THE WISCONSIN EARLY CHILDHOOD LONGITUDINAL DATA SYSTEM (WI EC-LDS) PROJECT Briefing for Department of Children and Families December 12, 2011

 Background ◦ What is an EC-LDS? ◦ What can an EC-LDS do?  The WI EC-LDS Project ◦ What have we done so far? ◦ Project objectives  Next Steps—get involved! ◦ Data Round Table ◦ Data Systems Survey  Questions/Discussion

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 Are children, birth to 5, on track to succeed when they enter school and beyond?  Which children and families are and are not being served by which programs/services?  Which children have access to high-quality early childhood programs and services?  What characteristics of programs are associated with positive child outcomes for which children?  What are the education and economic returns on early childhood investments?

 Governor’s Early Childhood Advisory Council ◦ 2010 Wisconsin Early Childhood System Assessment Report reported: “While the state collects many types of data related to early childhood, we don’t have the capacity to connect it, track children’s progress, or use it to assess the system.”  Key Objective for : ◦ Create a comprehensive longitudinal data system to track child outcomes and improve decision-making Background

 Collect and maintain detailed, high-quality child-, staff-, and program-level data  Link these data to one another across entities (collections or data warehouses), over time  Enable the data to be accessible through reporting and analysis tools

 WI Act 59 (2009) ◦ Requires establishment of a P-20 longitudinal data system (LDS)  3 federal grants awarded to WI-Department of Public Instruction (DPI) ◦ US Department of Education LDS Grant Program ◦ Latest grant includes funding to develop a high quality plan for incorporating early childhood data

 A comprehensive data warehouse storing student and school data from a variety of sources  Links to post-secondary data  A security application (Access Manager) that ensures only authorized personnel view confidential data  Secured reporting tools; e.g., Multi-Dimensional Analytic Tool (MDAT) that allow authorized users to analyze and provide access to data, including student records  Public reporting on WI Information Network for Successful Schools (WINSS) and in School Performance Reports  Professional development

 32-point jump in readiness ◦ 81% of kindergarteners fully school-ready, up from 49% in and 78% last year. Source: Maryland State Department of Education

 Major increases among African- American & Hispanic children 76% of African- American kindergarteners fully school-ready in , up from 37% in % of Hispanic children are now fully school-ready, a 31-point readiness gain from

 DECE: ◦ Do children receiving WI Shares subsidies who attend higher quality child care (as designated by YoungStar) have better educational and health outcomes than those who attend lower quality child care?  DFES: ◦ Do children of families who receive TANF benefits fare better in school than children in poor families who do not participate in TANF? ◦ Do they receive more preventative health services?  DSP: ◦ How do infants and toddlers in foster care fare when they enter school? ◦ Is participation in prevention programs such as home visiting associated with better educational outcomes?  DES: ◦ How can we improve data sharing methodologies between departments? ◦ How can we leverage technology advances from other data systems?

 EC-LDS Project Team ◦ DCF, DPI, DHS, DWD ◦ ECAC Steering Committee  Hired staff at DPI ◦ Project Coordinator, Carol Noddings Eichinger ◦ Data Analyst, June Fox  Project Charter ◦ Signed by DCF, DPI, DHS Administrators

 Analyze current early childhood data environment  Establish data sharing methodologies  Create a work plan to begin data sharing and analysis process  Develop strategies for data governance, long term system usage, and sustainability

 Are children, birth to 5, on track to succeed when they enter school and beyond?  Which children and families are and are not being served by which programs/services?  Which children have access to high-quality early childhood programs and services?  What characteristics of programs are associated with positive child outcomes for which children?  What are the education and economic returns on early childhood investments? Key Policy Questions

◦ Subsidized Child Care (WI Shares, YoungStar) ◦ Licensed Child Care ◦ Individuals with Disability Education Act: (IDEA) Part B and Part C ◦ Individual Student Identifier System (DPI) ◦ Head Start/Early Head Start ◦ Home Visiting ◦ Health (immunization, Vital Records, etc) ◦ Tribal Health Data Collection ◦ AFDC/TANF (CARES) ◦ Child Support (KIDS) ◦ SNAP/Food Stamps (CARES) ◦ Child Protective Services (WiSACWIS) ◦ Medicaid/BadgerCare (CARES) ◦ Workforce and Corrections data Existing Data Sources

1. Unique statewide child identifier 2. Child-level demographic and participation information 3. Child-level data on child development 4. Link child-level data with K-12 and other key programs 5. Unique program identifier to link with children and workforce 6. Program site structural and quality information 7. Unique EC workforce identifier to link with sites and children 8. Individual-level data on EC workforce demographic, education and professional development information 9. Transparent privacy protection and security practices and policies 10. State governance body to manage data collection and use Fundamental Data Components

 High quality information about young children and the services they receive  Ability to measure children’s progress across programs and over time  Ability to document which services are effective for which children and target resources accordingly  Increased cross-agency collaboration and communication  Increased accountability

 Bring together diverse group of EC stakeholders  Facilitated by national EC-LDS experts  Proposed Goals ◦ Provide information and garner buy-in ◦ Make recommendations re: data governance ◦ Create/review communication plan ◦ Draft underlying policy questions ◦ Begin to align data elements to policy questions ◦ Identify next steps

 June Fox, EC-LDS Data Analyst  Objectives ◦ Identify what data elements are collected by which systems ◦ Gather data dictionaries ◦ Explore inter-operability and potential data linkages ◦ Identify data gaps

 Who should attend the February data round table? ◦ ~10 people per department ◦ Mix of executive, program, high-level data people  Who can provide June with information about your current data systems and data elements? ◦ Who knows the nitty-gritty details about your systems? ◦ How is data collected and accessed? ◦ Existing data connections?  Hilary will send out a follow-up

“The simple act of describing something can galvanize action. What gets counted gets noticed. What gets noticed, gets done.” --Glenn Fujiura, University of Illinois

 Rod Packard, DPI, LDS Project Director ◦  Carol Noddings Eichinger, EC-LDS Project Coordinator ◦  June Fox, EC-LDS Data Analyst ◦  Hilary Shager (DCF), EC-LDS Project Team Member ◦  Jane Penner-Hoppe (DCF), EC-LDS Project Team, ECAC Steering Committee ◦  Coral Manning (DCF), EC-LDS Project Team Member ◦ Contacts: