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Dropout Prevention through

Early Warning Indicators:

A Current Distribution in

West Virginia Schools

Robert Balfanz

Vaughan Byrnes

November 2010

Labor Market Outcomes

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Labor Market Outcomes TABLE OF CONTENTS

1. Prior Research ...............................................................................3

2. Methods & Data.............................................................................5

3. Sixth Grade ....................................................................................7

4. Ninth Grade .................................................................................17

5. Twelfth Grade Diplomas ............................................................27

6. Districts ........................................................................................28

7. References ....................................................................................30

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Labor Market Outcomes This report provides a snapshot of the 2008-09 school year‟s individual student level data

for the 6th

and 9th

graders across the state of West Virginia. In particular, it focuses on indicators

of students‟ academic behaviors: attendance rates, behavioral problems (e.g., suspension, other

disciplinary actions), and course marks (course failure or GPA). It then examines how 6th

and 9th

grade students exhibiting key behavioral warning signs that have been shown by research in

other states and districts to be good predictors of future dropout are distributed across the state‟s

schools in concentration and number. The purpose of examining the distribution of students with

these academic behaviors is to identify which schools most need additional resources in order to

reduce the number of dropouts state-wide. In addition, we identify the high schools where the

lowest proportions of 12th

graders receive diplomas. Finally, the report examines the

concentration of students with early warning indicators at the district level. The research in this

report was funded by and conducted in conjunction with the National Governors Association

Center for Best Practices dropout prevention and recovery state grants.

PRIOR RESEARCH

This study was theory-driven, building on prior research on the predictors of a dropout

outcome. While dropout rates are considerably higher among some demographic groups than

others (e.g., high poverty, Hispanic and Black, those with parents who did not complete high

school, those with siblings who have dropped out), Gleason and Dynarski (2002) have shown

that demographic factors do not efficiently predict which students will drop out. A more

promising focus for developing early warning indicators relies on the theoretical construct of

student engagement in schooling (e.g., Fredricks, Blumenfeld, & Paris, 2004) that has guided

much of the research on dropping out. Engagement has emotional, behavioral, and cognitive

components, which are sometimes classified as social and academic engagement (e.g., Wehlage,

et al., 1989). Engagement is itself influenced by individual student background, as well as by the

institutions (family and community, as well as the school itself) within which the individual

student is placed (Rumberger & Lim, 2008).

A variety of factors may influence the student to begin a process of disengagement with

schooling, a psychological process that generally manifests itself behaviorally in absenteeism,

failure to complete assignments, and failure to pass courses. While many of the factors leading to

student disengagement are not school-related, the behavioral indicators of student disengagement

leading to a dropout outcome, such as attendance and course failure, manifest themselves

directly at school and can be more readily influenced by school practitioners. In the nation‟s

“dropout factories” (Balfanz & Legters, 2004), mostly minority student U.S. high schools where

50 percent or more of students do not make it from ninth to twelfth grade on time, average daily

attendance rates of 80 percent or less are an overwhelming daily reality. Low levels of

attendance are a strong predictor of course failure, and course failure in ninth grade is a strong

predictor of dropping out (Allensworth & Easton, 2007; Finn, 1989; Lan & Lanthier, 2003; Lee

& Burkham, 2003; Neild & Balfanz, 2006a, 2006b; Neild, 2009; Roderick & Camburn, 1999;

Schargel & Smink, 2001). Balfanz, Herzog, and Mac Iver (2007) have shown that these

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Labor Market Outcomes behaviors manifested in sixth grade (attendance, a record of misbehavior, or course failure in

English or mathematics) predict roughly half of eventual dropouts.

Early indicators of dropout are powerful tools at the K-12 level because they can

potentially alert educators to students who need some level of intervention to stay on track to

graduation. Identifying the relevant indicators is just a first step, and the step that is arguably the

easiest. Indicators do no good if they are not followed up by action and further assessment to see

whether the actions taken have helped to keep students on-track to graduation. However,

appropriate action depends in part on a robust set of early warning indicators. A strong indicator

set has several characteristics:

1. They are empirically created. Powerful indicators are identified based on

analysis of longitudinal data that tracks individual student progress over time. In

essence, indicators use the experience of previous cohorts to intervene when

students in subsequent cohorts begin to show behaviors associated with dropout

among their elder siblings.

2. They are simple and easily collected. Early warning indicators use readily

available data that are typically maintained by schools – variables such as grades,

attendance, and behavior in the classroom. They do not necessarily require

complex statistical modeling techniques or access to data from surveys or

interviews.

3. The set of indicators has been refined to include a few key variables. In

indicator systems, a few key indicators are easier for teachers to monitor than a

large set of predictors. K-12 analyses have demonstrated that although the

underlying issues that produced the poor grade or weak attendance may be

complex and may vary from student to student, there are a small number of flags

that alerts educators to a student potentially falling off-track. By extension, a

good indicator system also identifies variables that are not the strongest predictors

of eventual dropout.

4. They capture the majority of students who eventually become dropouts. A

good indicator system avoids the “1% problem,” or indicators that are highly

predictive but only identify a small percentage of dropouts.

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Labor Market Outcomes The research cited above has identified several key indicators across multiple districts

and states. For 9th

grade these typically include:

Attendance below 85%;

Two or more suspensions or serious disciplinary incidents;

Two or more semester course failures;

Failing a Mathematics course; and/or

Failing an English Language Arts course.

For the 6th

grade these typically include:

Attendance below 90%;

One or more suspensions or serious disciplinary incidents;

One or more semester course failures;

Failing a Mathematics course; and/or

Failing an English Language Arts course.

Indicators at the 9th

grade level require lower cut-off levels given that all students tend to

struggle more when making the transition to the 9th

grade and high school. For example, 9th

graders have lower attendance and more course failures, on average, than 6th graders. Thus in

the middle grades where most students remain on-track, students who are the most likely to

dropout in later years can be identified by more modest signs of disengagement.

METHODS & DATA

The research cited above employed model based methods, such as logistic regression

modeling, to determine which factors were the most related to the outcome of a dropout event on

the part of students. Using longitudinal analyses that tracked cohorts of students (first time 9th

graders, 8th

graders, or as early as 6th

grade in some samples), these studies determined which

factors were the most significant in predicting future dropout events and what the effects were on

a student‟s odds (or odds-ratio) of dropping out versus graduating on-time if a student should fall

below a specific threshold level for several key factors. These studies have consistently

determined that students‟ in-school behaviors (attendance, discipline, and course marks) were

more predictive than either demographic factors or achievement scores from standardized tests.

