How Dialogue Empowers
Assumption Testing
Creating Confident Data Users
Lynn Sawyer,
Miravia Training Associate
[email protected]
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 can’t 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
year’s 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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It’s 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
into assumptions
surfacing and inquiring
into assumptions
developing shared
meaning
developing agreement
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 you’ll be
quite fond of it.”
Susan Scott, Fierce Conversations
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Effective Participation Patterns for
Data-Driven Dialogue
Begin with the individual’s 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 5’11” 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
1. Choose a partner
2. Read silently to the designated stopping point.
3. When each partner is ready, stop and
“Say Something”.
* a key point, insight, personal connection, or
question
4. 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 one’s
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
Organizing
and
Integrating
and
Engaging
Managing
Modeling
Mediating
Monitoring
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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 World’s
Children” 2007
A table showing 21 rich countries listed in order of
average rank--1=high-- for 6 dimensions of child wellbeing (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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More…about 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 Q’s
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
Facilitator’s 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?
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