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Preventing Student Disengagement 1
Running Head: PREVENTING STUDENT DISENGAGEMENT IN MIDDLE SCHOOLS
Preventing Student Disengagement and Keeping Students on the Graduation Track in High-
Poverty Middle-Grades Schools: Early Identification and Effective Interventions
Robert Balfanz
Johns Hopkins University
Liza Herzog
Philadelphia Education Fund
Douglas J. Mac Iver
Johns Hopkins University
Robert Balfanz, Center for the Social Organization of Schools, Johns Hopkins
University; Liza Herzog, Philadelphia Education Fund; Douglas Mac Iver, Center for the Social
Organization of Schools, Johns Hopkins University.
This research was supported in part by a grant from the William Penn Foundation and by
the Research on Learning and Education (ROLE) Program at the National Science Foundation,
grant number 0411796.
Correspondence concerning this article should be addressed to Robert Balfanz, Center for
the Social Organization of Schools, 3003 N. Charles Street, Suite 200, Baltimore, MD 21218. E-
Mail: [email protected]
Preventing Student Disengagement 2
Abstract
Do many students in high-poverty schools become disengaged at the start of the middle
grades? Does this disengagement reduce the likelihood that they will eventually graduate?
Longitudinal analyses -- following more than 12,000 of Philadelphia’s sixth-grade students from
1996 until 2004 -- show that the answer to both questions is “yes.” Four simple predictive
indicators (attending less than 80% of the time, receiving a poor final behavior grade, failing
math, or failing English) identify sixth graders who have high odds of falling off the graduation
track. Sixth-graders with one or more of these indicators had only a 29% chance of graduating;
these indicators flagged 60% of those who left without graduating. Additional research indicates
that student disengagement which leads to dropping out can be reduced by combining effective
whole school reforms, early identification of students who need sustained intervention, and
practical, personal, and research-based attendance, behavioral, and extra-help interventions.
Preventing Student Disengagement 3
Preventing Student Disengagement and Keeping Students on the Graduation Track in High-
Poverty Middle-Grades Schools: Early Identification and Effective Interventions
The Middle Grades in general, and high-poverty middle-grades schools in particular,
continue to be the underperformers of the U.S. educational system. Drops in achievement and
student engagement have been well documented. Raising student achievement in high-poverty
middle-grades schools requires intensive, comprehensive, and multi-dimensional reforms. Most
current reform efforts have focused on reforming the roles, skills, and outlooks of the adults who
teach and administrate in the middle grades or the instructional materials they use. Sustained
attempts have also been made to make the middle grades more developmentally appropriate for
young adolescents and more caring and supportive learning environments. Much less attention
has been paid to understanding the magnitude of student disengagement in high-poverty middle-
grades schools, its impact on student achievement, and ultimately the role it plays in driving the
nation’s graduation rate crisis.
In our own work in developing and evaluating the Talent Development Middle and High
School comprehensive reform models for high-poverty schools, these questions have become
paramount. A middle or high school student’s decision to not attend school regularly, to
misbehave, or to expend low effort are all consequential indicators of his or her disengagement
from school. Thus, we have documented how student attendance, behavior and effort all have
independent and significant impacts on the likelihood that students attending high poverty
middle-grades schools will close their achievement gaps (Balfanz & Byrnes, 2006). We have
also learned how course failures and low attendance in 8th grade are powerful and almost
deterministic predictors of failing to earn promotion out of the 9th grade and ultimately dropping
Preventing Student Disengagement 4
out (Neild & Balfanz 2006a, 2006b). These findings led us to the questions which we pursued in
the research and development activities detailed in this article.
First, how widespread and how early in the middle grades does serious student
disengagement from schooling occur? In high-poverty urban schools, does the intersection of
early adolescence and the environmental/social conditions of concentrated, neighborhood
poverty, produce high levels of disengagement as early as the entry grade to middle school?
Second, are there indicators schools can easily use to identify sixth graders who are
beginning to disengage from schooling in a significant and consequential manner? Are there
indicators which signal -- absent substantial and sustained intervention – that there are high odds
that “this student is in trouble, will struggle academically, and ultimately dropout?” In other
words, can we trace the intermediate roots of the dropout crisis in high poverty neighborhoods to
the start of the middle grades?
Finally, given our findings, are there effective preventions and interventions and can they
be assembled into a comprehensive set of reforms that are implementable by high-poverty
schools?
Early Adolescents, High-Poverty Neighborhoods and the Roots of Disengagement from
Secondary Schools
We chose to begin our study with the sixth grade because, based on the current literature
on adolescent development and high-poverty neighborhoods (Bowen & Bowen, 1999; Halpern-
Felsher et al., 1997, Kowaleski-Jones, 2000), we had reason to believe that the combination of
the onset of adolescence and the transition to secondary schooling had the potential to create a
unique set of risk factors, which are heightened by the impact of concentrated poverty. In other
words, it was our theory that the combination of becoming an adolescent, moving into new
Preventing Student Disengagement 5
organizations of schools with more complex academic demands, and living in a high-poverty
area create unique conditions which can push students off the path to high school graduation
regardless of their prior schooling experience and as such, require proactive and preventative
middle-grades interventions.
Even as overall poverty rates fell in the 1990’s, the proportion of children living in
distressed and high poverty neighborhoods increased in many states. As children become young
adolescents (11 and 12) in these neighborhoods, they face a daunting array of environmental
challenges and circumstances that can interfere with forming a strong and positive attachment to
middle-grades schooling. They may have to take on increased caregiver roles within their
families. They also become targeted for recruitment into gangs and the illegal drug trade which
needs a constant supply of cheap labor. This in turn increases the likelihood that they will be
arrested or come in contact with the justice system. They also become more likely to be crime
victims themselves as they travel longer distances through dangerous neighborhoods. In large
cities, they are often required to take mass transportation to school which makes it easier for
them to disappear off on their own adventures. In addition, there may be few organized
opportunities for them to express the normal rambunctiousness of adolescents in parks,
recreation centers, or organized after-school activities leading this energy to be expressed in
more inappropriate manners (Connell & Halpern-Felsher, 1997). This is all occurring as they
undergo the normal stresses of adolescence brought on by developmental changes and the
increasing cognitive complexity and volume of schoolwork. Finally, high-poverty middle-grades
schools are often marked by high degrees of teacher turnover and even teacher vacancies. So,
students entering the middle grades in high poverty neighborhoods are more likely than in the
primary grades to experience chaotic, disorganized, under-resourced classrooms and schools.
