Data Driven Decision Making and The Data Coach

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Data Driven Decision Making and The Data Coach

• JANE COOK
• LITERACY TECHNOLOGY COACH, EASTCONN
• BETH MCCAFFERY
• SCHOOL IMPROVEMENT COORDINATOR, LEARN

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Purpose

• To highlight characteristics of high quality
  coaching practices and review the roles of a
  Data Coach
• To review the coaching process and learn tools
  to use as a Data Coach to improve Data Driven
  Decision Making (DDDM)
• To develop an action plan for implementing data
  coaching practices to support DDDM

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Objectives

• Participants will
• Identify the roles and responsibilities of a
  coach and effective models for coaching.
• Apply coaching behaviors that influence best
  practices.
• Examine the research on coaching that supports
  DDDM.

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Questions To Consider

• Why are Data Coaches needed?
• What is the theory behind data coaching?
• What is an effective Data Coach?
• What do effective Data Coaches do?
• What tools can Data Coaches employ to help
  educators use data to inform curriculum,
  instruction and assessment?

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What are the characteristics of an effective
Data Coach?

• Directions (See pp. 3-6 in your handout)
• Individually write 5 characteristics that an
  effective coach should possess.
• Share your response with another person.
• Join another partner group to form a group of
  four
• Select 5 distinct responses to the question.
• Write each response on sentence strips.
• Place responses on chart paper posted around the
  room and have participants do an Affinity
  Diagram.
• Summarize the results gathered by the coaches.

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What does the research say?

• Characteristics of Effective Coaches
• Beliefs
• Teaching expertise
• Coaching skills
• Relationship skills
• Content expertise
• Leadership skills
• See page 7 in handout

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The Roles of the Data Coach

• The Data Coach
• Is data literate
• Develops Data Literacy skills in others
• Facilitates
• Leads for sustainability
• Source The Data Coachs Guide to Improving
  Learning for All Students by Nancy Love, et al

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Data Literacy

• The Four Phases of the Data-Driven Dialogue
• Predict
• Go visual
• Observe
• Infer/Question
• Source The Data Coachs Guide to Improving
  Learning for All Students by Nancy Love, et al

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Facilitating Coaches as Questioners

• How can my use of questions probe others
  thinking?
• How do I pose questions that promote
  reflection?
• What are some examples of Data Team Leader
  questions?
• How can these questions be adapted for use by
  Data Coaches?

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Sustainability

• Collaborative inquiry
• Professional development
• Change theory
• School culture
• Vision
• Systems thinking
• Source The Data Coachs Guide to Improving
  Learning for All Students by Nancy Love, et al

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Reflecting on Coaching

• As an individual, review the quotes on p. 16 in
  your handout.
• Put an asterisk in front of the one or two that
  most intrigue you. Spend three minutes writing
  about why those quotes resonate for you and how
  they apply to coaching.
• Form a triad and use the Microlabs Protocol to
  discuss these quotes. See Activity Questions at
  the bottom of p. 17 in your handout.

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Coaches Help Mine the Data

• Creating safe environments
• Accessing and organizing data
• Analyzing and interpreting
• Sustaining Professional Learning Communities

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Text Rendering Protocol

• Read through the text completely.
• Mark the sentence or phrase that holds
  particular significance for you.
• Re-read and reflect until everyone is finished
  reading.

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Steps for Coaching Data Teams

• Build relationships
• Help teams request your services with an
  identified need or area of concern.
• Observation of Data Team
• Feedback
• Reflection

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Resources

• Exploring http//calicoaches.wikispaces.com
• Text-based resources See bibliography on p.
  26 in your handout

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Your Feedback

• Please take the time to complete the feedback
  form provided.
• Make sure you have signed the CALI sign- in
  sheet before you leave (if you have not done so
  already).