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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]

mailto:[email protected]


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 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


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


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.


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


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


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?


(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


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


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.


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.


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


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.


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


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.


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


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


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.


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,


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


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


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


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


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,


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


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,


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


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;


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.


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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.


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.


Table 3

How Well Did Sixth Graders who Failed English in 1997 (n=1409) Stay on Track to Graduate in


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


on

time

6

one year

late


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.


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%


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


on

time

4

one year

late


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.


Table 6

How Well Did Poorly-Behaving Sixth-Graders in 1997 (n=4893) Stay on Track to Graduate in


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


on

time

5

one year

late


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.


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%


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%


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%)


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


on

time

1

one year

late


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.


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


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