Preventing Student Disengagement 6
Not surprisingly, many of these students conclude that not much productive is going on (Wilson
& Corbett, 2001). In short, students entering the middle grades in high poverty neighborhoods
can experience a range of pull and push factors which may promote disengagement from
schooling. The extent of this disengagement can be vividly seen in the sharp drop in attendance
which often occurs between the elementary and middle grades. For example, Table 1 shows how
the percent of students missing a month or more of schooling escalates sharply and even
becomes the norm in some of Baltimore’s high-poverty neighborhoods.
Prior Efforts at Early Identification of Students Who Are Falling off The Graduation Track in the
Middle Grades
Given that high school dropouts have been a concern for over forty years and that
dropping out has consistently been linked to student disengagement, it is surprising that the field
of early indicators is grossly underdeveloped (Jerald, 2006). There have been, at most, a handful
of studies which have attempted to follow cohorts of students over extended periods of time to
establish the contexts, points in time, and school outcomes or events associated with students
falling off the graduation track (e.g., Alexander, Entwisle & Kabbani, 2001; Ensminger &
Slusarcick 1992). Even fewer of these studies have attempted to develop typologies which
establish how different sets of factors and contexts derail different types of students or provide
different types of paths to dropping out (Battin, Abbott, Hill, Catalano & Hawkins, 2000;
Roderick, 1993, Cairns, Neckermann & Cairns, 1989). Fewer yet have then tested the predictive
validity of these typologies with different sets of students or the complete universe of students
within a school district (Janosz, Le Blanc, Boulerice, & Tremblay, 2000).
In this regard, we note that most prior work on dropout indicators has been based
primarily on repeated surveying of a relatively small number of students. This can lead to rich
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insight into the underlying complexities and interplay between individual, social, and school
factors in triggering dropping out. Predictive indicators based on survey constructs, however, are
of limited utility to schools because neither repeated surveying nor the interpretation of
constructs is readily reproducible by schools.
One clear finding from prior work on dropout predictors that does inform our work is that
students fall of the graduation track from both academic and behavioral points of departure. In
short, while different students begin their disengagement from school for different reasons, two
clear paths emerge: one rooted primarily in academic struggle and failure and another grounded
more in behavioral responses to the school environment. (Jansoz et al., 2000.)
Another finding of importance to our study is that the impact of a risk factor often varies
depending upon when it occurs in the life course. For example, Alexander et al (2001) followed
a sample of first graders in Baltimore through high school and showed that different predictors
had more or less power depending on when in a student’s progression through school they
occurred. For example, retention in any grade turned out to have a negative impact on a
student’s odds of making it through the ninth grade, but retention in the middle grades was
particularly problematic.
Finally, our study embraces the work of Gleason & Dynarski (2002) which suggests that,
to be useful, dropout predictors need a high predictive yield. A predictor has a high yield when
most students with the characteristics eventually fail to graduate and the characteristics alone or
in combination with other variables identifies a significant portion of the students who will not
graduate. Gleanson and Dynarski argue that dropout predictors based on status variables (race,
gender, age, special education or ELL status) have often been of limited practical utility because
either many of the students with the characteristics ultimately graduate (so directing additional
Preventing Student Disengagement 8
interventions towards them drains resources that could be better used elsewhere) or the
predictors do not net the majority of students who will dropout (so many students in need of
extra interventions do not receive them because they have not been successfully identified). The
few studies which have been able to identify high yield predictors have done so using small
populations of students from a single high school or modest-sized town. These studies though,
have consistently found course grades, attendance, and behavior measures in the middle grades
to be high yield predictors (Barrington & Hendricks, 2001; Lloyd, 1974; Morris, Ehren, & Lenz,
1991).
No study that we are aware of, however, has examined the questions of most interest to
us: How early in the middle grades can a significant number of students in high-poverty school
districts be identified who, absent intervention, will fall of the graduation track? How large a
role does student disengagement play in falling off the graduation track in the middle grades?
And equally important, can students be identified in a reliable and valid manner with indicators
readily available and interpretable to school teachers and administrators?
Part A: Developing Indicators of Student Disengagement and Examining Their Impact on Falling
Off the Graduation Track Early in the Middle Grades
Methods and Data
Three questions guided our analyses:
(1) Are significant numbers of students in high-poverty urban schools showing
unmistakable signs of disengagement by sixth grade?
(2)Do sixth graders who exhibit unmistakable signs of disengagement by struggling
academically, not coming to school on a regular basis, and/or behaving inappropriately fall off
the path to graduation in significant numbers?
Preventing Student Disengagement 9
(3) Can we identify a set of indicators that flag sixth graders who have high odds of
falling off the graduation track and do these indicators collectively flag a substantial percentage
of the students who do not graduate with a high school diploma?
We also wanted to end up with a parsimonious set of variables. In order to be useful to
schools, we looked to have a small set of indicators that were not overwhelming in complexity.
For this reason, we decided that we wanted to emerge with a unique set of indicators, each with
its own independent and additive predictive power rather than composites or indexes. We
decided to set a 75% yield as our initial screening level. Could we find a set of predictive
indicators that individually identified sixth-graders who had a 75% or more risk of not
graduating from the school district?
The outcome variable we used was whether or not the sixth graders in the cohort we
followed graduated from the school district on time or within one extra year of their expected
graduation date. We chose this as our outcome partly because of time and data constraints but
also because in-depth analyses of the school district’s data reveal that the overwhelming majority
of graduates get their high school diploma on time, or within one extra year (Neild and Balfanz
2006b). Thus, extending the analysis to two or more years beyond the expected graduation date
would at most increase the graduation rate by a few additional percentage points. We also
decided to focus on graduation rather than dropping out. This means that students who transfer
out of the school district are included in the analysis as non-graduates. We do examine the
impact of the identified indicators on whether the student dropped out vs. transferred in a
secondary analysis, but, for our primary analysis, our focus is on whether the sixth grader
ultimately graduated from the school district. Thus, our graduation rates will be lower than
official calculations which adjust for transfers out. However, Rumberger (2004) and others have
Preventing Student Disengagement 10
shown and argued that adolescents who transfer after experiencing school difficulties ultimately
drop out in high numbers, and that the common practice of excluding transfers when computing
graduation rates likely leads to large overestimates of the actual graduation rates. In short, it is
not safe to assume that a student who exhibits serious disengagement from schooling at the start
of the middle grades, and then transfers to another school district prior to completing high school
will ultimately graduate. Thus, if the goal is to establish the magnitude of student disengagement
at the start of the middle grades, so that effective and preventive action can be taken, it is
important to keep all students in the analysis.
