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Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved Data Driven Dialogue: Data Driven Dialogue: Facilitating Collaborative Facilitating Collaborative Inquiry Inquiry Developed by Bruce Wellman and Laura Lipton Day Four

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Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Data Driven Dialogue:Data Driven Dialogue: Facilitating Collaborative InquiryFacilitating Collaborative Inquiry

Developed byBruce Wellman and Laura Lipton

Day Four

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Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Seeing Systems 3 – 2 – 1 +1Seeing Systems 3 – 2 – 1 +1H/O p. 26H/O p. 26

3 – Strong suits

2 – Growth areas

1 – point to ponder

“Now this little model is special-made for committees…………….It comes equipped with one gas pedal, four steering wheelsand ten sets of brakes.”

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

The “D’s” of DataThe “D’s” of Data

Deny

Discover

Distort Determine

Defend Decide

Delete Deliberate

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

A Change FormulaA Change Formula

Change = AxBxC>X

A- Shared dissatisfaction

B- Shared vision

C- Knowledge of practical next steps

X- Cost of change

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Seeing Systems 3 – 2 – 1 +1Seeing Systems 3 – 2 – 1 +1

3 – Strong suits

2 – Growth areas

1 – point to ponder

Leading SystemsLeading Systems

Clear and Agreed Upon StandardsClear and Agreed Upon Standards

Technological Infrastructure: How we know

Managing Data

• Identifying

• Organizing

• Accessing

• Displaying

Leading SystemsLeading Systems

Clear and Agreed Upon StandardsClear and Agreed Upon Standards

Organizational and Individual Capacities: What we talk about

Structural Capacities

• Aligned curriculum

• Aligned instruction

• Aligned assessments

Technical Capacities

• Assessment literacy

• Data analysis skills

• Learning-focused instruction

• Learning-focused supervision

• Learning-focused professional development

• Standards-based grading/reporting

Professional Capacities

• Knowledge of the structure of the content discipline(s)

• Knowledge of self (values, beliefs, standards

• Knowledge of teaching skills and strategies

• Knowledge of learners and learning

Leading SystemsLeading Systems

Clear and Agreed Upon StandardsClear and Agreed Upon Standards

Sociological Infrastructure: How and why we talk

Attention to Task

• Learning-focused

• Time and energy efficient

• Data-driven

Attention to Process

• Shared tools and structures

• Learning-focused conversations

• Data-driven dialogue

Attention to Relationship

• Shared norms and values

• School culture

• Professional community

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Compass PointsCompass Points

North– Just get it done.

East – Look at the big picture.

South– Consider everyone’s

feelings

West – Pay attention to details.

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Compass PointsCompass Points

Go to your compass point of preference.

Form clusters of 3-4.

Create a T-Chart listing the strengths and limitation of that preference.

Strengths Limitations

When a person pausesin mid sentence to choose a word,

that’s the best timeto jump in and change the subject.

“It’s like an interception in football! You grab the other guy’s idea and run the opposite way.”

The more sentences you complete. The higher your score. The idea is to block the other guy’s thoughts and express your own. That’s how you win.

“Conversations aren’t contests.”

OK. A point for you.But I’m still ahead.

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Blocks to Understanding:Blocks to Understanding:“I” Listening“I” Listening

Be aware of

• Personal Referencing

• Personal Curiosity

• Personal Certainty

Pg. 20

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Join your

and share some of your experiences with these listening patterns.

Pair and SharePair and Share

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““I” ListeningI” Listening

Personal referencing

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““I” ListeningI” Listening

Personal curiosity

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

““I” ListeningI” Listening

Personal certainty

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Intervening withIntervening with “I”“I” ListeningListening

• Personal Referencing“How much detail do you need to move forward

with this item?”

• Personal Curiosity“How many of you are interested enough in this

topic to stick with it at this point in time?”

• Personal Certainty“How many of you are still exploring possibilities

here and are not yet ready to move to solutions or proposals for action?”

Pg. 20

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BREAKBREAK

Please return at 10:15

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

COLLABORATIVE LEARNINGCOLLABORATIVE LEARNING CYCLE - Pg.44 CYCLE - Pg.44

Managing

Modeling

Mediating

Monitoring

Activating

and

Engaging

Exploringand

Discovering

Organizingand

Integrating

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COLLABORATIVE LEARNINGCOLLABORATIVE LEARNING CYCLE CYCLE

Managing

Modeling

Mediating

Monitoring

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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Question BrainstormingQuestion Brainstorming

• On your own -- generate 2-3 questions about gender and reading at the 3rd grade level. Record each of these on a post-it note.

