where have all the 9th graders gone? a descriptive study of three first-time 9th grade student...

8/14/2019 Where Have All the 9th Graders Gone? A Descriptive Study of Three First-Time 9th Grade Student Cohorts

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LTF Descriptive Analysis - 1

Where Have All the 9th Graders Gone?

A Descriptive Study of Three First-Time 9th Grade Student Cohorts

Prepared by

Kenneth T. Wilburn, Ph.D.

University of North Florida

And

Dax M. Weaver, MPH

Senior Research and Evaluation Consultant

Health-Tech Consultants, Inc.

For

The Community Foundation in Jacksonville

Learning to Finish Project

For additional information, contact

Ms. Pam D. Paul, Vice President, Community Initiatives

The Community Foundation in Jacksonville

121 West Forsyth St., Suite 900

Jacksonville, FL 32202

(904) 356-4483; www.jaxcf.org

November 20, 2008


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Acknowledgements

We wish to express our sincere appreciation to Timothy R. Ballentine, Executive

Director, Instructional Research and Accountability, and his staff for their assistance in providing

the data for this study, reviewing the data analysis, and development of the final report. In

particular, we would like to acknowledge the contribution of Glenna Goings, Supervisor,

Performance Analytics, for efforts well beyond the call of duty and for her advice and counsel in

completing this study.

This study was sponsored and funded by The Community Foundation in Jacksonville,

121 West Forsyth Street, Suite 900, Jacksonville, FL 32202, Nina Waters, President,

http://www.jaxcf.org/index.html.


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Table of Contents

Page

Introduction.. 8

Graduation Rate for Floridas Public Schools .. 8

Ways for a Foundation to Assist a School District ........... 9

Design of the Study .. 10

Purpose of Phase I of the Study .... 11

Data Source ... 11

Analysis Methods and Procedures .. 13

Determination of Graduation and Dropout Rates . 13

Identification of Cohort Groups. 13

Study Population ......... 14

Gender and Race/Ethnic Groups of the Study Cohorts ... 15

Age, Home Language, and Socioeconomic Status .. 15

Academic Program Information ... 16

Home School . 17

High School Graduation Rates and Characteristics of Those Who Graduated .. 19

2002 Cohort Graduation Analysis 19

2003 Cohort Graduation Analysis 212004 Cohort Graduation Analysis 22

Combined Cohort Graduation Analysis 23

Overall Graduation Status by Gender and Race ... 24

Students Who Failed To Graduate by School Type . 25

Matriculation Patterns of the Three Cohorts . 26

Comparing Graduating and Non-Graduating Students ... 28

Mathematics: Comparing Graduates and Non-Graduates 28

Language Arts: Comparing Graduates and Non-Graduates . 29

FCAT Scores: Comparing Graduates and Non-Graduates .. 30

Attendance: Comparing Graduates and Non-Graduates ... 31


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Page

Conduct: Comparing Graduates and Non-Graduates ... 32

Between-School Student Transfers: Comparing Graduates and

Non-Graduates .. 32

Socio-economic Status: Comparing Graduates and Non-Graduates. 33

Identifying Dropouts ... 34

Gender of Dropouts .. 36

Race/Ethnic Identification of Dropouts 36

Time and Age Distribution of Dropouts ... 37

Socioeconomic Status of Dropouts ... 38

Summary and Discussion 39

Study Population ... 39

Number of 9th Grade Students Graduating in 3, 4 or 5 Years ... 40

Percentage of First-Time 9th

Grade Students Who Withdrew or

Unofficially Dropped Out of School ... 40

Personal and Academic Characteristics of Those Who Failed to

Graduate 41

Differences Between Non-Graduates and Graduates ... 42

Personal and School Characteristics That Could Be Used to

Identify Students at High Risk of Not Graduating From High

School ... 42

Policy and Practice Implications of the Study .. 43

Future Directions.. 45

References ... 46


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List of Tables

Page

Table 1. Cohort Graduation Rates Among Floridas Large Urban School

Districts .. 8

Table 2. Duval Data Set Information: Primary Identifying Codes (Variables) for

Each Student .. 12

Table 3. Identifying the 9th

Grade Cohort for 2002 14

Table 4. Cohort Demographics: Gender and Race/Ethnic Group 15

Table 5. Cohort Demographics: Age, Home Language and Free-Reduced Lunch

Program 16

Table 6. Cohort Academic Program Information .. 17

Table 7. Ninth Grade Home Schools of Cohort Students .. 18

Table 8a. A 5-Year Analysis of Withdrawal Codes for Students in the 2002

Student Cohort 20

Table 8b. Summary Analysis of the Withdrawal Codes for Student Cohort

2002 . 20


Student Cohort 21

Table 9b. Summary Analysis of the Withdrawal Codes for Student Cohort2003 . 21


Student Cohort 22

Table 10b. Summary Analysis of the Withdrawal Codes for Student Cohort

2004. 23

Table 11. Combined 2002, 2003, and 2004 Cohorts: Summary Analysis of the

Withdrawal Codes ... 23

Table 12a. Ninth Grade Cohort Graduation Status by Gender and Race/Ethnic

Group: Individual Cohorts .. 24

Table 12b. Ninth Grade Graduation Status by Gender and Race/Ethnic Group:

Combined Cohorts .. 25


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Page

Table 13. Students Who Failed to Graduate by School Type: Combined

Cohorts. 26

Table 14. Matriculation of Cohort Students Through 4-Year of High School ... 27

Table 15. Final Math and Algebra I Grades: A Comparison of Eventual Diploma

and Non-Diploma Earning 9th Grade Students ... 29

Table 16. Final Language Arts and English I Grades: A Comparison of Eventual

Diploma and Non-Diploma Earning 9th Grade Students 29

Table 17. FCAT Reading and Mathematics Developmental Scores: A

Comparison of Eventual Diploma and Non-Diploma Earning 9th

Grade

Students ... 30

Table 18. Ninth Grade Attendance: A Comparison of Eventual Diploma and

Non-Diploma Earning 9th Grade Students .. 31

Table 19. Combined Cohort 9th

Grade Discipline ... 32

Table 20. Numbers of Transfers Between Schools: Graduates Versus Non-

Graduates 33

Table 21. Percent of Graduates and Non-Graduates Who Participated in the

Free/Reduced Lunch Program 33

Table 22. Estimated Number of Dropouts .. 35

Table 23. Gender of Students Who Dropped Out ... 36

Table 24. Grade at the Time of Dropping Out: Combined Cohorts ... 37

Table 25. Age at the Time of Dropping Out: Combined Cohorts ... 38

Table 26. Percent of Dropouts Who Participated in the Free/Reduced Lunch

Program .. 38

Table 27. Number and Percent of Students who Graduated Each Year . 40

Table 28. Estimated Number and Percent of Students Who Dropped Out of the

Study Cohorts .. 41


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List of Figures

Page

Figure 1. Combined 2002, 2003, 2004 Cohorts 4-Year Grade Distribution ... 27

Figure 2. Combined 2002, 2003, 2004 Cohorts: Dropouts by Race or Ethnic

Group .. 37


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Introduction

Graduation Rate for Floridas Public Schools

Like many large urban school systems, the Duval County Public Schools (DCPS) has

struggled to maintain high graduation rates, with students obtaining a standard diploma within 4

years of entering high school. According to the Florida Department of Education (FDOE), the

cohort graduation rate for cohorts 2003-2004 to 2006-2007 was 64.3%. For this time period,

Duval ranked 6th out of the 7 largest school districts in Florida.