For this study, we created a dataset using attendance, demographic, administrative,
course and credit, and test data at the individual level provided by the School District of
Philadelphia. We constructed an individual level longitudinal data set that let us follow 12, 972
students enrolled in sixth grade in 1996-1997 over an eight-year time period through to 2003-
2004, or one year beyond expected graduation for the cohort. We were able to create a universe
sample of all students who showed their intent to be educated in the school district by being
enrolled as sixth graders in October 1996 in Philadelphia public schools and by attending enough
to have at least one course mark, behavior mark and/or attendance data for at least one reporting
period that school year. Of course, when examining specific predictors or specific combinations
of predictors, we necessarily examined slightly smaller sub-samples of this universe due to
missing data.
We created four distinct sets of predictor variables based on prior work on student
disengagement and falling off the graduation track:
1) Academic performance variables: standardized test scores from the spring
of 5th grade and final course marks from 6th grade
Preventing Student Disengagement 11
2) Behavior variables: end-of-year behavior marks in each course, in-school
and out of school suspensions
3) Attendance: Both total days absent and by descending cut points i.e.
percent attending 80% or less
4) Status variables that might indicate underlying but unmeasured academic
or behavioral outcomes: special education status, English as a Second Language status,
and being one or more years overage for grade.
Test scores were assessed at the 5th grade for the purposes of this analysis. We used the
student’s scaled reading and math scores on the Pennsylvania System of School Assessment
(PSSA), a statewide criterion-referenced test used to measure attainment of academic standards
in reading and math. During the time of this study, the PSSA was administered in the 5th, 8th and
11th grades. Beginning in 2004-05, the state began to administer the exam in grades 3 through
11.
Final behavior marks were determined by taking the behavior mark assigned to each
student by each classroom teacher in the final marking period. The mark represents the teacher’s
cumulative behavior assessment for the student during the sixth-grade year. For each course, a
student earns an academic mark (A through F) and a behavior mark (Excellent, Satisfactory, or
Unsatisfactory). It is important to note that beginning in 2004-05, the District revised its marking
policy and replaced the behavior metric with teacher narrative. This shift may make it more
cumbersome for District schools to use unsatisfactory behavior in a course as a predictive flag in
the future.
Preventing Student Disengagement 12
Suspensions were determined from District administrative records. Suspensions were
separated into in-school and out-of-school suspensions, by marking period.
Attendance rates were calculated for each student by dividing the number of days the
student was marked present by the total number of days the student was enrolled in a given
school year.
English courses were included in this analysis if a course was described as a core English
or Reading course taken in the sixth grade. We created a dichotomous variable to divide
students into a) earning 65 or above (the District’s pass/fail cut)—these students passed English;
and b) students earning less than 65—these students failed English. It should be noted that in the
exceptional case that a student was enrolled in both an English and a Reading course during the
same marking period, and failed one course and passed the other, that student was considered to
have failed English.
Math courses were included in this analysis if a course was described as a core math
course taken in the sixth grade. Supporting and remedial math courses were not included. We
created a dichotomous variable to divide students into a) earning 65 or above (Passed Math); and
b) students earning less than 65 (Failed Math).
Graduation status was determined by examining the student’s enrollment records. For
the purposes of this analysis, a student was considered to have graduated in a particular school
year if his or her enrollment status was designated “G” (Graduated) as of October 1st following
the close of the school year in question. Our rationale was to include in the graduation rate for a
given year not only those who graduated in the spring but also those who successfully completed
needed summer coursework to become early fall graduates.
Preventing Student Disengagement 13
Dropout status was determined by examining the drop code list designed by the School
District. For the purposes of this analysis, all leavers who were assigned District drop or non-
drop withdrawal codes were considered to have left the District. Transfers and moves were
considered to be non-drop withdrawals. Of our 1996-97 sixth grade sample, 82% of all “leavers”
-- students who were “active” enrollees in a given year and coded as a) drop, b) non-drop
withdrawal, or c) missing altogether the following year -- are drops and 15% are non-drop
withdrawals.
Demographic variables were determined by using District demographic files: in 1996-97,
the sixth grade ethnic breakdown was African American (64%), White (19%), Latino (12%),
Asian (5%) and Other (<1%).
Special status including special education or ESL designation was determined by a
student status variable in District administrative files.
Findings
We found four variables that met our criteria of having high predictive power (75% or
more students with the indicator did not graduate from the school district on time or one year
late) and high yield (identifying a significant percentage of the students from the cohort who did
not graduate). They are:
Failing Math
Failing English
Attend school 80% or less of the time
Out of School Suspensions
A fifth variable, receiving an unsatisfactory final behavior mark in any subject, did not quite
meet our initial screening level of having 75% or higher predictive power but was kept it in the
Preventing Student Disengagement 14
analysis because a) its predictive power was still substantial at 71%, b) it identified a large
number of future non-graduates, and c) it was found to be a powerful covariate.
Academic achievement. Consistent with earlier findings, course failure was a better
predictor of not graduating than were low test scores. Students who failed either a mathematics
or English/Reading course in the sixth grade rarely graduated from the school district. Fourteen
percent of the sixth graders in the sample (n= 1801) failed mathematics in sixth grade and only
19% of these students ultimately graduated from the school district within one year of on-time
graduation. Eleven percent of the sixth graders failed (n=1409) an English or reading course and
only 18% graduated from the school district within one year of on-time graduation. The students’
progression through the school district can be seen in Tables 2 and 3.
Fifth grade test scores in reading and mathematics by comparison turned out to be very
poor predictors of who would stay on the graduation track. Table 4 below shows that its only
students with the very lowest test scores (10th percentile or less) that have significantly lower
rates of reaching 12th grade on time and even then failing math or failing English in sixth grade is
much more predictive of falling off the graduation track.
Attendance. Attending school less than 90% of the time in sixth grade increases the
chance that students will fall off the graduation track. When attendance dips below 80% (missing
36 days or more in the year) our a priori threshold of 75% or more of the students falling off the
graduation track is reached. In Philadelphia, 1934 or 15 percent of sixth graders in our cohort
attend school less than 80% of the time. Only 17% of these students ultimately graduated from
the school district within one year of expected graduation. Their progression can be seen in Table
5 below.
Preventing Student Disengagement 15
Suspensions. Students who were suspended in sixth grade fell off the graduation track in
large numbers. Eight hundred and forty five (6%) sixth graders in the cohort received one or
more out of school suspensions. Only 20% of these students graduated within one year of on-
time graduation. Two hundred and twenty two sixth graders received in-school suspensions and
only 17% of them remained on the graduation track. The odds decreased even further for the 136
sixth graders who had two suspensions and the seventy four students who had three or more.
