Highlights from CRDC and Other Data Systems: Key Takeaways and - - PowerPoint PPT Presentation

Samantha Murphy – Allegheny County Department of Human Services Cheryl Kleiman – Education Law Center

Highlights from CRDC and Other Data Systems: Key Takeaways and Emerging Issues

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Civil Rights Data Collection: What is it?

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Collected by U.S. Dept of Education’s Office of Civil Rights

• Since 2011, collected from every school, every other school year
• A collection of data on key education and civil rights issues in public schools
• Uniquely disaggregated - race, gender, disability (IDEA & 504), English-learner

Includes

• Student enrollment and demographics
• Program and curriculum offerings
• School Climate (discipline, restraints, bullying, law enforcement)
• Staff and Resources

How and Why its Used?

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Part of Multi-Level Strategy to Combat Discrimination

– Used in investigations of discrimination complaints – Determine level of compliance to civil rights laws – Inform guidance and policy

Important tools for schools, parents, and advocates

– Helps identify issues – Data as evidence – Building power through transparency and accountability

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HIGH LEVEL DISCIPLINE REPORT Additional Reports

• Suspension and Expulsion
• School Days Missed Due to OSS
• Transfer to Alternative Schools

Restraints and Seclusion

QUESTIONS TO ASK

• Most striking?
• Disproportionality?
• By race, gender,

disability?

• Previous year’s

data?

• Statewide data?
• Correlation

between funding or policy decisions?

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Data Sharing with School Districts

• MOU I

– one way data sharing, the school district to DHS

• MOU II

– DHS can share data back with school districts – Child Welfare placement, Homelessness, “at risk” flag

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School Data in DHS’ Data Warehouse

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Human Service at a DHS Partner District

39% 36% 43% 28% 24% 33% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% District-wide Elementary School High School

Human Service Involvement, DHS Partner District

Prior Involvement (2002 to present) Involved in 2016-17

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Detailed School District Involvement

16% 4% 18% 2% 4% 1% 20% 4% 3% 1% 12% 1% 4% <1% 14% 2% 0% 5% 10% 15% 20% 25% Child Welfare Child Welfare Placement Mental Health Drug & Alcohol Homeless & Housing Supports Assisted Housing Public Benefits (SNAP/TANF) Juvenile Probation (Ages 10+)

Human Service Involvement by Program Area – District-level

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Academic Performance – Chronic Absence

19% 17% 28% 13% 24% 10% 13% 0% 5% 10% 15% 20% 25% 30% Any Human Service Any DHS Service Child Welfare Mental Health Public Benefits

Chronic Absence* by Service Type - 2016-17

Involved with Human Services in 2016-17 Never Involved with Human Services All District Students

*Students were chronically absent if they missed 10% or more (about 18 days) of days enrolled in school. This includes excused absences, unexcused absences, and out-of-school suspensions.

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Responding to national priorities

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Example: Multi-year comparisons

• Examined educational outcomes for youth in Child Welfare placement

(24 hour substitute care, foster home, shelter, group home, respite)

• Attendance
• Includes data from 6 local school districts
• Compared 2016-17 findings to 2013-14
• How did school attendance outcomes change for youth post-placement?
• Examined outcomes for 663 students from 2013-14
• Examined outcomes for 695 students from 2016-17

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Example: Multi-year comparisons

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Attendance Trends for Youth in Placement, 2013/14 vs. 2016/17 2013/14 (N=663) 2016/17 (N=695) Post-Placement Attendance Improvement 62% 67% Chronic Absence Rate* 38% 31%

• * Students were chronically absent if they missed 10% or more of days enrolled in school.
• In 2016-17, the chronic absence rate for the 6 partner districts included was 27%.
• Compared to 2013-14, the 2016-17 cohort of youth in placement was more likely to have a post-

placement improvement to attend, and less likely to be chronically absent.

How Data-sharing has been Used

• Grants and Proposals

– Proposal for school-based mental health units – RFP for innovative usage of school data

• Projects

– Afterschool program targeting youth with high standardized test scores but poor attendance/GPA

• General internal reporting and research

– School discipline trends – Racial disproportionality – Geographic disproportionality

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Themes and Takeaways

• 1. Discrimination Persists

– Discipline, Restraints, Referrals to Law Enforcement – Look at disproportionality as well as raw numbers – Interventions need to be race, gender, and disability specific

• 2. Limitations of Data

– Can be incomplete and inconsistent – More disaggregated than most, but still gaps – View in tandem with other data sources

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Themes and Takeaways

• 3. Data Provides Opportunities for Improvement

– Be informed – your school, your district, your state – Available to parents, community, and advocates – Regular review by district leadership – models available

CHERYL KLEIMAN, ESQ. Education Law Center ckleiman@elc-pa.org 412-258-2124

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For More Data-Sharing Information:

Samantha Murphy, Resource Services Manager and Education Liaison: samantha.murphy@alleghenycounty.us Sanjeev Baidyaroy, Data Analyst: sanjeev.baidyaroy@alleghenycounty.us

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