As a result of these findings, many districts and states are developing Early Warning Systems

using administrative data to identify students who exhibit these key off-track indicators in order

to target them for intervention programs in efforts to stem the dropout problem in their regions.

The descriptive summaries below were based upon student level data taken from the

2008-09 school year for the state of West Virginia. The data were provided by the West Virginia

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Labor Market Outcomes Department of Education from their state-wide management information system that serves all

schools and school districts in the state. The sample included data for 21,244 6th

grade students,

25,315 9th

grade students, as well as 20,315 12th

graders, for a total of 66,874 students across the

three grades. For all tables below listing schools and districts by name, institutional educational

programs and schools for the deaf and the blind were excluded.

The majority of tables in the report list the 25 schools with the most students displaying a

given indicator. The rationale behind such tables was to show what percent of students with the

various early warning signs are a) concentrated in a relatively few schools, b) how this varied by

indicator, and c) the extent to which these were the same or different schools for each indicator,

all of which provides important insights for planning an effective and efficient response. Some

tables additionally list some smaller schools where fewer numbers of students displayed a given

indicator but where the overall percentages of students with the indicator were high enough to

consider the school as being „at risk‟.

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Labor Market Outcomes ACADEMIC BEHAVIOR INDICATORS IN THE 6

TH GRADE

The sample of data for 6th

grade included 21,244 6th

grade students, as well as 223

schools with the 6th

grade. For each of the key indicators, Table 1 shows the overall percentage

of students state-wide with that indicator during the 2008-09 school year. The table also shows

the percentage of all 223 schools to have certain numbers of students with each indicator. For

schools where fewer numbers of students are exhibiting early warning indicators, ie: 10-50

students, this represents schools where problems are small enough that interventions could be

handled by one to two dedicated staff members. However in schools where 50 or more students

exhibit early warning signs, this represents a challenge large enough in scope to require the

implementation of whole school reforms.

Table 1 – Overall State-Wide Rates of 6th

Grade Students with Key Indicators

Attendance

<90%

>=1

ISS/OSS

Fail >=1

Courses

Fail

Math

Fail

English

>=2 of all

Indicators

% of all 6th

Grade

Students (N = 21,244) 20% 18% 10% 5% 4% 13%

% of All Schools with

0 students with

Indicator

9% 10% 29% 39% 93% 17%


10 or more students

with Indicator

59% 54% 28% 13% 12% 38%


25 or more students

with Indicator

29% 27% 13% 4% 3% 16%


50 or more students

with Indicator

8% 8% 4% 1% 0% 4%


75 or more students

with Indicator

1% 1% 1% 0% 0% 1%


100 or more students

with Indicator

0% <1% 0% 0% 0% 0%

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Labor Market Outcomes Table 2 below shows the concentration of 6

th grade students with each indicator within schools.

Table 2 – Concentration of 6th


50% of Students with

this indicator are

concentrated in “X”

Schools


this indicator are


Schools


this indicator are


Schools Attendance <90% 43 Schools 69 Schools 84 Schools

1 or More Minor Incidents 38 Schools 61 Schools 75 Schools

1 or More Course Failures 24 Schools 35 Schools 49 Schools

Failed Math 16 Schools 30 Schools 44 Schools

Failed English 18 Schools 29 Schools 39 Schools

2 or More of any Indicators 30 Schools 53 Schools 67 Schools

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Labor Market Outcomes Attendance has proven to be the driving factor in getting students back on-track to

graduation, ahead of other academic indicators. When students attend school more, their

behavior improves and their failures rates decrease with increased class attendance and work

completion. Thus, efforts directed towards attendance rates can also lead to indirect

improvements in the other indicators of behavior and course marks.

Table 3 – 25 Schools with the Most Students Attending Under 90%

SCHOOL DISTRICT NUMBER OF

STUDENTS

% OF

STUDENTS

6th

GRADE

ENROLLMENT

MOUNT VIEW HS MCDOWELL COUNTY 62 48% 128 ROBERT BLAND MS LEWIS COUNTY 66 36% 185 MAN MS LOGAN COUNTY 43 35% 124 MADISON MS BOONE COUNTY 71 31% 226 COLLINS MIDDLE FAYETTE COUNTY 58 29% 202 BLUEFIELD MS MERCER COUNTY 57 29% 197 WAYNE MS WAYNE COUNTY 48 28% 170 BECKLEY-STRATTON MS RALEIGH COUNTY 65 27% 241 HUNTINGTON MS CABELL COUNTY 58 27% 217 RIPLEY MS JACKSON COUNTY 67 26% 260 PRINCETON MS MERCER COUNTY 56 26% 215 SUMMERSVILLE MS NICHOLAS COUNTY 51 26% 199 HORACE MANN MS KANAWHA COUNTY 45 26% 172 SISSONVILLE MS KANAWHA COUNTY 43 26% 168 MARTINSBURG SOUTH BERKELEY COUNTY 80 25% 324 ELKVIEW MS KANAWHA COUNTY 61 25% 246 WASHINGTON IRVING HARRISON COUNTY 60 24% 253 BEVERLY HILLS MS CABELL COUNTY 47 23% 204 MUSSELMAN MS BERKELEY COUNTY 97 22% 451 MARTINSBURG NORTH BERKELEY COUNTY 50 22% 225 LOGAN MS LOGAN COUNTY 45 21% 211 ANDREW JACKSON MS KANAWHA COUNTY 53 19% 281 B-U MS UPSHUR COUNTY 51 19% 267 EDISON MS WOOD COUNTY 45 19% 236 EASTERN GREENBRIER GREENBRIER COUNTY 43 16% 270

ADDITIONAL AT RISK SCHOOLS (FEWER NUMBERS OF STUDENTS BUT HIGH OVERALL PERCENTAGES)

FORT GAY MS WAYNE COUNTY 22 37% 59 GLADE MS WEBSTER COUNTY 19 37% 52 SANDY RIVER MS MCDOWELL COUNTY 32 33% 97 DUVAL PK-8 LINCOLN COUNTY 25 33% 75 FAYETTEVILLE ES FAYETTE COUNTY 34 32% 106 CEDAR GROVE MS KANAWHA COUNTY 22 32% 69 ASHTON ES MASON COUNTY 17 32% 53 WHEELING MS OHIO COUNTY 20 31% 64 SOUTHSIDE K-8 MCDOWELL COUNTY 17 31% 55

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Behavioral indicators are often the first sign of falling off-track, emerging before drops in

attendance rate or increase in course failures. Students often begin to show behavioral issues,

such as increase in suspensions, years prior to dropping out, whereas their attendance and course

marks tend not to experience dramatic declines until the year(s) immediately preceding a dropout

event. Increases in disciplinary incidents can represent the beginning of a student‟s

disengagement from school, where a student whose attendance and course marks begin to slide

may already be highly disengaged from the system.

