How Dialogue Empowers Assumption Testing

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How Dialogue EmpowersAssumption Testing
Creating Confident Data Users

• Lynn Sawyer,
• Miravia Training Associate
• lsawyer50_at_aol.com

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Creating Confident Data Users

• Shifting practitioners from being data-givers to
  responsible, informed, collaborative, data-users

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Levels of Competence

• Conscious of Unconscious Competence
• Unconscious Competence
• Conscious Competence
• Conscious Incompetence
• Unconscious Incompetence

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Best Outcomes

• Collective understanding that merges the best of
  multiple perspectives
• Challenges for both the data literate and the
  data shy
• Embrace a mission to reduce the phobia and
  toxicity of data

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• You cant fake
• farming
• or
• teaching.
• John Soderman,
• Douglas County, Nevada, Superintendent

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• What are some of the indicators that a farmer
  might use to determine if he is successful
  throughout the course of a years time?

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Switch it out Professionals Learning in Community

• Great conversations are the hallmark
• Difficult conversations happen regularly and
  without toxicity

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Its in the dialogue

• Leaders such as Schmoker, Dufour, Wheatley,
  Costa, Eisner, Scott,
• Garmston, and Senge
• tell us that
• change in schools will happen,
• must happen,
• one conversation at a time.

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• How we talk is effecting who we are becoming---
• DIALOGUE ----- DISCUSSION

thinking holistically thinking
analytically making connections making
distinctions
surfacing and inquiring surfacing and
inquiring into assumptions into assumptions
developing shared developing agreement meaning
on action
Seeking common understanding Seeking decisions
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Why data-driven dialogue may be counter-cultural

• A feeling of inadequacy about technical and
  statistical knowledge
• Competing priorities for TIME
• The way data have been used--collected for
  delivery to someone else, or placing blame,
  rather than for self-assessment, reflection and
  improvement of practice

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Conversation Protocols

• Are brain-compatible
• Ensure psychological safety
• Chosen by facilitator with deliberate
  intentionality, otherwise data becomes something
  to fear and defend against
• Support the group in being unsettled or
  uncomfortable while talking about the right
  things

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Protocols provide the place to eat the frog

• If you have a frog to eat, eat it in the
  morning, because by noon youll be quite fond of
  it.
• Susan Scott, Fierce Conversations

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Effective Participation Patterns forData-Driven
Dialogue

• Begin with the individuals ideas and
  perspectives to establish readiness
• Rarely start with a whole-group pattern
• Provide scaffolding for structured sharing and
  meaning-making as an active, personal, AND social
  process

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Meaning is defined in and by relationship

• Up/down, large/small, close/far
• Is an individual who is 511 short or tall?
• As a member of a professional basketball team?
• Is Kenai, AK, near or far?
• How about in relationship to Florida?

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57 degrees is data

• Mid-March day in New England, unusually warm--57
  degrees (balmy)
• Teachers filing in to a workshop in
• El Paso wearing hats, gloves, coats, while
  complaining of the cold-- also 57 degrees
• 57 degrees is just data
• Hot or cold is interpretation

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The real methodology for system change
begins and ends with ongoing, authentic
conversations about important questions.Tony
Wagner
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Trigger inquiry by use of a 3rd point

• Student work samples
• Teacher-made common assessments
• Lesson plans or teaching artifacts
• Thought-provoking articles or books
• Selected readings

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Say Something

• Choose a partner
• Read silently to the designated stopping point.
• When each partner is ready, stop and
• Say Something.
• a key point, insight, personal connection,
  or question

1. Continue the process until you have completed the
   selection.

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Guiding Assumptions

• Data have no meaning
• Knowledge is both a personal and social
  construction
• There is a reciprocal influence between the
  culture of the workplace and the thinking and
  behavior of its members
• Understanding should precede planning
• Cycles of inquiry, experimentation and reflection
  accelerate continuous growth and learning
• Norms of data-driven collaborative inquiry
  generate continuous improvements in student
  learning

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Examine and Interrogate Assumptions

• Causal theories are always based on assumptions
• See what does not want to be seen
• (The longer we avoid an issue, the easier it
  gets to avoid it)
• Faced with the choice between changing ones
  mind and proving that there is no need to do so,
  almost everybody gets busy on the proof. John
  Kenneth Galbraith

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COLLABORATIVE LEARNING CYCLE -
Activating and Engaging
Organizing and Integrating
Managing Modeling Mediating Monitoring
Exploring and Discovering
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COLLABORATIVE LEARNING CYCLE
Activating and Engaging

• Surfacing Experiences and Expectations
• What are some predictions we are making?
• With what assumptions are we entering?
• What are some questions we are asking?
• What are some possibilities for learning that
  this experience presents to us?

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A Data Display

• From UNICEF report The State of the Worlds
  Children 2007
• A table showing 21 rich countries listed in order
  of average rank--1high-- for 6 dimensions of
  child well-being (material well-being, health and
  safety, education, family, behaviors and risks,
  subjective well-being)
• From performance of 15 year olds in developed
  countries on international achievement tests
• Not listed due to insufficient data are
  Australia, Iceland, Japan, Mexico, New Zealand

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Moreabout the report

• Netherlands is the most densely populated and has
  the greatest diversity, Finland is the least
  diverse
• Higher scoring countries do not believe that
  child well-being is the sole responsibility of
  the educational system
• What are your predictions, assumptions and
  questions?

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COLLABORATIVE LEARNING CYCLE
Activating and Engaging

• Surfacing Experiences and Expectations
• What are some predictions we are making?
• With what assumptions are we entering?
• What are some questions we are asking?
• What are some possibilities for learning that
  this experience presents to us?

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Possible Qs

• What countries are included?
• What measures were compared?
• What were the dependent/independent variables?

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COLLABORATIVE LEARNING CYCLE
Exploring and Discovering

• Analyzing the Data
• What important points seem to pop-out?
• What are some emerging patterns, categories or
  trends ?
• What seems to be surprising or unexpected?
• What are some things we have not yet explored?

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COLLABORATIVE LEARNING CYCLE
Organizing and Integrating

• Generating Theory
• What inferences/explanations/conclusions might we
  draw? (causation)
• What additional data sources might we explore to
  verify our explanations? (confirmation)

• What are some solutions we might explore as a
  result of our conclusions? (action)
• What data will we need to collect to guide
  implementation? (calibration)

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Data begets data

• What have other countries learned that we need to
  know?
• What are some other data bits that you need to
  confirm causal theories?
• What is of interest/importance related to the
  work you now do?

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Assumptions Worth Testing

• That the data-literate have it right
• That we have common frames of reference and
  mental models that shape our assumptions
• That we do what we know (that there is no
  Knowing-Doing Gap)
• That students understand the expected standard
  and how quality work looks

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• Teachers blaze the path to knowledge generation
  when pairs, small groups, and entire faculties
  intentionally and purposely use data as a source
  for analyzing progress and proactively planning
  for improvement.
• Lipton and Wellman, Data-Driven Dialogue A
  Facilitators Guide to Collaborative Inquiry, 2004

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You as dialogue starter

• Orchestrate epiphanies--ideas with lights around
  them and exclamation points
• Cause thinking that is intellectually and
  emotionally compelling
• Susan Scott--Fierce Conversations

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• What are the implications of dialogue to empower
  the testing of assumptions?