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THE WISCONSIN EARLY CHILDHOOD LONGITUDINAL DATA SYSTEM (WI EC-LDS) PROJECT
Briefing for Department of Children and FamiliesDecember 12, 2011
Presentation Overview
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
3
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
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 2011-12:◦ Create a comprehensive longitudinal data system to
track child outcomes and improve decision-making
Background
What can a comprehensive early childhood longitudinal data system do?
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
Foundation upon which to build
Components of DPI’s Current LDS 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
Other States: Maryland
32-point jump in readiness◦ 81% of
kindergarteners fully school-ready, up from 49% in 2001-2002 and 78% last year.
Source: Maryland State Department of Education
Other States: Maryland Major increases
among African-American & Hispanic children
• 76% of African-American kindergarteners fully school-ready in 2010-2011, up from 37% in 2001-2002
• 70% of Hispanic children are now fully school-ready, a 31-point readiness gain from 2001-2002
Other States: Rhode Island
Potential DCF Questions 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?
The WI EC-LDS: First Steps
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
Project Charter Objectives
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 information3. Child-level data on child development4. Link child-level data with K-12 and other key programs5. Unique program identifier to link with children and workforce6. Program site structural and quality information7. Unique EC workforce identifier to link with sites and children8. Individual-level data on EC workforce demographic,
education and professional development information9. Transparent privacy protection and security practices and
policies10. State governance body to manage data collection and use
Fundamental Data Components
Expected Outcomes 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
Next Steps: Data Round Table
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
Next Steps: Data Systems Survey
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
What we need from you...
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 email
“The simple act of describing something can galvanize action. What gets counted gets noticed. What gets noticed, gets done.”
--Glenn Fujiura, University of Illinois
Questions/Discussion
Rod Packard, DPI, LDS Project Director◦ [email protected]
Carol Noddings Eichinger, EC-LDS Project Coordinator◦ [email protected]
June Fox, EC-LDS Data Analyst◦ [email protected]
Hilary Shager (DCF), EC-LDS Project Team Member◦ [email protected]
Jane Penner-Hoppe (DCF), EC-LDS Project Team, ECAC Steering Committee◦ [email protected]
Coral Manning (DCF), EC-LDS Project Team Member◦ [email protected]
Contacts:
