess the
expenditure items and to discuss whether these represent an effective use of funds. In-
depth details of this model are presented in the literature review of this study. The
selection of this model as the main theoretical framework is due to its direct alignment to
educational strategies that have been proven to yield positive student achievement results.
Statement of the Problem
Schools across the nation struggle to meet the demands of No Child Left Behind
(NCLB). California’s public schools are in a state of urgency as budget cuts continue to
have an impact on education. Some districts, as a means to balance the budget, resort to
cutting programs, shortening the school year, eliminating/combining positions as well as
implementing furlough days. In a time when the demands are high, the public school
system is held accountable for educating all students with a limited amount of financial
resources. Although there are many ways to solve a problem, the strategic allocation of
human resources can facilitate the progress of improving student achievement.
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Limited resources are a problem that educational leaders will encounter as they
budget and reallocate resources. A shift in educational funding took place that is
different from past practices where the revenues per pupil increased on a yearly basis
(Odden & Picus, 2008). California schools need research-based direction on how to best
allocate resources in order to avoid low-achievement that can lead them to attain, or
remain in, a Program Improvement status. Fortunately, researchers (Darling-Hammond,
2002; Duke, 2006; Fermanich et al, 2006; Odden, 2009; Reeves, 2003; Togneri &
Anderson, 2003; Williams et al., 2005) can now identify some of the common strategies
applied within high achieving schools that can be implemented in underperforming
schools. The Center on Education Policy (2009) reports that schools that have exited
restructuring utilized numerous strategies to improve student achievement. These
strategies include professional development, a standards-driven curriculum, student
assessments to guide instruction, and research-based instructional programs.
The literature review presents solutions by identifying a number of major topics
that can support the allocation of limited resources through best practices. The chapter is
divided into four sections: Allocation and Use of Human Resources, School Improvement
Strategies, Limited Resources/Fiscal Constraints, and Gap Analysis. The Evidence-
Based Model (Odden & Picus, 2008) is used as the framework to benchmark the district
of study and its current allocation of human resources.
Purpose of the Study
The purpose of this study was to find ways to help districts use human resources
to improve student performance. Data from six comprehensive middle schools was
collected and analyzed in comparison to the Evidence-Based Model (Odden & Picus,
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2008). The Evidence-Based Model is research-based and designed to assist schools in
allocating human resources in areas that have proven to deliver positive outcomes.
Benchmarking the Evidence-Based Model to the district of study provides information on
how closely aligned the school sites are in their allocation of resources according to best
practices. Hence, this gap analysis case study provides policymakers and educational
leaders with resources that can affect student achievement.
This study answers the following questions:
1. What research-based human resource allocation strategies improve student
achievement?
2. How are human resources allocated across the district of study and its schools?
3. Is there a gap between current human resource allocation practices and what the
research suggests is most effective?
4. How can human resources be strategically re-allocated to align with strategies that
improve student achievement?
Importance of the Study
This study, in addition to the findings of fifteen other members of a thematic
dissertation group at USC, provides policymakers, district managers, and administrators
with research-based resources that support effective use of funding. Approximately 80%
to 85% of a school’s and a district’s budget is designated for personnel expenditures
(Alvy & Robbins, 1998). Therefore, as personnel expenditures constitute such a large
percentage of a school’s budget, it is of prime importance to identify the most cost-
effective method to allocating human resources. In conclusion, this study serves several
purposes: 1) To provide information specific to the middle school setting; 2) To analyze
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schools and districts that have implemented resource allocation strategies to improve
student achievement; 3) To identify the advantages of the Evidence-Based Model; and 4)
To will provide policymakers and educational leaders data that can support changes in
the reallocation of personnel funds.
Methodology
In an effort to study the allocation of human resources in an identified district, this
study examined the allocation of personnel in six middle schools located in an urban city
in Southern California. The gap analysis uses the Evidence-Based Model (Odden &
Picus, 2008) as the comprehensive resource allocation framework to determine the
district’s alignment to best practices. Although this study is embedded in a qualitative
research design, quantitative data will be provided to support the analysis.
The district of study meets the following criteria: 1) located in an urban city, 2)
serves a diverse student population, and 3) 70% or more of the student population
receives free or reduced-price meals. Interviews were scheduled with district officials to
gather information relevant to the allocation of human resources. Additionally,
documents such as Single Plan for Student Achievement, School Accountability Report
Card, County Summary Reports, and district allocation of personnel documents were
analyzed and input into a database developed by David Knight and Lawrence O. Picus
(2012). The database serves multiple purposes: 1) To assist in the organization of data by
site and district, 2) To generate the gaps in comparison to the Evidence-Based Model, and
3) To provide a basis for recommendations to the six middle school sites and the district.

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Limitations
As with any research, there are certain limitations to this study. First, this study is
not generalizable since it focuses on a specific sample group. Second, the sample size is
not representative of all schools in California. Lastly, the study is conducted in a short
period of time and with resources applicable to the 2012-2013 school year. Therefore,
additional budget cuts may alter the results of this study.
Delimitations
This study was conducted in six public middle schools that serve a diverse student
population in southern California. Only one adequacy model, the Evidence-Based
Model, was used to benchmark the district’s and the school sites’ human resource
allocation.
Assumptions
This study employed the following assumptions:
1. All data collected was representative for this particular study.
2. All people interviewed provided accurate information to the best of their
knowledge.
3. Literature review is substantial enough to allow for -proceeding with
recommendations on human resource allocation practices.
Definitions
1. Academic Performance Index (API): California’s numerical index used to
measure schools’ academic performance and growth (California Department of
Education, 2012).
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2. Achievement Gap: The observed difference in performance between groups of
students.
3. Adequacy: The sufficient amount of funding necessary to educate each student to
high standards of performance (Odden, 2003).
4. Adequate Yearly Progress (AYP): Accountability system implemented as a result
of No Child Left Behind (NCLB) that requires all students to demonstrate
proficiency in the areas of Mathematics and English Language Arts (California
Department of Education, 2012).
5. American Recovery and Reinvestment Act (ARRA): A stimulus package that
provided over $100 billion for schools nationwide.
6. Average Daily Attendance (ADA): Districts receive school funding based on
student attendance. The average daily attendance is calculated by dividing the
days of attendance by the total days of instruction, then multiplying it by the per
pupil revenue limit.
7. Brown v. Board of Education (1954): A landmark legal decision by the United
States Supreme Court that declares segregation in public schools based on race is
unconstitutional.
8. California Standards Test (CST): Tests developed in California to measure
students' progress toward achieving California's state-adopted academic content
standards in English–language arts (ELA), mathematics, science, and history–
social science (Standardized Test and Reporting Program, 2012).
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9. Categorical Funds: Funds allocated for specific programs or to support students
with special needs, such as students with disabilities and English Learners
(EdSource, 1996-2012).
10. Class Size Reduction (CSR): Program established in 1996 with the intent of
improving student achievement by reducing class sizes in grades K-3.
11. Elementary Secondary Education Act (ESEA): A United States federal statute
enacted April 11, 1965. The act funds primary and secondary education,
emphasizing the importance of equal access to education. ESEA is reauthorized
every five years; the current reauthorization of ESEA is the No Child Left Behind
Act of 2001.
12. Evidence-Based Model (EBM): A model that takes into account the essential
personnel positions and funding levels for schools and students to achieve growth
(Odden et. al, 2005).
13. Gap Analysis: An approach used to identify, diagnose, and close the gap between
the actual and desired performance (Clark & Estes, 2002).
14. No Child Left Behind (NCLB): The No Child Left Behind Act of 2001 is the
reauthorization of the Elementary Secondary Education Act (ESEA). NCLB
increased the accountability of the public education system by setting the 100%
proficiency target by 2014. It places focus on testing and accountability, parent
involvement, and quality instruction by highly qualified teachers.
15. Proficiency: In accordance with NCLB, all students are expected to demonstrate
mastery of the content areas tested by 2014.
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16. Per Pupil Funding: The amount of funding provided to schools districts for the
enrollment of each student.
17. Professional Development: The opportunity for adult learners to build capacity
through trainings that incorporate strategies for instructional and school
improvement.
18. Proposition 13: Limits property tax rates and provides a cap for increases in
assessed property values.
19. Proposition 98: Guarantees schools a minimum level of funding.

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Chapter 2: Literature Review
The purpose of this chapter is to review the literature in four particular areas:
school improvement strategies, allocation of human resources, limited resources/fiscal
constraints, and Clark and Estes’ (2008) gap analysis approach. School improvement
strategies (Darling-Hammond, 2002; Duke, 2006; Fermanich et al., 2006; Odden, 2009;
Reeves, 2003; Togneri & Anderson, 2003; Williams et al., 2005; ) are analyzed in order
to provide educational leaders and policymakers a synthesis of the common strategies
used by high-achieving schools. Knowledge of these common strategies will assist
educational leaders strategically allocate resources in ways that benefit school reform.
This chapter provides an in-depth description of the allocation of personnel resources as
identified in the Evidence-Based Model (Odden & Picus, 2008). The Evidence-Based
Model is the conceptual framework that used in this study.
School Improvement Strategies
Darling-Hammond (2002) analyzes the similarities of successful restructured
schools in New York City and identified them as small alternative organizations driven
by motivation and incentives. These schools form part of the Center for Collaborative
Education (CEC). They service low-income students of color who have been labeled at-
risk in traditional school settings. To highlight their success, Darling-Hammond shares
the twelve principles and practices implemented at four of the high schools which
demonstrated success through their 90% and over graduation and college-going rate: 1)
school purposes, 2) high and universal academic standards, 3) interdisciplinary,
multicultural curriculum, 4) small size and personalization, 5) commitment to a goal, 6)
performance-based assessments, 7) respectful tone and values, 8) family involvement, 9)
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shared decision making, 10) commitment to diversity, 11) selection of the school by
student choice and 12) administrative and budget targets. Through administrative and
budget targets, schools strategically allocate funds in order to provide small class sizes
and time for teacher collaboration. Some of the common practices include completion of
graduation portfolios, changes in the master schedule to meet student needs,
interdependent teams, teachers as advisors, parents as group members, and built-in
professional development. Similar to business organizations, the success of these schools
relies on the collective decision-making of all stakeholders, with benefits and incentives
for all.
In 1995, Reeves (2003) conducted research that defied the common assumption of
schools’ inability to gain academic achievement due to students’ ethnicity and
socioeconomic status. This study observed schools from elementary through high school
in Milwaukee, Wisconsin, which had over 90% of their students eligible for free or
reduced-price lunch, over 90% of their students’ forming part of an ethnic minority
group, and over 90% of their students’ meeting the state and district standards. This
study coined the term “90/90/90.” Through various observations in the form of site visits
and data analysis, the following common characteristics were identified: a focus on
academic achievement, clear curriculum choices, frequent assessments, emphasis on
nonfiction writing, and collaborative scoring of student work. Similar to the upcoming
Common Core, 90/90/90 schools require written responses in performance assessments.
Results of this study indicate that there is not one solution or instructional program that
can be credited with the school’s academic growth. It is the outcome of teaching quality
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with emphasis on instruction and assessments, in addition to the collective work of all
stakeholders, that contributes to students’ academic achievement.
In an effort to learn more about how districts promote good instruction, Togneri
and Anderson (2003) identified five high-poverty school districts that demonstrated
success in increasing and maintaining student achievement across grade levels. The
schools and districts examined represent sample populations from Texas, California,
Maryland, Minnesota, and Rhode Island. In conjunction with Learning First Alliance
leaders and researchers, this study gathered over 200 interviews in addition to school
visits and focus groups. The findings revealed a common system-wide approach through
the implementation of the following seven strategies: 1) acknowledgment of poor
performance as indicated by the data, followed by the seeking of solutions, 2) a system-
wide approach to instructional improvement that includes curriculum that aligns to state
standards, professional development on specific set of strategies, and shared instructional
leadership, 3) the development of a vision focused on learning and instructional
improvement, presented to the community, students, and staff through board meeting
agendas and newsletters, in addition to being outlined in the strategic plans and school
improvement plans, 4) data-based decision making, 5) the adoption of new approaches to
professional development with coherent, district organized strategies to improve
instruction, and 6) a shared leadership approach.
Williams et al. (2005) conducted an extensive analysis with the objective of
responding to the research question, “why do schools servicing similarly challenged
student population vary in their performance on the API by as many as 250 points?” The
overall objective of the study was to identify what high-performing schools may be doing
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in comparison to low-performing schools. The research involved 257 California
elementary schools from 147 districts with similar student populations. Surveys were
provided to teachers and principals to identify the intensity of practice implementation.
Four common school practices were identified as predictors of high performance, as
analyzed in the surveys of high performing schools: 1) prioritizing student achievement
through high expectations, a vision aligned to student learning, and a commitment to
improving student achievement, 2) implementing a coherent, standards-based curriculum
and instructional program by identifying essential standards and organizing instruction in
accordance to state academic standards, 3) using assessment data to improve student
achievement, and 4) ensuring availability of instructional resources.
Similar to Reeves (2003) and Togneri and Anderson (2003), Duke (2006) studied
the factors that influence schools’ academic growth, particularly within turnaround
schools. The purpose of the study was to identify what is known and unknown in the
process of improving low-performing schools with the intent of sharing this information
with the Virginia School Turnaround Specialist Program. The study took into account
the findings of five studies, conducted between 1999 and 2004, focused on schools
servicing a large percentage of students from low socioeconomic status that demonstrated
impressive academic growth. In identifying the commonalities between the five studies,
Duke found the following top six characteristics: prompt student assistance, teacher
collaboration, data-driven decision making, team leadership, adjustments to the
organizational structure, and staff development. Other characteristics emphasized in the
meta-analysis, though not prevalent in all five studies, include the alignment of
EDUCATIONAL RESOURCE ALLOCATION 26