Table 1. Cohort Graduation Rates Among Floridas Large Urban School Districts

Rank District Graduation Rate

1st Hillsborough 79.1%

2nd Orange 71.7%

3rd Palm Beach 71.4%

4th Broward 66.3%

5th Pinellas 64.5%

6th Duval 64.3%

7th Dade 63.9%

State Average 72.4%

Source: FDOE, Cohort Graduation Rates, Cohort 2003-2004 to 2006-2007

According to a variety of published studies, Duvals 4-year graduation rate is not

completely out of line with other large urban school districts across the country. For example, the

2003 cohort graduation rate for the Boston Public Schools was 51%; the 2003 New York City 9 th

grade cohort had a graduation rate of 52%, and only 50% of Portland 9th

graders graduated on

time in 2000 (Editorial Projects in Education, 2008). Indeed, the dropout crisis has become a

national problem. As the latest edition ofEducational Evaluation and Policy Analysis reported

(Kahne, Sporte, de la Torre, Easton, 2008), the United States ranks 16th out of the top 21

Organizations for Economic Co-Operation and Development nations with respect to high school

graduation rates (Kirsch, Braun, Yanmamoto, & Sum, 2007). Furthermore, only 34% of the

students who entered 9th grade in U.S. public schools left with regular diplomas and the

qualifications to attend a 4-year college (Greene & Winters, 2005).


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While the graduation rate appears as a school statistic, the impact of having a high

number of students in a community without the necessary tools and credentials to seek skilled

jobs and/or enroll in postsecondary education and training programs reaches far beyond the

classroom. Studies of systemic reform have revealed that often outsiders can serve as catalysts,

helping a school district determine what actions to undertake to address the issue of high dropout

rates as well as opening up a pathway for greater involvement from key community stakeholders

(Murphy, 2006).

Ways for a Foundation to Assist a School District

In regard to addressing the national dropout crisis, the Youth Transition Funders Group

stated the following:

The most important investment a foundation can make is to support a district and its

research partners in using data to segment the population of students who are not

graduating. We simply cannot improve graduation rates if we do not understand which

students are falling off the pathway to graduation and why. (2007, p. 6)

During the past year, The Community Foundation in Jacksonville has engaged in an

exploratory investigation into the failure of a significant number of DCPS students to receive a

standard high school diploma. As part of this effort, they have established the Learning to Finish

(LTF) study team, which includes members from DCPS, local civic leaders, and representatives

from a wide range of community organizations. This group has spent considerable time

reviewing research on the issue of high school completion and publishing the results from their

investigations in a series of educational briefings. The Community Foundation has also

established a working relationship with Kathy Mullin and Larry Myatt of the Boston public

school system, which is currently engaged in systemic reform to address issues related to

Bostons dropout rates. We have conducted telephone conferences with Robert Balfanz, Ph.D.,

of Johns Hopkins University, a nationally known expert in the study of school dropouts. In

addition, key members of the teams have conducted site visits to the large urban school districts

of Boston and Philadelphia, which are engaged in a variety of reform efforts to address the

dropout crisis in their districts.


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It is important to note that the LTF projects leadership team has decided not to assume

that other large urban school districts research findings and strategies will be applicable to

Jacksonville. Instead, they have begun a community-wide investigation into the specific issues

facing Jacksonville. This study serves as one piece of The Community Foundations

investigation of the dropout issues in Jacksonville.

Design of the Study

In general, the purpose of this study is to determine how applicable national research

studies are to Jacksonville. This will be accomplished, in part, by reproducing a portion of a

research study conducted by Dr. Balfanz of the Philadelphia public schools, Unfulfilled Promise:

The Dimensions and Characteristics of Philadelphias Dropout Crisis, 2000-2005, then building

upon his lessons learned in that effort as we attempt to discover what might be of value to

Jacksonville.

The study is structured as a three phase, multi-methods process. Phase 1 is a quantitative,

descriptive study of historical and recent data on high school completion. We chose to study 9th

grade students because being on track at the end of the 9th grade has been shown to be a strong

predictor of graduation (Allensworth & Easton, 2005, 2007). Balfanz (2007) also reported

student success in the 9th grade as one of the most valid predictors of success in graduating from

high school. Therefore, the population for this phase of the analysis consisted of three cohorts of

students who had completed the middle school grades in the DCPS and were entering the 9 th

grade for the first time. The students from Cohort 1 entered 9th

grade in 2002-2003 and were

expected to graduate in 2005-2006; the second cohort began 9 th grade in 2003-2004 and ideally

would have graduated in 2006-2007; and the third cohort entered in 2004-2005 with a planned

graduation date in 2007-2008.

As described in greater detail in the methods section of this paper, the composition of the

cohorts was limited to continuing DCPS middle school students who were in the 9 th grade for the

first time and were enrolled in an education program for which a standard diploma was the

anticipated outcome. In the second phase of the study, data from the same cohort population will

be used to conduct an analysis of the correlation between personal and academic characteristics


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Table 2. Duval Data Set Information: Primary Identifying Codes (Variables) for Each Student

Demographics

o Student number

o Grade level 2001: Prior to the year of inquiry

o Grade level

o School year

o School number

o Gender

o Ethnic preference

o Date of birth

o Withdrawal code: Official reason for withdrawal

o English language learner

o Native/home language

o Free/reduced lunch eligibility status

o ZIP code

Academic Information

o Promotion/retention status 2001: Prior to the year of inquiry

o Promotion/retention status for school year

o Grade point average for the year

o State Standards Reading Scale Score

o State Standards Reading Achievement Level

o State Standards Reading Developmental Scale Score

o State Standards Mathematics Scale Score

o State Standards Mathematics Achievement Level

o State Standards Mathematics Developmental Scale Score

o Language arts final course grade

o Mathematics final course grade

o End of year credits earned

o End of year grade point average

Attendance & Discipline Information

o Days present

o Days absent

o Major or zero tolerance violations charged to the student


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o Action taken by school officials

o Duration of action taken by school officials

Educational Program Information

o Primary exceptionality if in the ESE program

o Dual enrollment status/program indicator

o Dropout prevention program participation indicator

o Program/curriculum followed by student

Source: DCPS Data Warehouse

Analysis Methods and Procedures

This section of the study provides information about how the graduation rate was

calculated and the criteria used to extract the cohort groups to be included in the study from the

total data set.

Determination of Graduation and Dropout Rates

Generally, graduation rate calculation methods fall into one of two types: annual rates

and cohort rates. The annual rate provides information on the number of students who graduate

or drop out of school in a single year. For example, when the state of Pennsylvania reports a

statewide dropout rate of 1.9% for students in grades 7-12, it means that during this single year

1.9% of the enrolled students dropped out. The cohort rate is considered the gold standard for

determining the graduation and dropout rates and is the method used by the FDOE. The cohort

rate provides information about the graduation and/or non-graduation rate of a single cohort of

students. For example, in 2007 the FDOE reported that of the 12,867 9 th grade students enrolled

in the DCPS in the 2003-2004 school year, 64.3% had earned a standard high school diploma by

the end of the 2006-2007 school year. This study employs the FDOE recommended cohort

method for investigating school completion and non-completion rates.

Identification of the Cohort Groups

As indicated above, when the FDOE calculates the cohort completion rate for the states

school districts, it includes all students enrolled in the 9th grade who are engaged in a diploma

awarding program. However, because of the special goals and objectives of this study, not all of


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the enrolled 9th

grade students for a given year were included in the study cohorts. The following

student groups were not included in this study:

Students who were not enrolled in the DCPS during the year prior to the 9 th grade;

Students who were repeating 9th grade;

Students who were enrolled in an ESE program (e.g., mentally handicapped,

behavioral/learning disabilities) for which a standard diploma was not an expectation.

A cohort of 9th grade students was identified using a data set that contained a unique

student number, prior year enrollment, and current educational program. Table 3 illustrates how

the process was conducted for the 2002 cohort and the student count at each stage of the process.

Table 3. Identifying the 9thGrade Cohort for 2002

Steps Description Students

Step 1 Identify the total number of students in 9th grade in 2002 11,339

Step 2 Remove 9th grade students not enrolled in DCPS during the

prior year, as well as students repeating the 9th grade.

-3,874

Step 3 Remove duplicate student numbers due to dual enrollment in

multiple programs.

-32

Step 4 Remove all ESE students enrolled in programs for which a

standard diploma is not an expectation.

-965

Step 5 Establish final first-time 9th grade cohort membership. 6468

In order to establish initial membership for each cohort, this process was followed for the 2003

and 2004 cohorts. Once the membership of each cohort group was identified, these specific

students were tracked, in regard to the variables previously listed in Table 2, for the next 5 years.