Behavior grades. Receiving a final unsatisfactory behavior grade in any subject in the
sixth grade significantly reduced the chances that sixth graders would graduate from the school
district within one year of expected graduation. A very large number (4893) and percent (38%)
of sixth graders received at least one final unsatisfactory behavior grade. Only 24% of these
students graduated on time from the school district and an additional five percent graduated
within one extra year. Seventy one percent of the sixth graders who received one or more poor
final behavior grade fell of the graduation track. As a result, unsatisfactory behavior grades fell
just short of our a priori predictive threshold of 75% or more. But this predictor yields a very
large number of the school district’s future non-graduates. In fact, the number of students with at
least one final poor behavior grade is greater than the number of students who fail math, fail
English, and are suspended combined. The progression of students with a poor final behavior
grade through the school system can be found below in Table 6.
In addition to being a significant indicator in and of itself, a poor final behavior mark is
also a powerful covariate. Students who fail math or English and also have a poor final behavior
mark fall off the graduation track at even greater rates than students who fail math and English
but receive good behavior marks. This can be seen in Table 7. Thus, it is significant that the
Preventing Student Disengagement 16
majority of students who failed math or English/Reading in the sixth grade cohort also received a
poor final behavior mark in at least one subject.
Finally, it is revealing that receiving a poor final behavior mark in any subject and having
none of the other highly predictive indicators (failing math or English, attending less then 80% of
the time) is as predictive of falling of the graduation track as being suspended (and having no
other indicators). Thirty eight percent of the sixth graders with only a poor final behavior mark
graduated within one year of their expected graduation year compared to thirty six percent of the
students who were suspended. This indicates that, 1) a single behavioral episode significant
enough to bring suspension and 2) sustained, more mild misbehavior (or perceived lack of effort)
in a single class that leads to a poor final behavior mark both have strong, approximately equal,
impacts in knocking students off the graduation track.
Status variables. Being either a special education student or an English Language
Learner in sixth grade reduced students’ odds of remaining on the graduation track. However, the
impact of these variables fell substantially short of our required threshold for highly predictive
indicators. Being overage for sixth grade initially appears to be highly predictive that sixth
graders will not graduate within one year of their expected graduation date. Only 29% of the
2406 overage students in our sixth grade cohort stayed on the graduation track. However, this is
primarily because a high percentage of overage students either failed math, failed English,
attended less than 80% of the time, or had a poor final behavior grade. The one third of the
overage students who did not have any of our highly predictive indicators (fail math, fail
English, poor attendance, suspended, or poor behavior grade) graduated at the same rate as the
overall cohort.
Preventing Student Disengagement 17
The Big Five becomes the Big Four. Our analysis of achievement, attendance, and
behavioral variables unearthed five variables with high predictive yield. Failing math, failing
English, attending less than 80% of the time, being suspended, and receiving a poor final
behavior grade. Following our desire to parsimonious as possible we decided to use poor final
behavior grades as our primary behavior variable in the analysis which follows. This is because
almost all students who were suspended also received a poor final behavior grade and four times
as many students received a poor final behavior grade as were suspended. It should be noted that
in locales and districts that do not record behavior grades, our analysis indicates that being
suspended does serve as behavior measure with high predictive yield.
Combinations of the Big Four predictors. As has been noted in several instances above,
many of the sixth graders in the cohort have combinations of our predictive indicators, and the
more indicators a sixth grader has, the lower their odds of staying on the graduation track. In
fact, if students only have one indicator -they only fail math, fail English, attend less than 80% of
the time, or receive a poor final behavior mark than their odds of not graduating are closer to two
out of three, then three out of four or lower. Table 8 shows how the odds of falling off the
graduation track increase as students have multiple high yield indicators. Table 9 shows the
number of students with different combinations of the big four predictors.
The Prepared and Engaged Comparison Group. We created a comparison group of
students who exhibited behaviors and accomplishments consistent with being prepared for and
engaged in middle schools in order to see if these behaviors and accomplishments served as
protective factors which increased students’ odds of graduation. In other words, if being
disengaged during the sixth grade seriously diminishes the chances that a student will graduate,
does being engaged in schooling and academically prepared for grade level work enhance the
Preventing Student Disengagement 18
odds of graduating? We called this comparison group, Prepared and Engaged Students, defined
as those who were enrolled in 1996-97 as sixth graders in Philadelphia public schools, attended
school 90% or more of the time, passed math and English, had no final poor behavior marks, and
scored 1275 or higher on the reading section of the PSSA and 1312 or higher on the math section
of the PSSA in the spring of 5th grade. These cut scores were chosen as the scaled score
equivalent of the current “proficiency floor” for Pennsylvania, where a school achieves Adequate
Yearly Progress (AYP) via proportion of its student population scoring at or above Proficient. In
identifying Prepared and Engaged students in this way, we were necessarily limited to the pool
of sixth graders who had attended the school district in the Spring of 5th grade and who had not
been absent during both the math and reading tests (and were also non-missing on the high yield
indicators). Complete data were available for a total of 8,340 students, a smaller universe than
the 12,037 students used to develop the high yield indicators. Out of these 8,304 students only
604 or about 7% of the students met our criteria for being considered Prepared and Engaged
sixth-graders: students who entered the middle grades with proficient academic skills and who
come every day, behave, and pass their courses. Examining these students’ progression through
the school system and their graduation outcomes shows us how prepared and engaged students
fare in the School District of Philadelphia. Table 10 shows that these sixth-graders have a 71%
graduation rate (on time or one year late) from the school system. This contrasts with the
district’s overall rate for 1996-1997 sixth-graders of 43%, its 56% rate for sixth-graders with
none of the high yield risk factors, and its 29% rate for sixth graders with 1 or more the risk
factors.
Logistic regression. We used logistic regression to estimate the association between
graduating from high school and the occurrence of a high yield indicator in the sixth grade
Preventing Student Disengagement 19
students. The model also helps us verify that each of the indicators has independent predictive
power, and how much of a difference it makes, in graduating from high school. When we tested
the relationships between variables, we found each of the high yield indicators – failing math,
failing English, attending less than 80% of the time and receiving a poor final behavior mark --
to be independently statistically significant and consistently negative predictors of graduation.
Table 11 presents the data from a logistic regression analysis that measures the odds of
dropping out of school for students having one of the indicators versus those students who do
not. For example, from the table we can see that students who attend 80% or less of the time are
significantly less likely to graduate, controlling for the bad behavior marks, course failures, and
race/ethnicity. More specifically, as one can see by subtracting the poor attendance odds ratio
from 1.00, sixth graders who attend 80% or less of the time are 68% less likely to graduate than
students who attend more frequently. Likewise, students with one of more poor final behavior
grades are 56% less likely to graduate than students who do not have poor behavior grades. The
results also indicate that, all else being equal, the negative impact of attendance on the odds of
graduating is the greatest, followed by poor behavior and failing math, and then failing English.
