How LEA’s Use Data to Inform Practice:  The Opportunities for and Challenges to Use In Schools and...
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How LEA’s Use Data to Inform Practice: The Opportunities for and Challenges to Use In Schools and Districts Ellen B. Mandinach CNA and REL Appalachia October, 2009. Why is Data-Driven Decision Making Important. Mandates from the new administration. NCLB continues.

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Why is Data-Driven Decision Making Important

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Why is data driven decision making important

How LEA’s Use Data to Inform Practice: The Opportunities for and Challenges to Use In Schools and DistrictsEllen B. MandinachCNA and REL AppalachiaOctober, 2009

Why is data driven decision making important

Why is Data-Driven Decision Making Important

  • Mandates from the new administration.

  • NCLB continues.

  • The context of accountability and compliance.

  • The shift to continuous improvement.

  • The realities of classrooms and schools.

Key quotes

Key Quotes

  • “I am a believer in the power of data to drive our decisions. Data gives us the roadmap to reform. It tells us where we are, where we need to go, and who is most at risk.” (Duncan, 2009)

  • “Our best teachers today are using real time data in ways that would have been unimaginable just five years ago. They need to know how well their students are performing. They want to know exactly what they need to do to teach and how to teach it.” (Duncan, 2009)

  • “Data and data analyses are powerful tools that must be used to improve schools.” (Easton, 2009)

Why is data driven decision making important

Information is the key to holding schools accountable for improved performance every year among every student group. Data is our best management tool. I often say that what gets measured, gets done. Once we know the contours of the problem, and who is affected, we can put forward a solution. Teachers can adjust lesson plans. Administrators can evaluate curricula. Data can inform decision making. Thanks to No Child Left Behind, we’re no longer flying blind.Secretary Margaret Spellings (2005)

Philosophical transformation

Philosophical Transformation

  • Now - Data for continuous improvement.

  • Past - Data for accountability and compliance.

  • But this means some fundamental shifts in data systems, data collection, and data use……

Old and new

Old and New

  • What is not new: Data-driven decision making. Educators have been using data for a long time.

  • What was new: The coupling of data and accountability for compliance.

  • What is new: Discussions around data for continuous improvement and helping students to succeed, not just schools attaining AYP.

  • Yet there is a further distinction…..

Accountability from berliner 2007 and petrides 2006

Accountabilityfrom Berliner (2007) and Petrides (2006)

  • Assessments of learning.

    • Provides data on what and how much students have learned.

  • Assessments for learning.

    • Provides data to help improve teaching and student learning.

    • And we all know the kind of accountability depicted here…..

The emerging curriculum

The Emerging Curriculum

The disconnect state assessments from stecher and hamilton 2006

The Disconnect: State Assessmentsfrom Stecher and Hamilton (2006)

  • Features of state assessments used for accountability limit their utility for data-driven decision making.

  • They present a conflict of data use for accountability as opposed to inform instructional practice.

  • As depicted in yet another classic cartoon…..

The disconnect

The Disconnect

Why now the good the bad and the ugly

Why Now?: The Good the Bad, and the Ugly

  • Technological Advances.

    • State-wide longitudinal data systems.

    • Data warehouses and SIS.

    • Accountability systems such as Grow and NYStart.

    • Handhelds.

  • The Proliferation of Data.

  • Human Capacity or Lack Thereof.

Why is data driven decision making important

To Understand How States, Districts, and Schools Use Data and Technology Solutions to Create Data Cultures

Components of a conceptual framework for data driven decision making

Components of a Conceptual Framework for Data-Driven Decision Making

Data continuum

Data Continuum

  • Data - exist in a raw state without meaning.

  • Information - data given meaning in context.

  • Knowledge - collection of information deemed useful to guide action.

Decision making feedback loop

Decision Making Feedback Loop

  • Make a Decision.

  • Implement the Action.

  • Determine the Action’s Impact.