Study PopulationIn this section of the study, we provide a description of the characteristics of the students

selected from the DCPS data set to serve as the study population. First the population will be

described by gender and race/ethnic group. This will be followed by a description of the group in


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reference to age, home language, participation in the districts free/reduced lunch program,

academic program assignment, and school.

Using the procedures previously described, a cohort of first-time 9th grade students was

selected from the 9th

grade students enrolled during the 2002-2003, 2003-2004 and 2004-2005

school years. These procedures rendered a total study population of 20,065 students, with 6,468

in the 2002-2005 cohort, 6,846 in the 2003-2006 cohort, and 6,751 in the 2004-2007cohort.

Gender and Race/Ethnic Groups

Table 4 provides the race and gender demographics for each cohort. These data indicate

that the gender and racial composition of the cohorts were a valid representation of the overall

DCPS high school student body. As described by the codes in the Duval data set, approximately

54% of the students were female and the largest race and/or ethnic groups were White (48%),

Black (43%), Hispanic (4%), and Asian (4%). Other racial and/or ethnic groups, such as Native

American and Multi-racial, accounted for approximately 1% of the overall population.

Table 4. Cohort Demographics: Gender and Race/Ethnic Group

Gender Race/Ethnic Group

Cohort Data Female Male White Black Hispanic Asian Other

2002 Number 3,543 2,925 3,156 2,770 235 250 57

N=6,468 Percent 54.8 45.2 48.8 42.8 3.6 3.9 0.9

2003 Number 3,654 3,192 3,349 2,915 278 229 75

N=6,846 Percent 53.4 46.6 48.9 42.6 4.1 3.3 1.1

2004 Number 3,526 3,229 3,167 2,892 314 294 88

N=6,751 Percent 52.2 47.8 48.9 42.8 4.6 4.4 1.3

Total Number 10,723 9,346 9,672 8,577 827 773 220

N=20,065 Percent 53.43% 46.57% 48.19% 42.74% 4.12% 3.85% 1.10%

Age, Home Language, and Socioeconomic Status

As reported in Table 5, the majority of the students in the cohort, 60%, were 15 years of

age during their 9th

grade school year. About 33% of the students were 1 year above the expected

age for the 9th grade (16 years old), and 5% were 2 years above age (17 years old). Less than 1%


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Table 6. Cohort Academic Program Information

Special Program Enrollment GPA

Cohort

Data

Type

Standard

Program

English

Language

Exceptional

Education(1)

Dropout

Prevention(2)

9t

Grade

(All Students)

2002 Number 5,827 103 281 277 2.45

N=6,468 Percent 90.0% 1.6% 4.3% 3.9%

2003 Number 6,062 114 417 253 2.46

N=6,846 Percent 88.5% 1.6% 6.1% 3.7%

2004 Number 6,080 154 289 232 2.40

N=6,755 Percent 90.0% 2.3% 4.3% 3.4%

Total

N=20,065

Number 17,969 371 987 762

Percent 89.5% 1.8% 4.9% 3.8% Mean=2.441

Includes students in gifted education programs;2

May be for less than 180 days

Home Schools

The DCPS data set used in this analysis provided the school where each student was

enrolled for each year. For tracking purposes, we have used the school where the student was

enrolled for the 9th

grade as their home school. Table 7 provides the number and percent of the

students in the cohorts assigned to each of the major secondary schools and alternative schoolcenters during the 9th grade. As the data illustrates, all of the districts senior high schools were

represented in the cohort, with the largest student populations being from the districts three

largest high schools. Sandalwood High School was the home school for approximately 9.22% of

the study cohort, with 8.12% from Mandarin High School and 6.7% from Fletcher High School.

The smallest number of students was from Baldwin Junior/Senior High School.


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Table 7. Ninth Grade Home Schools of Cohort Students2002 Cohort 2003 Cohort 2004 Cohort Combined

School N % N % N % N %

#33 Lee HS 310 4.79% 355 5.19% 450 6.67% 1,115 5.56%

#35 Jackson HS 344 5.32% 319 4.66% 307 4.55% 970 4.83%

#38 Baldwin MS/HS 74 1.14% 85 1.24% 104 1.54% 263 1.31%

#75 Paxon Ad

Studies

388 6.00% 418 6.11% 353 5.23% 1,159 5.78%

#86 Parker HS 386 5.97% 419 6.12% 392 5.81% 1,197 5.97%

#90 Englewood HS 278 4.30% 369 5.39% 344 5.10% 991 4.94%

#96 Ribault HS 377 5.83% 292 4.27% 169 2.50% 838 4.18%

#107 Anderson Sch

Arts

227 3.51% 199 2.91% 239 3.54% 665 3.31%

#153 Stan Coll.

Prep.

371 5.74% 416 6.08% 425 6.30% 1,212 6.04%

#165 Raines HS 328 5.07% 331 4.83% 262 3.88% 921 4.59%

#223 Fletcher HS 410 6.34% 475 6.94% 478 7.08% 1,363 6.79%

#224 Wolfson HS 430 6.65% 439 6.41% 387 5.73% 1,256 6.26%

#237 Sandalwood

HS

548 8.47% 653 9.54% 648 9.60% 1,849 9.22%

#241 Forrest HS 303 4.68% 257 3.75% 275 4.07% 835 4.16%

#248 White HS 393 6.08% 415 6.06% 358 5.30% 1,166 5.81%

#260 Mandarin HS 531 8.21% 536 7.83% 563 8.34% 1,630 8.12%

#265 First Coast HS 303 4.68% 335 4.89% 388 5.75% 1,026 5.11%

#280 Peterson Acad 215 3.32% 281 4.10% 329 4.87% 825 4.11%

#285 Randolph

Acad

165 2.55% 141 2.06% 159 2.36% 465 2.32%

Alternative andSpecial Schools

87 1.35% 111 1.62% 121 1.79% 319 1.59%

Total 6,468 6,846 6,751 20,065 100%


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High School Graduation Rates and Characteristics of Those Who Graduated

In this section of the study, we describe the results of the records analysis for the

students tracked from the 9th grade through the 4th or 5th year of high school. The cohort

graduation rate is calculated and the number of students who exited the public school system

prior to graduation, along with their reasons for exiting, is discussed. The second portion of this

section is devoted to an analysis of the differences between those who graduated and those who

did not. In the final section, we identify those students who dropped out of school and describe

the characteristics of this group.

One of the major objectives of this study was to gain a better understanding of the

students in the three cohorts who received a standard high school diploma at the end of their 4 th

or 5th year of high school. Using a data set labeled as GPA-Promotion-Retention and each

students unique identification number, we were able to trace each student beginning in the 9th

grade through the next 4 to 5 years. This enabled us to determine: a) which students earned a

standard high school diploma; b) when the diploma was earned (i.e., high school year 2, 3, 4 or

5); c) which students officially withdrew from school and their official reason for withdrawing;

d) which students stayed in school for 4 years but earned a certificate of completion rather than a

standard diploma; and e) which students failed to enter school as expected from year to year

without notifying the school district that they were dropping out.

2002 Cohort Graduation Analysis

The results from the analysis of withdrawal codes for the 2002 student cohort is presented

in Table 8a and summarized in Table 8b.

As indicated by the data presented in Tables 8a and 8b, over the 4 years of high school,

the number of students who remained in the DCPS and were able to keep pace with their cohort

dropped from a 1st year adjusted enrollment of 6,286 to a beginning 4 th year enrollment of 4,202.

In other words, approximately 67% of the first-time 9th graders were in their senior class 4 years

later. After accounting for the 156 students (0.2%) who officially withdrew from school during

years 1-4, our analysis indicates that approximately 63.2% of the original first-time 9th

grade


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students graduated from high school within 4 years. A more detailed analysis of what may have

happened to those who did not withdraw or graduate will be provided later in this report.