Partial coefficients of determination calculated for each set of variables indicate that the
high yield indicators contribute vastly more explanatory power than the race/ethnicity variables
(the most common status variables used in prior attempts to develop indicators). The partial
coefficient of determination for the combined four risk factors was .1523, while the coefficient
for the combined race variables was .0045.
The goodness-of-fit of a reduced form of the model, including only the four risk factors
and no race variables, was examined by regressing the predicted odds on the observed odds.
Preventing Student Disengagement 20
These were highly correlated, with an R-squared of .95. In other words, the logistic model fits
the differing graduation rates of different groups extremely well.
Total predictive yield of the Big Four. Overall, if failing math, failing English, attending
less than 80% of the time, and receiving a poor final behavior mark are used as our final set of
predictive variables, 60% of the sixth graders in the cohort who will not graduate from the school
system within one year of expected graduation can be identified. Collectively, students with one
or more of these predictive characteristics have only a 29% graduation rate from the school
district.
Although our analyses demonstrate the importance of the Big Four risk factors, almost
one-quarter of the students have none of the Big Four risk factors and still never graduate from
the District. We hypothesized that a considerable portion of these students—those with no risk
factors who did not graduate—were transfers or moves. As it turns out, of the 2765 students in
this zero-risk/no-grad group, 574 (21%) transferred or moved, 2038 (74%) dropped out, and 145
(5%) were still in school. As we move to the one-risk/no-grad group (2224), we expected there
to be proportionally fewer transfers and moves, more drops and more active enrollees. This
proved to be the case, with 318 (14%) transfers/ moves, 1759 (79%) drops, and 141 (6%) still in
school. For the two-risk/no-grad group, there were 104 (10%) transfers or moves, 877 (83%)
drops, and 70 (7%) still in school. For the three-risk/no-grad group, there were 49 (9%)
transfers/ moves, 445 (83%) drops, and 40 (7%) still in school. Finally, for the all four-risk/no-
grad group, we had 23 (8%) transfers or moves, 273 (90%) drops, and 5 (2%) still enrolled.
Discussion
We were able to find four variables with a very high predictive yield that identify the majority of
sixth graders who fall off the graduation track. These variables, moreover -- poor attendance,
Preventing Student Disengagement 21
poor behavior marks, and failing math or English-- each are readily and commonly measured at
the school level and collectively capture a significant portion of a district’s future dropouts. Our
results also confirm and extend prior findings suggesting that students fall of the graduation track
in different but identifiable ways. In the sixth grade, by far the most common occurrence was for
students to either have a single risk factor, especially poor behavior or poor attendance, or two
risk factors, especially poor behavior plus course failure in English or mathematics. Only 12%
of the sixth graders with at least one of the indicators had both poor attendance and a bad
behavior grade. Likewise only 11% percent failed both Math and English. Just 16% had more
than two indicators. We can regard these findings as hopeful because they indicate that, in sixth
grade, nearly all students who can be identified at high risk for falling off the graduation track
are only demonstrating difficulty in one academic subject and/or in one behavioral realm. At this
point in their schooling, most of the students who are falling off the graduation track are not
demonstrating a multiplicity of difficulties (not coming to school, behaving badly when they are
in school, and failing multiple courses) that is typical of many struggling high school students.
On the other hand, the data also indicate that significant numbers of students are falling off the
graduation track in the sixth grade and that schools may need to provide different types of
supports for different sets of students during the entry year of the middle grades. This finding, in
turn, greatly complicates school management and resource needs.
We also found evidence to support our theory that the combination of the onset of
adolescence and the beginning of a new organizational form of schooling, in the context of high-
poverty neighborhoods, brings its own set of risk factors which lead to large numbers of students
becoming increasingly and critically disengaged from schooling. This can been seen in
overriding importance of attendance and behavior as both independent factors and powerful
Preventing Student Disengagement 22
covariates in identifying students with high odds of falling off the graduation track. 92% of the
sixth graders with one or more of the high yield indicators had poor attendance or a poor final
behavior grade, as either their only indicator or in combination with course failure in either
mathematics of English/Reading. In addition, 81% percent of the students who failed math and
83% percent of the students who failed English had either poor attendance or an unsatisfactory
final behavior grade as a covariate. Finally, students who failed math or English and also had
poor attendance or a poor final behavior grade failed to graduate in even higher percentages than
students who just failed one of these courses. Also significant is the fact that receiving a poor
final behavior mark from a classroom teacher is as significant a predictor of falling off the
graduation track as receiving an out of school suspension (given no other indicators). This
suggests, that even mild levels of disconnect between perceived or actual behavior and the
behavioral norms expected by middle grade teachers in high poverty schools can increase a
student’s odds of not graduating. Thus, it is not just major infractions like fighting but also
sustained mild misbehaviors like not paying attention, not completing assignments, or talking
back in class which indicate critical levels of student disengagement.
The most significant finding, we believe, is that our results show that early manifestation
of academic and behavioral problems at the start of the middle grades do not self-correct, at least
within the context of middle grade schools that serve high-poverty populations. A common
response to students who struggle in sixth grade is to wait and “hope they grow out of it,” to
attribute early struggles to the natural commotion of early adolescence and experiencing new
organizational structures of schooling. Our evidence clearly indicates, that at least in high-
poverty schools, students who are missing 20% or more school, receiving poor behavior marks
or failing math or English in 6th grade do not recover. On the contrary, they drop out. This says
Preventing Student Disengagement 23
that early intervention is not only productive but is absolutely essential. Without it, these
students will not succeed.
Part B: Designing an Effective Prevention and Intervention Program to Keep Middle Grade
Students on the Graduation Track
The large numbers of students who fall of the graduation track early in the middle grades
clearly require substantial and sustained supports to become engaged in schooling and
successfully pass their courses. The paramount influence of attendance and behavior on pushing
early adolescences off the graduation track indicates something which seems apparent on face
value but is often overlooked in attempts to reform low performing middle grade schools.
Students need to attend school regularly, behave and try to succeed in school.
In an attempt to design an effective prevention and intervention program we undertook a
four stage process. First, we used survey data for six high poverty middle schools to examine the
factors which influence behavior, attendance, and effort. Second, we examined the impact the
existing Talent Development Middle Grades model had on keeping middle grade students on the
graduation track. Third, we searched the literature for evidence of effective behavioral,
attendance, and course failure interventions. Finally, we put all these elements together to
develop a comprehensive prevention and intervention program which we are currently piloting in
two high poverty middle grade schools.
