Coaching Data Inquiry to Close Achievement Gaps

Through CESAsFacilitated by:

WISExplore Team Members Mary Ann Hudziak, CESA 6

Lisa Arneson, CESA 3Jesse Harness, CSN Commissioner

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WISExplore

● Since 2012

● Funding--grant sources: GEARUP and SLDS

● State provided a new Longitudinal Data System for schools and districts called WISEdash.

● Why WISExplore? persistent gaps, lack of district- and school-level knowledge regarding effective data use,

● Future goals--to close achievement gaps

https://dpi.wi.gov/wisexplore 3

About the WISExplore Project

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Statewide Capacity Through Cooperative Educational Service

Agencies in Collaboration with the Wisconsin Department of Instruction

● 12 locally funded CESAs supported legislatively

● CESA Statewide Network (CSN) liaison between the Department of Public Instruction (DPI) and the 12 CESAs

● CSN statewide project funded through the DPI

● Staffed by CESA consultants who develop resources and training

● The goal is to develop CESA expertise to build statewide capacity in local districts

● Annual deliverables provide onsite coaching and skill-building support

● Opportunities are provided for CESAs to embed and expand the work locally

Focus

• Data Inquiry Process: Developing collaborative inquiry processes to inform improvement actions focused on achievement gaps

• WISEcoaching: Developing regional WISEcoaching capacity to support data-driven school improvement

• WISEsupport: Providing technical assistance and support for data accuracy

• Collaboration: Fostering collaborative relationships to embed inquiry throughout state initiatives

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Session Outcomes● Become aware of the WISExplore data inquiry process

● Learn how structured guided inquiry sharpens continual improvement actions through heightened focus on disparities among student groups, clarity in student data findings, as well as the powerful evidence of root causes in practice

● View stories from WISEcoaches about their experiences

● Demonstrate how the WISExplore project promotes collaboration across the state

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Data Inquiry Process

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8https://dataqualitycampaign.org/

Why Data Inquiry?

• Persistent achievement gaps exist statewide over the past 5 years for these students:• Economically Disadvantaged (low-income)

• Black, Hispanic and Native American students

• English Learners (ELs)

• Students with Disabilities

• Schools and educators that effectively use data to inform their practice are improving student achievement.

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Wisconsin’s Approach to Continuous Improvement

A focus on key ideas from school improvement research:

• Administration leading for equity,

• Schools using data to inform decision-making, and

• Evidence-based teaching and learning

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Statewide System of Support

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EQUITY

Empowering Environment

Effective Practices

Effective Implementation

EQUITY

This combination of practices, implementation, and environment is key to equity: improving student outcomes and closing gaps.

Improved student outcomes are integral in DPI’s goal of Every Child a Graduate, College & Career Ready.

EffectiveImplementation

Strategies to support effective practices.

Stage-based process that supports equity, incorporates data, and focuses on teaching and learning within MLSS.

EffectivePractices

Research supported practices within an equitable MLSS.

Focuses on the universal level.

Goal is to improve student outcomes and close equity gaps.

EmpoweringEnvironment

Supportive structures for implementationof effective practices to address equity gaps.

Resources and tools to support change.

Training and coaching through linked teams.

Sharpening the focus on equity through systematic inquiry and coaching support.

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WISExplore’s Data Inquiry ProcessFor Continuous Improvement

Continuous Improvement

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One process for many entry points into continuous improvement.

Inquiry is a critical component throughout continuous cycles of improvement

Improvement Cycles with Embedded Ongoing Inquiry

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ActApply adjustments in actions as determined from analyzing and interpreting the student and practices data studied to improve the process and outcomes

PlanIdentify specific improvement actions, barriers and necessary data inquiry to develop a blueprint for implementation and ongoing monitoring

StudyAnalyze both student and practices data to determine the degree to which intended outcomes are being achieved; clarify and communicate findings

DoFollow planned implementation of actions and data collection with fidelity

Hear from a WISEcoach. . .

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Chris Van Hoof, CESA 8Continuous Improvement

Critical Contexts for Data Inquiry

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Interconnected Data Inquiry Process

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Student data inquiry provides the pathway to inquiry and analysis of hypothetical root causes in practices.

The combination of student data inquiry and subsequent practices data inquiry provides an informed foundation for proposing priorities for improvement actions.

Student Data Inquiry Process

• Question• Investigate• Clarify• Hypothesize

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Although inquiry begins intentionality around critical questions about student achievement, the process is iterative and cyclical in that investigations lead to related additional questions and more data findings will be clarified. The steps help teams stay on track and avoid “bird walking” away from the original intended question. It is an adaptive process, rather than a technical one.

Purposeful Questioning

Purposeful student data inquiry begins with important questions about student performance within a critical context.

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Question Focus Attainment Growth

Big Picture QuestionsAll students ● School-wide, grade level,

classroomTo what levels of proficiency have

students achieved?

… currently?… in recent years?

What is the measured progress for these

students?

Is the progress adequate to reach the

attainment goal?

Equity Gap QuestionsComparisons between student groups● School-wide, grade level,

classroom

Content QuestionsComparisons among content skills● School-wide, grade level,

classroom

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Investigate Detailed Data

The WISEdash for Districts, a secure portal with additional dashboards, provides different data views and data at the student detail level.

Clarify

While observing the data captured, teams clarifythe data by …

• Analyzing data patterns and trends• Interpreting the data• Summarizing data findings• Prioritizing data according to criticality

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Summarize

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Data Finding for Improvement

In the middle school, for the past three years, the % of EcD students that reach proficiency on the ELA portion of the state assessment has been declining (from 23% in 2012 to 15% in 2015), whereas the % of non-EcD reaching proficiency has been increasing (42% in 2012 to 51% in 2015). The gap is increasing between EcD and non-EcD.

After observing, analyzing and interpreting data, teams collaborate to summarize what they found.

Prioritize Data Findings

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● What’s the risk for students if we don’t improve this data pattern?

● Will students’ futures be compromised?

● The highest priority data finding can be called a “problem statement”. (There would be only a very few (1-2) problem statements for improvement)

HIGH Priority

MEDIUM Priority

LOW Priority

Teams Hypothesize about Likely Root Causes

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• Step 1: Based on the critical student data findings, teams write a problem statement.

• Step 2: Teams generate hypotheses about likely root causes for the data problem.

• Step 3: Teams use the “Fishbone” process to organize selected hypotheses of practices into important categories.

• Step 4: Teams use a wheel of influence process to identify high leverage practices to investigate further.

Source: https://www.Openclipart.org

CATEGORIES OF LIKELY ROOT CAUSES

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Examples

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From Student Results Data to Practices Inquiry

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Practices Inquiry

1. QUESTION. Based on the most likely hypotheses of practices, teams ask - what evidence do we need about this practice that will inform our next steps?

2. INVESTIGATE. Based on the question(s), teams study relevant practices within the school, district and in the research to gather evidence.

3. CLARIFY. Teams identify and summarize findings from the investigation and prioritize practices for future actions.

4. PROPOSE. Teams propose a theory of action and plan for implementation. 28

Next Steps ...

Data Inquiry leads to …

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Hear from a WISEcoach . .

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Al Betry, CESA 9Inquiry

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WISEcoaching

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WISEcoaching Support

● Supported through the WISExplore Grant

● Each CESA identifies consulting staff○ At least two in

each of the 12 CESAs

○ Staff who are involved in school improvement

● To support area educators in effective data inquiry work

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WISEcoaches are provided ...

● Training○ Face-to-Face and Webinars○ Updates on state data tools (WISE

suite of tools)○ Inquiry process updates

● Guidance for local coachinginquiry projects

● Resources○ Templates○ Slides○ Guidance documents

● Networking opportunities and structure 34

WISEcoaching

Deliverables:Each WISEcoach submits their ongoing work through:

● Journal of their in-district coaching

● Log of events where the tools are embedded and shared (i.e. Network meetings, workshops, inter-agency training, contracted work, etc.)

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Value Add:● Embed inquiry in the

individual project work● Coordination throughout

all state initiatives● Embed and apply training

and data understanding for the in-district contracted coaching

● Reliable data experts across the State

Hear from a WISEcoach . .

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Billie Finco, CESA 4Coaching

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Collaboration

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Collaboration

• Wisconsin Department of Public Instruction• Wisconsin RtI Center• Wisconsin Center for Educational Research

(WCER)• Association for Wisconsin School

Administrators (AWSA)• Wisconsin Association for District

Administrators (WASDA)• Wisconsin Council of Administrators of

Special Services (WCASS)

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Collaboration Projects

• Implementation of Continuous Improvement Planning• Data Leadership Academy• Summit for Data-Informed Leadership• Data Leadership Tools

• WISExplore Data Inquiry Process• Self-Assessment of Data-Informed Leadership• Data Source Audit• PLC Inquiry Protocol• Inventory of Data Use• Practices Surveys for

Literacy and Math

40Source:

https://www.Openclipart.org

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For more information about WISExplore Inquiry Process or WISEcoaching, contact:

Mary Ann Hudziak: mhudziak@cesa6.orgLisa Arneson: larneson@cesa3.org

Jesse Harness (CSN): jyharness@gmail.com

For more information about WISEdash, contact

Melissa Straw at DPI: melissa.straw@dpi.wi.gov