  • Feedback for Additional Decision Making Actions.

  • Here’s what the framework looks like…..

Conceptual framework for dddm

Conceptual Framework for DDDM

  • Click to edit Master text styles

    • Second level

      • Third level

        • Fourth level

          • Fifth level

John easton s data cycle

John Easton’s Data Cycle

  • Identify problems.

  • Identify solutions to problems.

  • Provide continuous monitoring.

  • Target research to address the problem.

  • Another feedback loop

The chops challenges to and opportunities for dddm

The CHOPS: Challenges to and Opportunities for DDDM

  • To answer pressing questions.

  • To address programmatic questions, of “Does it work?”.

  • To provide data according to state and federal mandates.

  • To provide compliance and accountability data.

  • To get the right data to support continuous improvement for student learning.

  • To increase awareness and gain a clear dynamic depiction of what is happening, not a static snapshot.

  • To improve teaching and learning.

Easton s cycle of data

Easton’s Cycle of Data

The goal for dddm

The Goal for DDDM

  • How do we move teachers, schools, districts, and states from being data-rich but information poor to using data and transforming them into usable knowledge?

    • Adequate and targeted professional development.

    • Technological infrastructure and tools.

    • Determining what are the right data elements and collecting them BEFORE someone asks, Does it work?

    • Having the right data.

    • Explicit vision.

    • Explicit need.

    • SUPPORT.

A new and valuable resource

A New and Valuable Resource

  • IES Practice Guide from the What Works Clearinghouse

  • Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.



  • Recommendations

  • Action Steps

  • Roadblocks

  • Vetted References (with caveats)

Recommendation 1

Recommendation 1

  • Make data part of an ongoing cycle of instructional improvement.

    • Collect and prepare a variety of data about student learning.

    • Interpret data and develop hypotheses about how to improve student learning.

    • Modify instruction to test hypotheses and increase student learning.

Recommendation 2

Recommendation 2

  • Teach students to examine their own data and set learning goals.

    • Explain expectations and assessment criteria.

    • Provide feedback to students that is timely, specific, well formatted, and constructive.

    • Provide tools that help students learn from feedback.

    • Use students’ data analyses to guide instructional changes.

Recommendation 3

Recommendation 3

  • Establish a clear vision for school-wide data use.

    • Establish a schoolwide data team that sets the tone for ongoing data use.

    • Define critical teaching and learning concepts.

    • Develop a written plan that articulates activities, roles, and responsibilities.

    • Provide ongoing data leadership.

Recommendation 4

Recommendation 4

  • Provide supports that foster a data-driven culture within the school.

    • Designate a school-based facilitator who meets with teacher teams to discuss data.

    • Dedicate structured time for staff collaboration.

    • Provide targeted professional development regularly.

Recommendation 5

Recommendation 5

  • Develop and maintain a district-wide data system.

    • Increase a variety of stakeholders in selecting a data system.

    • Clearly articulate system requirements relative to user needs.

    • Determine whether to build or buy the data system.

    • Plan and stage the implementation of the data system.

A valuable resource a national survey

A Valuable Resource: A National Survey

Means, B., Padilla, C., & Gallagher, L. (in press). Use of education data at the local level: From accountability to instructional improvement. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.

Another resource an edited volume

Another Resource: An Edited Volume

Mandinach, E. B., & Honey, M. (2008). Data-driven school improvement: Linking data and learning. New York: Teachers College Press.

Final comments

Final Comments

  • The time is ripe. The funds for the SLDSs. A Secretary of Education who believes in data. The support of IES and NCES. All of this is unprecedented.

  • Given the CHOPS, the OP’s far outweigh the CH’s. It will not be easy. It will be labor intensive and costly. There needs to be a vision and a commitment across all levels of the education system.

  • This is about helping all students, regardless of their learning strengths and weaknesses, to learn, achieve, and become self-sustaining, lifelong learners.

  • This is a MUST, not an if or when. The time is now.

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