What School Reforms Influence Attendance, Behavior, and Effort in High Poverty Middle Grade
Schools?
In order to gain a better understanding of what school factors influenced student
attendance, behavior, and effort, we analyzed survey items (focused on students’ perceptions of
mathematics and their mathematics classrooms and teachers) that we had previously collected in
Preventing Student Disengagement 24
Philadelphia. Our survey data include observations for 2,334 5th to 8th grade students from 6
representative high-poverty high-minority middle schools in the school district. The data was
observed during the 2002-03 school year. Nineteen survey items were used to measure the five
major concepts which had been linked to either student motivation or academic achievement in
the middle grades. They were, teacher support (how well students felt supported and encouraged
to succeed as well as the extent to which they believed their teachers cared about them) ,
academic press (the extent to which students felt both teachers and peers expected them to work
hard and do their best) , parental involvement (how often parents helped with homework and the
degree to which they felt welcome in the school), utility (the extent to which students believed
that the mathematics they were studying would be useful in life), and intrinsic interest (the extent
to which students found mathematics classes interesting and exciting). One additional survey
item, asking students how hard they worked in math class that year, was used to measure their
Effort. Finally, a measure of classroom quality, High Learning Growth Math Section, was
measured as a dummy variable, where any section whose students averaged an NCE gain from
fall to spring that was greater than 5 (on the TerraNova Mathematics Test) were coded ‘1’.
To best model the dynamic relationships between these variables and student
achievement, we used Structural Equation Modeling (SEM) and Confirmatory Factor Analysis
(CFA) (Bollen, 1989; Long, 1983; Mueller, 1996). SEM and CFA allow us to first model the
relationships between our survey items and the larger unobserved latent concepts for which we
hypothesize them to be indicators, and then second to test the relationships between the latent
factors and our other measures of student engagement and academic performance.
We found that Academic Press had a large effect upon student behavior, Utility was the
strongest determinant of student effort, and that Parental Involvement and Intrinsic Interest had
Preventing Student Disengagement 25
significant effects upon both students’ level of effort and their attendance (Balfanz and Byrnes
2006). While Teacher Support did not have a significant effect upon any of the student outcomes
we examined, this may be due to its very high correlations with the other latent factors.
Overall, we found that our latent factors were very important in determining student
engagement. But that different factors impacted the different elements of attendance, behavior,
and effort. Our findings strongly support the use of comprehensive school reforms that attempt to
improve student engagement through many mutually supporting mechanisms. A singular focus
on any one lever can lead to some level of engagement gains, but it is only when all related
factors addressed in a systematic and integrated manner that all of the factors which signal
disengagement and push students off the graduation track are addressed.
Impact of Talent Development Middle Grades Comprehensive Whole School Reform Model on
Keeping Middle Grade Students on the Graduation Track
The Talent Development Middle Grades (TDMG) model combines research-based
instructional programs in the core academic subjects (mathematics, English/reading, science, and
history) with extensive teacher training and support (e.g. in-classroom coaching) to enable
implementation of more active and engaging pedagogies. It also provides targeted extra help
through elective replacement mathematics and reading labs which students take in addition to
their regular mathematics or English course in lieu of an elective. The extra-help labs are
designed to both close skill and knowledge gaps and to preview upcoming classroom instruction
so students are better able to understand the new material they are being taught. Evaluations of
the instructional programs and extra-help labs have shown that they significantly improve
student achievement when implemented with reasonable fidelity (Balfanz & Byrnes, 2006;
Balfanz, Mac Iver, & Byrnes, 2006; Herlihy & Kemple, 2004, 2005; Mac Iver, Balfanz, & Plank,
Preventing Student Disengagement 26
1998; Mac Iver, Balfanz, Ruby, Byrnes, Lorentz, & Jones, 2004; Mac Iver, Ruby, Balfanz, &
Byrnes, 2003; Mac Iver, Ruby, Balfanz, Jones, Sion, Garriott, & Byrnes, in press; Ruby, in press,
2006). In addition to its strong instructional programs and intensive teacher support, the TDMG
model also helps schools make organizational changes which increase the communal nature of
schooling. Combinations of small learning communities, teacher teams, and vertical looping are
used to create learning environments where students and teachers come to know and care about
one another (Balfanz, Ruby, & Mac Iver, 2002)
Given what we had learned about the predictors which indicate that middle grade
students are falling off the graduation track and how attendance, behavior, and effort are
influenced by academic press, intrinsic interest, utility, and parental involvement, we surmised
that the TDMG model, with its focus on effective and engaging instruction, substantial extra-
help, and a communal nature of schooling, may serve as effective counter to at least some of the
forces pushing students off the path graduation. We tested our hypothesis by comparing the
extent to which the first cohort of students in three TDMG schools to experience the Talent
Development reforms during all of their middle school years experience the big 4 indicators
compared to matched comparison schools and the extent to which the TDMG students ultimately
graduated at high rates (Byrnes & MacIver, 2006). We found that middle grade students in the
TDMG schools did have lower rates of poor attendance (11% of the TDMG students versus 18%
of the control students were poor attenders) and lower course failure rates (3% of the TDMG
students vs. 15% of the control students failed math; 6% of the TDMG students versus 9% of the
control students failed English). However, TDMG and control students had similar rates of poor
behavior. We also found that students in TDMG schools had significantly higher graduation
rates; across the three pairs of schools TDMG students outgraduated control students by eleven
Preventing Student Disengagement 27
percentage points. Furthermore, a multivariate binary logistic model controlling for race, gender,
special education and English Language Learner status found that students who attended a
TDMG school for three years (in 6th, 7th and 8th grade) were 55% more likely to graduate on time
than were control students.
Search for Effective Interventions for Behavior and Attendance
Although, the existing TDMG model has a significant impact on keeping middle grade
students on the graduation track, our experience working in a wide range of high poverty middle
grade schools, as well as the analytic work on the high yield predictors indicated that additional
interventions specifically focusing on improving behavior and attendance needed to be woven
into the model. Fortunately, our search for effective interventions revealed that while the fields
of attendance and behavior interventions are not well developed, particularly in the secondary
grades, there are interventions with solid research bases and evidence of effectiveness. In both
areas, a common set of strategies have been found effective. First, positive behavior and good
attendance is constantly recognized, modeled, and promoted. Second, the first absence or
incident of misbehavior brings a consistent response. Third, simple data collection and analysis
tools are developed which enable teachers and administrators to identify when, where, and which
students misbehave or do not attend. Fourth, attendance and behavior teams composed of
teachers, administrators, counselors and sometimes parents regularly meet to analyze the data
and devise solutions. Individually-targeted efforts are undertaken to understand why certain
students are unresponsive, continuing to misbehave or not attend despite the positive incentives
and recognition. Effective strategies in reaching an unresponsive student typically requires
assigning a specific adult, usually one of the student’s main teachers, with the responsibility of
shepherding the student (i.e., building a closer, more personal relationship with the student,
Preventing Student Disengagement 28
checking-in daily with the student and giving that student immediate feedback). If the student is
a chronic poor attender, this shepherding might include calling the student each day the student is
absent to communicate that the student was missed and to ask the reason for the non-attendance.