assessments to curriculum and instruction, high expectations, parent involvement, and
scheduling adjustments.
In a district analysis, Fermanich et al. (2006) conducted an in-depth study of 9
successful districts and 31 schools from the state of Washington. For the purposes of the
study, success was determined by meeting the established criteria as represented by a
score between 24 and 36 with 36 being the highest. One, the study found that districts in
this range averaged a spending of $5,600 per pupil in comparison to the state average
spending of $5,422. Two, successful schools focused their resources on improving
teaching and learning. Lastly, the study identified six key improvement elements of
successful districts: focus on educating all students, data-driven decision making,
rigorous curriculum and alignment to state standards, effective professional development,
changes to the learning environment, and extended learning opportunities for struggling
students.
Odden (2009) identified ten strategies used by schools and districts that managed
to double student performance as measured by the state’s testing system. The following
represent Odden’s ten strategies:
1. Understanding the performance challenge by getting the “sense of urgency” to
improve student performance.
2. Setting ambitious goals that are reasonable and attainable.
3. Change the curriculum program and create a new instructional vision that
includes teacher collaboration and the embodiment of the National Board for
Professional Teaching Standards.
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4. Benchmark and formative assessments and data-based decision making to
continuously measure student performance, provide student feedback, and
identify areas of improvement.
5. Providing ongoing intensive professional development to ensure active learning
and collaboration that impacts student improvement and instruction.
6. Using time efficiently and effectively by making the necessary changes, which
may include extending the school year, time for instruction, time for
collaboration, and schedule changes.
7. Extending learning time for struggling students is one of the determining factors
that influence doubling student performance. Extended learning time may be in
the form of one-to-one tutoring, small group tutoring, double periods, or summer
school programs.
8. Collaborative cultures and distributed leadership as described in professional
learning communities (DuFour et al., 2006) is proven to improve teacher
engagement and commitment through shared decision-making.
9. Professional and best practices include reaching outside for knowledge on recent
research and practices implemented at other sites and districts.
10. Human capital side of doubling student performance involves recruiting top
talent, in addition to developing talent from within to meet the needs of the
specific school sites and district.
Recurring themes in the literature. Four recurring themes throughout the body
of research have proven to be essential in the success of school improvement. These four
themes are analyzed in depth, and additional research studies that support the
EDUCATIONAL RESOURCE ALLOCATION 28

implementation of such strategies are provided. These recurring themes are leadership,
assessment and data-based decision making, curriculum and instruction, and professional
development. Table 1 provides a quick reference to the four recurring themes.
Table 1
School Improvement Strategies According to Research Studies
Researchers Curriculum
and
Instruction
Data Professional
Development
Leadership
Darling-
Hammond
(2002)
High standards
and an
interdisciplinary
multicultural
curriculum
Collaborative
planning
Shared
decision
making
Reeves
(2003)
Clear
curriculum
choices, quality
instruction, and
emphasis on
nonfiction
writing
Frequent
assessments
and multiple
opportunities
for
improvement
Teacher
collaboration and
collaborative
scoring of student
work with
emphasis on
feedback
Works
collectively
with all
members and
values all
adults in the
system
Togneri &
Anderson
(2003)
System-wide
approach
Data-Based
Decision
Making
Coherent and
district-organized
Redefined
leadership to
shared
leadership
Williams et
al. (2005)
Coherent,
Standards-based
Instructional
Program
Assessment
Data to
improve
student
achievement
Redefined
leadership to
managers of
the school
improvement
Duke
(2006)
Curriculum and
instruction
aligned to
assessments
Data-driven
decision
making
Teacher
collaboration
Team
leadership

EDUCATIONAL RESOURCE ALLOCATION 29

Table 1, continued
Fermanich
et al. (2006)
Rigorous
curriculum and
alignment to
state standards
Data-driven
decision
making
Professional
Development

Odden
(2009)
Change the
curriculum and
adopt a new
instructional
program
Data-Based
Decision
Making
Ongoing
professional
development with
in-district support,
knowledge of out-
of-district
practices, and
updated research
on best practices
Distributed
leadership

Leadership. Studies of school improvement strategies reveal the importance of
school leadership as an integral component for school improvement and student
achievement (Marzano et al., 2005; Northhouse, 2010). The concept of leadership has
been redefined throughout the years; the literature indicates that successful schools
practice shared leadership (Duke, 2006; Odden, 2009; Reeves, 2003; Spillane et al. 2001;
Togneri & Anderson 2003) whereby decisions that affect the school culture and student
learning are developed by everyone as a team. Distributed leadership facilitates the
accomplishment of the goals through a laser-like focus on instruction and student
learning. Bolman and Deal (2008) suggest that a leader’s success in developing shared
leadership can be attributed to the leader’s ability to understand the organization through
multiple lenses: structural, human resources, political, and symbolic.
Secondly, leaders at successful schools are focused (Patterson, 2001); they share a
clear vision and mission supported by ambitious goals (Duke, 2006; Hallinger & Heck,
2002; Marzano et al. 2005; Odden, 2009; Reeves, 2003;Togneri & Anderson, 2003; ).
Leadership in successful schools is not an individual function; it is a shared practice that
EDUCATIONAL RESOURCE ALLOCATION 30

values the contributions of all members (Elmore, 2005; Reeves, 2003; Spilllance et al.
2001).
Assessment and data-based decision making. A second recurring theme in
successful schools is their implementation of making decisions based on data (Duke,
2006; Fermanich et al., 2006; Odden, 2009; Togneri &Anderson, 2003; Williams et al.,
2005). Research confirms that schools that demonstrated improvement placed
assessments and data as the determining factor for future instructional activities and
interventions (Lezotte & McKee Snyder, 2011; Odden, 2009; Odden & Archibald, 2009;
Reeves, 2003; Williams et al., 2005 ;). Although testing received a negative connotation,
research (Lezotte & McKee Snyder, 2001; Odden, 2009) suggests that frequent testing as
a monitoring tool serves as a proactive measurement to determine the extent of re-
teaching and intervention. Furthermore, testing does not have to be given in the
traditional form of paper and pencil, as students can demonstrate their understanding
through oral responses, end-of-the lesson exit cards, written reflections, and portfolios.
Assessments are informational tools that can be resourceful to the student when given
with specific data (Lezotte & McKee Snyder, 2001; Reeves, 2003) and with ample
opportunities for improvement (Reeves, 2003). Aligned with curriculum and instruction,
standards-based benchmarks and formative assessments assist in maintaining the school
focused on student achievement.
Curriculum and instruction. A third recurring theme is the alignment of
curriculum and instruction to state standards. Curriculum and instruction have proven to
be effective when implemented as a system-wide approach (Darling-Hammond, 2002;
Mac Iver & Farley, 2003; Togneri & Anderson, 2003Williams et al., 2005) in which
EDUCATIONAL RESOURCE ALLOCATION 31

systems are designed to support instruction and student learning. As an on-going
process, curriculum is developed and revisited by teachers, instructional coaches, and
district administrators to ensure school-wide instructional consistency and alignment to
state academic standards. Instructional consistency is attainable through the
identification of system-wide best practices supported by professional development,
instructional leaders, and coaches. Instruction by effective teachers, as indicated in
Reeves (2003) is the highest indicator of student academic achievement.
Other views in the literature (Fermanich et al., 2006; Odden, 2009) identify new
curriculum programs as effective for school improvement. As part of school reform,
Odden (2009) reports that schools with high student performance adopt new curriculum
and instructional approaches. This allows teachers to experience a new standards-based
curriculum followed by professional development.
Professional development. Professional development implemented system-wide
yields positive academic achievement results (Odden, 2009; Togneri & Anderson, 2003).
According to Odden (2009), effective professional development results in a positive
change in teachers’ classroom-based instructional practice. Recently, the concept of
professional development evolved from the traditional delivery form of workshops and
conferences to on-site trainings, learning communities, and coaching.
The most common form of professional development is on-site training, in many
cases, delivered by in-district experts and on-site instructional coaches (Odden, 2009;
Togneri & Anderson, 2003). On-site trainings are beneficial resources when addressing
instructional strategies, interpreting multiple data to guide instruction, and building
capacity on newly adopted curriculums. Joyce and Showers (2003) identify four key
EDUCATIONAL RESOURCE ALLOCATION 32

component of training: knowledge, modeling, practice, and peer coaching. Two of these
four components are evident during on-site trainings; the other two, modeling and peer
coaching, are more attainable during learning communities and classroom observations.
Allocation of Human Resources
It is important for educational leaders to be aware of the best practices proven to
be most effective in raising student achievement. Although an obstacle to education
might be proper funding, the awareness of best practices permits educational leaders to
make wise choices when allocating human resources. The following section examines
the changes in resource allocation patterns and concludes with a description of the
Evidence-Based Model (Odden and Picus, 2003) as an adequacy tool that meets the needs
for student achievement.
History of expenditures. Although it is a common belief that revenues per pupil
for education have decreased, Hanushek and Rivkin (1997) report that per-pupil spending
increased by 3.5% between 1890 and 1990. Most recently, data supports the continuous
growth of dollars per pupil since the 1970s (Figure 1). For the 2009 fiscal year, the
National Public Education Financial Survey reports average per-pupil spending at
$10,591 (Figure 2). Other changes in the history of expenditures include the shifting
responsibilities from local, state, and federal funding.

EDUCATIONAL RESOURCE ALLOCATION 33

Figure 1. Total Revenues Per Pupil: 1970-2009

Figure 2. Current per-pupil expenditures in the United States: 2008–09

EDUCATIONAL RESOURCE ALLOCATION 34

Changes in resource allocation patterns. Due to the increase in revenues per
pupil, the next area to explore is the changes or patterns in the allocation of resources.
Odden and Picus (2008) identify six areas that have demonstrated spending patterns:
instruction, instructional support, student support, administration, operations and
maintenance, and food and transportation.
Several studies (Adams, 1994; Firestone et al., 1994; Hannaway et al., 2002; Lankford &
Wyckoff, 1995) have been conducted in an attempt to respond to the allocation of new
resources. A common assumption is that the greatest impact of the increase in revenues
is seen in the area of instruction; however, research confirms otherwise.
Under the Kentucky Education Reform Act (KERA) of 1990, approximately $490
million was allocated to school districts statewide with the intent of improving student
performance. Adams (1994) conducted a study to investigate how four districts, two of
low-wealth and two of high-wealth, utilized their money to improve student learning.
The expenditure categories included administration, instruction, transportation,
operation/maintenance, fixed costs, capital, and other expenses. His findings revealed
that between 1990 and 1992 expenditure patterns remained the same in six of the seven
categories and there was a slight increase in instruction. Reform dollars in Kentucky
increased spending on teacher salaries, staff development and training, instructional
materials, and technology.
Similar to Adams (1994), Firestone et al. (1994) analyzed the expenditures of 11
districts in New Jersey after the enactment of the Quality Education Act (QEA). QEA
increased state aid by $800 million, and approximately $257 million of that was allocated
toward low-wealth urban districts. The study is divided into three categories which
EDUCATIONAL RESOURCE ALLOCATION 35

identify the districts of study: special-needs districts (poor urban districts), transition-aid
districts (wealthiest school districts), and foundation aid (districts that fall between
special-needs and transition aid). In an attempt to study changes in revenue between
high-wealth and low-wealth districts, four areas were examined: personnel, materials and
resources, facilities, and program changes. The findings of the study indicate that,
although QEI increased state aid by $800 million, no major changes were evident in the
allocation of resources.
In a similar attempt, Lankford and Wyckoff (1995) questioned the allocation of
resources and expenditures of school districts in New York. During the 1991-92 term,
New York spent $12 billion more than it did in the 1979-1980 term. Many changes in the
allocation of resources are attributed to student population, educational philosophy,
management practices, and educational policies. The study identified that a significant
amount of funds were allocated to teaching children with disabilities. Although the
objective is to increase student achievement, Lankford and Wyckoff report that most of
the funds are consumed by the high cost of expenditures due to inflation, accountability
toward providing services to students with special needs, and by increases in teacher
salaries and health benefits.
In response to changes brought about by standards-based reform and
accountability, Hannaway et al. (2002) conducted a study with the goal of analyzing how
resources were reallocated as a result of reform demands. At the time, four of the
identified reform states were Kentucky, Maryland, North Carolina, and Texas. As an
area of focus, the study specifically analyzed funding changes in instruction, pupil
support services, instructional support services, district administration, and school
EDUCATIONAL RESOURCE ALLOCATION 36

administration. The findings revealed an overall increase of 7% from 1992 to 1997. The
areas of higher, although not substantial, increase were instruction, instructional support
services, and school administration. In pupil personnel services, the subarea that received
the most increase dealt with special education. Overall, pupil support services
demonstrated the greatest percent change of 29.8 from 1992 to 1997. This supports the
increase in spending for additional teaching staff as referred in Odden and Picus (2008).
Recurring themes in the literature. Three areas that have been affected the
most as a result of additional funding are teacher salaries (Adams, 1994; Hannaway et al.,
2002; Lankford & Wyckoff, 1995), special education (Hannaway et al., 2002; Lankford
& Wyckoff, 1995; Odden & Picus, 2008), and instructional support services (Adams,
1994; Hannaway et al., 2002; Odden & Picus, 2008). The following section briefly
describes the three areas of impact.
Teacher salaries. Although it may appear that teacher salaries are low, Hanushek
and Rivkin (1997) document the history of spending growth and its impact on the
increase of teacher salaries. In comparison to other careers, teacher salaries have
remained low; however, in the arena of education, teacher salaries represent the one
category that increased throughout the years. Many contributing factors aided the
increase of teacher salaries: increase in real dollars per pupil, student enrollment growth,
and unionization pressures (Hanushek & Rivkin, 1997).
Special education. Hanushek and Rivkin (1997) document the growth and
representation of special education students in the general school population as 8.7% in
1978 to 11.6% in 1990. The growth is partly due to the passage of the Education for All
Handicapped Children Act of 1975; this action legally expanded the offering of services
EDUCATIONAL RESOURCE ALLOCATION 37

to all students with special needs. As a result, the educational system placed a particular
focus on the allocation of resources and its benefits toward special education programs
(Hanushek & Rivkin, 1997; Langford & Wycoff, 1995; Odden & Picus, 2008).
Furthermore, additional categorical funding for special education students added pressure
toward identifying students’ eligible for or in need of special education services.
Instructional support services. Instructional support services range from
expenditures for professional development to support staff salaries (Odden & Picus,
2008). Similar to the documented increase in teacher salaries and special education
services, Hanushek and Rivkin (1997) report an increase in support staff between 1960
and 1980.
Adequacy. Traditionally, districts and schools have allocated resources according
to what is considered an appropriate education. As identified by Baker (2005), the
traditional approach is similar to a service delivery model with no focus on student
learning objectives; therefore, it treats education as a one-size-fits-all model. As a result,
it is of prime importance that schools and districts strategically allocate resources that
reflect a focus on student achievement. An approach that re-evaluates spending
allocation according to student needs is the adequacy model. As defined in Chapter 1,
adequacy helps students reach academic achievement by allocating resources according
to their specific needs. Unfortunately, this has not been the case in past practices. Steifel
et al. (2004) studied schools identified as best and worst in the state of New York, and
their findings revealed no relationship between school ratings and their resource
allocation practices. The study also found that schools identified as “best” most often did
not have a specific plan for spending, while “worst” schools attempted to be strategic in
EDUCATIONAL RESOURCE ALLOCATION 38

their spending strategies. This study demonstrates how the traditional approach does not
follow a systemic method that takes into consideration strategies that lead to student
achievement.
In an effort to provide support to schools and district leaders, Mead (2005)
provides an example of a diagnostic tool known as the Financial Condition Indicator
System (FCIS) developed for New York schools to assess their current allocation of
resources before making any fiscal changes. Similarly, Odden et al. (2003) developed the
Evidence-Based Model (EBM) as a tool to assist district officials in identifying the
location of spending. The overall goal is to provide a starting point to schools and
districts as they transition from a traditional approach to an adequate spending model.
Adequacy studies. The following studies (Perez, 2007; Sonstelie, 2007) examine
the cost of educating students in California through the lens of an adequacy model.
Sonstelie (2007) randomly selected 568 public school teachers, principals, and
superintendents to simulate what it would require for schools to reach some minimum
achievement level. The results indicated that California school budgets should be at least
twice the current funding level to achieve student academic proficiency.
In a “Getting Down to Facts” study, Perez et al. (2007) explore the relationship
between school resources and student performance. The study compares the spending of
high-performing schools with low-performing schools, the findings revealed no
relationship between school resources and academic achievement. Factors that may
contribute to academic success as observed in schools that are “beating the odds” include
high quality teachers and staff, standards based curriculum, and coherent instruction.
EDUCATIONAL RESOURCE ALLOCATION 39

These studies suggest that additional funding is required in order for students to
receive an adequate level of education that ensures academic achievement as measured
by state standards. The following section addresses the Evidence-Based Model as an
alternative method to allocating resources aligned with an adequate education.
Evidence-Based Model. The Evidence-Based Model developed by Odden et al.
(2003) uses a wide array of school level factors required to provide an adequate education
for all students in addition to determining the cost for these resources. This model is
embedded in research-based school improvement strategies that have proven to be
effective in raising student achievement. This model (Figure 3) is built from the core,
which addresses student to teacher ratio, and gradually expands to the funding of the
outer layers: specialists, extended support, specialized education, and professional
development. Odden, Goetz, and Picus (2007) also developed a list of recommendations
for adequacy resources for prototypical elementary, middle, and high schools (Table 2)

Figure 3. The Evidence-Based Model. Borrowed from Odden and Picus, 2009
EDUCATIONAL RESOURCE ALLOCATION 40

Table 2
Recommendations for Adequate Resources for Prototypical Elementary, Middle and High
Schools
School Element Elementary
Schools
Middle Schools High Schools
School
Characteristics

School
Configuration
K-5 6-8 9-12
Prototypical
School size
432 450 600
Class size K-3: 15
4-5: 25
6-8: 25 9-12: 25
Full-day
Kindergarten
Yes NA NA
Number of teacher
work days
190 teacher work
days, so an increase
of 5 days
190 teacher work
days, so an increase
of 5 days
190 teacher work
days, so an increase
of 5 days
Percent of students
with disabilities
13.7% 13.7% 13.7%
Percent poverty
(free and reduced-
price lunch)
36.3% 36.3% 36.3%
Percent ELL 10.6% 10.6% 10.6%
Personnel
Resources

1. Core teachers 24 18 24
2. Specialist
teachers
20% more assuming
a 6 period day with
each FTE teaching
5 periods: 4.8
20% more assuming
a 6 period day with
each FTE teaching
5 periods: 3.6
33% more assuming
a 90 minute block
schedule with each
FTE teaching 3
blocks a day: 8.0
3. Instructional
facilitators/coache
s
1/200 students: 2.2 1/200 students: 2.25 1/200 students: 3.0
4. Tutors for
struggling
students
1/100 poverty
students: 1.57
1/100 poverty
students: 1.63
1/100 poverty
students: 2.18
5. Teachers for ELL
students
An additional 1
teacher/100 ELL
students: 0.46
An additional 1
teacher/100 ELL
students: 0.48
An additional 1
teacher/100 ELL
students: 0.64

EDUCATIONAL RESOURCE ALLOCATION 41

Table 2, continued
6. Extended day 1.31 1.36 1.74
7. Summer school 1.31 1.36 1.74
8. Students with
mild disabilities
Additional 3
professional teacher
positions and 0.5
aides for each
special education
teacher
Additional 3
professional teacher
positions and 0.5
aides for each
special education
teacher
Additional 4
professional teacher
positions and 0.5
aides for each
special education
teacher
9. Students with
severe disabilities
100% state
reimbursement
minus federal funds
100% state
reimbursement
minus federal funds
100% state
reimbursement
minus federal funds
10. Resources for
gifted/talented
students
$25/student $25/student $25/student
11. Substitutes 10 days/FTE 10 days/FTE 10 days/FTE
12. Pupil support
staff
1/100 poverty
students: 1.32
1/100 poverty
students plus 1
guidance/250
students: 3.18 total
1/100 poverty
students plus 1
guidance/250
students 4.25 total
13. Supervisory
aides
2 2 3
14.
Librarians/media
specialists
1 1 1 librarian
1 library technician
15. Principal 1 1 1
16. School site
secretary
1 secretary and
1 clerical
1 secretary and
1 clerical
1 secretary and 3
clerical
Dollar per Pupil
Resources

17. Professional
development
Included above:
Instructional
facilitators
10 summer days
Additional:
$100/pupil for other
PD expenses-
trainers,
conferences, travel,
etc.
Included above:
Instructional
facilitators
10 summer days
Additional:
$100/pupil for other
PD expenses-
trainers,
conferences, travel,
etc.
Included above:
Instructional
facilitators
10 summer days
Additional:
$100/pupil for other
PD expenses-
trainers,
conferences, travel,
etc.
18. Technology and
equipment
$250/pupil $250/pupil $250/pupil

EDUCATIONAL RESOURCE ALLOCATION 42

Table 2, continued
19. Instructional
materials, including
textbooks,
formative
assessments
$165/pupil $165/pupil $200/pupil
20. Student
activities
$250/pupil $250/pupil $250/pupil
Other
Expenditures

22. Operations and
maintenance
$890/pupil $890/pupil $890/pupil
23. Transportation $375/pupil $375/pupil $375/pupil
24. Food services Self supporting Self supporting Self supporting
Borrowed and adapted from Odden, Goetz, and Picus, 2007.
Limited Resources/Fiscal Constraints
California school finance overview. California’s school finance has been
primarily shaped by three events: Serrano v. Priest (1971), Proposition 13 (1978), and
Proposition 98 (1988). The following section presents a brief description of each event
and its significant impact on school funding.
Serrano v. Priest. Prior to Serrano v. Priest (1971), public education was mainly
funded through local property taxes. This type of funding system led to financial
inequities due to the disparity between the generated revenues of low-wealth districts in
comparison to high-wealth districts. As a result, Serrano v. Priest challenged the courts
by asserting that school funding through local property taxes violated students’ equal
protection under the law (EdSource, 1999). Consequently, Assembly Bill 65 passed in
1977 and had the intent of equalizing the revenue limits by adjusting for inflation over
time. Although the intent was made to equalize funding, certain exceptions existed; for
example, categorical funds were not required to meet the Serrano case mandates.
EDUCATIONAL RESOURCE ALLOCATION 43

Proposition 13. In addition to Serrano v. Priest, California voted on a ballot
measure that addressed the tax inequities as outlined in the Serrano case. In response to
the tax inequity concern, Proposition 13 capped the property tax rate at 1% for all
homeowners. This proposition reduced property taxes across the state, initiating a more
defined shift in public school funding from local to state.
Proposition 98 (1988). In a continuous attempt to equalize school funding,
Proposition 98 was passed as a revised system for educational funding. Proposition 98
guarantees a minimum amount of state and local property tax money toward K-14 public
education. Today, this minimum guaranteed funding is determined by the schools’ and
districts’ Average Daily Attendance (ADA) rate. Proposition 98 accounts for 72% of the
funds received by K-12 schools. Figure 4 outlines the sources of Proposition 98; they
represent the funding levels in the Budget Act of 2008.

Figure 4. California’s Education Revenue System
EDUCATIONAL RESOURCE ALLOCATION 44

Federal Response to Fiscal Crisis. In 2009, President Obama signed the
Recovery and Reinvestment Act (AARA) as a response to the national fiscal crisis. This
act became commonly known as “the stimulus package.” The stimulus package boosted
federal spending by over $800 billion and provided tax cuts meant to encourage
additional spending to ameliorate the economy. Over $100 billion of these monies were
allotted toward education, making it the largest one-time funding increase (Mead et al.,
2010).
In addition to the allotment of ARRA funds, a reform agenda was established to
ensure the proper usage of educational funds. This reform agenda carried out by the State
Fiscal Stabilization Fund (SFSF) included implementing standards and assessments
aimed at producing career and college-ready students, increasing teacher effectiveness,
creating data systems equipped to track students from preschool to college, and reforming
consistently low performing schools. Committed states were awarded one-time stimulus
funds with the condition of demonstrating implementation toward achieving the four
areas in the school reform agenda.
However, although ARRA and SFSF provided some relief, certain problems arose
due to the design of the one-time funds. For example, the intent of these funds was to
improve education with one-time monies but for a long-term reform (Mead et al., 2010)
As a result, states had the opportunity to adjust their current budget, yet not to plan for
fiscal longevity (Roza, 2010). In addition, states began to use the received funds to
supplant rather than supplement, which was not the original plan of the design. Large
urban districts reported that ARRA funds were not sufficient to balance the state and
local budget cuts (Center of Education Policy, 2010).
EDUCATIONAL RESOURCE ALLOCATION 45

State Level Response to Fiscal Crisis. In an attempt for improvement, California
responded by reducing part of the burden placed by the crisis on schools. Policy changes
were instituted in six different areas: 1) increased flexibility in funding, 2) flexibility
with class size reduction, 3) waivers for instructional materials adoptions, 4) reductions in
the number of school days, 5) flexibility in the funding of maintenance projects, and 6)
flexibility in financial reserve requirements. Although none of these changes produced
new money, it allowed schools the flexibility of using the existing funds in ways that
benefited the operation and function of the schools.
1. Increased flexibility in funding: To allow districts financial creativity, the
State approved flexibility of funding which was originally intended for categorical
programs in exchange for additional cuts (Shambaugh et al., 2011; Weston,
2011;). This initiative allowed the 39 categorical programs to be swept into the
general fund. Therefore, it allowed districts to use funds according to need and
with no specific limitations (California Education Code 42605).
2. Flexibility with class size reduction: California offered funding incentives to
districts with reduced class sizes since 1996. As a result, many districts took
advantage of this incentive by reducing their class sizes to fewer than 20 students
in kindergarten through third grade (CDE, 2009). In an attempt to provide relief
to struggling districts, California allowed districts to receive class size reduction
funds without meeting the 20 students requirement until 2013.

EDUCATIONAL RESOURCE ALLOCATION 46

Table 3
Class Size Reduction Relief, 2008-2009 to 2011-2012
Under Senate Bill 311, 2004 New Flexibility: 2008-09 through 2001-12
Average number
of students per
class
Penalty Average number of
students per class
penalty
Up to 20.44 No penalty Up to 20.44 No penalty
20.45-20.94 20% penalty 20.45-21.44 5% penalty
20.95-21.44 40% penalty 21.45-22.44 10% penalty
21.45-21.84 80% penalty 22.45-22.94 15% penalty
21.85+ 100% penalty 22.95-24.94 20% penalty
24.95+ 30% penalty

3. Waivers for instructional materials adoptions: In an effort to ensure that students
are taught the most current and relevant content matter, California requires
districts to adopt new material every three to four years. However, as a form of
relief, districts were allowed to keep their non-renewed adoption until 2013.
4. Reductions in the number of school days: In 2008, policymakers in California
reduced the required amount of school days by five. This alleviated some of the
district expenses in operation maintenance and staffing. However, for district
employees this reduction resulted in furlough days, which meant a reduction in
salary. Furlough days did not come easily, negotiations had to take place with
teachers’ union in order to agree to the implementation process (EdSource, 2011).
5. Flexibility in the funding of maintenance projects: In 2009, as an attempt to
lessen the financial pressures, California abolished the requirement of having
districts allocate between 1% and 3% of its funds for maintenance operations. In
support, the state continued to provide funds for these operations without
expecting for the district to match their spending amount (Shambaugh et al.,
2011).
EDUCATIONAL RESOURCE ALLOCATION 47

6. Flexibility in financial reserves requirements: California also revised the
requirement of financial reserves, this allowed districts to reserve less and
compensate for some of the budget cuts.
Summary. These budgetary compromises provided short-term relief to school
districts in California. Consequently, most districts have been able to cover basic
operating costs with these relief funds. However, it is unclear how much longer districts
will be able to stay afloat with limited funding. The strategic allocation of resources may
become the deciding factor in the future health of schools and districts
Gap Analysis
Districts can obtain a clear picture of their fiscal status quo by analyzing their
current resource allocation to the desired goal. This section reviews the literature on gap
analysis as defined by Clark and Estes (2008). Gap analysis is a tool designed to
compare and increase the effectiveness of an organization. Clark and Estes’ (2008) gap
analysis approach identifies the causes of performance gaps with the intent of
implementing solutions that will improve the entirety of the organization. This approach
identifies three main causes of performance gaps: knowledge/skills, motivation, and
organizational barriers. This study utilizes these causes of performance gaps in support
of the proposed human resource allocation recommendations for the district of study.
To facilitate the implementation of the gap analysis approach, Clark and Estes
(2008) developed a gap analysis process model that consists of six steps: 1) Identify key
business goals, 2) identify individual performance goals, 3) determine performance gaps,
4) analyze gaps to determine causes, 5) identify and implement solutions, and 6) evaluate
EDUCATIONAL RESOURCE ALLOCATION 48

results, tune systems and revise goals. The following is a brief description of the steps in
the process model.
1. Identify key business goals: This step requires the understanding of the goals by
all members of the organization.
2. Identify individual performance goals: All members of the organization play a
vital role in the success of the company. It is important for all members to
understand how their contribution affects the overall goal.
3. Determine Performance gaps: To determine the performance gaps it is important
to specify the desired performance levels.
4. Analyze gaps to determine causes: Clark and Estes (2008) suggest the analysis of
the gaps in order to determine the causes and close the performance gaps. The
causes are referred to as the “Big Three,” which represent the following:
knowledge/skills, motivation, and organizational barriers. These three factors
must be in place and aligned with each other in order to achieve the goal (Clark &
Estes, 2008).
5. Identify and implement solutions: Once the causes of the performance gaps have
been assessed, appropriate solutions should be selected and put into action.
6. Evaluate results, tune systems and revise goals: The success of organizations lies
in the continuous examination of input and output; therefore, step 6 stresses the
importance of measuring the results to determine whether achievement has been
met. Step 6 recommends the revision and tuning of goals when necessary.

EDUCATIONAL RESOURCE ALLOCATION 49

Chapter 3: Methodology
This chapter begins with a description of the methodology of this study, including
the research questions, sample and population, instrumentation and data collection, and
data analysis. The purpose of this study was to inform policy makers and educational
leaders of resource allocation strategies that support student achievement. To do so, this
study analyzes the allocation of personnel resources in a district serving over 35,000
students while benchmarking the existing spending patterns to the Evidence-Based Model
(EBM) (Odden et al., 2003). This study specifically focused on the district’s six middle
schools, all of which are located in a low-wealth urban area, as demonstrated by the
percentage of students receiving free or reduced-price meals. Findings of this study will
permit for recommendations regarding how to reallocate resources while staying within
the allotted school and district budget. Recommendations will also include research-
based strategies that been shown to improve student achievement.
Overview of Methodology
Public education experienced changes with the goal of improving student learning
and student achievement while, at the same time, working toward optimizing the limited
amount of funding resources. The unanswered question that this study aimed to answer
is how to allocate resources, specifically human resources, in ways that research suggests
will help districts increase student performance and student achievement. In search of
solutions, this study examined six middle schools to determine how personnel resources
are used while utilizing the Evidence-Based Model (Odden & Picus, 2008) as a
comprehensive resource allocation framework. The Evidence-Based Model (EBM), in
addition to the performance gaps identified in by Clark and Estes (2008) were used as the
EDUCATIONAL RESOURCE ALLOCATION 50

framework for determining the extent of the gaps. This study utilized a qualitative
research design, and quantitative data was included to support the analysis.
Research Questions
To better understand the decisions and practices used by the district, the research
this study sought to answer four research questions:
1. What research based human resource allocation strategies improve student
achievement?
2. How are human resources allocated across the district of study and its schools?
3. Is there a gap between current human resource allocation practices and what the
research suggests is most effective?
4. How can human resources be strategically re-allocated to align with strategies that
improve student achievement?
Sample and Population
This study used a purposeful sample of six middle schools located in the same
school district. The district is located in an urban area and was identified as a district in
year three Program Improvement (PI) status (California Department of Education, 2011).
Due to the large size of this district and in an attempt to focus, only the middle schools
were analyzed. All of the sample schools are traditional public schools with no specific
entrance requirements. The district chosen for this study meets the following criteria: 1)
located in an urban city, 2) serves a diverse student population comparable to California
ethnic demographics, and 3) 70% or more of the student population receives free or
reduced-price meals.
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The selection process began by creating a County Summary Report using the
database on the California Department of Education website (California Department of
Education, 2011). A report of 30 districts was generated with the following purposefully
selected information:
Socio-economic Indicator
- Free or Reduced Price Meals
Student Data
- Enrollment
Student by Ethnic Designation-Number and Percent
- All ethnic groups were selected
Staffing
- FTE Administration
- FTE Pupil Services
- FTE Teacher
- # Classified Staff
To narrow the selection of districts and achieve greater focus, the list was
condensed into 10 schools districts. Elementary districts and districts with an enrollment
of 20,000 or fewer students were removed from the list. The next elimination step
targeted districts with fewer than 70% of the students receiving free or reduced-price
meals. This reduced the selection sample to four districts with diverse student
populations. As a starting point, two of the districts were contacted via email. The
district of study responded the following day, while the other district never responded.
At a meeting with the Superintendent, the following information was discussed and
EDUCATIONAL RESOURCE ALLOCATION 52

provided: contact information of the USC Dissertation Chair, statement of the problem,
purpose of the study, and research questions. The Superintendent agreed to provide
necessary information for the study and expressed particular interest in research-based
professional development.
Tables 4 and 5 provide student enrollment information for the six middle schools
in the district. All of the middle schools serve 6
th
through 8
th
grade; all exceed 1,000 in
total enrollment. Table 4 outlines the student enrollment at the six middle schools by
group. The last column on Table 5 identifies the student racial/ethnic makeup in
California for 2010-2011. The largest group represented in the district, as well as in
California, is the Hispanic/Latino population.
Table 4
Student Total Enrollment
Student Enrollment by School Total Enrollment
School A 1,330
School B 1,484
School C 1,300
School D 1,325
School E 1,298
School F 1,571
Source: 2010 – 2011 School Accountability Report Card

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Table 5
Student Enrollment by Group
Group Percent of Total Enrollment
School A School B School C School D School E School F CA
American Indian
or Alaska Native
0.5 0.3 0.7 0.5 0.4 0.3 0.7
Asian

1.3 2.3 3.2 2 1.6 1.4 8.52
Black or African
American
15.3 21.6 17.2 23.5 22.7 14.4 6.69
English Learners

54.5 29.4 43.8 35.6 31.7 57
Filipino

0.8 2.5 3.2 2.9 1.4 1.5 2.56
Hispanic or
Latino

75.2 54.2 64.4 57.4 56.2 75.4 51.4
3
Native Hawaiian
Pacific Islander
0.4 0.3 0.2 0.9 0.7 1.1 0.58
Socioeconomicall
y Disadvantaged
90.1 67.9 76.5 73.2 74.1 89.1
Students with
Disabilities
13.2 10.1 11.2 10.2 12.6 12.2
Two or More
Races
0.8 0.8 0.8 0.8 0.8 0.6 1.81
White

5.4 16.5 9.2 11.5 15.6 5 26.6
3
Sources: 2010 – 2011 School Accountability Report Card and California Department of Education, Student
Racial/Ethnic Makeup for 2010-11
The value of studying the district of study consists in that it is relatively similar to
other districts in California. Therefore, the information gathered from this study will
serve other districts as they prepare to allocate human resources adequately, using
research-based findings that have been demonstrated to double student performance.
Nevertheless, this study does focus on one school district. Its findings are not
generalizable, yet may prove helpful to district leaders encountering similar
circumstances.

EDUCATIONAL RESOURCE ALLOCATION 54

Instrumentation and Data Collection
This study includes qualitative methods of data collection and analysis. The
Evidence-Based Model (Odden & Picus, 2008) and Clark and Estes’ (2008) leadership
frames are used as conceptual frameworks for analyzing the school and district level
allocation of human resources to improve student achievement. The examination of
current human resource allocation practices at the district of study and what research
suggests is most effective will assist in better understanding the identification of
performance gaps, as defined in the literature review.
This study was conducted in coordination with a thematic dissertation group at the
University of Southern California’s Rossier School of Education. To ensure consistency
throughout the study, student researchers met and maintained an open communication via
email with the USC Dissertation Chair, Dr. Lawrence O. Picus. In this study, three
primary forms of data were collected: 1) quantitative data on staffing ratios, 2) school and
district documents, and 3) information gathered from interviews. Initial contact with the
district Superintendent was made via email, followed by a meeting where the purpose of
study was reviewed and discussed. The Superintendent gave the approval to contact
district officials for the purposes of the study.
Quantitative data. An Excel spreadsheet developed by Picus and Knight (2012)
was used as the tool to collect information on the allocation of school staff across the six
middle schools in the district. This tool is based on the Evidence-Based Model (EBM)
(Odden & Picus, 2008), which reflects a strategic plan for improving student
achievement. Data was collected through the school and district documents in addition to
EDUCATIONAL RESOURCE ALLOCATION 55

the interviews. The following data was input into the database spreadsheet with the
objective of comparing the current allocation and the desired allocation to the EBM:
• Enrollment data by grade
• Student demographic data
• Class sizes
• Staff roles, certificated, classified, and support staff
• Number of staff in extended day programs
• Number of staff in summer school programs
• Expenditures in school resources
Documents. Document analysis took place through the collection of the
following data: 2011-2012 district and site budget, Single Plan for Student Achievement,
School Accountability Report Cards, County Summary Report, master schedules, staff
roster, professional development plans, and mission and vision statements. Collected
data will assist in identifying the gap between the current allocation and the EBM.
Interviews. Interviews were scheduled with the Superintendent, Assistant
Superintendent of Business Services, and the Assistant Superintendent of Educational
Services. Interviews will be conducted in a semi-structured, open-ended format. All of
the information was used to respond to the research questions and to provide the district
of study with simulations and trade-offs that can improve student achievement through
the careful allocation of human resources.
Data Analysis
Data analysis for this study involved a number of phases: 1) Initial contact with
the District Superintendent, 2) Gathering, input, and analysis of district and school data,
EDUCATIONAL RESOURCE ALLOCATION 56

3) Identification of the performance gaps, and 4) Presentation of simulations and possible
trade-offs to the district representatives. There were three main sources of information
analyzed: numerical data, documents, and interviews. All of this information was used to
determine the existing human resource allocation gaps through the instrumentation of the
Evidence-Based Model and the Excel spreadsheet developed by Picus & Knight (2012).
Performance gaps will also be analyzed, using Clark & Estes (2008) as the theoretical
gap analysis framework.
First research question. The first question guiding this study asked what
research-based human resource allocation strategies improve student achievement. A
number of studies were identified in the literature review to present common strategies
used by high performing schools. Included in Chapter 2 is also an in-depth description of
the Evidence-Based Model with supporting facts for selecting this model as the adequacy
framework.
Second research question. The second question that guides this study asked
how human resources are allocated across the district of study and its schools. All of the
quantitative district and site data was input into the database spreadsheet designed to
calculate and disaggregate the data according to categories. The spreadsheet includes the
following main categories: Student Input Data, Staff Input Data, Current Allocation
Output, Desired Allocation Output, EB Model Output, and Gap Analysis. Qualitative
data in the form of interviews and informational documents served to examine the
alignment of the district and site to its vision and mission.
Third research question. The third question asked whether there was a gap
between current human resource allocation practices and what the research suggests is
EDUCATIONAL RESOURCE ALLOCATION 57

most effective. Gathered information from data, interviews, and documents was
synthesized through the use of the database spreadsheet and Clark and Estes’ (2008) gap
analysis framework. Clark and Estes’ (2008) framework, as identified in Chapter 2, is a
theoretical tool used to identify performance gaps in the areas of knowledge and skills,
motivation, and organization. The identification of performance gaps was presented to
the district along with recommendations on how to close the gaps.
Fourth research question. The last question asked how human resources can be
strategically re-allocated to align with strategies that improve student achievement. Once
the information was collected and input into the database, it was analyzed in comparison
to the Evidence-Based Model. The database was designed to identify the gaps according
to categories; therefore, simulations and trade-offs could be manipulated for the purposes
of demonstrating the reallocation of resources according to the district and site needs.
However, the overall goal was to present a template closely or completely aligned to the
Evidence-Based Model, while staying within the district’s financial possibilities.
Summary
The purpose of this chapter was to describe the methodology utilized to conduct
and assist in answering the four research questions. This research examined the
allocation of human resources in a particular district, yet took in consideration the current
status of California’s financial situation. Therefore, this study offered the district the
most recent findings on how to improve student achievement through the allocation of
human resources that would result in positive outcomes for individual sites and the
district as a whole. The following chapter provides the details of the human resource
allocation and performance gaps.
EDUCATIONAL RESOURCE ALLOCATION 58

Chapter 4: Study Results
This chapter presents the findings of the research questions and study in addition
to recommendations from the researcher. Data collected from the district’s human
resource and educational services department were input into the simulation model
developed by Picus and Knight (2012) to analyze gaps between the district’s current
allocations and the recommended allocations of the Evidence-Based Model. Anecdotal
data obtained from the district’s business services were also used to answer the study’s
research questions. Gaps were analyzed using the framework of Clark and Estes (2008)
to determine the performance areas that need improvement.
This chapter is divided into two sections. The first section of this chapter presents an
overview of the district and the six middle schools of study. The second section
addresses and responds to the following research questions as interpreted through the
collected data and literature review.
• What research based human resource allocation strategies improve student
achievement?
• How are human resources allocated across the district of study and its schools?
• Is there a gap between current human resource allocation practices and what the
research suggests is most effective?
• How can human resources be strategically re-allocated to align with strategies that
improve student achievement?
Overview of the District
This section provides an overview of the district and the six middle schools that
participated in the study. The district is located in an urban area, serving over 36,000
EDUCATIONAL RESOURCE ALLOCATION 59

students. It is composed of 23 elementary schools, 6 middle schools, 5 high schools, and
5 alternative schools. Although some academic growth was achieved as measured by the
California Academic Performance Index (API), the increase is not significant enough to
close the achievement gap. As a result, the district is still categorized as a district in need
of improvement, most commonly known as Program Improvement (PI) status (California
Department of Education, 2011).
Due to time constraints this study focused on the six middle schools in the district
and not on the entire district. Out of respect to the participating schools and district, the
schools’ names will be replaced by numbers. All of the sample schools are traditional
public schools with no specific entrance requirements. As an attempt for school
improvement, three of the six middle schools receive additional funding through the
Quality Education Investment Act (QEIA) of 2006. QEIA is the result of a settlement
between the California Teachers Association, et al. v. Schwarzenegger, et al., which
provides additional funding to elementary, secondary and charter schools that rank in
decile 1 or 2 as measured by the Academic Performance Index (API) base (California
Department of Education, 2013).
All of the middle schools exceed the Evidence-Based Model prototypical school
size of 450. The six middle schools service a total of 8,087 students, averaging 1,348
students per site. All sites provide services to English language learners (ELL) and
special education students. Sites 1 and 5, as indicated in Table 6, have the highest
enrollment of English learners, in addition to the greatest percent of students receiving
free and reduced lunch (FARL). Overall, the six middle schools provide free or reduced-
price lunch to 6,088 students, which is equivalent to 75% of the total enrollment.
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Table 6
Demographics for all Six Middle Schools in Sample District
Middle
School #
Grade
Level
Total
6
th
7
th
8
th
ELL

Count %
SPED

Count %
FARL

Count %
1 1284 435 413 436 322 25.1
%
155 12.1
%
1071 83.4%
2 1424 482 484 458 202 14.2
%
129 9.1
%
1022 71.8%
3 1261 404 431 426 212 16.8
%
137 10.9
%
914 72.5%
4 1207 421 406 380 147 12.2
%
151 12.5
%
871 72.2%
5 1518 521 507 490 313 20.6
%
157 10.3
%
1267 83.5%
6 1393 455 495 443 105 7.5% 120 8.6
%
943 67.7%
Subtotal
for
Middle
Schools
8087 2718 273
6
2633 1301 16% 849 10% 6088 75%

Student achievement. Tables 7 and 8 outline the schools’ academic performance
and growth over a three-year period of time. The tool of measurement used in California
is the Academic Performance Index (API), a numerical index that ranges from a low of
200 to a high of 1000. In accordance with No Child Left Behind Act (NCLB), schools
that do not achieve Adequate Yearly Progress (AYP) for two consecutive years are
placed under Program Improvement (PI) status. PI schools must develop a two-year plan
that identifies how professional development and parent involvement will be
implemented to increase student achievement. In California, only schools that receive
Title I funds can enter Program Improvement status. As noted in Chapter 1, AYP is the
accountability system implemented as a result of NCLB that requires all students to
demonstrate proficiency in the areas of Mathematics and English Language Arts
(California Department of Education, 2012).
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Table 7
Student Achievement Data for Sample Middle Schools
Middle
Schools
2012
Met
AYP?
API
2010
API
2011
API
2012
3 Year
Change
Program
Improvement
Status
1 N 667 689 724 +57 Year 5
2 N 677 698 693 +16 Not in PI
3 N 730 762 789 +59 Year 5
4 N 708 720 747 +39 Not in PI
5 N 708 742 751 +43 Year 5
6 N 772 790 796 +24 Not Title 1
Average N 710.3 733.5 750 +39.7
Note. California Department of Education (2013)
Table 8
Subgroup Achievement Data – Three Years Growth
Middle
Schools
African-
American
Hispanic English
Learners
Students
with
Disabilities
White Socio-
economically
disadvantaged
1 +25 +49 +37 +54 +46 +44
2 +48 +22 +18 +32 +14 +31
3 +72 +57 +59 +64 +38 +62
4 +25 +49 +37 +54 +46 +44
5 +37 +50 +38 +55 N/A +45
6 +18 +22 +23 +52 +43 +27
Average +37.5 +41.5 35.3 +51.8 +37.4 +42.2
Note. California Department of Education (2013)
Funding conditions. Within the five years prior to this study, the district of study
weighed the necessity of certain services and programs. The budget crisis in California
forced the district of study to make expenditure changes in order to sustain the daily
educational process without a major interruption to the organization. Although the goal
was to save positions, decreases in educational funding resulted in personnel cuts and
furlough days. Expenditure changes and cuts began and gradually increased in the
following manner: 1) reduction of programs and services that are not necessarily attached
EDUCATIONAL RESOURCE ALLOCATION 62

to people, 2) elimination of programs that data showed to be ineffective, this resulted in
reduction of personnel, 3) increased class size, eliminated about 85% of class size
reduction, 4) transportation services were eliminated at the high school level, 5) the
Transportation Department increased the walking distance required to receive
transportation services and 6) furlough days were negotiated and scheduled into the
academic calendar.
District officials expressed that the district’s greatest challenge was meeting the
demands from the state and federal level with limited funds. To resolve the issue,
adequate funding allocation needs to be addressed along with the inequity of expenditure
per pupil across the states. In 2011-2012, California received a national ranking of 42 in
current expenditures for public K-12 schools per student in ADA (National Education
Association, 2013). Currently, California spends $2,094 less than the national average
and $14,896 less than the top state, Vermont. In 2011-2012, the district of study
experienced a decline of 2.7% in total revenues per student when compared to the
previous year (Ed-Data, 2013). Within the five years prior to this study, the district’s
general fund revenues decreased by $33,171,222, and this serves as justification for
changes and cuts in personnel and programs.
This section provides information on the findings for each of the four research
questions. The results of this section are a product of the combined gathered information
from interviews, district personnel allocation documents, and literature review articles.
Most of the information was synthesized and input into the simulation model in order to
identify gaps between the schools’ current human resource allocation and the
recommended allocations from the Evidence-Based Model.
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Findings for Research Question One
The first research question asked what research-based human resource allocation
strategies improve student achievement. The literature review in Chapter 2 revealed four
recurring themes that improve student achievement: leadership, assessment and data-
based decision making, curriculum and instruction, and professional development.
Findings of how the district of study implements these strategies will be reviewed below.
Leadership. Research reveals that schools that have demonstrated school
improvement practice shared decision-making (Darling-Hammond, 2002; Duke, 2006;
Togneri & Anderson, 2003) where all adults in the system are valued (Reeves, 2003).
Furthermore, leaders at successful schools are focused (Patterson, 2001); they share a
clear vision and mission supported by ambitious goals (Duke 2006; Hallinger & Heck,
2002; Marzano et al. 2005; Odden, 2009; ; Reeves, 2003; Togneri & Anderson 2003). A
focused leadership is crucial in order to meet the high stakes accountability measures of
the state and federal government. In aiming for excellence, administrators need to
strategically allocate human resources in a manner that benefits the entire organization
and ultimately results in student achievement. In an attempt at change, the
superintendent developed and presented a list of expectations (Table 9). In return,
principals also developed their vision and site expectations aligned to the
superintendent’s expectations. These expectations are continuously reviewed at the
Principals’ Meetings, which are held once a month at the District Office.

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Table 9
Leadership – Superintendent’s Expectations
Superintendent’s Expectations for 2011-2012

1. Meet Academic Achievement Goals (AYP, API)
2. Conduct Daily walkthroughs and turn in a monthly log
3. Acquire knowledge of four overarching themes
4. Facilitate the implementation of an effective Professional Learning Community
5. Implement a systemized Response to Intervention (RTI) for every student
6. Administer and upload common assessments and benchmarks
7. Establish and meet Safety and Attendance Site Initiatives and Goals
8. Participate in a summit presentation of data and planned instructional focuses
9. Identify a liaison for communications as part of adjunct duties
10. Prepare for Ready Rooms walkthroughs August 24, 2011
11. Maintain attendance of two Board meetings a year
12. Administer functioning SSC, ELAC, AAPAC when applicable
13. Support Parent Volunteers
14. Promote Outreach to Partners and Adopters
15. Maintain Positive Attitude
16. Execute Excellence on Purpose
17. Maintain High Quality English Language Learners and Special Education
Programs
18. Identify Professional Development Needs
19. Implement Positive Behavior Intervention Systems
20. Reduce Suspensions and Expulsions

Note. Source Sample District-Office of Superintendent (2012)
During the 2011-2012 school year, all principals attended the Principal’s Summit,
which resulted in the development of the school-wide and district-wide expectations.
The school administrators under the leadership of the superintendent developed the
district’s Four Areas of Focus: Student Achievement, Learning Environment,
Collaboration, and Resource Management (Table 10). Administrators were also
introduced to the book, 212: The Extra Degree, which continues to be used by the
superintendent as a source of inspiration and motivation. The book promotes that
individuals are responsible for their results and that attitude, kindness, belief, focus, and
perseverance can impact the outcome. As a leader, the superintendent intertwines the
EDUCATIONAL RESOURCE ALLOCATION 65

message of the book with the district’s four areas of focus, providing all administrators
with a sense of purpose and direction.
Table 10
Leadership – Four Areas of Focus
Four Areas of Focus
1. Student Achievement Ensure all students demonstrate academic proficiency by
meeting or exceeding standards
2. Learning Environments Maintain a safe and effective learning environment
3. Collaboration Build collaborative partnerships in which all members
share responsibility supporting excellence in student
achievement
4. Resource Management Manage all District resources to maximize student
achievement
Note. Source Sample District-Office of Superintendent (2012)
These four overarching themes created a common language for principals and a
consensus on the delivery message to the community and stakeholders. In 2011-2012, the
district experienced academic growth and increase in business partners. District officials
linked this achievement to the superintendent’s focus and leadership.
First area of focus: student achievement. In 2011-12, the district set the
expectation to meet academic achievement goals for both AYP and API. In reviewing
the presentations of the Principals Meeting, November 2012, administrators indicated that
reasons for student achievement in their particular site was due to focus and emphasis on
reading, increased academic support through coaching, collection of data, time for
collaboration, and professional development. One site included the benefits of block
scheduling, 1) allowing for increase minutes in Language Arts and Mathematics and 2)
intervention within the allocated instructional time. Two of the areas mentioned,
collection of data and professional development will be discussed in further detail as this
chapter unfolds. As an extension to the 2011-12 goal, the district added an “extra
EDUCATIONAL RESOURCE ALLOCATION 66

degree” (connection to the book, 212: The Extra Degree) for 2012-13 by including the
following: 1) ensure that each student has Added Value, and 2) One band growth for
every student each year, one CELDT level for EL students, and maintenance of advanced
proficiency for higher performing students.
Second focus area: learning environment. Learning environment became the
second area of focus, in part due to the large number of suspension and expulsion of
African-American students. In 2011-12, the data was presented to the leadership team.
In 2012-13, the goal included to reduce suspension and expulsion rates and engage
offenders in Service Learning Projects and Ambassadors of Compassion. Currently, one
school is piloting the program Ambassadors of Compassion, funded in part by the district
and by the program. The program teaches students about character traits, community
service, and citizenship.
Third focus area: collaboration. The district’s focus on collaboration is different
from the school site’s focus; although the district recognizes the importance of teacher
collaboration, this goal is specific to the community and stakeholders. In 2011-12, the
collaboration goal included the support of currently functioning programs such as the
School Site Council, the District English Learners Advisory Committee, the Parent
Teacher Association, the African American Parent Advisory Council and the Adopt-a-
School Program. At the time of this study, the district demonstrated growth by showing
an increase in business partners by 200%; each school has 3 to 4 adopters. Due to its
success in partnerships, the district included the support of functioning programs and
fostering of community partnerships in the 2012-2013 collaboration goals.
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Fourth focus area: resource management. In the area of resource management,
the district developed the objectives of maintaining a fiscally solvent budget, hiring
outstanding employees, providing high quality professional development, and
maintaining facilities. In 2012-13, the goal included the full implementation of
Professional Learning Communities and the implementation of Direct Interactive
Instruction (DII). Direct Interactive Instruction is the adopted instructional delivery
method used by the district, and is composed of the following four components: 1)
Standards and Measurable Objectives, 2) Lesson Structure and Sequence, 3) Student
Engagement, Feedback, and Correctives, and 4) Proactive Classroom Management. The
district hired an outside consultant to guide the staff in the implementation process of
DII.
Assessment and data-based decision making. The second recurring theme that
has been demonstrated to improve student achievement is assessment and data-based
decision making. Research confirms that schools that have demonstrated improvement
placed assessments and data as the determining factor for future instructional activities
and interventions (Lezotte & McKee Synder, 2001; Odden, 2009; Odden & Archibald,
2009; Reeves, 2003; Williams et al., 2005). At the district of study, data is used in an on-
going basis to determine the areas of need, strengths, and possible allocation of resources.
Student data is input, scanned, and analyzed via the data information system EADMS.
Common assessments for all core subject areas are designed at the site level and reviewed
at the site’s department meetings. District-wide formative assessments in both English
Language Arts and Math are utilized to identify specific areas of need and interventions.
EDUCATIONAL RESOURCE ALLOCATION 68

Prior to the beginning of the academic school year, English Language Arts and
Math department leads reviewed the alignment of formative assessments, also known as
benchmarks, with the designed pacing guides and standards. These meeting were held at
the Professional Development Center and are organized and led by the district’s
Secondary Specialists. Teachers were compensated at an extra-duty hourly rate for their
time during the non-contractual hours. Department lead teachers continued to meet
throughout the academic school year with the district content area specialists to revise
and ensure alignment with the state standards. Math and ELA content area teachers were
given strict deadlines to scan their quarterly assessments into the data system EADMS.
All sites had access to scanners, and teachers were able to immediately view the results
with a description of the most commonly missed problems by strand and standards.
Results were analyzed and shared at the site department meetings. Not all sites were at
the same level of trust when revealing assessment scores by teacher; therefore, some sites
used numbers rather than teacher names when displaying school-wide benchmark results.
Although parents did not have access to EADMS, the sites offered trainings on how to
use the parent and student portal Infinite Campus. Infinite Campus allows parents and
students to view grades, attendance, and assignments, which include formative and
summative assessments.
Curriculum and instruction. Curriculum and instruction aligned to assessments
and state standards is the third recurring theme that has proven to improve student
achievement (Duke, 2006; Fermanich et al., 2006). Curriculum is an area that the district
found challenging due to moving from Content State Standards to Common Core State
Standards (CCSS) and its anticipated influence on textbooks and digital curriculum. The
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district had not adopted new textbooks over the prior years due to limited funds. At the
time of this study, purchasing textbooks would not have been cost-effective due to the
unpredictability of alignment with the new Common Core standards. Curriculum and
instruction are both vital and have proven to be effective when implemented as a system-
wide approach (Darling-Hammond, 2002; Mac Iver & Farley, 2003; Togneri &
Anderson, 2003; Williams et al., 2005). Therefore, the district took steps to make
instructional delivery changes that could be transferred when the common core became
fully implemented in 2014-15. Outside consultants, Action Learning Systems, Inc. and
InnovateED partnered with the district to coach and mentor teachers on instructional
delivery with the objective of preparing all students toward an academic and career
pathway.
Professional development. Professional development is the fourth recurring
theme. In accordance with Odden (2009) and Togneri and Anderson (2003), the most
common form of professional development is on-site training delivered by in-district
experts and on-site instructional coaches. Under Educational Services, the district of
study employed ten district experts, and five were part of the Secondary Professional
Development Team composed of the following specialists: ELA, ELL, Special
Education, Read 180, and Mathematics. The professional development team worked
with individual schools to plan and implement individualized staff training programs for
their professional development needs. Under Business Services, the district offered
trainings through the Information Systems/Technology Department. Schools were able
to require on-site teacher trainings on how to navigate the informational data system
EADMS and Infinite Campus. Specific school sites allocated resources to fund Assistant
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Administrators of Instructional Improvement and Coaching in addition to Teachers on
Special Assignments (TOSA) carrying out the duties of instructional coaches.
At the district level, experts outlined a 2012–2015 implementation plan that
incorporated the changes of the Common Core State Standards (CCSS). The initiation of
this professional development plan took place in August 2012 at the Leadership Summit
with the attendance of site administrators and lead teachers. The plan was then
introduced to the entire school site staff between the months of November and December
2012. District experts used the article “5 Things Every Teacher Should be Doing to Meet
the Common Core State Standards” by Davis (2012) to present the following five shifts:
1) Lead High-Level, Text-Based Discussions, 2) Focus on Process, Not Just Content, 3)
Create Assignments for Real Audiences with Real Purposes, 4) Teach Argument, Not
Persuasion and 5) Increase Text Complexity. All of the information was delivered in
Modules. Module 1was titled “Common Core State Standards an Overview to Transition”
and Modules 2 through five were titled “Transitioning to Common Core State
Standards.” District experts strategically designed a plan to build capacity on school
administrators and site leads to facilitate the implementation process at the school sites.
The following is embedded in the implementation process: revising Scope and Sequence
documents to transition current standards to common core, piloting 13 schools to be part
of the 2013 Smarter Balanced Pilot Test, development of suited professional
development, curriculum, and benchmark assessments.
At the site level, individual sites worked toward meeting the District’s focus in
addition to implementing walk-throughs, data analysis, monitoring EL progress,
mitigating drop-out rates, and improving attendance and reduction of suspension and
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expulsion. All of these areas of need were included in the site’s professional
development plan. Professional development varied from site to site; for example, not all
sites had available funds to employ instructional coaches or academic assistant
administrators. Some sites, due to program improvement requirements, had the
flexibility to implement professional development on a weekly basis. Professional
development at these sites was possible through a once week early release day for
students. Table 11 represents a collection of topic areas covered at the individual school
sites; it does not represent one specific school site.
Table 11
Professional Development
Professional Development
• Common planning, using Scope and Sequence and benchmarks to address skills
and guide planning
• Development of common assessments
• Share and identify best practices
• Identify re-teaching based on EADMS data
• Direct Interactive Instruction (DII) training
• Sheltered Instruction Observation Protocol (SIOP) and Specially Designed
Academic Instruction in English (SDAIE) strategies
• Building capacity among math and language arts staff by enhancing teaching and
planning strategies
• Data Teams to predict mastery of standards before state testing
• Action Learning Walks by department to identify professional development
needs
• Technology trainings, includes document cameras, Smart Boards, student
responders, etc.
• Leadership and staff book study to build capacity
• Ongoing PLC training
• Ongoing Capturing Kids Hearts training
• Classroom management
• Performance tasks
• Writing effective learning objectives

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Summary and Recommendations from Research Question One
This section reviewed the four recurring themes that improve student achievement
and its implementation at the district of study. It also identified the district’s four areas of
focus and its progress within the past two years. Through the interviews, it was inferred
that the district experienced changes as a result of new leadership. From the researcher’s
observation and collection of data, it can be determined that the district experienced
academic growth, yet not significant to close the achievement gap. The superintendent’s
expectations were clear, and it was evident that all sites were, at some level, focusing on
student achievement and professional development, though it is not clear how sites were
held accountable. Classroom walk-throughs were mentioned, but there was not a district-
wide system in place on how these observations were communicated to the staff. A
suggested recommendation is for the district’s secondary specialists to develop a district-
wide observation template that meets the needs of all classrooms, regardless of content
matter, and serves as a form of professional growth. A second recommendation is for
sites to work closely with their professional development specialists and instructional
coaches to improve instruction and management in the classroom. At the time of this
study, there were only two sites with instructional coaches; this is an area that will be
further discussed in the re-allocation of resources section. Hiring of instructional coaches
would assist in supporting all of the recurring themes: leadership, assessments,
curriculum and instruction, and professional development.
Findings for Research Question Two
The second research question asked how human resources were allocated across
the district of study. Human resource allocation data provided by the Business Services
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division were collected and input into the Staff Input Tab of the dissertation simulation
model. Data on certificated teaching staff, certificated staff, classified staff, and office
staff is herein analyzed in relation to the Evidence-Based Model.
Certificated teaching staff. The phrase “certificated teaching staff” refers to
core teachers, specialist teachers, special education teachers, English learner teachers, and
those serving in positions of academic extra help. This section presents findings
regarding each of these categories of staff members.
Core teachers. In middle school, those who teach in the areas of English
Language Arts, mathematics, science, social studies, and world languages are categorized
as core teachers (Odden & Picus, 2008). All middle school students are expected to
receive instruction in the core areas. Teachers utilize the California State Standards as
the framework for instruction, and these standards are used as the basis for the
development of scopes in sequence, benchmarks, and common assessments. In middle
school, it is not uncommon for teachers to teach both core and elective courses to
different grade levels. For example, a teacher may teach 2 periods of Language Arts to
6
th
grade, 2 periods of Language Arts to 7
th
grade, and 1 period of Yearbook. At the
district of study, all middle school teachers received one preparatory period, which could
be used to plan lessons, collaborate, or conference with parents.
In the sample district, core teachers made up 68.6% of certificated teaching staff.
A total of 256.2 core teachers were spread across the six middle schools. The Evidence-
Based Model (EBM) recommends a class size of 25 students for Grades 6 through 8.
From the sample middle schools, the only sites with a class size of 25 or less in the
EDUCATIONAL RESOURCE ALLOCATION 74

content core areas were those that received Quality Education Investment Act (QEIA)
funds.
Specialist teachers. Specialist teachers teach noncore academic classes. For the
most part, these courses take place during the regular classroom teachers’ preparatory
period, providing core teachers with the opportunity to collaborate and lesson plan. The
EBM recommends specialist teachers to make up 20% or more of the core teachers. At
the district of study, specialist teachers made up 19.4% of the certificated teaching staff.
The noncore academic courses offered at the six middle schools include the following:
computers, art, home economics, band/choir, dance, Spanish, drama, yearbook, physical
education, and Associated Student Body (ASB). Some sites offered elective courses
based on the following college-bound programs: Advancement Via Individual
Determination (AVID) and Mathematics Engineering Science Achievement (MESA).
Special education teachers. Special education teachers assist students with
special needs and an Individualized Education Plan (IEP). In Table 12, the column of
SPED Staff includes special education and inclusion teachers. The special education
teachers identified in this column teach the regular classroom curriculum in a self-
contained setting to students with mild to moderate disabilities. Students with mild to
moderate disabilities who are ready to be mainstreamed are assigned to a regular
classroom with an Inclusion teacher. A total of 40 special education teachers were
identified in the six middle schools, representing 10.7% of the certificated teaching staff.

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Table 12
Certificated Teaching Staff
Certificated Teaching Staff
Middle Schools Core Specialist SPED Staff ELL Staff
1 47.92 10.05 7.00 .40
2 36.99 11.35 7.00 .73
3 52.75 14.00 6.00 1.45
4 33.84 8.16 6.00 0.00
5 49.65 14.85 7.00 2.50
6 34.98 14.02 7.00 0.00
Subtotal for
Middle Schools

256.2

72.4

40.0

5.1
% Of Teaching
Staff

68.6%

19.4%

10.7%

1.3%

English learner teachers. The ELL Staff column in Table 12 includes teachers
who work specifically with students learning English as a second language. This column
also includes EL Specialists who coach and teach for a certain percent of their time.
Most schools offer one or two periods of ELD (English Language Development),
depending on their EL population. Only one school has a full-time EL Specialist, and
schools 4 and 6 do not employ an EL Specialist or offer EL titled courses (Table 12). All
sites had credentialed teachers with the proper certification to teach English learners;
therefore, EL strategies were embedded into their daily instructional lessons. According
to the Evidence-Based Model, one additional teacher is recommended for 100 EL
students. A total of 1,301 English learners attend the six middle schools, and 5.1 EL
certificated staff members work specifically with English Learners. The total amount of
English learners does not include students who have been reclassified fluent English
proficient (RFEP) and are still part of the EL subgroup in the California Standardized
Test (CST). The EL label is removed only when the student has met proficiency in
English Language Arts for two consecutive years. Therefore, even though some
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reclassified students are not accounted in the total amount, schools are still obligated to
provide monitoring services for two years after their reclassification date.
Academic extra help. In addition to Inclusion teachers, special education, and
English as a Second language courses, academic extra help includes extended-day,
summer school, and district alternative programs. In the sample district, extended-day
took place in two forms: After School Homework Help Program and THINK Together
Program. Schools that received Title I funds offered one-hour of Homework Help every
day of the week. Certificated teachers received extra-duty pay for their afterschool
instructional services. Four out of the six middle schools offered the THINK Together
Program, which took place at the site for three hours after school. THINK Together is an
extended learning program that focuses on academic and enrichment courses. The
program hires their own staff, which does not require the services of site certificated
teachers.
Summer school was no longer offered at the middle school sites. Per the
interview with the Assistant Superintendent of Educational Services, some sites may have
allocated categorical funds to provide summer school, yet, due to limited funds, schools
allocated categorical monies to fund instructional coaches, professional development, and
technology. Summer school was offered to high school students in need of graduation
credits.
Pilot Tutoring Project (PTP) was an alternative recovery program put forth by the
district’s ELL Program director. Once a week, this project offered evening tutoring
services at the district’s Board Room. It was open for students’ grades 3-12 in need of
tutoring in the content areas of English Language Arts and Math. There was not an
EDUCATIONAL RESOURCE ALLOCATION 77

actual personnel cost for this program, since college students volunteered their tutoring
services.
Certificated Non-classroom Instructional Staff. In addition to core and
specialist teachers, the Evidence-Based Model recommends non-classroom instructional
staff, which includes instructional coaches and librarians. Due to budget cuts, the district
of study eliminated the librarian positions and replaced it with library paraprofessionals;
this position is discussed in the Classified Staff section of this chapter.
Instructional coaches. The district’s Professional Development Center offered
10 experts (26% FTE for each site) who served as instructional coaches. Their task was to
meet the district’s academic vision in conjunction with the site needs. Aside from district
expert specialists, sites were able to allocate funds to employ instructional coaches as
Teachers on Special Assignment. Sites 1 and 5 (Table 13) are the only schools that
allocated funds for an English Language Arts and Math coach. In reviewing their source
of funding, these positions are covered by Title I and QEIA funds.
The Evidence-Based Model (EBM) recommends one instructional coach for
every 200 students at the middle school level. In accordance with the EBM, the total
amount of instructional coaches for a total enrollment of 8,087 would be 40.4. The
district employed 10 coaches for 39 schools, in addition to the two site coaches at schools
1 and 5, for a total of 5.6 instructional coaches at the middle school setting.

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Table 13
Certificated Staff
Certificated Staff
Middle School By
Number
Total Enrollment Instructional Coaches
1 1284 2.26
2 1424 0.26
3 1261 0.26
4 1207 0.26
5 1518 2.26
6 1393 0.26
Subtotal for
Middle Schools

8087

5.6

Pupil Support Staff. Pupil support staff consists of the following: school nurses,
counselors, parent liaisons, psychologists, and speech and language pathologists. Some
of these positions were completely funded by the school site, while others are funded by
the district and shared throughout the 39 sites.
School nurses. The Evidence-Based Model identifies school nurses as part of a
non-instructional expenditure, due to their non-academic support to students. The district
of study employed a total of 9 school nurses for the entire district. Each site received
23% of a full-time position; enrollment size was not taken into account. The EBM
recommends one full-time nurse for every 750 students. Although nurses were not
stationed in the sites full-time, each school provided health care through the health
technicians.
Non-academic pupil support staff. The positions input into the non-academic
pupil support staff in Table 14 include counselors, guidance assistants, parent liaisons,
psychologists, and speech language pathologists. According to the Evidence-Based
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Model, each site should provide one non-academic pupil support staff for every 250
middle school students.
Each site employs counselors and guidance assistants. Counselors provide socio-
emotional and academic counseling in addition to providing individual and group
counseling. Guidance assistants assist with counseling, scheduling, and uploading of
grades into the data system. All schools employed a minimum of 3 counselors and a
guidance assistant with the exception of school number 5. School number 5 was the only
middle school site with four counselors and a parent liaison funded by Title I and QEIA.
The parent liaison worked 3.5 hours a day, and was responsible for scheduling parent
meetings, attending parent meetings, providing outside resource information, and
translating documents.
Similar to nurses, psychologists were also distributed across the 39 sites in the
district. Special education funds a total of 23 psychologists, and each site receives 59%
of a full-time position. School sites were flexible with their allotted time, depending on
student needs, identification of special education students, and assessment deadlines.
Speech language pathologists were also funded through special education; the district
employed a total of 24 pathologists, which represents 62% of a full-time position for each
site.

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Table 14
Pupil Support Staff
Pupil Support Staff
Middle School By
Number
Nurses Non-academic Pupil Support Staff
1 0.23 4.43
2 0.23 4.43
3 0.23 3.93
4 0.23 3.93
5 0.23 5.03
6 0.23 4.20
Subtotal for Middle
Schools

1.4

26.0

Classified Staff. Classified staff consists of non-certificated employees who
provide support to teachers and students. The classified staff in Table 14 includes the
following: special education aides, instructional aides, non-instructional aides, and library
technicians.
Special education aides. The district of study employed special education aides
in two instructional settings, the mainstream classroom and the special day class (SDC).
Students in SDC receive academic support and basic life skills in a self-contained setting.
Special day classes service students identified as developmentally delayed, emotionally
disturbed, or severely handicapped. For the purposes of this study, only Special
Education aides who support students with mild and moderate disabilities in the
mainstream classroom were accounted for in the simulation model. Special Education
aides in the mainstream classroom service Special Education students as indicated in the
student’s Individualized Education Plan (IEP). The Evidence-Based Model recommends
one Special Education aide for every 300 students. The district of study employed 27.6
EDUCATIONAL RESOURCE ALLOCATION 81

Special Education aides in the mainstream classroom with a total enrollment of 8,087
throughout the six middle schools.
Instructional aides. All of the Instructional Aides employed throughout the six
middle schools were funded through Economic Impact Aid (EIA) and Quality Education
Investment Act (QEIA) funds. Economic Impact Aid is categorical funds designated to
support English language acquisition for English learners (EL). The Instructional Aides
in the six middle schools were placed in either ELD classes or mainstream classes with a
high number of EL students. The Evidence-Based Model does not recommend the
employment of Instructional Aides; it recommends an additional certificated teacher for
every 100 EL students. A total of 9.7 Instructional Aides were hired in the sample
middle schools for the purpose of assisting English learners.
Non-instructional aides. The column of Non-Instructional Aides in Table 15
includes campus supervisors, noon/breakfast duty aides, and health technicians. The
category of Non-Instructional Aides consists of positions that support students outside of
the instructional setting, providing a safe and healthy school environment. A total of 38.4
Non-Instructional Aides were spread throughout the six sample middle campuses.
Library technicians. The Evidence-Based Model recommends one library/media
specialist at each middle school site; it does not recommend a library technicians or
paraprofessionals. However, as a result of budget cuts, certificated librarians were one of
the eliminated positions. Library paraprofessionals, also known as Library Media
Assistants, assist with the basic technical media operations, textbook inventory,
distribution of instructional materials, and check-out of library books to students and
staff. Library Media Assistants are also responsible of maintaining a library visitation
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schedule, but are not required to supervise classroom visitations. There was one library
paraprofessional employed at each middle school site.
Table 15
Classified Staff
Classified Staff
Middle
School By
Number
SPED Aides Instructional
Aides
Non-
Instructional
Aides
Library
Paraprofessionals
1 4.75 1.44 5.56 1.00
2 4.13 1.40 7.12 1.00
3 4.31 1.40 5.56 1.00
4 4.25 1.40 5.56 1.00
5 4.88 3.09 7.57 1.00
6 5.25 0.94 7.06 1.00
Subtotal
for Middle
Schools

27.6

9.7

38.4

6.0

Administration. Administration at the middle school setting includes principals,
assistant principals, and clerical staff. The Evidence-Based Model recommends one
principal per site, one assistant principal for every 450 students, and two secretaries for
each prototypical middle school site.
Principals/assistant principals. The district of study employed one principal per
site; the number of assistant principals varied per site. Site 1 employed two assistant
principals; one was identified as the Assistant Administrator of Instructional
Improvement and Coaching (AAIIC). This position was funded through Title 1. Site 5
employed 2.5 assistant principals; their AAIIC was funded through QEIA. The
remaining sites, due to budget constraints, employed either 1 or 1.5 assistant principals.
Principals and assistant principals were responsible for the management, instructional
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direction, and community/district relations of the overall site. A total of 16 principals and
assistant principals were employed throughout the six middle schools.
Secretaries. The district of study employed secretaries with the purpose of
maintaining daily office and department functions. Most of the sites consisted of the
following clerks: ASB (Associate Student Body), bilingual, attendance, registrar, special
education, and administrative. A total of 39.2 secretaries were employed throughout the
6 middle schools.
Table 16
Office Staff
Office Staff
Middle School
By Number
Enrollment Principal Assistant
Principal
Secretary
1 1284 1.00 2.00 6.38
2 1424 1.00 1.50 6.38
3 1261 1.00 1.50 6.38
4 1207 1.00 1.00 6.38
5 1518 1.00 2.50 8.28
6 1393 1.00 1.50 5.38
Subtotal for
Middle Schools

8087

6.0

10.0

39.2

Summary of Findings from Research Question Two
This section provided information regarding how human resources were allocated
across the six middle schools. Research question three utilized this human resource data
to identify the gap between the current human resource allocation and the Evidence-
Based Model.

