Transcript The Impact of Student Mobility on Urban Districts in Massachusetts Mary Jo Rossetti, Somerville School Committee Tony Pierantozzi, Superintendent Somerville Public Schools Mary M.

The Impact
of
Student Mobility
on
Urban Districts in Massachusetts
Mary Jo Rossetti, Somerville School Committee
Tony Pierantozzi, Superintendent Somerville Public Schools
Mary M. Bourque, Superintendent Chelsea Public Schools
April 23, 2012
10:15-11:30 AM
Boston Convention & Exhibition Center, 212
1
What do you know about student mobility?
• On the post-its in front of you, please write
down all that you know about student
mobility.
• Small group share-out.
• So what do we know? Whole group share
out.
2
By the end of today’s session we
will have discussed the:
• Problem
• Causes
• Consequences
• Future
3
Student Mobility
• Student mobility is the constant flow of students enrolling
in and transferring out of a school or school district
throughout the school year. High student mobility
negatively impacts the learning of both the mobile and
non-mobile students as well as the larger school
community.
• The constant state of flux caused by high rates of
student mobility in urban schools prevents schools from
providing consistent and coherent instruction to both the
mobile and the non-mobile student populations—Russell
Rumberger refers to schools with high student mobility
as functioning in a setting of pervasive chaos (2003).
4
Student Mobility:
The Context of the Problem
Education reform initiatives, including the federal
legislation, No Child Left Behind (2002),
implicitly assume “all students will attend a
specific school consistently enough that the
school can make a difference in their
achievement” (Kerbow, 1996, p. 1).
The traditional kindergarten to grade 12 education
does not exist for many students in our country,
particularly in our urban areas.
5
The Problem
•
Absence of a common language used to describe the problem.
•
Lack of standard formula consistently used for calculation.
•
Non-urban school districts with stabile student populations do not collect
and analyze student mobility data.
•
Weak understanding of the cumulative and longitudinal nature of the
problem.
•
Failure of education policy makers to recognize student mobility as a factor
impacting education.
•
Students enter a school at a different point in the curriculum –academic
gaps emerge—or students enter only to repeat a curriculum topic/objective.
6
Prior Research
• Researchers have found that highly mobile students are
more likely to have :
–
–
–
–
–
lower academic performance and achievement
be at risk for dropping out
exhibit behavior problems and disrupted peer relationships
be retained or fail a grade
suffer varying degrees of psychological and social adjustment
difficulties
– Overtime schools may have more new students than stabile
students.
– Schools experience “curriculum lag.”
(Rumberger, 2002; Rumberger, Larson, & Palardy, 1999;
Alexander, Entwisle, & Dauber, 1996; Kerbow, 1996; GAO,
1994).
7
Potential impact on schools:
– higher teacher turnover
– lack of curriculum coherence
– lags in curriculum pacing
– increased fiscal responsibilities
(Hartman, 2006; Hirsch, 2006; Kerbow, 1996).
8
Research Questions
Part I: Massachusetts
1) What is the extent of student mobility in urban and
non-urban school districts in Massachusetts?
2) What is the relationship among student mobility.
resources, and academic achievement.
3) What is the comparative magnitude of student
mobility; what are the socio-demographic
characteristics of the mobile student population, and
what are the student achievement outcomes in
urban and non-urban school districts in relation to
student mobility?
9
Student Mobility in Massachusetts
• Student Mobility in MA
– 10 % (2005-2006, 9-month)
– 15% (2005-2006, 12-month)
– 11% (2006-2007, 9-month)
• There is a spectrum of student mobility in school districts across MA
of some magnitude.
• Identify and name the categories on the spectrum
– Level I: Mobile (0-9.9 percent)
– Level II: Highly mobile (10-19.9 percent)
– Level III: Hypermobile (20 percent).
• The majority of urban school districts have mobility rates
that place them in Level II and Level III compared to nonurban school districts. The data also suggest a range of
student mobility magnitude within each Level: high,
middle, and low.
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12-month student mobility rates
October 1st -September 30th
• As expected, the 12-month student mobility rates among all school
districts in the study document a significant increase in student moves
as compared to the nine-month student mobility calculation (more
evident in urban).
• Across the state the majority of school moves for all school districts
take place during June, July, and August.