Table 4 – 25 Schools with the Most Students with 1 or More In-School or Out-of-School

Suspensions


STUDENTS

% OF

STUDENTS

6th

GRADE

ENROLLMENT

EAST BANK MS KANAWHA COUNTY 71 51% 139 VAN DEVENDER MS WOOD COUNTY 54 41% 133 MOUNT VIEW HS MCDOWELL COUNTY 51 40% 128 BLUEFIELD MS MERCER COUNTY 75 38% 197 WEIR MS HANCOCK COUNTY 57 38% 151 WESTERN GREENBRIER GREENBRIER COUNTY 47 38% 123 SISSONVILLE MS KANAWHA COUNTY 60 36% 168 MAN MS LOGAN COUNTY 45 36% 124 STONEWALL JACKSON KANAWHA COUNTY 56 33% 169 CAPON BRIDGE MS HAMPSHIRE COUNTY 46 32% 146 MOUNTAINEER MS HARRISON COUNTY 43 31% 140 MADISON MS BOONE COUNTY 66 29% 226 MARTINSBURG SOUTH BERKELEY COUNTY 91 28% 324 MARTINSBURG NORTH BERKELEY COUNTY 64 28% 225 MUSSELMAN MS BERKELEY COUNTY 124 27% 451 WASHINGTON IRVING HARRISON COUNTY 68 27% 253 BECKLEY-STRATTON MS RALEIGH COUNTY 66 27% 241

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Labor Market Outcomes PARK MS RALEIGH COUNTY 43 27% 157 HUNTINGTON MS CABELL COUNTY 57 26% 217 HAYES MS KANAWHA COUNTY 56 26% 217 COLLINS MIDDLE FAYETTE COUNTY 53 26% 202 B-U MS UPSHUR COUNTY 68 25% 267 EASTERN GREENBRIER GREENBRIER COUNTY 65 24% 270 SUMMERSVILLE MS NICHOLAS COUNTY 44 22% 199 JOHN ADAMS MS KANAWHA COUNTY 44 17% 257


ENSLOW MS CABELL COUNTY 36 38% 96 LASHMEET/MATOAKA SCHOOL

MERCER COUNTY 20 38% 52

SHORT LINE SCHOOL WETZEL COUNTY 18 34% 53 HARPERS FERRY MS JEFFERSON COUNTY 40 33% 121

Course marks, and course failures, are consistently the single strongest predictors of

future dropping out. Simply put, students who failed to pass their courses also fail to accumulate

the necessary number of credits need to receive a diploma and graduate successfully from high

school. While any course failure is a significant predictor of dropping out, failure of core courses

such as mathematics and English are particularly strong.

Table 5 – 25 Schools with the Most Students Failing 1 or More Courses


STUDENTS

% OF

STUDENTS

6th

GRADE

ENROLLMENT

GUYAN VALLEY MIDDLE LINCOLN COUNTY 43 45% 96 SISSONVILLE MS KANAWHA COUNTY 63 38% 168

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Labor Market Outcomes BECKLEY-STRATTON MS RALEIGH COUNTY 87 36% 241 MARTINSBURG NORTH BERKELEY COUNTY 69 31% 225 ROBERT BLAND MS LEWIS COUNTY 56 30% 185 MOUNT VIEW HS MCDOWELL COUNTY 39 30% 128 WASHINGTON IRVING HARRISON COUNTY 69 27% 253 HUNTINGTON MS CABELL COUNTY 57 26% 217 BEVERLY HILLS MS CABELL COUNTY 53 26% 204 PARK MS RALEIGH COUNTY 40 25% 157 HORACE MANN MS KANAWHA COUNTY 37 22% 172 HAYES MS KANAWHA COUNTY 46 21% 217 B-U MS UPSHUR COUNTY 50 19% 267 INDEPENDENCE MS RALEIGH COUNTY 36 19% 189 RIPLEY MS JACKSON COUNTY 48 18% 260 MADISON MS BOONE COUNTY 40 18% 226 COLLINS MIDDLE FAYETTE COUNTY 37 18% 202 MUSSELMAN MS BERKELEY COUNTY 76 17% 451 MARTINSBURG SOUTH BERKELEY COUNTY 54 17% 324 WEST FAIRMONT MS MARION COUNTY 30 17% 172 HAMILTON MS WOOD COUNTY 30 17% 181 MILTON MS CABELL COUNTY 30 14% 216 ANDREW JACKSON MS KANAWHA COUNTY 37 13% 281 HEDGESVILLE MS BERKELEY COUNTY 31 13% 238 ELKVIEW MS KANAWHA COUNTY 28 11% 246

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Labor Market Outcomes Table 6 – 25 Schools with the Most Students Failing a Mathematics Course


STUDENTS

% OF

STUDENTS

6th

GRADE

ENROLLMENT

BECKLEY-STRATTON MS RALEIGH COUNTY 69 29% 241 MARTINSBURG NORTH BERKELEY COUNTY 54 24% 225 WASHINGTON IRVING MS HARRISON COUNTY 56 22% 253 PARK MS RALEIGH COUNTY 31 20% 157 SISSONVILLE MS KANAWHA COUNTY 32 19% 168 ROBERT BLAND MS LEWIS COUNTY 33 18% 185 INDEPENDENCE MS RALEIGH COUNTY 30 16% 189 CALHOUN M/HS CALHOUN COUNTY 14 15% 96 PHILIPPI MS BARBOUR COUNTY 12 14% 84 MARTINSBURG SOUTH MS BERKELEY COUNTY 35 11% 324 WILDWOOD MS JEFFERSON COUNTY 17 11% 154 MUSSELMAN MS BERKELEY COUNTY 44 10% 451 HEDGESVILLE MS BERKELEY COUNTY 21 9% 238 ELKVIEW MS KANAWHA COUNTY 21 9% 246 HUNTINGTON MS CABELL COUNTY 20 9% 217 BEVERLY HILLS MS CABELL COUNTY 19 9% 204 HAMILTON MS WOOD COUNTY 16 9% 181 HORACE MANN MS KANAWHA COUNTY 15 9% 172 KEYSER P/MS MINERAL COUNTY 15 8% 189 WEST FAIRMONT MS MARION COUNTY 14 8% 172 ANDREW JACKSON MS KANAWHA COUNTY 19 7% 281 RIPLEY MS JACKSON COUNTY 17 7% 260 SPRING MILLS MS BERKELEY COUNTY 15 7% 207 B-U MS UPSHUR COUNTY 16 6% 267 JOHN ADAMS MS KANAWHA COUNTY 14 5% 257