If the student has behavior problems, the shepherding might involve asking each of the student’s
teachers to complete a simple behavioral checklist and then checking at the end of the day how
the student did. If these modest shepherding efforts do not succeed, then it is time to seek even
more intensive, individualized, and clinical interventions often involving one-on-one services
from helping professionals. Fortunately, simple shepherding has been found to be implementable
by teachers and schools (though not without the struggles involved in implementing anything
new) and has been shown to make significant impacts on improving attendance and behavior
(Horner, Sugai, Todd, & Lewis Palmer, 2005, Crone, Horner, & Hawken 2004, Reid, 2000).
Implicit in this and explicit in the prevention literature is a three stage model that
involves: 1) school-wide reforms aimed at alleviating 75% or so of the problem behaviors
(including poor attendance), 2) individually-targeted shepherding efforts for the 15 to 20% of
students who need additional supports beyond the school-wide reforms and 3) intensive efforts
involving specialists (counselors, social workers, etc.) for the 5 to 10% of students who need
more clinical types of supports.
Next Step: Putting it All Together to Develop a Comprehensive Intervention and Prevention Plan
to Keep Middle Grade Students on the Graduation Track
Currently we are taking all that we have learned from the analytic work on the high yield
indicators, the Talent Development Middle Grades model, and the literature on improving
attendance and behavior to develop and pilot a comprehensive approach to keeping middle grade
students on the graduation track. Table 12 details the interventions we are putting in place for
Preventing Student Disengagement 29
attendance, behavior and course failure in the sixth grade in two high poverty middle schools.
All of these interventions have proven to be individually effective. What we need to find out is
their cumulative and collective impact: What percent of students who absent intervention would
fall of the graduation track can be kept on track through the implementation of the
comprehensive set of interventions? Over the next several years, we will extend the supports to
the 7th and 8th grade and then compare the number of students with a high yield indicator (bad
behavior, poor attendance, and course failure) to numbers in prior years and, more importantly,
monitor improvements in the percent of students staying on track to graduation.
Conclusion
We began our investigation by asking, to what extent do students become disengaged
from schooling in significant numbers at the start of the middle grades (6th grade), and does this
disengagement have a negative impact on the likelihood that middle grade students will
eventually graduate? Unfortunately, at least in middle grade schools which serve high poverty
populations, we found that the answer to both questions was yes. In fact, our analytic work on
high yield predictors showed that four simple factors -- attending less then 80% of the time,
receiving a poor final behavior grade, failing math or failing English in sixth grade -- could
identify close to half of the students who would ultimately fail to graduate from the school
district. Combined with on-track predictor work that has recently been done in the 8th and 9th
grade (Allensworth, 2005, Allensworth & Easton, 2005; Neild & Balfanz, 2006b), it has become
clear that the vast majority of dropouts, at least in large cities, are highly identifiable and
predictable before they have entered or spent much time in high school. This work also indicates
that student disengagement and course failures are the proximate causes of students falling off
the graduation track. Our analysis of the factors which influence attendance, behavior, and effort;
Preventing Student Disengagement 30
the evidence of the positive impacts of the Talent Development Middle and High School models;
and the promising results of our search for effective behavioral and attendance interventions all
suggest that many of these dropouts are preventable. Early identification of students who, absent
sustained intervention, will fall off the graduation track, combined with effective whole school
reforms, and research-based and practice-validated attendance, behavioral, and extra help
interventions provide a promising path for confronting the nation’s graduate rate crisis head on.
Preventing Student Disengagement 31
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Preventing Student Disengagement 37
Table 1
Elementary and Middle-Grades Attendance in Selected High-Poverty Neighborhoods in
Baltimore, 2003-04
High-Poverty
Neighborhood
Percent of Elementary
Students (Grades 1-5)
Missing 20+ Days
Percent of Middle-Grades
Students Missing 20+
Days
Clifton-Berea 15% 46%
Greenmount 15% 50%
Madison 21% 65%
Midway 6% 55%
Park Heights 17% 57%
Note: The source of these data is the Baltimore Neighborhood Indicators Alliance.
Preventing Student Disengagement 38
Table 2
How Well Did Sixth Graders who Failed Math in 1997 (n=1801) Stay on Track to Graduate in
2003 (on time) or in 2004 (one year late)?
Year
% Who were in … ‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04
6th grade 17
7th grade 66 20 <1 <1
8th grade 5 59 18 1
9th grade 6 58 44 16 4 1
10th grade 1 24 20 9 1
11th grade 1 16 8 2
12th grade 1 19 8
% of students who actually graduated 13
on
time
6
one year
late
Cumulative % of students who left the
District 12 15 23 30 47 61 75
Note. The percentage of 1997 6th-graders failing math who were in the expected grade in eachfollowing year is in bold.
Preventing Student Disengagement 39
Table 3
How Well Did Sixth Graders who Failed English in 1997 (n=1409) Stay on Track to Graduate in
2003 (on time) or in 2004 (one year late)?
Year
% Who were in … ‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04
6th grade 20
7th grade 60 22 <1 <1
8th grade 5 56 20 1
9th grade 6 55 43 16 5 1
10th grade <1 2 23 19 9 2
11th grade 1 14 8 2
12th grade 1 16 9
% of students who actually graduated 12
on
time
6
one year
late
Cumulative % of students who left the
District 14 15 23 32 50 62 74
Note. The percentage of 1997 6th-graders failing English who were in the expected grade in eachfollowing year is in bold.
Preventing Student Disengagement 40
Table 4
How Did the Percentage of Sixth-Graders from 1996-1997 Who Would Entered 12th Grade On
Time (In 2002-2003) Vary Depending Upon Students’ End-of –Fifth-Grade Scores in
Mathematics and Reading on the Pennsylvania System of School Assessment (PSSA)?
Student’s Statewide
Percentile on the PSSA
Math Reading
1st-10th 40% 39%
11th-20th 49% 50%
21st-30th 52% 53%
31st-40th 55% 58%
41st-50th 60% 59%
51st-60th 63% 59%
61st-70th 61% 61%
71st-80th 66% 62%
81st-90th 61% 65%
91st-99th 68% 63%
Preventing Student Disengagement 41
Table 5
How Well Did Low Attenders Who Were Sixth-Graders in 1997 (n=1934) Stay on Track to
Graduate in 2003 (on time) or in 2004 (one year late)?
