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SLDS State Support Team Webinar

SLDS Webinar

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Early Childhood Data Use Part III

Introduction to Early Childhood

Data UsePart III

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PanelistsAvisia Whiteman (Minnesota)Casey Hanson (Missouri)Dr. Lori Bland (George Mason University)

Agenda• Review of early childhood data use - Plan & Create

phases from the first two webinars• Introduction to using early childhood data use –

Support phase• Learn about examples from other states creating

early childhood products and use• Share ideas about future possibilities of using EC

Data• Questions from participantsSLDS Webinar

Welcome

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What does it mean to effectively use early

childhood data?

Early Childhood Data Use

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Data Use Framework

Mission and Goals

• What is the point?

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Review: PLAN

Identification and prioritization of users

• Who are we serving?

Identification of uses

• What types of decisions and/or actions will the system inform?

Stakeholder Engagement

• How do we involve those whom we intend to serve?

Products/Resources

• What types of products and resources will the SLDS generate?

Delivery

• How will you deliver data to key users?

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Review: CREATE

User Support

• How will users know how to use the system?

• How will users understand the data provided by the system?

• How will users know what to do with the data provided by the system?

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Data Use Framework: SUPPORT

Evolution and Sustainability

• How do we continue to support users and their needs as they expand and evolve?

• How do we make the system an essential resource for users?

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Data Use Framework: SUPPORT (continued)

• How do we ensure we have the resources to continue meeting users’ needs?

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Data Use Framework

Mission & Goals

Identification & Prioritization of Users

Identification of Uses Stakeholder

Engagement

Products/Resources

User SupportEvolution & Sustainability

Delivery

How will users know how to use the system? How do we continue to support users and their needs as they expand and evolve?

Data Use: Support Strategies

Minnesota Example Missouri Example

• Provide group-based and one-on-one training, wiki

• Work with early adopters to revise and refine to provide more support and value for others

• System built by relationships• Ask users to help prioritize

improvements

• Engagement in process from Day 1

• Training & technical assistance• Development and automation

of data collection, analysis• Working to institutionalize

roles and responsibilities

How will users understand the data provided by the system?

Data Use: Support Strategies

Minnesota Example Missouri Example

• Constant communication • Ideally, the core group of

users will involve a person from each agency across program areas to ensure deep understanding

• Minnesota is standardizing data entry through a Minnesota manual for source system element codes and report standards

• High level of detail and explanation in pilot reports, but time intensive

• This question is the struggle for continuing to fund analysis o Automationo Who can have access?

How will users know what to do with the data provided by the system? How do we ensure we have the resources to continue meeting users’ needs?

Data Use: Support Strategies

Minnesota Example Missouri Example

• Data Analysis Plan prior to coding

• Provide examples of how other agencies have used it, ideally indexed to standard federal reporting and common local needs

• Work with user group to prioritize the largest impact improvements

• Head Start ownership of data and analysiso Data Element and Protocol

workgroupo Governance committee

• Annual revisiting

How do we make the system an essential resource for users (sustainable)?

Minnesota Example Missouri Example

• Data Analysis Plan identifying opportunities to support core initiatives within Head Start

• Provide reports as a way to intensify relationships with partners

• For example, do children with high needs really attend full day kindergarten after Head Start?

• Open dialogue with partners, programs, and practitionerso Eventually, parents

and policymakers• Continued adaptation

and development

Data Use: Support Strategies

Research has typically:• been conducted in K-12• focused on use of student achievement data, in this

order1. state-level accountability tests2. district-level benchmark tests3. classroom assessment

• focused on use of specific technological tools (fewer studies)

• been conducted with, in this order1. special education teachers (response to

intervention)2. classroom teachers3. leadership (principals and superintendents (but

in broader context))

Applications of principles can be made to Early Childhood.

Understanding the Context for the Research

What does research say about how users of the data (state

administrators, policy makers, program administrators) understand

the data provided by the data system?

Research in Practice

• Data is complex and multi-layered: • build understanding of relationships

across data.

• Use is influenced by user understanding and social interactions among users: • build understanding with organizational

groups.

• Data use isn’t and shouldn’t be linear:• build understanding of data use as an

iterative and recursive process.

User Understanding of Data & Data Use

What does the research say about whether users know what to do with

the data provided by the data system?

Research in Practice

• Incorporation of micro-level (classroom) data is key because macro-level data (state) is less useful.

• Specific feedback from data impacts learning.

• Skill drill should not be the only focus for data use – because development of conceptual understanding and deep learning is lost.

• User anxiety about DDDM affects efficacy for using data tools and understanding the data which, in turn, affects collaboration about data use and ability to internalize DDDM as a regular practice.

User Use of Data

What does the research say about how we continue to support users

and their needs as they expand and evolve?

Research in Practice

• Leader at meso-level (organization) is a key player to model data use, but typically lacks training:• Cultivate leadership.

• DDDM + instructional coaching within an organization influences practice and increases outcomes: • Cultivate a coaching culture.

• Use is based on data skills, data use skills, and content understanding of users: • Cultivate expertise development in context/content.

Continuous Support of Users

• Use is influenced by the context of the decision and/or content area in which the decision is made (i.e., literacy data and decisions are different than those for numeracy).o Construct sessions to build efficacy for data

skills, data understandings, and suggestions for use

o To do this, use modeling, elaboration, and feedback.

o Cultivate collaborative work groups within organizations.

o Focus workshops on one context/content area.o Identify key problems for users based on data

analysis.o Bring in content experts to help with solutions.o Disseminate easily understood, colorful 1-2

page flyer with visualized data, problem identified by the data, solutions to the problem, and implications for decisions.

Continuous Support of Users(continued)

What does the research say about decision making resulting from data

use?

• Focus on continuous organizational or implementer improvement, rather than on summative judgment.

• Focus on underlying processes related to client outcomes--focus decisions that improve learning rather than test scores.

• Use programs that have at least good evidence of effectiveness (See IES What Works Clearinghouse.)

• Capacity for use is necessary, but not sufficient:o Start with goals, then align data to goalso Embed within systemic changeo Create common language and cultureo Develop multiple structures/layers for decision-

making• Translate Numbers into Meaning• Translate Meaning into Action

For Effective Decision Making

• Biesta, 2007.• Bowers, 2009.• Datnow, Park, &

Kennedy-Lewis, 2012.• Dunn, Airola, Lo, &

Garrison, 2013.• Ikemoto & Marsh,

2007.• Luo, 2008.• Mandinach, 2012.• Mandinach, Honey, &

Light, 2006.

Sources• Marsh, McCombs, &

Martorell, 2010.• Park & Datnow,

2009.• Rogosa, 2005.• Slavin, et al., 2013.• Supovitz, 2009.• Swan & Mazur,

2011.• Wohlstetter,

Datnow, & Park, 2008.

• Wohlstetter, Park & Datnow, 2008.

• Wu, 2009.

SLDS Webinar 5-18-11 26

SLDS State Support Team Webinar

Questions or comments?

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State Support Team & SLDS Team: Missy Cochenour, (909) 241-4264Missy.cochenour@sst-slds.org Maddie Fromell, (202) 219-7117Maddie.fromell@ed.gov

Panelists:Casey Hanson, hansoncb@missouri.edu Avisia Whiteman, avisia.whiteman@state.mn.usDr. Lori Bland, lbland2@gmu.edu

SLDS Webinar

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