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Labor Market Outcomes Table 7 – 25 Schools with the Most Students Failing an English Course


STUDENTS

% OF

STUDENTS

6th

GRADE

ENROLLMENT

GUYAN VALLEY MIDDLE LINCOLN COUNTY 24 25% 96 BECKLEY-STRATTON MS RALEIGH COUNTY 44 18% 241 HUNTINGTON MS CABELL COUNTY 34 16% 217 BEVERLY HILLS MS CABELL COUNTY 32 16% 204 SISSONVILLE MS KANAWHA COUNTY 26 15% 168 PHILIPPI MS BARBOUR COUNTY 13 15% 84 PARK MS RALEIGH COUNTY 20 13% 157 MARTINSBURG SOUTH MS BERKELEY COUNTY 40 12% 324 HORACE MANN MS KANAWHA COUNTY 20 12% 172 DUNBAR MS KANAWHA COUNTY 19 12% 159 CENTRAL PRESTON MS PRESTON COUNTY 11 11% 96 B-U MS UPSHUR COUNTY 26 10% 267 STONEWALL JACKSON MS KANAWHA COUNTY 17 10% 169 RAVENSWOOD MS JACKSON COUNTY 13 10% 125 MARTINSBURG NORTH BERKELEY COUNTY 21 9% 225 ROBERT BLAND MS LEWIS COUNTY 17 9% 185 HAMILTON MS WOOD COUNTY 16 9% 181 MUSSELMAN MS BERKELEY COUNTY 37 8% 451 WASHINGTON IRVING MS HARRISON COUNTY 20 8% 253 JOHN ADAMS MS KANAWHA COUNTY 20 8% 257 CHAPMANVILLE MS LOGAN COUNTY 12 8% 150 COLLINS MIDDLE FAYETTE COUNTY 14 7% 202 RIPLEY MS JACKSON COUNTY 16 6% 260 TAYLOR COUNTY MS TAYLOR COUNTY 11 6% 172 ANDREW JACKSON MS KANAWHA COUNTY 14 5% 281

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Labor Market Outcomes Table 8 below shows the schools with the largest number of students to have two or more

of any of the five key indicators: Attendance under 90%; one or more suspensions; failed one or

more courses; failed math; and failed English.

Table 8 – 25 Schools with the Most Students with 2 or More Key Indicators


STUDENTS

% OF

STUDENTS

6th

GRADE

ENROLLMENT

BECKLEY-STRATTON MS RALEIGH COUNTY 93 39% 241 SISSONVILLE MS KANAWHA COUNTY 59 35% 168 MOUNT VIEW HS MCDOWELL COUNTY 45 35% 128 MARTINSBURG NORTH BERKELEY COUNTY 72 32% 225 WASHINGTON IRVING HARRISON COUNTY 77 30% 253 ROBERT L. BLAND MS LEWIS COUNTY 51 28% 185 PARK MS RALEIGH COUNTY 43 27% 157 HUNTINGTON MS CABELL COUNTY 56 26% 217 HORACE MANN MS KANAWHA COUNTY 42 24% 172 BEVERLY HILLS MS CABELL COUNTY 47 23% 204 MADISON MS BOONE COUNTY 47 21% 226 COLLINS MIDDLE FAYETTE COUNTY 43 21% 202 MUSSELMAN MS BERKELEY COUNTY 91 20% 451 MARTINSBURG SOUTH BERKELEY COUNTY 65 20% 324 B-U MIDDLE SCHOOL UPSHUR COUNTY 54 20% 267 INDEPENDENCE MS RALEIGH COUNTY 38 20% 189 WILDWOOD MS JEFFERSON COUNTY 31 20% 154 RIPLEY MS JACKSON COUNTY 47 18% 260 HAYES MS KANAWHA COUNTY 40 18% 217 WEST FAIRMONT MS MARION COUNTY 31 18% 172 BLUEFIELD MS MERCER COUNTY 31 16% 197 ANDREW JACKSON S KANAWHA COUNTY 42 15% 281 HEDGESVILLE MS BERKELEY COUNTY 33 14% 238 ELKVIEW MS KANAWHA COUNTY 33 13% 246 JOHN ADAMS MS KANAWHA COUNTY 33 13% 257


GUYAN VALLEY MIDDLE LINCOLN COUNTY 30 31% 96

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Table 9 below highlights which schools appear most frequently across all of the above

Tables 3-8 showing those with the largest numbers of students with each key indicator. The

schools listed below are probably those where intervention efforts in general could most

effectively lead to a future reduction of the dropout rate, given that they have the most students

now showing signs of disengagement of school, and most likely to dropout in the years ahead.

Table 9 – Schools that Appear Most Frequently Across All Tables


TABLES SPECIFIC TABLES

B-U MS UPSHUR COUNTY 6 A, S, F1, FM, FE, Any HUNTINGTON MS CABELL COUNTY 6 A, S, F1, FM, FE, Any MARTINSBURG NORTH BERKELEY COUNTY 6 A, S, F1, FM, FE, Any MARTINSBURG SOUTH BERKELEY COUNTY 6 A, S, F1, FM, FE, Any MUSSELMAN MS BERKELEY COUNTY 6 A, S, F1, FM, FE, Any SISSONVILLE MS KANAWHA COUNTY 6 A, S, F1, FM, FE, Any WASHINGTON IRVING HARRISON COUNTY 6 A, S, F1, FM, FE, Any ANDREW JACKSON KANAWHA COUNTY 5 A, F1, FM, FE, Any BECKLEY-STRATTON RALEIGH COUNTY 5 A, F1, FM, FE, Any BEVERLY HILLS MS CABELL COUNTY 5 A, F1, FM, FE, Any HORACE MANN MS KANAWHA COUNTY 5 A, F1, FM, FE, Any RIPLEY MS JACKSON COUNTY 5 A, F1, FM, FE, Any ROBERT BLAND MS LEWIS COUNTY 5 A, F1, FM, FE, Any PARK MS RALEIGH COUNTY 5 S, F1, FM, FE, Any COLLINS MIDDLE FAYETTE COUNTY 5 A, S, F1, FE, Any ELKVIEW MS KANAWHA COUNTY 4 A, F1. FM, Any MADISON MS BOONE COUNTY 4 A, S, F1, Any MOUNT VIEW HS MCDOWELL COUNTY 4 A, S, F1, Any

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Labor Market Outcomes ACADEMIC BEHAVIOR INDICATORS IN THE 9

th GRADE





grade. Table 10 shows the overall percent of students state-wide to have each

key indicator during the 2008-09 school-year. The table also shows the percent of all 162 schools

to have certain numbers of students with each indicator. For schools where fewer numbers of

students are exhibiting early warning indicators, ie: 10-50 students, this represents schools where

problems are small enough that interventions could be handled by one to two dedicated staff

members. However in schools where 50 or more students exhibit early warning signs, this

represents a challenge large enough in scope to require the implementation of whole school

reforms.

