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Data-driven school improvement: making the grade for students experiencing homelessness

Data-driven school improvement: making the grade for students experiencing homelessness. Heather Denny Montana OPI State Homeless Coordinator DJ QuirinMai Montana OPI Federal Programs Data Analyst. Which Students are Homeless?.

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Data-driven school improvement: making the grade for students experiencing homelessness

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  1. Data-driven school improvement: making the grade for students experiencing homelessness Heather Denny Montana OPI State Homeless Coordinator DJ QuirinMai Montana OPI Federal Programs Data Analyst

  2. Which Students are Homeless? • Students may be identified as homeless if they are living in any of the following situations – • In a shelter – domestic violence, emergency homeless shelter, transitional housing, etc. • Doubled-Up – living with friends or family members due to loss of housing related to a natural disaster (fire/flood), domestic violence, lack of affordable housing, job loss, or other financial crisis • Hotel or motel – because they cannot afford housing • Unsheltered – this includes living in a tent/RV, sleeping in a place not intended to be a home (barn, storage facility, etc) or living in substandard housing

  3. Consolidated State Performance Report (CSPR) • State and District Level Data that is reported to the USED annually • EdFacts Data Files and CSPR • Compare your district to similarly sized districts with similar demographics • Westeros v. Shannara v. Narnia • Do we have more or fewer homeless students? • Do our students score higher or lower on state tests?

  4. District level data • Westeros Public School District • Total Enrollment = 16,545 • Poverty Data – free/reduced lunch data, but also look at your community poverty data (shelters, food banks, soup kitchens, etc.) • Estimated Homeless – 10% of children on free/reduced lunch • Actual Homeless count • Cohort graduation/dropout rates (new federal requirement under ESSA)

  5. Compare schools • Feeder patterns are a good way to go • Community characteristics Winterfell HS Dragonstone HS Casterly Rock HS

  6. Elementary data

  7. MS and HS Data

  8. Drop-Out Data - cohort

  9. Graduation Rate – Cohort

  10. School level trends • Demographics of students experiencing homelessness • Gender/Ethnicity • Grade level • Unaccompanied Status • IEP/504 • EL/Migrant • OSS/ISS/Behavior Issues • Absences/Tardies • Mobility – in-district, out-of-district, # of moves in high school, # of total moves • Compare highly mobile populations: homeless – military – foster – migrant

  11. Focus on Test scores • Are all students identified as homeless accessing Title I services? • Is there an achievement gap between homeless and low income students? • Our test is the ACT – do students take the test seriously if they do not think they college bound? • Test scheduling – are we scheduling the test when families have run out of SNAP (Food Stamp) benefits? • Studies from Duke and the University of Wisconsin show that student test scores are lower when families have run out of SNAP benefits.

  12. Focus on Grade levels • Absences/tardies in elementary school – setting habits for a lifetime, can we intervene with family engagement strategies? • Family engagement to help parents understand why attendance is important in the early grades • Transitions – from school to school and grade to grade • Head Start/PK – kindergarten • Elementary to middle • Middle to HS • HS to college/career

  13. Focus on Staff • Track schedules of students identified as homeless • Do you see trends with attendance/tardies • Can we reschedule core classes? • Do we need to check on transportation? • Which period of the day are they missing? • Look at teachers: Are students identified as homeless failing the same class/teacher? • Does the teacher need professional development or support? • Do students need access to computer labs, supplies, etc.? • Classroom climate – are students identified as homeless struggling because of other student personalities (bullying/harassment issues) • Scheduling • Are we putting students identified as homeless in classes they can’t pass? And why can’t they? • Are they all failing classes that are required to graduate?

  14. Tracking student’s progress

  15. CTE as an intervention

  16. Youth Risk Behavior Survey Data Statewide the reported rate of homelessness was almost 5%, in Montana. In some “urban” districts in Montana it was as high as 10% of all students who responded to the survey. 2017 Youth Risk Behavior Survey Special Report: Homelessness

  17. Risk Factors for Montana Students Self-Reporting as Homeless *Data from the 2016-17 Youth Risk Behavior Survey (YRBS) for Montana

  18. Building a Culturally responsive program • Create a collaborative relationship with the community (tribal education, elders, community leaders, etc.) • Selecting the right person to be the homeless liaison • Examine your own cultural bias • Renaming the program to fit within the culture of the community āissṗoōmmoǒtsiiyō•ṗ "we help each other" FIT program – Browning’s new program

  19. Resources • Montana OPI’s Homeless Education Page • Montana OPI’s Homeless Student Data Dashboard • National Center for Homeless Education – www.nche.ed.gov • School House Connection – www.schoolhouseconnection.org • National Association for the Education of Homeless Children and Youth – www.naehcy.org

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