Year
% Who were in … ‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04
6th grade 8
7th grade 70 11 <1 <1
8th grade 6 64 10 <1
9th grade 6 60 36 12 3 1
10th grade 2 25 15 6 1
11th grade <1 1 15 4 1
12th grade <1 1 17 5
% of students who actually graduated 13
on
time
4
one year
late
Cumulative % of students who left the
District 16 19 28 37 57 69 79
Note. The percentage of 1997 6th-grade low attenders who were in the expected grade in each
following year is in bold. Low attenders were students attending 80% or less as sixth-graders.
Preventing Student Disengagement 42
Table 6
How Well Did Poorly-Behaving Sixth-Graders in 1997 (n=4893) Stay on Track to Graduate in
2003 (on time) or in 2004 (one year late)?
Year
% Who were in … ‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04
6th grade 6
7th grade 80 10 <1 <1
8th grade 3 74 10 <1
9th grade 4 69 34 12 3 1
10th grade <1 1 38 20 7 1
11th grade <1 1 27 7 2
12th grade <1 1 31 8
% of students who actually graduated 24
on
time
5
one year
late
Cumulative % of students who left the
District 10 12 19 26 40 52 64
Note. The percentage of 1997 6th-graders receiving a final poor behavior mark who were in the
expected grade in each following year is in bold.
Preventing Student Disengagement 43
Table 7
Percent of Sixth-Graders Graduating On Time and One Year Late by Course Failure and Poor
Behavior Combinations
Course Failure/Behavior
Combinations
On-Time Grads One-Year-Late Grads
Fail Reading in Sixth Grade
Good Behavior in All
Classes (n=176) 14% 7%
Poor Behavior in Any Class
(n=725) 6% 5%
Poor Behavior in Reading
(n=450) 6% 5%
Fail Mathematics in Sixth Grade
Good Behavior in All
Classes (n=298) 16% 8%
Poor Behavior in Any Class
(n=1006) 8% 5%
Poor Behavior in Math
(n=625) 6% 4%
Preventing Student Disengagement 44
Table 8
Graduation Rates for 6th Graders with Different Numbers of High Yield Predictive Indicators
Number of Risk
Factors
N Percent Who Graduate On Time or One Year Late from the
District
0 6265 56%
1 3498 36%
2 1329 21%
3 619 13%
4 326 7%
1 or More Risk Factors 5772 29%
Preventing Student Disengagement 45
Table 9
Big Four indicator combinations and counts for all 12,037 6th grade students in the School
District of Philadelphia in 1996-1997
Risk Category N
Sixth graders with no indicators (N-= 6265, 52%)
Sixth graders with one or more indicators(N = 5772, 48%)
Sixth graders with 1 indicator (N = 3498, 29%)
Attendance 524
Behavior 2577
Fail Math 255
Fail English 142
Sixth graders with 2 indicators (N = 1329, 11%)
Attendance and Behavior 367
Attendance and Math 63
Attendance and English 53
Behavior and Math 449
Behavior and English 304
Math and English 93
Sixth graders with 3 indicators (N = 619, 5%)
Att. + Beh. + Math 142
Att. + Beh. + Engl. 95
Beh. + Math +Engl. 307
Att. + Math + Engl. 75
Sixth graders with all four indicators (N= 326, 3%)
Preventing Student Disengagement 46
Table 10
How Well Did Prepared and Engaged Sixth Graders from 1997 (n=604) Stay on Track to
Graduate in 2003 (on time) or in 2004 (one year late)?
Year
% Who were in … ‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04
6th grade
7th grade 93 <1
8th grade <1 90 <1
9th grade <1 81 3 1 <1
10th grade <1 <1 77 4 1
11th grade <1 72 2 1
12th grade <1 <1 73 1
% of students who actually graduated 70
on
time
1
one year
late
Cumulative % of students who left the
District 7 10 18 20 23 24 28
Note. The percentage of Prepared and Engaged 6th-graders from 1997 (students who attend 90%
or more of the time, have excellent behavior marks, pass math and English, and score at or above
Proficient on the 5th Grade PSSA) who were in the expected grade in each following year is in
bold.
Preventing Student Disengagement 47
Table 11
Logistic account of factors associated with high school completion for 1996-1997 sixth graders
in the School District of Philadelphia
Predictor Odds RatioParameter
EstimateStandard Error p-Value
High Yield Indicators
Poor Attendance
(<80%)0.318 -1.1447 0.0730 <.0001
Bad Behavior 0.435 -0.8316 0.0449 <.0001
Fail Math 0.459 -0.7780 0.0738 <.0001
Fail English 0.577 -0.5493 0.0821 <.0001
Race
Asian 0.893 -0.1133 0.1000 0.2562
White 0.794 -0.2309 0.0554 <.0001
Hispanic 0.692 -0.3684 0.0671 <.0001
Other 0.537 -0.6216 0.4627 0.1790
Preventing Student Disengagement 48
Table 12Comprehensive Plan for Keeping Middle Grades Students on the Graduation Track
Focus of interventionType of Intervention Attendance Behavior Course Failures
School-Wide (AllStudents)
Every AbsenceBrings a Response
Create Culturewhich saysAttending EverydayMatters
Positive SocialIncentives for GoodAttendance
Data tracking atteacher team level
Teach, Model,Expect GoodBehavior
Positive SocialIncentives andrecognition forGood Behavior
Advisory
Research BasedInstructionalPrograms
In-Classroomimplementationsupport to enableactive and engagingpedagogies
Targeted (15-20%of Students
2 or moreunexcused absencesin a month bringsBrief Daily CheckBy an Adult
Attendance TeamInvestigates andproblem solves, whyisn’t studentattending(teacher, counselor,administrator,parent)
2 or more officereferrals bringsinvolvement ofBehavior Team
Simple behaviorchecklist broughtfrom class to classchecked each day byan adult
Mentor assigned
ElectiveReplacement ExtraHelp Courses-tightly linked tocore curriculum,preview upcominglessons, fill inknowledge gaps
Targeted ReducedClass Size forstudents whosefailure is rooted insocial-emotionalissues
Intensive (5-10% ofStudents)
Sustained one onone attention andproblem solving
Bring in appropriatesocial service orcommunity supports
In-depth BehavioralAssessment-why isstudent misbehaving
Behavior contractswith familyinvolvement
Bring in appropriatesocial service orcommunity supports
One on OneTutoring