Table 10 – Overall State-Wide Rates of 9th


Attendance

<85%

>=2

ISS/OSS

Fail >=2

Courses

Fail

Math

Fail

English

>=2 of all

Indicators

% of all 9th

Grade

Students (N = 25,315) 17% 15% 15% 16% 12% 19%


0 students with

Indicator

14% 9% 10% 11% 11% 9%


10 or more students

with Indicator

65% 57% 54% 52% 50% 64%


25 or more students

with Indicator

43% 31% 36% 30% 29% 43%


50 or more students

with Indicator

19% 12% 15% 17% 12% 22%


75 or more students

with Indicator

7% 8% 6% 9% 4% 10%


100 or more students

with Indicator

3% 5% 4% 4% 1% 5%

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Labor Market Outcomes Table 11 below shows the concentration of 9

th grade students with each indicator within schools.

Table 11 – Concentration of 9th



this indicator are


Schools


this indicator are


Schools


this indicator are


Schools Attendance <85% 32 Schools 49 Schools 59 Schools

2 or More Minor Incidents 23 Schools 41 Schools 52 Schools

2 or More Course Failures 25 Schools 40 Schools 50 Schools

Failed Math 22 Schools 35 Schools 45 Schools

Failed English 24 Schools 38 Schools 48 Schools

2 or More of any Indicators 27 Schools 43 Schools 53 Schools

Prior research has shown that all students in general struggle more in high school than in

the middle grades. Thus, starting with 9th

grade, overall rates of attendance decline on average

for all students while rates of course failures and disciplinary incidents increase. As a result,

distinguishing those students that are most likely to dropout from other students requires

somewhat modified indicator measures in order to account for the fact that all students struggle

more in the ninth grade. Key indicators for indentifying likely dropouts using 9th

grade data are:

attendance under 85%; two or more suspensions; two or more course failures; and failing

mathematics or English, as in the middle grades.

19

Labor Market Outcomes Attendance has proven to be the driving factor in getting students back on-track to

graduation, ahead of other academic indicators. When students attend school more, their

behavior improves and their failures rates decrease with increased class attendance and work

completion. Thus, efforts directed towards attendance rates can also lead to indirect

improvements in the other indicators of behavior and course marks.

Table 12 – 25 Schools with the Most Students Attending Under 85%


STUDENTS

% OF

STUDENTS

9th

GRADE

ENROLLMENT

SCOTT HS BOONE COUNTY 75 35% 214 WESTSIDE HS WYOMING COUNTY 64 35% 183 WAYNE HS WAYNE COUNTY 57 29% 196 RIVERSIDE HS KANAWHA COUNTY 109 26% 412 LINCOLN COUNTY HS LINCOLN COUNTY 85 25% 342 CAPITAL HS KANAWHA COUNTY 88 24% 369 LIBERTY HS HARRISON COUNTY 55 24% 230 HERBERT HOOVER HS KANAWHA COUNTY 63 23% 275 LOGAN SENIOR HS LOGAN COUNTY 55 23% 239 HUNTINGTON HS CABELL COUNTY 111 22% 495 MARTINSBURG HS BERKELEY COUNTY 136 21% 646 PRESTON HS PRESTON COUNTY 83 20% 412 ROBERT C. BYRD HS HARRISON COUNTY 55 20% 274 CABELL MIDLAND HS CABELL COUNTY 105 19% 563 PARKERSBURG HS WOOD COUNTY 102 19% 532 BROOKE HS BROOKE COUNTY 60 19% 323 SAINT ALBANS HS KANAWHA COUNTY 66 18% 369 MUSSELMAN HS BERKELEY COUNTY 78 17% 452 SPRING VALLEY HS WAYNE COUNTY 58 17% 349 SOUTH CHARLESTON KANAWHA COUNTY 57 17% 334 PARKERSBURG SOUTH WOOD COUNTY 71 16% 437 PRINCETON SENIOR HS MERCER COUNTY 53 16% 327 HEDGESVILLE HS BERKELEY COUNTY 85 15% 573 WHEELING PARK HS OHIO COUNTY 65 13% 490 MORGANTOWN HS MONONGALIA COUNTY 55 12% 474


KANAWHA COUNTY SCHOOLS ACADEMY

KANAWHA COUNTY 40 60% 67

BIG CREEK HS MCDOWELL COUNTY 28 47% 59 TUG VALLEY HS MINGO COUNTY 52 41% 127 IAEGER HS MCDOWELL COUNTY 39 35% 110 MOUNT VIEW HS MCDOWELL COUNTY 47 33% 141 SHERMAN HS BOONE COUNTY 47 33% 142 WYOMING COUNTY EAST HS

WYOMING COUNTY 43 32% 133

MAN SENIOR HS LOGAN COUNTY 33 32% 103

20


Behavioral indicators are often the first sign of falling off-track, emerging before drops in

attendance rate or increase in course failures. Students often begin to show behavioral issues,

such as increase in suspensions, years prior to dropping out, whereas their attendance and course

marks tend not to experience dramatic declines until the year(s) immediately preceding a dropout

event. Increases in disciplinary incidents can represent the beginning of a student‟s

disengagement from school, where a student whose attendance and course marks begin to slide

may already be highly disengaged from the system.

Table 13 – 25 Schools with the Most Students with 2 or More In-School or Out-of-School

Suspensions

SCHOOL DISTRICT

NUMBER

OF

STUDENTS

% OF

STUDENTS

9th

GRADE

ENROLLMENT

BLUEFIELD HS MERCER COUNTY 80 33% 239 MOUNT VIEW HS MCDOWELL COUNTY 45 32% 141 BUCKHANNON UPSHUR UPSHUR COUNTY 108 31% 349 JEFFERSON HS JEFFERSON COUNTY 102 31% 331 RAVENSWOOD HS JACKSON COUNTY 46 30% 155 RIVERSIDE HS KANAWHA COUNTY 120 29% 412 POINT PLEASANT HS MASON COUNTY 65 29% 226 WHEELING PARK HS OHIO COUNTY 137 28% 490 WASHINGTON HS JEFFERSON COUNTY 97 28% 343 LIBERTY HS HARRISON COUNTY 58 25% 230 HEDGESVILLE HS BERKELEY COUNTY 137 24% 573 CABELL MIDLAND HS CABELL COUNTY 136 24% 563 PRINCETON SENIOR HS MERCER COUNTY 78 24% 327

