THE WISCONSIN EARLY CHILDHOOD LONGITUDINAL DATA SYSTEM (WI EC-LDS) PROJECT Briefing for Department...

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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◦ Rod.Packard@dpi.wi.gov

Carol Noddings Eichinger, EC-LDS Project Coordinator◦ Carol.Eichinger@dpi.wi.gov

June Fox, EC-LDS Data Analyst◦ June.Fox@dpi.wi.gov

Hilary Shager (DCF), EC-LDS Project Team Member◦ Hilary.Shager@wisconsin.gov

Jane Penner-Hoppe (DCF), EC-LDS Project Team, ECAC Steering Committee◦ Jane.PennerHoppe@wi.gov

Coral Manning (DCF), EC-LDS Project Team Member◦ Coral.Manning@wisconsin.gov

Contacts:

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