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Findings for Research Question Three
The third research question asked whether there was a gap between the resource
allocation already in place and the resource allocation that research suggests is most
effective. The Evidence-Based Model (EBM) allocates personnel according to research-
based findings that have proven to increase student achievement. In analyzing the results
of the simulation model developed by Picus and Knight (2012), positive and negative
gaps were identified between the current allocation and the recommendations of the
EBM. The positive gaps are addressed in the discussion regarding research question four
and are followed by re-allocation recommendations.
Table 17 displays the gap results, and the three columns below the “Position
Counts” heading provide information on the current, desired, and EBM recommendations
of personnel counts. The two columns below the “Gap” heading provide information of
the gap difference between current to desired and current to EBM. All negative gaps are
identified in parentheses while all positive gaps are identified without parentheses It is
evident, when referring to Table 17 that negative gaps outweigh the positive gaps in both
columns. These gaps were analyzed using the recommendations of the EBM and the gap
analysis framework of Clark and Estes (2008). As reviewed in Chapter 3, Clark and
Estes (2008) identify three potential causes that may impede the positive performance of
an organization: motivation, knowledge/skills, and organizational barriers.

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Table 17
Human Resource Allocation Gaps for Six Middle Schools
District Total
Position Counts Gap
Title Current Desired EB Current-
Desired
Current-EB

Principals 6.0 6.0 6.0 0.0 0.0
Assistant Principals 10.0 35.9 12.0 (25.9) (2.0)
Instructional
Coaches
5.6 27.0 40.4 (21.4) (34.9)
Core teachers 256.2 404.4 323.5 (148.2) (67.3)
Specialist teachers 72.4 80.9 64.7 (8.4) 7.7
SPED teachers 40.0 53.9 53.9 (13.9) (13.9)
ELL teachers 5.1 13.0 13.0 (7.9) (7.9)
Academic extra help
staff
2.0 60.9 60.9 (58.9) (58.9)
Non-academic pupil
support
32.6 93.2 93.2 (60.6) (60.6)
Nurses 1.4 8.1 10.8 (6.7) (9.4)
Extended
day/summer school
staff
0.0 111.3 101.5 (111.3) (101.5)
Instructional aides 9.7 80.9 0.0 (71.2) 9.7
Supervisory aides 38.4 35.9 35.9 2.5 2.5
SPED aides 27.6 27.0 27.0 0. 6 0.6
Librarians 0.0 6.0 6.0 (6.0) (6.0)
Library technicians 0.0 18.0 0.0 (18.0) 0.0
Library
paraprofessionals
6.0 18.0 0.0 (12.0) 6.0
Secretaries/clerks 39.2 35.9 35.9 3.2 3.2
Note: Model developed by Picus and Knight (2012)
Negative gaps between current district allocations and the EBM. The most
significant negative gaps existed in the following categories: instructional coaches, core
teachers, and non-academic pupil support. Another negative gap that will be briefly
discussed is the extended day/summer school staff.
Instructional coaches. A deficit of 34.9 instructional coaches existed between
the current allocation and the Evidence-Based Model (EBM). According to the EBM,
EDUCATIONAL RESOURCE ALLOCATION 86

there should be one full-time instructional coach for every 200 students. In the district of
study, one instructional coach for every 200 students would be equivalent to 40.4
coaches. The district expressed the desire of 4 content area coaches per site, regardless of
the size of enrollment. At the time of this study, the only schools that employed
instructional coaches were sites that received Title I and QEIA funds. The district
employed 10 content area specialists who provided professional development to the sites;
however, it does not replace the effectiveness of having coaches stationed at each site.
Hiring instructional coaches appears to be an organizational gap, since the district was
aware of the benefits of hiring instructional coaches, yet was unable due to the lack of
funding. There was a possibility that this deficit might also increase when QEIA reached
its end term, causing sites to re-evaluate their allocation of funds.
Core teachers. The next significant gap was found in the number of core
teachers, which showed a deficit of 67.3. Schools that fell under QEIA requirements
adjusted their class size to 25; the remaining sites were at 36 students per class. Sites with
QEIA funds had the flexibility of hiring and creating positions that would affect student
performance. Courses taught by specialist teachers provided the opportunity of balancing
the average content area class size, since these classes can exceed the 25- and 36-students
enrollment. Like QEIA, the EBM recommends one teacher for every 25 students. The
difference between the current number and the desired number is even larger; if funding
were not an issue, district leadership expressed the desire of 20 students per class.
Similar to the first significant gap, this gap was also due to an organizational barrier.
Non-academic pupil support staff. Non-academic pupil support staff includes
both certificated and classified positions that support students outside of the classroom
EDUCATIONAL RESOURCE ALLOCATION 87

setting. These positions include counselors, guidance counselors, nurses, health assistant
paraprofessionals, school psychologists, speech language pathologists, social workers,
and family/community liaisons. As revealed through the study, many of these positions
were only in existence at the school sites for a limited amount of hours per day; some
were completely eliminated due to budget cuts. A deficit of 60.6 non-academic pupil
support staff positions exists between the current allocation and the EBM. The model
recommends one full-time position for every 100 students classified as “at risk.”
Students who quality for free or reduced-price lunch fall under the category of “at risk.”
Extended day/summer school staff. Summer school is an area that has been
completely eliminated at the middle school sites. Extended day is made possible through
an outside organization that works with the district yet employs its personnel. Some sites
do offer homework help, but their starting and ending dates vary. This becomes a
motivational gap for students and staff since there is no consistency in the schedule.
Teachers who do participate receive extra-duty pay through Title 1 funds. The EBM
recommends .833 of a full-time position per 100 students classified “at risk” for each
category. A deficit of 101.5 extended day/summer school staff positions existed in
comparison to the EBM.
Summary of Findings for Research Question Three
This section identified two types of gaps: 1) gaps between the current and desired
allocations and 2) gaps between the current allocation and that recommended by the
Evidence-Based Model. The next section takes into consideration the gaps and provides
trade-off recommendations to benefit the improvement of student achievement.

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Findings for Research Question Four
The Evidence-Based Model (EBM) provides a detailed list of position counts for
a prototypical school. Picus and Knight’s (2012) database model (Table 17) outlines the
positive and negative gaps. Given this information, question four asks about the re-
allocation of human resources in order to improve student achievement. To respond to
this question, the researcher examined the positive gaps between the current district
allocation and the EBM. Using research from Picus and Odden (2008), in addition to the
research identified in the literature review, the researcher proposes trade-offs that will
increase the amount of instructional coaches. This recommended proposal takes into
consideration all positions and relies on data to determine which positions focus on
student achievement.
The researcher determined the positions that could be eliminated or traded by
analyzing the positions that have little or no direct effect on student achievement. Odden
and Picus (2008) identify the following as additional resources: administrators, aides,
pupil support personnel, and librarians. In general, all of these positions provide support
to the school. From the gap analysis results, administration is not an area that exceeds the
EBM recommendations; therefore, it is not an area that would be cut or traded, although
it is a position that has little or no direct effect on student achievement. Research
identified instructional aides as personnel who do not add value to students’ academic
achievement. Although research (Odden and Picus, 2008) suggests that instructional
aides can have an impact on students when adequately trained and closely supervised, the
district’s funding situation did not allow for the allocation of resources for training
instructional aides. Similarly, pupil support personnel, supervisory aides, and librarians
EDUCATIONAL RESOURCE ALLOCATION 89

are not positions that directly affect student achievement; therefore, these positions were
considered for trade-off with the objective of increasing the number of instructional
coaches.
Instructional coaches. Positive gaps were observed in the following positions:
specialist teachers, instructional aides, supervisory aides, special education aides, library
paraprofessionals, and secretaries. Most of these positions were taken into consideration
with the attempt of increasing the number of instructional coaches. Instructional coaches
affect the classroom-learning environment through their direct interaction with staff by
providing on-going mentoring, applicable feedback, and supplemental resources.
Although there are many areas with negative gaps, increasing the number of instructional
coaches benefits core teachers, ELL teachers, and all staff who work with “at risk”
students. Picus and Odden (2008) identify instructional coaches as the key to
professional development. Therefore, in reviewing all positions, instructional coaches
represent the position that can benefit all students and staff school-wide.
Trade-offs. Research (Odden & Picus, 2008) does not support the employment
of instructional aides at the middle school level; there is no evidence of improved student
performance. As a result, it is recommended to trade the 9.7 full-time instructional aid
positions to employ 3 additional instructional coaches. The district of study employed
more supervisory aides (2.5 full-time positions), special education aides (0.6 full-time
positions), and secretaries (3.2 full-time positions) than the EBM recommends. These
combined positions assist in employing 2 additional instructional coaches. At the time of
this study, the district employed 5.6 instructional coaches, which includes the 10 district
professional development content specialists at 26% service per site. When deducting the
EDUCATIONAL RESOURCE ALLOCATION 90

district’s professional development specialists, the sites were left with 4 full-time
instructional coaches. The trade-off from the aides and secretaries, in addition to the 7.7
specialist teachers over the EBM recommendation, make the employment of 13
instructional coaches possible which guarantees the employment of 17 instructional
coaches. It is not possible to allocate the number of instructional coaches recommended
by the EBM; however, it is possible to allocate instructional coaches at each site. The
researcher recommends the numbers indicated in Table 18 due to the total amount of
student enrollment per site. Although these proposed changes will increase the number
of instructional coaches per site, the district’s funding budget makes it impossible to meet
the desired and EBM goals.
Table 18
Recommended Number of Instructional Coaches per Site
Middle School
#
Grade Level
Total
Recommended Number of
Instructional Coaches
1 1284 3
2 1424 3
3 1261 2.5
4 1207 2.5
5 1518 3
6 1393 3
Subtotal for
Middle Schools

8087

17

Chapter Summary
This chapter presented the findings of the four research questions as they apply to
the district of study. Data and information were collected from research articles,
interviews, and district documents. The chapter began with an overview of the district’s
sample schools, its students’ achievement, and how funding conditions affected the
overall district. Following the overview of the district, the findings of the four research
EDUCATIONAL RESOURCE ALLOCATION 91

questions were presented. Four strategies that have proven to improve student
achievement were identified and analyzed in relation to how they were implemented at
the district of study. All of the personnel employed at the sample schools were identified
and matched according to the categories of the Evidence-Based Model (EBM).
Personnel counts were input into the simulation model developed by Picus and Knight
(2012) to quantify the amounts according to the categories. The simulation model also
displayed the positive and negative gaps, which facilitated the interpretation of the
differences among the current allocation, the desired allocation, and the allocation
recommended by the EBM. This information was then used to recommend trade-offs to
help improve student achievement school-wide and meet the current fiscal budget.
Chapter 5 concludes the study with recommendations and implications for future
research.

EDUCATIONAL RESOURCE ALLOCATION 92

Chapter 5: Conclusions
This study examined human resource allocation strategies that help improve
student achievement while maximizing the limited amount of public school funding. In
Chapter 1, the researcher presented a brief history of school finance and the implications
of limited resources. Chapter 2 addressed the following topic areas: school improvement
strategies, allocation of human resources, limited resources/fiscal constraints, and gap
analysis. Four school improvement strategies were identified as positive indicators of
student achievement: leadership, assessment and data-based decision making, curriculum
and instruction, and professional development. Chapter 3 presented the methodology
used to respond to the four research questions developed by the USC cohort in relation to
the district of study. Picus and Odden’s (2008) Evidence-Based Model (EBM), Clark
and Estes (2008) gap analysis framework, and Picus and Knight’s (2012) simulation
database model were utilized as the primary sources in this study. Chapter 4 explained
how all of the sources mentioned in Chapter 3 were implemented to respond to the four
research questions. A gap analysis was conducted through Picus and Knight’s simulation
database model, which identified the gaps among the EBM and the district’s current and
desired human resource allocations. The simulation database model facilitated the
interpretation of data, making it possible to identify areas of trade-off that can be
recommended to the district of study to improve student achievement.
Purpose of the Study
The purpose of this study was to address the problem that California faces due to
limited public school funding and to provide resources to maximize the use of dollars
through the reallocation of human resources. This study used a purposeful sample of six
EDUCATIONAL RESOURCE ALLOCATION 93

middle schools. Data were collected and analyzed and interviews were conducted with
the intent of providing the district meaningful information that can be utilized at school
sites to increase student achievement.
Importance of Study
This study, combined with the findings of the fifteen cohort members of the
thematic dissertation group from USC, provides policymakers, district managers, and
administrators an overview of how districts throughout southern California allocate
human resources. The interpretation of data, made possible by Picus and Knight’s (2012)
simulation database model, along with the findings of the literature review, facilitate
recommendations on how to reallocate human resources according to Picus and Odden’s
(2008) Evidence-Based Model. Educational leaders and decision-makers are presented
with information that can be implemented or taken into consideration for their specific
school districts.
Methodology
In an effort to study the allocation of human resources in an identified district, this
study examined the allocation of personnel in six middle schools located in an urban city
in Southern California. The gap analysis used the Evidence-Based Model (Odden &
Picus, 2008) as the comprehensive resource allocation framework to determine the
districts alignment to best practices. Although this study was embedded in a qualitative
research design, quantitative data were used to provide support to the analysis.
The district of study met the following criteria: 1) located in an urban city, 2)
serves a diverse student population, and 3) 70% or above receive free or reduced-price
meals. Interviews were scheduled with the District’s Superintendent, Assistant
EDUCATIONAL RESOURCE ALLOCATION 94