Table 8a. A 5-Year Analysis of Withdrawal Codes for Students in the 2002 Student Cohort

2002(9th) 2003(10th) 2004(11th) 2005(12th) 2006(12th)

Withdrawal Category 1st Year 2nd Year 3rd Year 4th Year 5th Year Total

Number of Students 6468 4733 4413 4202 49

Promoted 4815 4572 4187 4029 33

Retained 1653 161 226 173 16

Received Standard Diploma 15 161 3799 23 3998

Received Cert. of Completion 8 227 8 243

Transferred Out of District/State 39 8 14 4 65

Transferred to Private School 22 4 5 2 1 34

Withdrew: Voluntary/Over Age 8 4 6 3 21

Withdrew: Death 13 1 14

Withdrew: Non-Attendance 12 9 6 1 28

Withdrew: Home Education 6 1 7

Withdrew: Adult Studies 4 5 3 12

Withdrew: Senior Year Without aDiploma or Certificate

1 1

Table 8b. Summary Analysis of the Withdrawal Codes for Student Cohort 2002

Data Category Total

1st Time 9th Grade Beginning Cohort 6468

Total Students Who Officially Withdrew 182

Adjusted Cohort 6286

Total Diplomas 3975Percent Receiving Standard High School Diploma (4 Years) 63.2%


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Results from the withdrawal code analysis for the 2003 student cohort are presented in

Table 9a and summarized in Table 9b. These data show that when students who officially

withdrew from school were accounted for, the 4-year graduation rate for the 2003 cohort of first-

time 9th

grade students was approximately 59.23%.


2003(9th) 2004(10th) 2005(11th) 2006(12th) 2007(12th)

Withdrawal Category 1st Year 2nd Year 3rd Year 4th Year 5th Year Total

Number of Students 6846 5176 4603 4222 80

Promoted 5374 4371 4039 4001 47

Retained 1472 805 564 221 33Received Standard Diploma 12 149 3747 36 3944

Received Cert. of Completion 43 224 267

Transferred Out of District/State 31 19 11 1 1 63

Transferred to Private School 14 9 1 2 2 28

Withdrew: Voluntary/Over Age 3 7 8 5 3 26

Withdrew: Death 1 1

Withdrew: Non-Attendance 3 2 7 6 1 19

Withdrew: Medical 0

Withdrew: Home Education 2 2 4

Withdrew: Adult Studies 2 9 11


38 1 39

Table 9b. Summary Analysis of the Withdrawal Codes for Student Cohort 2003

Data Category Total

First-Time 9th Grade Beginning Cohort 6846

Total Students Who Officially Withdrew 191

Adjusted Cohort 6655

Total Diplomas 3944

Percent Receiving Standard High School Diploma (4 Years) 59.26%

As indicated by the data presented in Tables 9a and 9b, over the 4 years of high school,

the number of students who remained in the DCPS and were able to keep pace with their cohort


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dropped from a 1st

year adjusted enrollment of 6,655 to a beginning 4th

year enrollment of 4,222.

In other words, approximately 63% of the first-time 9th graders were in their senior class 4 years

later. After accounting for the 191 students (0.3%) who officially withdrew from school during

years 1- 4, our analysis indicates that approximately 59% of the cohorts original first-time 9th

grade students graduated from high school within 4 years. Again, more detailed analysis of what

may have happened to those who did not withdraw or graduate will be provided later in this

report.


The results from the analysis of withdrawal codes for the 2003 student cohort are

presented in Table 10a and summarized in Table 10b. At the time this report was developed, data

from the 2007-2008 school year (the 5th cohort year) were not available.


2004(9th) 2005(10th) 2006(11th) 2007(12th)

Withdrawal Category 1st Year 2nd Year 3rd Year 4th Year Total

Number of Students 6755 5028 4566 4224

Promoted 4998 4181 4062 4021

Retained 1757 847 504 203

Received Standard Diploma 13 138 3808 3959

Received Cert. of Completion 11 4 15

Transferred Out of District/State 36 14 8 2 60

Transferred to Private School 15 7 3 6 31

Withdrew: Voluntary/Over Age 7 4 4 15

Withdrew: Death 9 9

Withdrew: Non-Attendance 4 3 6 10 23

Withdrew: Home Education 5 5

Withdrew: Adult Studies 3 10 10 23


39 39


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Overall Graduation Status by Gender and Race

According to national studies of school dropouts, there is some evidence that males and

minorities make up a disproportionate number of those who drop out of school. In order to

explore this issue among our study population, we analyzed the diploma earning and non-

diploma earning groups by gender and race. Results from this analysis for each individual

student cohort are presented in Table 12a with results for the three cohorts combined in Table

12b.

Table 12a. Ninth Grade Cohort Graduation Status by Gender and Race/Ethnic Group: IndividualCohorts

Race/Ethnic Group

Cohort Gender Graduation Status All White Black Hispanic Asian Other

2002 Cohort Female % Non-Diploma 51.5 43.6 49.1 4.2 2.2 0.9% in 2002 Cohort 47.9 43.8 3.6 3.8 0.9

Male % Non-Diploma 46.5 43.2 50.5 4.4 2.0 0

% in 2002 Cohort 49.8 41.6 3.7 3.9 1.0


2003 Cohort Female % Non-Diploma 49.5 46.7 46 4.2 1.6 1.5

% in 2003 Cohort 49.0 43.4 3.6 3.0 1.0

Male % Non-Diploma 50.5 41.6 49.8 4.6 2.6 1.4

% in 2003 Cohort 48.9 41.6 4.6 3.8 1.1


2004 Cohort Female % Non-Diploma 50.5 42.9 49.3 3.9 2.9 1.0% in 2004 Cohort 45.8 44.6 4.1 4.4 1.1

Male % Non-Diploma 49.5 42.5 47.2 5.6 3.2 1.5

% in 2004 Cohort 49.1 39.8 5.1 4.5 1.5

The tables provide the percent of female and male students who failed to earn a diploma

by race, as well as the proportion of the cohort. Among females, the failure to earn a diploma

was slightly higher among Black females in the 2002 and 2004 cohorts and virtually the same as

their White counterparts in the 2003 cohort. However, among male students, the failure to earn a

diploma was higher among Black males for each of the cohorts. For students failing to earn a

degree in each of the three cohorts, White and Asian students were underrepresented and Black

and Hispanic students were over represented. While these differences do not appear to be

statistically significant, they do model other national studies on graduation rates and race. For

example, White females made up 47.9% of the 2002 cohort, but only accounted for 43.6% of the


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females who failed to earn a diploma. On the other hand, Black females make up 43.8% of the

2002 cohort, but accounted for 49.1% of the females who did not earn a diploma.

It is important to note that while there are some differences in graduation rates between

boys and girls and according to racial groups, failure to graduate from high school was certainly

not restricted by gender or to a specific racial or ethnic group. As reported in Table 12b, the

overall percentage of first-time 9th grade students who did not graduate was almost evenly split

between females (50.5%) and males (48.9%) and the difference between White (43.4%) and

Black (48.6%) students was approximately 5%. These data indicate that while failing to achieve

a high school diploma may be somewhat greater for certain groups, it is a phenomenon that is

spread widely across gender and racial boundaries.

Table 12b. Ninth Grade Graduation Status by Gender and Race/Ethnic Group: CombinedCohorts

Race and or Ethnic Group

Gender Graduation Status All White Black Hispanic Asian Other

Female % Non-Diploma 50.5 44.4 48.1 4.1 2.2 1.1

% Standard Diploma 49.5 41.4 3.5 4.6 0.9

% in Total Cohort 47.6 43.9 3.8 3.7 1.0

Male % Non-Diploma 48.9 42.4 49.2 4.9 2.6 1.0

% Standard Diploma 54.4 35.0 4.2 5.2 0.9

% in Total Cohort 49.3 41.0 4.5 4.1 1.2

Students Who Failed to Graduate by School Type

Table 13 provides a summary of the students who failed to graduate by the type of school

they were enrolled in during the 9th grade. First, the percentage of the combined cohort

population represented by each school group is provided, along with the percentage of all

students who failed to graduate for each group. Our analysis indicates that for the total

population, approximately 15% of the students were enrolled in special admission (magnet) high

schools, 6% in technical academies, 1.6% in alternative schools, and 80% in neighborhood high

schools. In contrast, only 5.1% of the students who did not graduate were enrolled in special

admission (magnet) schools, while 7.9% were enrolled in academies, 1.5% in alternative schools,

and 85.4% in neighborhood schools. These data indicate that neighborhood high schools and


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technical academies are overrepresented in the non-graduating group while the special admission

and alternative high schools are underrepresented in the non-graduating group.