– Urban: 52% during the school year, 48% during June, July, and
August.
– Non-urban: 28% during the school year, 72% during June, July,
and August.
• Movement of students during the school year is more indicative
of the unplanned moves that result in the pervasive disruptions
to classroom instruction and thereby impact the entire education11
community.
Urban vs. Non-Urban:
A spectrum of urbanicity
• Data suggest a redefinition of the urban school district to
include a spectrum of five urbanicity factors that include
student mobility.
• School districts were categorized on the spectrum:
– High urban (enrollment 5000 and four factors)
– Moderate urban (enrollment 5000 and three factors)
– Urban tendencies (enrollment 5000, 4000, 3000, 2500, and two
factors).
Factors
1) minimum student population of 5000
2) 30 percent low income
3) 20 percent FLNE
4) 10 percent LEP
5) high student mobility (Level II or above)
12
Accountability
• Two years of data show non-urban districts in the State
with consistently higher Composite Performance Index
(CPI) scores than urban school districts at each grade
level and in each academic area tested.
• The urban school districts with lower overall CPIs are
categorized as Level II (highly mobile) and Level III
(hypermobile) school districts.
• Urban districts have higher student populations identified
as low-income, Limited English Proficient, and First
Language Not English (FLNE).
13
Student Mobility
and
Academic Achievement
In general, for the two years reviewed, student
mobility is a high correlate to student
populations who are:
– LEP (.78 and .82);
– FLNE (.77 and .79);
– LOWINC (.92)
– Student mobility was not found to be a strong
correlate to special education eligibility.
– % rental units (.77 and .82)
14
Implications
• These results suggest that student mobility be
considered a reliable predictor of lower
academic achievement in a manner comparable
to low income.
• Implications from these results suggest that
Massachusetts educators and policy makers
alike should look at student mobility and the
ways Massachusetts serves the mobile child in
order to improve academic outcomes.
15
Causes and Consequences from
District Superintendents:
Urban and Non-Urban
Urban
• Housing, employment,
poverty, immigration
• Immigrant versus urban
migrant (revolving door)
• Obstacle for all
• Cost (intervention and
support)
• Broad impact
• Sense of urgency
• Non-Urban
• Employment-timed
moves and for better job
opportunity
• Obstacle for mobile
student
• Not a major issue
• Gaps in learning (student
by student basis)
16
Research Questions
Part II: Chelsea, MA
3) What is the extent of student mobility in the
Chelsea Public Schools?
4) What are the patterns and likely causes of
student mobility in the Chelsea Public
Schools?
5) Is student mobility related to student
achievement?
6) To what extent does student mobility impact
17
the classroom and the school?
Part II: Chelsea, MA
Chelsea’s data suggest that the mobile student population is
overwhelmingly complex exhibiting multiple at-risk factors. The
mobile student population in Chelsea is overwhelmingly:
Extent
Chelsea 9 months
• Mobility rate
• LEP
• FLNE
• LOWINC
05-06
25.4
28.4
76.1
73.7
06-07
19.9
31.1
78.8
69.2
07-08
17.8
24.7
81.4
76.7
08-09
21.5
24.5
79.0
71.3
09-10
16.1
24.5
79.0
64.3
10-11
18.7
27.7
77.5
84.9
Chelsea 12 months
• Mobility rate
• LEP
• FLNE
• LOWINC
05-06
35.1
31.1
78.7
82.3
06-07
32.7
30.6
80.8
76.3
07-08
29.8
27.6
83.3
71.3
08-09
33.6
25.4
80.6
73.2
09-10
26.6
31.4
76.9
67.8
10-11
32.6
31.6
74.7
80.5
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Where from?
Immigrant and Urban Migrant
• 622 students entered the CPS in grades 1-12
from February 1, 2011 through January 31,
2012.
•
•
•
•
28 countries
24 states
37 MA communities (clusters of urban sharing)
91 re-enrollments including 27 from charter schools and 14
from parochial schools.
This data set did not indicate the number of moves in a
student’s educational career.
August, September, and January were the three highest
months for student registration.
19
Where to?
Immigrant and Urban Migrant
• 786 students transferred out of the CPS in
grades 1-12 from February 1, 2011 through
January 31, 2012.
• 15 countries/outside contiguous US (PR*)
• 23 states
• 45 MA communities (clusters of urban sharing)
Chelsea’s mobile student population can be categorized as
highly mobile (3-5 moves K-12), hypermobile (6 or more
moves k-12), and frequently mobile (from grade 3, one move
for each year of schooling).
20
Causes
• Results from staff completion the open response questionnaires
(N=90) five themes pertaining to the causes of student mobility in
Chelsea
• Economic and poverty
• Immigration and binationality
• Family dynamics
• Community
• Upward mobility
• Results from parent registration (N=683) and parents transfer survey
(N=615) suggest the causes for frequent moves as housing,
employment, and over one-third of the parents report that they were
joining friends and family already living in the school district.
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Consequences
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Lag in curriculum progression (Kerbow’s flattening of the curriculum).
New students enter with lower academic ability and skill development.
New student enter at a different point in the curriculum
Frequent metaphors: revolving door, educating a moving target,
students always needing to catch-up, having to constantly doubleback.
Being made to feel that educators are trying to use student mobility as an
excuse for low student performance.
The feeling that nobody is listening to educator’s instructional needs.
Administrative costs.
Accountability is negatively impacted.
New teachers find it overwhelming to teach in a school district with a high
rate of student mobility.
Program measurement is difficult and often inaccurate.
Conflicting instructional philosophies that do not apply to a highly
mobile student population: whole class grouping—differentiated
instruction.
Social-emotional and behavioral issues of highly mobile students.
Link to dropping out-the cumulative disillusionment and disengagement.
Reluctance of parents/guardians to become involved.
Educators’ sense of frustration, loss, and sadness.
22
Mobile and Non-Mobile Student Interviews
Interviews of the mobile and non-mobile students support previous findings on
the social-emotional impact of high student mobility for a school community.
Mobile Student (N=18)
Non-Mobile Student (N=17)
•
•
•
•
• Hardest part is introducing
yourself over and over
• Best part is making new
friends (evidence to the
contrary)
• The non-mobile students tend
to make friends with other nonmobile students.
• New students just appear
• Behavioral issues of new
students
Hardest part not knowing anyone
Easiest part is a fresh-start
Feel their grades are good.
Hard to engage in the life of the
school.
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Student Longevity
[Frustration is] not being able to see the growth in your students even as
they move on to older grades (Jennifer O’Brien, Language Teacher,
John Silber Early Learning Center).
• Of the kindergarten cohort of 1992-1993, only 15.8
percent graduated thirteen years later as the Class
of 2005.
– 1993-1994—13.8 percent (Class of 2006)
– 1994-1995—15.5 percent (Class of 2007)
– 1995-1996—15.4 percent (Class of 2008)
– 1996-1997—15.6 percent (Class of 2009)
– 1997-1998—16.2 percent (Class of 2010)
– 1998-1999—16.5 percent (Class of 2011)
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Academic Achievement
and Student Mobility in Chelsea
Public Schools
1)
Is there a difference in the academic achievement of the mobile
student compared to the non-mobile student?
•
Two consecutive years of Composite Performance Indices (CPIs) for Chelsea students
show a significant gap between CPI scores of the non-mobile and the mobile student
cohort.
•
T-Tests were used to determine the difference between means of mobile student MCAS
scaled scores and non-mobile student MCAS scaled scores. Two-tailed independentsamples t-tests were conducted for two cohorts 2006 and 2007. The tests were
significant (p<.05) for each grade and for each year compared. Results show that the
mean for non-mobile Chelsea student performance was significantly higher than the
mean for mobile student performance in both mathematics and ELA and at each grade
level.
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2) Do students who move out of the school district tend to be the
higher performing students compared to the academic
performance of the students enrolling or entering the school
district?
• For both Mathematics and ELA for 2006 and 2007, on average,
the students who left the school district had higher aggregate
CPI scores than the new students entering during the
timeframe under review. The difference was found most
prevalent at the elementary and middle grades. The annual loss
of higher achieving students only to be replaced by lower
achieving students tends to lower aggregate student
performance scores and unfairly holds urban school districts
accountable for societal conditions that are not within its
control.
26
3) Is there a relationship between the length of time a student attends
the Chelsea Public Schools (student longevity) and academic
achievement?
• A one-way analysis of variance (ANOVA) evaluated the
relationship between student academic achievement and length
of time in the Chelsea Public Schools. In all four analyses,
results of the one-way ANOVA showed students with higher
achievement had a longer percent o time in the school district;
the percent of time spent in Chelsea differs significantly by
achievement categories. Students in the Warning/Failing
category, on average, spent significantly lower percent of time
in the Chelsea schools than did students in all other MCAS
categories.
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From the mobile student:
(Student #29)
Interviewer: So you have moved 20 times since kindergarten.
Mobile student: Or more.
Interviewer: Or more. Tell me some of the communities that you have
lived in [without a moment of hesitation, the young woman rattles off
a list that leaves me speechless].
Mobile student: Harrisburg, PA; Syracuse, NY; Bronx, NY; East Boston,
MA; Chelsea, MA now; New Bedford, MA; Stoughton, MA; Medford,
MA;
Interviewer: Somerville?
Mobile student: Somerville.
Interviewer: What is the primary reason for moving do you think?
Mobile student: My mom.
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Other Findings
Social/Emotional Impact
on the Non-mobile Students
(Student #24)
Interviewer: What is the worst part about having
new students enter your class?
Non-mobile student: I think just adapting to the fact
that there is another person in there to kinda
take up the teacher’s time sometimes or even
just that they have to catch-up so the class gets
behind on certain occasions.
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(Student #24)
Interviewer: And throughout your years in
the Chelsea Public Schools have you ever
had close friends move away?
Non-mobile student: Ya, I have.
Interviewer: How many?
Non-mobile: A couple, more than a handful
actually. Sometimes it is hard because,
like, you want to spend your high school
career with them and you want to share
your memories with them…
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Part III: Somerville, MA
Somerville’s data also suggest that the mobile student population is
overwhelmingly complex exhibiting multiple at-risk factors. The mobile
student population in Somerville is overwhelmingly:
Extent
Somerville 9 months
• Mobility rate
• LEP
• FLNE
• LOWINC
05-06
16.1
28.1
20.7
17.3
06-07
20.2
36.8
22.8
22.3
07-08
18.5
29.1
19.7
19.4
08-09
17.8
28.6
19.3
18.3
09-10
17.2
30.4
18.8
17.5
10-11
17.3
26.6
17.5
15.7
Somerville 12 months
• Mobility rate
• LEP
• FLNE
• LOWINC
05-06
41.6
66.0
49.4
44.7
06-07
39.5
64.6
44.0
40.5
07-08
37.5
62.0
39.9
38.1
08-09
37.3
63.1
39.0
36.8
09-10
40.8
70.3
42.6
35.4
10-11
40.5
64.2
41.0
33.9
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Part IV: Conclusions
• Student mobility exists.
• Student mobility matters.
• Student mobility is a strong predictor of academic
outcomes comparable with lower student achievement
that is traditionally attributed to other socio-demographic
characteristics, including poverty.
• The negative impact of student mobility is ameliorated at
the local, state, and national policy levels –
32
Part V: Recommendations
and
Policy Implications
Federal
•
Formally recognize highly mobile students
as an at-risk subpopulation in predominantly
urban schools.
•
NEAP collection of data on number of
moves
•
Fund research and help to identify the most
effective practices and programs for mobile
student populations.
•
Provide competitive grants to fund
innovative strategies implemented and
successfully addressing the problem in
schools.
State
•
Include student mobility as an indicator in
defining “high-risk” students.
•
Design a virtual School for high risk
students.
•
Recognize the need for increased and
sustainable funding streams for highly
mobile students.
School
•
Sense of urgency
•
Parent/guardian workshops
•
Extend attendance at schools until a natural
break in the school year
•
Track annual and longitudinal student
mobility
•
Urban clusters
District
•
Systemic registration
and placement
•
Framework of diagnostic assessments—and
intervention programming
•
High school graduation credit recovery
online.
•
Innovative pathways toward high school
diploma.
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Future Research
• Relationship between student mobility and special
education eligibility.
• Relationship between student mobility and dropping out
of school.
• Relationship between student mobility and parent
involvement.
• Relationship between student mobility and effective
school improvement initiatives and instructional
practices.
• Models that work (including DoD schools).
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QUESTIONS?
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