21

Labor Market Outcomes SISSONVILLE HS KANAWHA COUNTY 43 23% 191 SCOTT HS BOONE COUNTY 48 22% 214 MARTINSBURG HS BERKELEY COUNTY 134 21% 646 HUNTINGTON HS CABELL COUNTY 103 21% 495 WOODROW WILSON RALEIGH COUNTY 88 21% 424 CAPITAL HS KANAWHA COUNTY 79 21% 369 INDEPENDENCE HS RALEIGH COUNTY 45 20% 221 RIPLEY HS JACKSON COUNTY 61 17% 350 MUSSELMAN HS BERKELEY COUNTY 68 15% 452 JOHN MARSHALL HS MARSHALL COUNTY 54 15% 371 PARKERSBURG HS WOOD COUNTY 64 12% 532 UNIVERSITY HS MONONGALIA COUNTY 52 12% 425


KANAWHA COUNTY SCHOOL ACADEMY


Course marks, and course failures, are consistently the single strongest predictors of

future dropping out. Simply put, students who failed to pass their courses also fail to accumulate

the necessary number of credits need to receive a diploma and graduate successfully from high

school. While any course failure is a significant predictor of dropping out, failure of core courses

such as mathematics and English are particularly strong.

Table 14 – 25 Schools with the Most Students Failing 2 or More Courses

SCHOOL DISTRICT

NUMBER

OF

STUDENTS

% OF

STUDENTS

9th

GRADE

ENROLLMENT

CAPITAL HS KANAWHA COUNTY 106 29% 369

22

Labor Market Outcomes LINCOLN HS HARRISON COUNTY 55 27% 202 RIVERSIDE HS KANAWHA COUNTY 108 26% 412 SAINT ALBANS HS KANAWHA COUNTY 96 26% 369 SOUTH CHARLESTON KANAWHA COUNTY 85 25% 334 EAST FAIRMONT HS MARION COUNTY 63 25% 250 HUNTINGTON HS CABELL COUNTY 109 22% 495 NORTH MARION HS MARION COUNTY 56 22% 249 CABELL MIDLAND HS CABELL COUNTY 120 21% 563 HERBERT HOOVER HS KANAWHA COUNTY 57 21% 275 UNIVERSITY HS MONONGALIA COUNTY 86 20% 425 ROBERT C. BYRD HS HARRISON COUNTY 54 20% 274 MARTINSBURG HS BERKELEY COUNTY 124 19% 646 BUCKHANNON UPSHUR UPSHUR COUNTY 66 19% 349 ELKINS HS RANDOLPH COUNTY 55 19% 294 HEDGESVILLE HS BERKELEY COUNTY 106 18% 573 GREENBRIER EAST HS GREENBRIER COUNTY 67 18% 363 PRESTON HS PRESTON COUNTY 68 17% 412 GEORGE WASHINGTON KANAWHA COUNTY 58 17% 344 WASHINGTON HS JEFFERSON COUNTY 54 16% 343 MORGANTOWN HS MONONGALIA COUNTY 71 15% 474 SPRING VALLEY HS WAYNE COUNTY 53 15% 349 PARKERSBURG HS WOOD COUNTY 77 14% 532 PARKERSBURG SOUTH WOOD COUNTY 55 13% 437 WHEELING PARK HS OHIO COUNTY 50 10% 490

23

Labor Market Outcomes Table 15 – 25 Schools with the Most Students Failing a Mathematics Course

SCHOOL DISTRICT

NUMBER

OF

STUDENTS

% OF

STUDENTS

9th

GRADE

ENROLLMENT

ROBERT C. BYRD HS HARRISON COUNTY 87 32% 274 SAINT ALBANS HS KANAWHA COUNTY 111 30% 369 CAPITAL HS KANAWHA COUNTY 109 30% 369 SOUTH CHARLESTON KANAWHA COUNTY 97 29% 334 RIVERSIDE HS KANAWHA COUNTY 110 27% 412 HUNTINGTON HS CABELL COUNTY 119 24% 495 BUCKHANNON UPSHUR UPSHUR COUNTY 84 24% 349 EAST FAIRMONT HS MARION COUNTY 57 23% 250 HEDGESVILLE HS BERKELEY COUNTY 126 22% 573 CABELL MIDLAND HS CABELL COUNTY 122 22% 563 PARKERSBURG SOUTH WOOD COUNTY 97 22% 437 GEORGE WASHINGTON KANAWHA COUNTY 71 21% 344 MARTINSBURG HS BERKELEY COUNTY 131 20% 646 UNIVERSITY HS MONONGALIA COUNTY 85 20% 425 HERBERT HOOVER HS KANAWHA COUNTY 56 20% 275 MORGANTOWN HS MONONGALIA COUNTY 89 19% 474 HURRICANE HS PUTNAM COUNTY 67 19% 350 PARKERSBURG HS WOOD COUNTY 97 18% 532 WASHINGTON HS JEFFERSON COUNTY 63 18% 343 WHEELING PARK HS OHIO COUNTY 81 17% 490 GREENBRIER EAST HS GREENBRIER COUNTY 60 17% 363 SPRING VALLEY HS WAYNE COUNTY 59 17% 349 PRESTON HS PRESTON COUNTY 64 16% 412 WOODROW WILSON RALEIGH COUNTY 65 15% 424 MUSSELMAN HS BERKELEY COUNTY 58 13% 452

24


Table 16 – 25 Schools with the Most Students Failing an English Course


STUDENTS

% OF

STUDENTS

9th

GRADE

ENROLLMENT

RAVENSWOOD HS JACKSON COUNTY 41 26% 155 CAPITAL HS KANAWHA COUNTY 85 23% 369 EAST FAIRMONT HS MARION COUNTY 57 23% 250 CABELL MIDLAND HS CABELL COUNTY 117 21% 563 SAINT ALBANS HS KANAWHA COUNTY 77 21% 369 LINCOLN HS HARRISON COUNTY 42 21% 202 HUNTINGTON HS CABELL COUNTY 97 20% 495 RIVERSIDE HS KANAWHA COUNTY 84 20% 412 SOUTH CHARLESTON HS KANAWHA COUNTY 67 20% 334 HERBERT HOOVER HS KANAWHA COUNTY 55 20% 275 ELKINS HS RANDOLPH COUNTY 57 19% 294 MARTINSBURG HS BERKELEY COUNTY 114 18% 646 NORTH MARION HS MARION COUNTY 45 18% 249 PARKERSBURG HS WOOD COUNTY 92 17% 532 GEORGE WASHINGTON KANAWHA COUNTY 56 16% 344 PRESTON HS PRESTON COUNTY 60 15% 412 GREENBRIER EAST HS GREENBRIER COUNTY 56 15% 363 BUCKHANNON UPSHUR UPSHUR COUNTY 53 15% 349 LINCOLN COUNTY HS LINCOLN COUNTY 50 15% 342 WASHINGTON HS JEFFERSON COUNTY 50 15% 343 MORGANTOWN HS MONONGALIA COUNTY 64 14% 474 UNIVERSITY HS MONONGALIA COUNTY 61 14% 425 SPRING VALLEY HS WAYNE COUNTY 44 13% 349 HEDGESVILLE HS BERKELEY COUNTY 63 11% 573 PARKERSBURG SOUTH WOOD COUNTY 40 9% 437

25


Table 17 below shows the schools with the largest number of students to have two or

more of any of the five key indicators: Attendance under 85%; two or more suspensions; failed

two or more courses; failed math; and failed English.

Table 17 – 25 Schools with the Most Students with 2 or More Key Indicators


STUDENTS

% OF

STUDENTS

9th

GRADE

ENROLLMENT

RIVERSIDE HS KANAWHA COUNTY 143 35% 412 CAPITAL HS KANAWHA COUNTY 121 33% 369 SAINT ALBANS HS KANAWHA COUNTY 104 28% 369 BUCKHANNON UPSHUR UPSHUR COUNTY 93 27% 349 SOUTH CHARLESTON HS KANAWHA COUNTY 91 27% 334 ROBERT C. BYRD HS HARRISON COUNTY 75 27% 274 LIBERTY HS HARRISON COUNTY 63 27% 230 CABELL MIDLAND HS CABELL COUNTY 146 26% 563 HUNTINGTON HS CABELL COUNTY 130 26% 495 EAST FAIRMONT HS MARION COUNTY 66 26% 250 MARTINSBURG HS BERKELEY COUNTY 164 25% 646 HERBERT HOOVER HS KANAWHA COUNTY 68 25% 275 HEDGESVILLE HS BERKELEY COUNTY 139 24% 573 WASHINGTON HS JEFFERSON COUNTY 78 23% 343 ELKINS HS RANDOLPH COUNTY 64 22% 294 PRESTON HS PRESTON COUNTY 86 21% 412 PARKERSBURG HS WOOD COUNTY 109 20% 532 UNIVERSITY HS MONONGALIA COUNTY 87 20% 425 GEORGE WASHINGTON KANAWHA COUNTY 67 19% 344 PRINCETON SENIOR HS MERCER COUNTY 61 19% 327 WHEELING PARK HS OHIO COUNTY 88 18% 490 GREENBRIER EAST HHS GREENBRIER COUNTY 66 18% 363 MORGANTOWN HS MONONGALIA COUNTY 81 17% 474 PARKERSBURG SOUTH WOOD COUNTY 74 17% 437 MUSSELMAN HS BERKELEY COUNTY 62 14% 452


KANAWHA COUNTY SCHOOLS ACADEMY


RAVENSWOOD HS JACKSON COUNTY 53 34% 155

26


Table 18 below highlights which schools appear most frequently across all of the above

Tables 12-17 showing those with the largest numbers of students with each key indicator. The

schools listed below are probably those where intervention efforts in general could most

effectively lead to a future reduction of the dropout rate, given that they have the most students

now showing signs of disengagement of school, and most likely to dropout in the years ahead.

Table 18 – Schools that Appear Most Frequently Across All Tables


TABLES

SPECIFIC TABLES

CABELL MIDLAND HS CABELL COUNTY 6 A,I, F2, FM, FE, Any CAPITAL HS KANAWHA COUNTY 6 A,I, F2, FM, FE, Any HEDGESVILLE HS BERKELEY COUNTY 6 A,I, F2, FM, FE, Any HUNTINGTON HS CABELL COUNTY 6 A,I, F2, FM, FE, Any MARTINSBURG HS BERKELEY COUNTY 6 A,I, F2, FM, FE, Any PARKERSBURG HS WOOD COUNTY 6 A,I, F2, FM, FE, Any RIVERSIDE HS KANAWHA COUNTY 6 A,I, F2, FM, FE, Any UNIVERSITY HS MONONGALIA COUNTY 6 A,I, F2, FM, FE, Any HERBERT HOOVER HS KANAWHA COUNTY 5 A, F2, FM, FE, Any MORGANTOWN HS MONONGALIA COUNTY 5 A, F2, FM, FE, Any PARKERSBURG SOUTH WOOD COUNTY 5 A, F2, FM, FE, Any PRESTON HS PRESTON COUNTY 5 A, F2, FM, FE, Any SAINT ALBANS HS KANAWHA COUNTY 5 A, F2, FM, FE, Any SOUTH CHARLESTON HS KANAWHA COUNTY 5 A, F2, FM, FE, Any WHEELING PARK HS OHIO COUNTY 5 A, I, F2, FM, Any WASHINGTON HS JEFFERSON COUNTY 5 I, F2, FM, FE, Any BUCKHANNON UPSHUR UPSHUR COUNTY 5 I, F2, FM, FE, Any GREENBRIER EAST HS GREENBRIER COUNTY 4 F2, FM, FE, Any ROBERT C. BYRD HS HARRISON COUNTY 4 A, F2, FM, Any MUSSELMAN HS BERKELEY COUNTY 4 A, I, FM, Any SPRING VALLEY HS WAYNE COUNTY 4 A, F2, FM, FE

27

Labor Market Outcomes PROPORTION OF 12

th GRADERS RECEIVING DIPLOMAS





grade. The table below shows the high schools with the lowest percentages

of 12th

grade students enrolled in the fall that managed to receive diplomas the following spring.

Overall, 87% of 12th

grade students enrolled in 2008-09 received diplomas in the spring of the

school year.