Table 13. Students Who Failed to Graduate by School Type: Combined Cohorts

School TypePercent of CohortPopulation

Percent Who Failed ToGraduate

Special Admissions (Magnet Schools) 15.1% 5.1%Technical Academy 6.4% 7.9%Neighborhood High School 76.9% 85.4%Other (Alternative & Special Schools) 1.6% 1.5%

Matriculation Patterns of the Three Cohorts

Because the overriding goal of this research was to gain a better understanding of the

complex issues related to high school completion, of special interest was the matriculation

pattern of the cohorts as they proceeded through the 4 years of high school. Table 14 provides a

summary of the grade placement of the cohort students during each of the 4 years of high school.

For example, each cohort began with 100% of the students in the 9 th grade during the 1st year of

high school. By the 3rd

year of high school, approximately 4% of the 2002 cohort students were

still classified as 9th graders, as were 3% of the 2003 and 2004 cohort students. At some point

during the 3rd

or 4th

year of high school, 64% of the 2002 cohort students were classified as being

in the 12th

grade, as were 65% of the 2003 cohort students and 62% of the 2004 cohort students.The combined grade distribution of all three cohorts is illustrated in Figure 1. As Table 13 and

Figure 1 illustrate, through the 3rd year of high school, between 10% and 12% of each student

cohort remained in school even though they were lagging 1 to 2 years behind their cohort group.

The matriculation and grade distribution data presented in this section seems to indicate

that there is a significant number of lagging students (i.e., approximately 670 800) in each 9th

grade cohort who should be participating in and/or could benefit from some type of grade

recovery or second chance academic program. For example, a program that was successful with

only half of these lagging students would have the effect of increasing the overall 2004 cohort

graduation rate from 60% to 66%.


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Table 14. Matriculation of Cohort Students Through 4 Years of High School

2002 Cohort Year 1 Year 2 Year 3 Year 4

9th Grade 100% 17% 4% 0%10th Grade 70% 8% 1%11th Grade 1% 64% 4%12th Grade 0% 2% 62%

2003 Cohort Year 1 Year 2 Year 3 Year 4


2004Cohort Year 1 Year 2 Year 3 Year 4


Figure 1. Combined 2002-2003-2004 Cohorts' Four Year

Grade Distribution

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

Year

Percent

9th Gr

10th Gr

11th Gr

12th Gr

9th Gr 100.0% 14.4% 3.4% 0.4%

10th Gr 72.2% 7.4% 0.9%

11th Gr 0.7% 64.4% 3.1%

12th Gr 0.1% 1.9% 61.4%

Year 1 Year 2 Year 3 Year 4


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Comparing Graduating and Non-Graduating Students

In this section of the report, we examine differences between the 9 th grade students in

each of the three cohorts who ended their high school careers by earning a standard diploma with

those who did not earn a diploma. As recommended by other researchers, at this time we have

focused our efforts on each cohort of 9th grade students. Specifically, we examine items that have

been identified by others (Balfanz, 2007; Neild & Balfanz, 2006) as valid indicators of school

success. The items included in this comparative analysis were:

Mathematics Final math grades and final grades in Algebra I;

Language arts Final language arts grades and final grades in English I;

Florida Comprehensive Assessment Test (FCAT) scores Developmental scores in

mathematics and reading;

Attendance Annual attendance;

Conduct Students with major or zero tolerance violations;

Transfers Transfer between schools;

Comparisons by gender and race.

Mathematics: Comparing Graduates and Non-Graduates

The first area examined was overall mathematics course grades. The DCPS program

allows for students to be simultaneously enrolled in more than one math course. Table 15

provides a comparison of final mathematics grades of 9 th grade students who, 4 or 5 years later,

either earned or failed to earn a standard high school diploma. Algebra I students are included in

this set of data. In the bottom section of Table 15, 9 th grade students enrolled in Algebra I are

extracted and reported independently.

There appears to be a significant difference in final mathematics grades between students

who eventually earned a high school diploma and those who did not. The percentage of studentswho earned a final grade of F in at least one mathematics course was approximately four times

higher for non-diploma earning students. While the difference in Algebra I grades was not as

pronounced, the course failure rate among non-diploma earning 9th grade students was still three

times greater than their classmates who did earn a diploma. In the next phase of this study, our


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analysis will be expanded to include 8th

grade students. At that time, we will be able to see if this

difference is also true for students who take Algebra I in the 8 th grade.

Table 15. Final Math and Algebra I Grades: A Comparison of Eventual Diploma and Non-

Diploma Earning 9thGrade Students

Percent of 9th Grade Students With a Final Math Grade of F in At Least One CourseGroup 2002 Cohort 2003 Cohort 2004 Cohort

Students Who Received A Diploma 8.4% 8.3% 8.0%Non-Diploma Students 35.2% 32.6% 31.1%

Percent of 9th Grade Students With a Final Grade of F in Algebra IGroup 2002 Cohort 2003 Cohort 2004 Cohort

Students Who Received A Diploma 10% 9.7% 11.6%Non-Diploma Students 36% 33.1% 35.4%

Language Arts: Comparing Graduates and Non-Graduates

The second academic area we examined was overall language arts course grades. As with

mathematics, the DCPS program allows for students to be simultaneously enrolled in more than

one language arts course. Table 16 provides a comparison of final language arts grades of 9 th

grade students who, 4 or 5 years later, either earned or failed to earn a standard high school

diploma. English I students are included in this set of data. In the bottom section of Table 16, 9 th

grade students enrolled in English I are extracted and reported independently.

Table 16. Final Language Arts and English I Grades: A Comparison of Eventual Diplomaand Non-Diploma Earning 9thGrade Students

Percent of 9th Grade Students With Final Language Arts Grade of F inat Least One Course

Group 2002 Cohort 2003 Cohort 2004 CohortStudents Who Received A Diploma 3.6% 3.7% 4.4%Non-Diploma Students 25.8% 19.9% 21.0%

Percent of 9th Grade Students With a Final Grade of F in English IGroup 2002 Cohort 2003 Cohort 2004 Cohort

Students Who Received A Diploma 6.4% 5.5% 6.4%Non-Diploma Students 33.2% 24.5% 25.5%

As with mathematics, there appears to be a significant difference in final language arts

grades between 9th grade students who eventually earned a high school diploma and those who

did not. The percentage of students who earned a final grade of F in at least one language arts


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course was approximately five times higher for non-diploma earning students. The difference in

English I grades for the two groups was approximately equivalent.

As our analysis continues beyond this initial phase, it will be important to examine the

impact of the districts grade recovery programs on students who fail math and language arts

courses in the 9th grade. How many of these students are able to beat the odds and recover in

time to graduate from high school?

FCAT Scores: Comparing Graduates and Non-Graduates

While some researchers have not found standardized test scores to be a good predictor of

high school graduation, with the continuing emphasis on testing by the FDOE, we thought that it

was important to include the FCAT in this comparative analysis. After consultation the DCPS

Executive Director of Instructional Research and Accountability, it was determined that the

Developmental Scale Score would be the most appropriate metric to use in this comparison.

Table 17 provides a comparison of FCAT reading and mathematics developmental test scores of

9th

grade students who, 4 or 5 years later, either earned or failed to earn a standard high school

diploma.