Superintendent of Business Services, Assistant Superintendent of Educational Services,
and personnel from the Human Resources office. Several documents were collected and
analyzed, such as the Single Plan for Student Achievement, School Accountability
Report Card, County Summary Reports, district allocation personnel documents, as well
as district informational documents. Data from the collected documents were input into a
simulation database model developed by David Knight and Lawrence O. Picus (2012).
The database served multiple purposes: 1) Organization of data by site and district, 2)
Computation of gaps in comparison to the Evidence-Based Model and 3) Serving as a
tool to provide recommendations to the six middle school sites and district.
Sample and Population
This study used a purposeful sample of six middle schools located in the same
school district. The district is located in an urban area and has been identified as a district
in year-three Program Improvement (PI) status (California Department of Education,
2011). Due to the large size of the district, and in an attempt to focus, only the middle
schools were analyzed. All of the sample schools were traditional public schools with no
specific entrance requirements.
The selection process began by creating a County Summary Report using the
database on the California Department of Education website (California Department of
Education, 2011). A report of 30 districts was generated. To narrow the selection of
districts and achieve greater focus the list was condensed into 10 schools districts.
Elementary districts and districts with an enrollment of 20,000 or fewer were removed
from the list. The next elimination step targeted districts with less than 70% of the
student population receiving free or reduced-price meals. This reduced the selection
EDUCATIONAL RESOURCE ALLOCATION 95

sample to four districts with diverse student populations. As a starting point, two of the
districts were contacted via email, and the district of study responded the following day.
A meeting was scheduled with the Superintendent yielded discussion regarding the
contact information of the USC Dissertation Chair, the statement of the problem, the
purpose of the study, and the research questions. The Superintendent agreed to provide
necessary information for the study as long as the district remained anonymous.
All of the middle schools in the district of study serve 6
th
through 8
th
grade; all
exceed 1,000 students in total enrollment. The value of studying this particular district
consists in that it is relatively similar to other districts in California. Therefore, the
information gathered from this study will serve other districts as they prepare to allocate
human resources adequately, using research-based findings that have demonstrated to
double student performance. Nevertheless, this study does focus on one school district
and its findings are not generalizable. They may, however, prove useful to district
leaders who face similar funding situations.
Summary of the Findings
The following section provides a summary of the findings of the research
questions for the specific district of study. Findings were obtained through a literature
review of strategies that help improve student achievement, collection of data from the
district of study, interviews with district officials, and interpretation of data through a gap
analysis.
Research question one. What research based human resource allocation
strategies improve student achievement? Four recurring principles for improving student
EDUCATIONAL RESOURCE ALLOCATION 96

achievement were identified in the literature review: leadership, assessment and data-
based decision making, curriculum and instruction, and professional development.
Leadership. In the area of leadership, findings revealed that the district embraces
the concept of shared decision-making. In the years prior to this study, the district
established its own four areas of focus: student achievement, learning environments,
collaboration, and resource management. These priorities were reinforced through
trainings led by the superintendent, district leaders, and outside consultants.
Assessment and data-based decision making. Data is used in an on-going basis
to determine the areas of need, strengths, and possible allocation of resources. Student
data is input, scanned, and analyzed via the data information system EADMS. Common
assessments for all core subject areas are designed at the site level and reviewed at the
site’s department meetings. District-wide formative assessments in both English
Language Arts and Math are utilized to identify specific areas of need and interventions,
and teachers are able to view the results with a description of the most commonly missed
problems by strand and standards. Results are analyzed and shared at the site department
meetings. An observed area of growth for the district of study is that of holding all
teachers accountable for their individual scores by openly discussing the results without
covering the names of teachers or students.
Curriculum and instruction. Curriculum is an area that the district finds
challenging due to the its moving from Content State Standards to Common Core State
Standards (CCSS) and this move’s anticipated influence on textbooks and digital
curriculum. The district had not adopted new textbooks over a few years due to limited
EDUCATIONAL RESOURCE ALLOCATION 97

funds. At the time of this study, purchasing textbooks would not have been cost-effective
due to the unpredictability of alignment with the new Common Core standards.
Professional development. The most common form of professional development
is on-site training, delivered by in-district experts and on-site instructional coaches.
Under Educational Services, the district of study employed 10 district experts, and 5 were
part of the Secondary Professional Development Team, composed of the following
specialists: ELA, ELL, Special Education, Read 180, and Mathematics. The professional
development team worked with individual schools to plan and implement individualized
staff training programs for their professional development needs. Under Business
Services, the district offered trainings through the Information Systems/Technology
Department. A recommendation in this area is to involve master teachers as Teachers on
Special Assignment (TOSA) to serve as on-site instructional coaches.
Research question two. How are human resources allocated across the sample
district? The simulation database model developed by Picus and Knight (2012) identifies
18 human resource positions; three are not necessarily supported by the model, yet they
are included in the list to account for personnel who are traditionally employed by the
district. These three positions are not supported due to lack of evidence on their impact
on school improvement and student achievement: instructional aides, library technicians,
and library paraprofessionals. Through the collection of data, provided by Human
Resources, Educational Services, and Business Services, it was revealed that the district
allocated resources in 15 of the 18 human resource positions, including instructional
aides and library paraprofessionals. Gaps between the current allocation and that which
EDUCATIONAL RESOURCE ALLOCATION 98

was desired in relation to the Evidence-Based Model were identified and addressed in the
section pertaining to research question three.
Research question three. Is there a gap between current human resource
allocation practices and what the research suggests is most effective? The simulation
database model revealed significant gaps between the current resource allocations and the
recommendations of the Evidence-Based Model (EBM). In comparison to the EBM,
principals are the only position aligned to the recommended count in the model. From
the 18 positions identified in the EBM, the district employed below the recommended
count in 10 positions, and exceeded the recommended amount in 6 positions. The
researcher utilized the excess amounts to recommend trade-offs that align to the EBM
and the desired district counts. Trade-off recommendations were offered in the section
regarding research question four.
Research question four . How can human resources be strategically re-
allocated to align with strategies that improve student achievement? Positive and
negative gaps were analyzed to determine the areas of reallocation that would benefit the
improvement of student achievement while meeting the needs of the district’s current
funding condition. The most significant negative gaps were identified in the numbers of
instructional coaches, core teachers, and non-academic pupil support staff. Positive gaps
were observed in the numbers of specialist teachers, instructional aides, supervisory
aides, special education aides, library paraprofessionals, and secretaries. Positions that
were overstaffed in comparison to the Evidence-Based Model (EBM) were taken into
account for the recommendation of trade-offs. The interpretation of data revealed that
many of the overstaffed positions were mainly classified, and, generally, three classified
EDUCATIONAL RESOURCE ALLOCATION 99

positions account for one full-time certificated position. Due to the limited amount of
position counts available for trade-off, the researcher recommended increasing the
number of instructional coaches and cut all of the instructional aid positions, in addition
to the overstaffed amounts of specialist teachers, supervisory aides, and secretaries. The
researcher recommends the increase in the number of instructional coaches due to their
impact on the classroom-learning environment through their direct interaction with staff
by providing on-going mentoring, applicable feedback, and supplemental resources.
Instructional coaches assist in the reinforcement and transfer of new and/or old material
that is presented in professional development and implemented in the classroom. As
professional partners, instructional coaches share research-based interventions that can be
weaved into the lesson to help differentiate instruction (Knight, 2009). Hence,
instructional coaches represent the position that can benefit all students and staff school-
wide
Limitations
The following list identifies the limitations present in the study:
• This study is not generalizable to other schools due to the small sample size.
• Elementary and high schools were not taken into account in this study; therefore,
the information presented applies only to the middle school sites in the district of
study.
• The study was conducted over a short period of months and during a time of
budgetary uncertainties.
• Although specific questions were formulated for the interviews, anecdotal
accounts were shared during the interview.
EDUCATIONAL RESOURCE ALLOCATION 100

• Information gathered from the interviews is limited to the understanding of the
district officials. Their response to the interview questions may differ from the
responses of all other educational leaders in the district.
• In a few years, some of the sites will no longer receive Quality Education
Investment Act (QEIA) funds; this loss can alter the trade-off recommendations
and human resource allocations at certain sites.
• Although positions were affected due to budget cuts, the passage of Proposition
30 may alleviate some of the negative gaps observed in Picus and Knight’s (2012)
simulation database model.
Implications for Practice
This study offers pertinent information to districts in California that experience
fiscal constraints due to budget cuts and demands from the state and federal level. The
study provides research-based information on strategies that help improve student
achievement. Districts can use this information to benchmark their current practices to
those identified in Chapter 2 of this study. Districts can examine the Evidence-Based
Model (EBM) to determine its value as a tool that can be implemented. In addition to the
EBM, districts can informally assess the cause of their performance gaps as either
knowledge/skill, motivational, and/or organizational.
Within the district of study, the researcher diligently analyzed all district
documents and interviews to provide an as best as possible account of the current human
resource allocations in comparison to the Evidence-Based Model. The overall intent was
to be of service to the district by providing information and recommendations based on
research studies that have proven to increase student achievement. Chapter 4 of this
EDUCATIONAL RESOURCE ALLOCATION 101

study provides a snapshot of the district and its current practices have been acknowledged
and compared to the four recurring school improvement strategies: leadership,
assessment and data-based decision making, curriculum and instruction, and professional
development. Position counts were input into Picus and Knight’s (2012) simulation
database model to provide a visual with computations that may serve as a discussion
point to educational school site leaders and district officials as they reallocate human
resources.
Lastly, this study allows for policymakers to observe a district’s situation and
struggle as leadership manages to meet the federal and state demands with limited funds.
This, in turn, opens the discussion on how the amount of per-pupil spending does not
compare to the amount of other states and makes it difficult to provide positions and
programs at the same level as others.
Recommendations for Future Research
The researcher recommends the following for future research:
• Due to time constraints, this research only studied the six middle schools at the
district of study. A recommendation is to include all grade levels to receive an in-
depth collection of data on the allocation of human resources. This would benefit
the district of study in allowing for the discrepancies and similarities to be
highlighted for all 39 schools in the district.
• It is also recommended that school finance researchers identify schools in
California that are currently aligned to the human resource allocation of the
Evidence-Based Model.
EDUCATIONAL RESOURCE ALLOCATION 102

• This study based the cause of performance gaps on the responses of the
interviewed district officials. It is recommended that site administrators be
interviewed for a more accurate basis on the cause of performance gaps.
Conclusion
Public school funding in California continues to be an area of concern as the
demands from the federal and state level increase while the amount of funding resources
decreases. California’s low national ranking in expenditures for public K-12 schools per
student in ADA (National Education Association, 2013) clearly demonstrates a
significant gap between California and states with high student spending, such as
Vermont, New Jersey, and New York. At the time of this study, California spent $2,094
less than the national average and $14,896 less than the top state. Although the district of
study made great efforts to meet the state and federal demands, California’s per pupil
funding does not facilitate the task.
As a result, the purpose of this study was to acknowledge California’s funding
situation while offering an alternative method through the recommendations of the
Evidence-Based Model. The Evidence-Based Model takes into account the results of
research and best practices to recommend the allocation of human resources in areas that
affect school improvement and student achievement. Although this research revealed
significant gaps between the district of study and the Evidence-Based Model, it also
revealed how limited funding is allocated to meet student needs. The collection of data,
district documents, and interviews support the good intentions of the district. It is then up
to policy makers to acknowledge California’s difficult task and to develop a system that
provides adequate funding for all schools.
EDUCATIONAL RESOURCE ALLOCATION 103

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Abstract (if available)

Abstract The purpose of this study was to analyze the human resource allocation of one urban school district located in Southern California in comparison to the recommendations of the Evidence-Based Model. Prior to collecting data from the district, the researcher identified four strategies for increasing student achievement. These strategies were utilized as a benchmark when analyzing the strategies used by the district. A mixed-methods approach was used in this study. Qualitative data in the form of anecdotal notes was used to comprehend the district's decision-making when implementing specific strategies and practices. Quantitative data on position counts was input into a simulation database model developed by Picus and Knight (2012) to identify gaps among the district's desired allocation, its current human resource allocation, and that recommended by the Evidence-Based Model. ❧ Overall, the study provides the district with responses and findings to the following research questions: 1) What research based human resource allocation strategies improve student achievement? 2) How are human resources allocated across the district of study and its schools? 3) Is there a gap between current human resource allocation practices and what the research suggests is most effective? 4) How can human resources be strategically re-allocated to align with strategies that improve student achievement? ❧ Findings of this study serve as a tool for the district of study, educational leaders, and policy makers. The study takes into account California's fiscal situation at the time of this study in addition to the difficulties of meeting state and federal demands with limited funds. The district's human resource allocation is displayed, demonstrating its effective use of resources in some areas and its lack of personnel in others. This study provides the district with recommendations regarding human resource allocation trade-offs that meet its current funding budget and benefit student achievement. Results of the study demonstrate that, although recommendations are applicable, they are not significant in closing the gap between the district's current human resource allocation and the Evidence-Based Model. Therefore, this study may be used as a sample for policy makers to examine the great efforts of one district and the barriers imposed due to lack of adequate funding.

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Aligning educational resources and strategies to improve student learning: effective practices using an evidence-based model

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Allocation of educational resources to improve student learning: case studies of California schools

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Personnel resource allocation in a Hawaii school complex

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Reallocating human resources to maximize student achievement: a critical case study of a southern California school district

Asset Metadata

Creator Garcia, Sandra (author)

Core Title Educational resource allocation at the middle school level: a case study of six middle schools in one California district

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 07/25/2013

Defense Date 05/29/2013

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

Tag allocation,effective strategies,finance,human resources,OAI-PMH Harvest,school improvement,student achievement

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Picus, Lawrence O. (committee chair), Escalante, Michael F. (committee member), Morgan, Helen E. (committee member)

Creator Email garcia21@usc.edu,SGarcia1021@hotmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-299902

Unique identifier UC11294013

Identifier etd-GarciaSand-1846.pdf (filename),usctheses-c3-299902 (legacy record id)

Legacy Identifier etd-GarciaSand-1846.pdf

Dmrecord 299902

Document Type Dissertation

Format application/pdf (imt)

Rights Garcia, Sandra

Type texts

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

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA