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The Impact of Student Mobility on Urban Districts in Massachusetts

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

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The Impact of Student Mobility on Urban Districts in Massachusetts

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  1. 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

  2. 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.

  3. By the end of today’s session we will have discussed the: • Problem • Causes • Consequences • Future

  4. 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).

  5. 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.

  6. 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.

  7. 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).

  8. Potential impact on schools: • higher teacher turnover • lack of curriculum coherence • lags in curriculum pacing • increased fiscal responsibilities (Hartman, 2006; Hirsch, 2006; Kerbow, 1996).

  9. Research QuestionsPart I: Massachusetts • 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?

  10. 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 non-urban school districts. The data also suggest a range of student mobility magnitude within each Level: high, middle, and low.

  11. 12-month student mobility ratesOctober 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 education community.

  12. 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)

  13. 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).

  14. 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)

  15. 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.

  16. 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) Causes and Consequences from District Superintendents: Urban and Non-Urban

  17. Research QuestionsPart 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 the classroom and the school?

  18. 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 05-06 06-07 07-08 08-09 09-10 10-11 • Mobility rate 25.4 19.9 17.8 21.5 16.1 18.7 • LEP 28.4 31.1 24.7 24.5 24.5 27.7 • FLNE 76.1 78.8 81.4 79.0 79.0 77.5 • LOWINC 73.7 69.2 76.7 71.3 64.3 84.9 Chelsea 12 months 05-06 06-07 07-08 08-09 09-10 10-11 • Mobility rate 35.1 32.7 29.8 33.6 26.6 32.6 • LEP 31.1 30.6 27.6 25.4 31.4 31.6 • FLNE 78.7 80.8 83.3 80.6 76.9 74.7 • LOWINC 82.3 76.3 71.3 73.2 67.8 80.5

  19. 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.

  20. 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).

  21. 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.

  22. 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 double-back. • 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.

  23. Mobile Student (N=18) 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. 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 non-mobile students. New students just appear Behavioral issues of new students 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.

  24. 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)

  25. Academic Achievementand Student Mobility in Chelsea Public Schools • 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 independent-samples 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.

  26. 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.

  27. 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.

  28. 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.

  29. Other FindingsSocial/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.

  30. (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…

  31. 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 05-06 06-07 07-08 08-09 09-10 10-11 • Mobility rate 16.1 20.2 18.5 17.8 17.2 17.3 • LEP 28.1 36.8 29.1 28.6 30.4 26.6 • FLNE 20.7 22.8 19.7 19.3 18.8 17.5 • LOWINC 17.3 22.3 19.4 18.3 17.5 15.7 Somerville 12 months 05-06 06-07 07-08 08-09 09-10 10-11 • Mobility rate 41.6 39.5 37.5 37.3 40.8 40.5 • LEP 66.0 64.6 62.0 63.1 70.3 64.2 • FLNE 49.4 44.0 39.9 39.0 42.6 41.0 • LOWINC 44.7 40.5 38.1 36.8 35.4 33.9

  32. 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 –

  33. Part V: Recommendations andPolicy 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.

  34. 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).

  35. QUESTIONS?

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