Table 17. FCAT Reading and Mathematics Developmental Scores: A Comparison of EventualDiploma and Non-Diploma Earning 9thGrade Students

Mean Reading and Mathematics Developmental Scores*

Group Test2002

Cohort2003

Cohort2004

Cohort MeanStudents Receiving Diploma Reading 1918.34 1922.84 1931.42 1924.20Non-Diploma Students Reading 1636.19 1707.07 1725.23 1689.50Students Receiving Diploma Mathematics 1926.36 1946.76 1964.71 1945.94Non-Diploma Students Mathematics 1750.55 1833.21 1836.85 1806.87*The FCAT developmental scores range from approximately 0 to 3000 across grades 3 through 10 for FCAT Readingand Mathematics. (http://fcat.fldoe.org/fcat_dev/score_report.asp)

For each cohort and in both the areas of reading and mathematics, the FCAT

developmental test scores were higher for diploma-earning 9th grade students than for those who

did not earn a standard diploma. The difference between the two student groups appears to be

significantly higher in reading. The difference between the mean scores of the three cohorts

between the two student groups in mathematics was 139.07, whereas the difference in mean


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scores for reading was 234.07. Consequently, the FCAT developmental scores in reading may be

a better predictor of high school success. This difference will be examined more thoroughly in

the second part of this study, where we will attempt to develop statistically reliable predictors of

success.

Attendance: Comparing Graduates and Non-Graduates

Historically, overall student attendance has proven to be a good indicator of which

students will be successful in school. The view of some educators is that unsuccessful students

gradually become less and less engaged in school, resulting in non-attendance. The viewpoint of

others is that it is the poor attendance that produces academic failure. Regardless of which comes

first, many studies have found a significant association between attendance and high school

completion.

Table 18 provides a comparison of the three cohorts diploma earning and non-diploma

earning attendance during the 1st

year of high school. Our analysis of these data indicated a

difference between the mean days of attendance between the two student groups of only 6.9

days. Both groups of students attended over 90% of the 180 day school year.

Table 18. Ninth Grade Attendance: A Comparison of Eventual Diploma and Non-DiplomaEarning 9thGrade Students

9th Grade Mean Days Present

Group Data2002

Cohort2003

Cohort2004

CohortOverall Meanand Percent

Students WhoReceived ADiploma

Number 174.6 173.9 177.26 174.5% of SchoolYear* 96.9%

Non-DiplomaStudents Number 164.3 166.6 172.1 167.6

% of SchoolYear* 93.1%

*Based on 180 day school year

These results led us to believe that with these student cohorts there was no significant

difference in the attendance patterns of the group of students who received a diploma and those

who did not. However, this finding must be understood in light of the way attendance is

calculated. According to the district procedures, students are considered present if they attend


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one class per day, even though they could be missing one or two periods consistently. Another

caution regarding the interpretation of attendance data is the districts use of A/B scheduling.

While a student may be absent only a few days, the absence may have more impact on student

performance if it is for a series of classes. Finally, attendance data from the districts database

system is only as accurate as the individual teachers recording procedures. For instance,

substitute teachers may not always mark attendance.

Conduct: Comparing Graduates and Non-Graduates

The DCPS code of conduct data allowed us to identify students within our three cohorts

who had been charged with a major or zero tolerance violation of the student code of conduct. A

major violation includes such items as being charged with multiple class II offenses: stealing,

making threats to kill another person, and battery. Zero tolerance charges stem from such acts as

drug possession, possession of firearms, and sexual battery or rape. Within the combined cohort

of approximately 20,065 students, a total of 457 students (2.3%) were charged with at least one

of these violations. However, as presented in Table 19, there appears to be a significant

difference between diploma earning and non-diploma earning students. In each of the three

cohorts, the percentage of disciplinary violations charged to non-diploma earning students was

three times greater than the percentage earned by 9 th grade students who went on to graduate

from high school with a diploma.

Table 19. Combined Cohort, 9thGrade Discipline

Number and Percent of 9th Grade Students With a Major orZero Tolerance Violation

Group Date 2002 Cohort 2003 Cohort 2004 Cohort OverallDiploma Number 40 40 26 106

% of Total 26.8% 23.8% 18.5%Non-Diploma

Number 109 128 114 351% of Total 73.2% 76.2% 81.4%

Total 149 168 140 457

Between-School Student Transfers: Comparing Graduates and Non-Graduates

Balfanz (2007), in his study of the dropout crisis in Philadelphias schools, found a

positive correlation between the numbers of times a student transferred between schools and

dropping out of school. In this study, we examined the difference in the number of transfers


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between the graduate and non-graduate student groups. As indicated in Table 20, in each of our

cohort groups, the number of transfers is greater for the diploma earning students.

Table 20. Numbers of Transfers Between Schools: Graduates Versus Non-Graduates

Number of Transfers

Cohort Group 2002 2003 2004 2005 2006 20072002 Graduates 278 223 53 0 0 -N=6468 Non-Graduates 144 62 39 2 22003 Graduates 256 42 22 3 0N=6848 Non-Graduates 181 72 47 0 02004 Graduates 108 93 41 0N=6751 Non-Graduates 72 46 13 4Combined Graduates 278 479 203 115 45 0

Non-Graduates 144 243 183 95 15 4

Contrary to the findings in the Philadelphia study, a higher level of transfers does not

seem to be correlated with dropping out of school. One could hypothesize that factors such as the

high growth rate of new housing and community development in Jacksonville and DCPSs

somewhat unique use of magnet programs produces school transfers for very positive reasons.

As a familys socio-economic status improves, they might move to a new and/or better

community. As the DCPS develops a strong magnet program within a local school, students may

be attracted to that program from throughout the school district. While beyond the scope of this

study, Floridas policy of allowing students to freely transfer out of failing schools may also be

having an impact on the positive outcomes associated with school transfer.

Socio-economic Status: Comparing Graduates and Non-Graduates

Participation in the public schools free and/reduced lunch program has traditionally been

used by school researchers as an indicator of student/family socioeconomic status. Table 21

provides a comparison between graduates and non-graduates in regards to the students who

participated in this program.

Table 21. Percent of Graduates and Non-Graduates Who Participated in theFree/Reduced Lunch Program

Groups 2002 Cohort 2003 Cohort 2004 Cohort OverallGraduates 15.3% 16.7% 24.7% 18.9%Non-Graduates 24.6% 26.6% 41.3% 30.8%


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Over the course of the three year period the percent of students who participated in the

program increased among both student groups, however the rate of increase was greater among

non-graduating students. In addition, the gap between non-graduates and graduates increased

from 9 percentage points in 2002 to 16.6 percentages points on 2004. These data indicate that

there is a critical difference between the socio-economic status of graduates and non-graduates.

Identifying Dropouts

In this final section of the study, we endeavored to identify the number of students in

each of the three cohorts who dropped out of the cohort and, as a result, may have dropped out of

school. Because of the complexity involved in formally identifying these students, this process

warrants a more detailed explanation.

A student who officially transfers or withdraws from the school system, contacts the

school that they have been attending and formally goes through a process that allows the school

district to enter a withdrawal code (e.g., W03, W05, W15) into its data management system. This

is also true for a student who exits school after receiving a diploma (e.g., W06, W27, WGA) or a

certificate of completion (e.g., W07, W08, W09). However, a student who drops out does not

notify the school system he or she simply stops attending. If the student stops attending during

the school year, the district takes steps to determine why and, in the district records, designatesan identifying code such as W15 withdrawal due to non-attendance. However, if over the

summer break a student decides not to return for the next school year, the school system has no

way of knowing for sure whether the student dropped out or began attending some other

educational program.

As an example, consider this scenario. During the summer, a student moves to another

state and, once there, the students parents decide to home school. With the childs birth

certificate and a copy of his or her most recent report card, the child can be approved for home

schooling in most states without any records from the DCPS. Consequently, in Duval, the child

is classified as not entering as expected and may be considered a dropout when, in actuality, the

child should be classified as having transferred to another state. For these reasons, unlike


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establishing a graduation rate, determining exactly how many students dropped out of school is

an inexact science.

In attempting to obtain some reasonable estimate of the number of dropouts within the

three cohorts under study, we established our own protocol for determining who was a dropout.

Because we were tracking a specific cohort of students, we were able to identify any student who

did not appear in our data set as expected for the next school year. We placed a special DNE

did not enter as expected code in each students record when he or she failed to appear as an

enrolled student in the 2nd, 3rd, or 4th year of high school. Once a cohort of DNE students was

identified, we then examined their official withdrawal codes to determine if they: a) transferred

to an educational program outside the district; b) officially withdrew from school; c) graduated

early or received a certificate of completion; or d) had any other official withdrawal code that

would explain their absence from the cohort. Those students who were missing from the cohort

who did not have a discernable reason for their absence were classified as dropouts.

Table 22 provides a summary on the student dropouts who did not enter as expected for

each cohort. Our calculations indicate that the number of students who may have dropped out

increased with each succeeding cohort. The 2002 cohort had an estimated dropout rate of 25.5%,

followed by 29.5% for the 2003 cohort, and rising to a high of 31.3% with the 2004 cohort.

When examined as a whole, the estimated dropout rate for the combined three student cohorts

was approximately 28.2%. What this means in terms of students lost is that over the 4 years of

high school, almost 2,000 students left each cohort without receiving diplomas.

Table 22. Estimated Number of Dropouts *

Cohort Group Data Set Year of High School TotalYear 2 Year 3 Year 4

2002 Cohort Number 593 543 517 1,653

% of Total Cohort 25.5%2003 Cohort Number 705 692 627 2,024

% of Total Cohort 29.5%2004 Cohort Number 770 694 649 2,113

% of Total Cohort 31.3%Combined Number 2,068 1,929 1,793 5,790

% of Total Cohorts 28.2%*Students who did not enter as expected the next school year without officially withdrawing from school.


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Table 25. Age at the Time of Dropping Out:Combined Cohorts

Age Percent

15 years 0.1%

16 years 12.2%

17 years 28.4%

18 years 33.0%

19 years 20.8%

20 years or older 5.5%

Total 100%

Socioeconomic Status of Dropouts

Table 26 provides a summary of the students identified as dropouts who participated in

the districts free and reduced lunch program. As previously discussed, to qualify for this

program, a students parents and/or legal guardian must have an income level which meet he

Federal guidelines for participation in the program. Participation in this program has traditionally

been used by school researchers as an indicator of student/family socioeconomic status.

Table 26. Percent of Dropouts Who Participated in theFree/Reduced Lunch Program

Cohort Percent

2002 Cohort 23.9%

2003 Cohort 24.5%

2004 Cohort 40.2%

According to the most current available data regarding the 2008 National School Lunch

Program, approximately 21% of the public school students in Florida participate in this program

(http://www.fns.usda.gov/pd/32sllatest.htm). These data seem to support the hypothesis that

remaining in school is linked to ones socioeconomic status. This finding also has implications

for dropout prevention and intervention programs. Programs that restrict a low income students

ability to earn additional income while in school would seem to be unwise, while programs that

enhance a students ability to gain job skills and be employed would have increased value to

these students.


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Summary and Discussion

The overall goal of this study was to gain a better understanding of issues surrounding the

graduation crisis and the dropout problem found in the DCPS. Simply stated, the design of the

study was to follow three cohorts of students from the time they first entered the 9 th grade

through their 4th

or 5th

years of high school. Using data provided by the DCPS Department of

Research and Accountability, we were able to select a study population that met our criteria for

inclusion in our analysis. As recommended by Balfanz (2007), we sought to study cohorts of 9th

grade students who had been enrolled in the DCPS during the previous grades, were enrolled in

the 9th

grade for the first time, and were engaged in an academic program for which a standard

high school diploma was a reasonable expectation. Furthermore, our desire was to have a study

population that was reflective, in regards to gender, race, and socioeconomic status, of the

overall 9th grade population.

Study Population

The selection process yielded a study population of 20,056 students, of which 6,468

began the 9th

grade in 2002, 6,846 in 2003, and 6,751 in 2004. The population was 53.43%

female and 46.57% male. Approximately 48.19% of the students were identified by DCPS as

White, 42.74% were Black, 4.12% were Hispanic, 3.85% were Asian, and other ethnic, racial ormulti-racial groups accounted for 1.10% of the population. The vast majority of the 9th grade

students, 92.14%, were 15 to 16 years in age, 21% were enrolled in the schools free/reduced

lunch program, and English was the primary language spoken at home for 94.1% of the students.

In regard to their educational programs, 89.5% were engaged in a standard high school program,

1.8% were enrolled in an English language learning program, 4.9% were in the gifted or other

special education programs, and 3.8% were participating in one or more of the districts dropout

prevention programs. All of the districts senior high schools were represented by the cohort

membership, with the largest populations being from Sandalwood, 8.12%, Mandarin, 8.12%, and

Fletcher, 6.79%.


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Number of 9th

Grade Students Graduating in 3, 4, or 5 Years

Our analysis indicates that by the end of the 3rd year of high school, approximately 488

students (2.5%) of the original first-time 9th

grade cohort had received their diploma. By the end

of the 4th year, the number of students who had graduated increased to 11,842 students (60.74%),

and by the end of the 5th

year, 11,901 (61.04%) of the students had received a standard high

school diploma. Table 27 provides a summary of the combined cohorts graduation rate by year

of high school. While approximately 61% of the first-time 9th grade students eventually

graduated, of this group, 95% of the students graduated in the customary 4 th year of high school.

Only a small percentage (4.1%) of the students graduated early, and an even smaller percentage

(0.005%) graduated later, during the 5th

year of high school.

Table 27. Number and Percent of Students WhoGraduated Each Year*

Year of High School Number Percent

Year 1 0 0.00%

Year 2 40 0.20%

Year 3 448 2.30%

Year 4 11,354 58.23%

Year 5 59 0.30%

Total 11,901 61.04%*5th year data was not available for the 2004 cohort

Percentage of First-Time 9th

Grade Students Who Withdrew or Unofficially Dropped Out of

School

Because students who withdraw from school do so by contacting the school and going

through an official withdrawal process, it is relatively easy to calculate how many students

officially withdrew from school, with a high degree of certainty. In addition, because each

withdrawing student is assigned a specific withdrawal code, we can report the official reason a

student withdrew from school. However, students who drop out do not inform the school; they

simply stop attending. Consequently, the way we determined the percentage of first-time 9th

grade students who dropped out of school in this study was to identify those who did not enroll

as expected the next school year. Once a student was identified as not entering the next grade as


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expected, we reviewed the students record to determine if the student had officially withdrawn,

been retained, or transferred to a special program with the district. If none of these had occurred,

then the student was labeled as a cohort dropout. Even then, we could only assume that the

student dropped out of school. In actuality, the student could have moved to another location or

entered another educational program that did not require official transcripts from the DCPS.

There is no way for the school district to know for certain whether or not a student has dropped

out.

Given these conditions and cautions, we estimate the percentage of students who dropped

out of the original 9th

grade cohorts as approximately 28% or 5,790 of the adjusted cohort (i.e.,

minus students who officially withdrew) of 19, 496 students (Table 28).

Table 28. Estimated Number and Percent of Students Who Dropped Out of the Study Cohorts

2002 Cohort 2003 Cohort 2004 Cohort Combined

Number 2,068 1,929 1,793 5,790

% of Combined Cohorts 28.2%

*Students who did not enter as expected the next school year without officially withdrawing from school.

Personal and Academic Characteristics of Those Who Failed to Graduate

In almost every demographic category, students who failed to earn a diploma were like

the overall student cohort. While not a significant difference, the cohort was approximately 47%

male and males made up 49% of the population that failed to graduate. However, there were

some differences by race/ethnic group. A significantly larger portion of African American

females and males made up the group who did not graduate in comparison with their

representation in the overall population. Whereas African American females and males made up

44% and 41% of the cohort, respectively, they accounted for 48% and 49% of the students who

failed to graduate. Neighborhood high schools and technical academies tended to beoverrepresented in the population of students who did not graduate, while special

admission/magnet schools were significantly underrepresented.


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Differences Between Non-Graduates and Graduates

As one would suspect, there were some significant differences regarding academic

performance between non-graduates and graduates. When compared to graduates, the percentage

of non-graduates who failed at least one math class during the 9 th grade was four times higher

than for graduates and three times higher than the percentage of graduates who failed Algebra I.

The differences between non-graduates and graduates was even greater in the academic area of

language arts, where the percentage of non-graduates who failed at least one language arts course

in 9th grade was six times higher than for graduates and five times higher in comparison to the

percentage of graduates who failed English I. Other important differences included lower FCAT

scores in both mathematics and reading for those who did not graduate, along with more

incidences of major or zero tolerance discipline violations. Conversely, we were surprised to find

very little difference in days present in school between non-graduates and graduates, and the

transfers between schools was actually higher among graduates than non-graduates. We believe

that this may, in part, be due to the way attendance is recorded in the secondary grades.

Personal and School Characteristics That Could Be Used to Identify Students at High Risk of

Not Graduating from High School

Personal demographics (i.e., gender, race or ethnic group and age), attendance, and

transfers between schools do not seem to be particularly promising identifiers of first-time 9th

grade students at high risk of failing to graduate from high school. However, 9th

grade academic

performance in language arts and/or mathematics and/or FCAT developmental scores in reading

appear very promising as early warning signs of eventual failure to graduate. Specifically, first-

time 9th grade students who do not qualify for English I based on the current school district

criteria or who fail English I especially appear to be at high risk of not graduating. Clearly,

middle school students who cannot meet the districts criteria for qualifying to enroll in 9th grade

English I or Algebra I are at significant risk of failing to graduate.

In the next phase of this study, we will be conducting an analysis of the factors identified

here in order to determine the correlation and statistical probability of failing grades in language

arts and/or mathematics and/or failing FCAT scores impacting graduation from high school. At

that time, we expect to be able to confirm the general associations identified here.


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Policy and Practice Implications of the Study

An overriding goal of this study was to aid in the process of identifying issues that may

be used to assist the school district and community in developing an early warning system for the

first chance school program and developing successful second chance support programs for

students who initially fail. While the findings of this study are preliminary and await

confirmation through more in-depth and statistically rigorous analysis, this study may enable the

DCPS and others to narrow and better define their approaches to increasing graduation rates.

First, it appears that an effective monitoring system, especially in the area of language

arts, beginning in the middle school grades and continuing through the 9th

grade, holds promise

as an early warning system for identifying students who are at high risk of not graduating from

high school. Unfortunately, the high between-school transfer rate and the complexities of the

DCPS data system make the deployment of a district-wide monitoring system difficult. The high

transfer rate serves to render a school-based monitoring system less effective than a district-wide

monitoring system. Conversely, the districts Genesis/data warehouse system seems to be

primarily designed for storing information for annual reporting to schools and to meet the

reporting requirements of state and federal agencies. Consequently, in its current application, it is

not an overly responsive system for monitoring and tracking the progress of an identified group

of students, grading period by grading period, from 6th through 12th grades. Tracking of these

students may be further complicated by their participation in community based intervention

programs that are not currently part of the DCPS student information management system.

Our second observation is that most students were very persistent in attempting to

graduate from high school. In our study group, approximately 87% of the students who

eventually failed to graduate attended school 1 year or more beyond the legal age (16 years) at

which they could have quit. While prevention must begin early, second chance programs should

be available to students throughout the high school years. Clearly, there may be other outside

influences that contribute to the willingness of these students to remain in school; perhaps

parents paying for car insurance or the lack of meaningful employment, for example, may serve

to keep these students in school. While not exactly an intended consequence of remaining in

school, the social environment the school provides also is important to students.


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Third, there seems to be a gap between the number of students currently enrolled in

dropout prevention and/or intervention program and the number of students who have need of

theses programs. Regardless of whether the FDOE graduation rate of approximately 64% is used

or the graduation rate for the students in our study (60%), this means that between 3,960 and

4,400 of the average size 9th

grade class of 11,000 students will fail to graduate. According to the

data provided for this study by the DCPS, this number far exceeds the number of 9 th grade

students engaged in school and/or community, primary, or secondary prevention and intervention

programs.

Fourth, with the exception of the high schools with restricted enrollments (Douglas

Anderson, Paxon, and Stanton) the non-graduation issue and associated dropout problem seems

to be prevalent throughout the neighborhood high schools. While some schools have a

graduation rate higher than others, each of the neighborhood high schools seems to have a

significant population of underachieving students who eventually drop out of school.

Consequently, while a district monitoring program may be in order, prevention and intervention

efforts may be more effective if they are decentralized and molded to the unique culture and

climate of each individual neighborhood school in such as way that each school and its

community partners have buy-in and ownership of their programs.

Currently, there is great debate and disagreement in the education and research

community about the national crisis in graduation rates and dropouts. One point of view is that

the crisis is a result of the recent emphasis on accountability (i.e., high stakes testing) and

increased high school graduation requirements, particularly in math and science. Another point

of view holds that this emphasis has simply identified a problem that has silently existed for

some time. Regardless of the viewpoint, when more than 30% of a group of students fail to meet

the districts graduation criteria, review of those criteria, with a goal of ensuring the criterias

appropriateness for all students, is certainly in order. A graduation model somewhat like that in

postsecondary and higher education, where graduation requirements beyond the basic elements

are set by each curriculum or degree awarding program may be worth considering. The DCPS,

with its system of international, advanced, magnet, special, and technical curricula, seems to be

only a few degrees away from developing such an approach. There are provisions within existing


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state laws to provide multiple high school diplomas and pathways to graduation. The use of a

single set of graduation criteria to serve both the most and least able student may serve neither

very well.

Future Directions

The next steps for this study will be to further explore the ideal of developing an early

warning system by examining factors identified in this descriptive analysis (i.e., academic

performance in language arts and mathematics, FCAT developmental test scores, disciplinary

violations, etc.) that may serve as predictors for students at risk of dropping out of high school.

In addition, a qualitative component will be developed consisting of interviews and focus groups

with students who have dropped out of school, their parents, and those who work with the school

and community agencies that provide services to these students. We hope that this qualitative

component will help us develop a better understanding of how outside influences, such as family,

student employment, and juvenile crime, impact each students decision to continue or drop out

of school.


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References

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graduation. Chicago: University of Chicago, Consortium on Chicago School Research.

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Allensworth, E., & Eaton, J. (2007). What matters for staying on-track and graduating in

Chicago public schools? Chicago: University of Chicago, Consortium on Chicago School

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Almeida, C., Johnson, C., & Steinberg, A. (2007).Making good on a promise: Whatpolicymakers can do to support the educational persistence of dropouts. Boston, MA:

Jobs for the Future (www.jff.org).

Balfanz, R. (2007, May). What your community can do to end its dropout crisis: Learnings from

research and practice. Prepared for the National Summit on Americas Silent Epidemic,Washington, DC. Retrieved from http://www.silentepidemic.org/resource/index.htm

Editorial Projects in Education. (2008). Graduation Rates Map. Retrieved from

http://mapsg.edweek.org/edweekv2/default.jsp

Florida Department of Education. (2008). Cohort Graduation Rate: Cohort 2002-2003 through

2005-2006[District handout]. Tallahassee, FL. Author.

Greene, J. R., & Winters, M. A. (2005). Public high school graduation and college-readiness

rates: 1991-2002 (Education Working Paper No. 8). New York: Manhattan Institute,

Center for Civic Innovation.

Kahne, J. E., Sporte, S. E., de la Torre, M., & Easton, J. Q. (2008, September). Small high

schools on a larger scale: The impact of school conversions in Chicago. Educational

Evaluation and Policy Analysis,30(3), 281-315.

Kahne, J. E., Sporte, S. E., & Easton, J. Q. (2005). Creating small schools in Chicago: An earlylook at implementation and impact.Improving Schools, 8(1), 7-22.

Kirsch, I., Braun, H., Yamamoto, K., & Sum, A. (2007).Americas perfect storm: Three forces

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Murphy, L. (2006, Summer). Educating vulnerable pupils. Voices in Urban Education, 36-44.

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