Table 19 – 25 Schools with the Lowest Proportions of 12th

Graders Receiving Diplomas

SCHOOL DISTRICT % OF

STUDENTS

12th

GRADE

ENROLLMENT

PHILIP BARBOUR HS COMPLEX BARBOUR COUNTY 75% 210 CAPITAL HS KANAWHA COUNTY 76% 292 BLUEFIELD HS MERCER COUNTY 77% 133 MAN SENIOR HS LOGAN COUNTY 79% 118 SISSONVILLE HS KANAWHA COUNTY 79% 162 RICHWOOD HS NICHOLAS COUNTY 80% 124 SAINT ALBANS HS KANAWHA COUNTY 80% 259 PIKEVIEW HS MERCER COUNTY 80% 164 FAIRMONT SENIOR HS MARION COUNTY 81% 204 NITRO HS KANAWHA COUNTY 81% 238 SUMMERS COUNTY HS SUMMERS COUNTY 81% 112 MOUNT VIEW HS MCDOWELL COUNTY 81% 102 BUCKHANNON UPSHUR HS UPSHUR COUNTY 82% 268 GREENBRIER WEST HS GREENBRIER COUNTY 82% 111 CAMERON HS MARSHALL COUNTY 82% 73 HUNTINGTON HS CABELL COUNTY 82% 367 JAMES MONROE HS MONROE COUNTY 83% 141 LOGAN SENIOR HS LOGAN COUNTY 83% 189 WEBSTER COUNTY HS WEBSTER COUNTY 83% 114 GREENBRIER EAST HS GREENBRIER COUNTY 84% 280 MONTCALM HS MERCER COUNTY 84% 61 EAST FAIRMONT HS MARION COUNTY 84% 196 SOUTH HARRISON HS HARRISON COUNTY 84% 92 CLAY COUNTY HS CLAY COUNTY 84% 154 LEWIS COUNTY HS LEWIS COUNTY 84% 192

28

Labor Market Outcomes DISTRIBUTION OF ACADEMIC BEHAVIOR INDICATORS ACROSS DISTRICTS

The table below shows the districts in which the largest number of students displayed two or

more of the five key indicators. The numbers include students from grades 6, 9, and 12, and use

the high school level indicators: Attendance under 85%; two or more suspensions; failed two or

more courses; failed math; and failed English.

Table 20 – 25 Districts with the Most Students with 2 or More Key Indicators

DISTRICT NUMBER OF

STUDENTS

% OF

STUDENTS

6th

, 9th

, & 12th

GRADE

ENROLLMENT

CABELL COUNTY 510 18% 2903 KANAWHA COUNTY 1173 17% 6885 UPSHUR COUNTY 152 17% 884 BARBOUR COUNTY 99 16% 602 BERKELEY COUNTY 638 15% 4257 LEWIS COUNTY 96 15% 634 MARION COUNTY 259 14% 1893 JACKSON COUNTY 172 14% 1249 HARRISON COUNTY 365 13% 2730 BOONE COUNTY 133 13% 1052 RALEIGH COUNTY 328 12% 2787 JEFFERSON COUNTY 232 12% 1872 FAYETTE COUNTY 192 12% 1625 LOGAN COUNTY 176 12% 1481 MCDOWELL COUNTY 102 12% 840 MERCER COUNTY 246 11% 2201 GREENBRIER COUNTY 140 11% 1272 PRESTON COUNTY 114 11% 1064 WOOD COUNTY 321 10% 3099 MONONGALIA COUNTY 247 10% 2552 WAYNE COUNTY 178 10% 1816 OHIO COUNTY 133 10% 1337 HANCOCK COUNTY 109 10% 1045 RANDOLPH COUNTY 99 10% 1002 PUTNAM COUNTY 143 7% 2071

29


The table below shows the districts in which the lowest percentages of 12th

grade students

enrolled in the fall managed to receive diplomas the following spring.

Table 21 – 25 Districts with the Lowest Proportions of 12th

Graders Receiving Diplomas

DISTRICT % OF

STUDENTS

12th

GRADE

ENROLLMENT

BARBOUR COUNTY 75% 210 SUMMERS COUNTY 81% 112 KANAWHA COUNTY 81% 2038 UPSHUR COUNTY 82% 268 MONROE COUNTY 83% 141 GREENBRIER COUNTY 83% 391 MERCER COUNTY 83% 601 WEBSTER COUNTY 83% 114 CABELL COUNTY 83% 818 CLAY COUNTY 84% 154 LEWIS COUNTY 84% 192 MARION COUNTY 84% 598 LOGAN COUNTY 85% 489 RITCHIE COUNTY 85% 102 OHIO COUNTY 85% 456 TAYLOR COUNTY 86% 154 MONONGALIA COUNTY 86% 789 NICHOLAS COUNTY 86% 334 DODDRIDGE COUNTY 86% 96 MINERAL COUNTY 87% 381 BRAXTON COUNTY 87% 182 ROANE COUNTY 88% 185 JEFFERSON COUNTY 88% 630 RANDOLPH COUNTY 88% 307 WOOD COUNTY 88% 949

30

Labor Market Outcomes REFERENCES

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

Chicago public high schools. Chicago: Consortium on Chicago School Research.

Balfanz, R. & Legters, N. (2004). Locating the dropout crisis. CRESPAR Report #70. Baltimore, MD and

Washington, DC: Center for Research on the Education of Students Placed at Risk.

Balfanz, R., Herzog, L. & Mac Iver, D.J. (2007). Preventing student disengagement and keeping students

on the graduation path in urban middle-grades schools: Early identification and effective interventions.

Educational Psychologist, 42, 4, 223-235.

Finn, J.D. (1989) Withdrawing from school. Review of Educational Research, 59, 117–142.

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept,

state of the evidence. Review of Educational Research, 74, 59-109.

Gleason, P. & Dynarski, M. (2002). Do we know whom to serve? Issues in using risk factors to identify

dropouts. Journal of Education for Students Placed at Risk, 7, 25-41.

Lan, W., & Lanthier, R. (2003). Changes in students‟ academic performance and perceptions of school

and self before dropping out of schools. Journal of Education for Students Placed at Risk, 8(3), 309–332.

Lee, V.E., & Burkam, D.T. (2003). Dropping out of high school: The role of school organization and

structure. American Journal of Education, 100, 420-453.

Neild, R. C., & Balfanz, R. (2006a). An extreme degree of difficulty: The educational demographics of

urban neighborhood high schools. Journal of Education for Students Placed at Risk, 11, 131-141.

Neild, R. C. & Balfanz, R. (2006b). Unfulfilled promise: The dimensions and characteristics of

Philadelphia’s dropout crisis, 2000-2005. Philadelphia, PA: Philadelphia Youth Transitions

Collaborative.

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done. The Future of Children 19, 53-76.

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school. American Educational Research Journal, 36, 303–344.

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California Dropout Research Project Report #15. Santa Barbara, CA: California Dropout Research

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Schargel, F., & Smink, J. (2001). Strategies to help solve our school dropout problem. Larchmont, NY:

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Wehlage, G., Rutter, R., Smith, G., Lesko, N., & Fernandez, R. (1989). Reducing the risk: Schools as

communities of support. New York: Falmer Press.

31


© 2010, The Johns Hopkins University, on behalf of the Everyone Graduates Center. All Rights Reserved.

Suggested citation:

Balfanz, R., & Byrnes, V. (2010). Current Distribution of Early Warning Indicators Analysis: West Virginia.

Baltimore: Johns Hopkins University Center for Social Organization of Schools.

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