• Share and categorize your post-it notes. Create labels for your categories.

• “Step back” from your categories. Surface the assumptions that underpin your ideas and categories.

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

LUNCHLUNCH

Please return at 12:15

“Play some Frisbee, chew on an old sock, bark at a squirrel.If that doesn’t make you feel better, eat some cheese with a pill in it.”

“My team is having trouble thinking outside the box.We can’t agree on the size of the box, what materials the box should be constructed from, a reasonable budget for the box, or our first choice of box vendors.”

“My team has created a very innovative solution,but we’re still looking for a problem to go with it.”

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

PredictionsPredictions

Based on this demographic information -- what are some of your predictions for?- The overall reading performance of these grade three students.

- (Level 1, Level 2, Level 3, Level 4) (1 is low -- 4 is high -- 3 is the standard)

- Student performance disaggregated by gender.

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Data SamplesData Samples

• Grade 3 Reading by gender for school.

Four levels - 1 is low -- 4 is high -- 3 is the standard

• Grade 3 Reading Questionnaire by gender (4 questions)

1. I’m a good reader.

2. I like to read.

3. I read by myself at home.

4. I read with someone older than me at home.

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DATA TEAMSDATA TEAMSH/O p. 13H/O p. 13

RECORDER:Be sure to check with each team member before recording observations

MATERIALS MANAGER:Organize data, display set up charts for viewing, recording

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DATA TEAMS DATA TEAMS

PROCESS CHECKER:Use the Collaborative Cycle (p.44) to guide the process: Monitor for balanced participation

ENVIRONMENTAL ENGINEER:Organize the physical arrangement for team work – chairs in a horseshoe around the central displays

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Data Station Set-UpData Station Set-Up

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

CCOLLABORATIVE LEARNINGOLLABORATIVE LEARNING CCYCLEYCLE

Managing

Modeling

Mediating

Monitoring

Exploring and DiscoveringAnalyzing 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?

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Principles of Data-Driven DialoguePrinciples of Data-Driven Dialogue

Conscious Curiosity

Purposeful Uncertainty

Visually Vibrant Information

2005-2006 Grade 3 Reading by Proficiency Level

by Gender for School

0

10

20

30

40

50

60

70

80

90

100

Percentage

Males School (n=36)3 8 36 33 3

Females School (n=33)0 12 21 61 3

NE1 Level 1 Level 2 Level 3 Level 4

Exempt

Males= 14%

Females=3%

2005-2006 Grade 3 Reading Questionnaire by Gender for School

Question #1 "I'm a good reader."

0

10

20

30

40

50

60

70

80

90

100

Percentage

Males0 3 39 58

Females0 0 48 52

No Response No Sometimes Yes

2005-2006 Grade 3 Reading Questionnaire by Gender for School

Question #2 "I like to read."

0

10

20

30

40

50

60

70

80

90

100

Percentage

Males0 16 48 35

Females0 6 27 67

No Response No Sometimes Yes

2005-2006 Grade 3 Reading Questionnaire by Gender for School

Question #3 "I read by myself at home."

0

10

20

30

40

50

60

70

80

90

100

Percentage

Males0 6 35 58

Females0 3 30 67

No Response No Sometimes Yes

2005-2006 Grade 3 Reading Questionnaire by Gender for School

Question #4 "I read with someone older than me at home."

0

10

20

30

40

50

60

70

80

90

100

Percentage

Males0 52 26 23

Females0 48 39 12

No Response No Sometimes Yes

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REFLECTIONREFLECTION

• What are you noticing about yourself as a participant?

• What do you want to be aware of when you apply this phase with others?

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

BREAKBREAK

Please return at 2:00

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

•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)

COLLABORATIVE LEARNINGCOLLABORATIVE LEARNING CYCLE CYCLE

Managing

Modeling

Mediating

Monitoring

Organizing and IntegratingGenerating Theory•What inferences/explanations/conclusions might we draw? (causation)

•What additional data sources might we explore to verify our explanations? (confirmation)

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Theories of Causation - h/o p. 14Theories of Causation - h/o p. 14

Framing: Observation, Question, Hypothesis (“story line”)

Use this space to record two possible theories of causation re: your observation, question, or hypothesis

1.

2.

Circle one theory. In this space, record at least three sources of data you could use to confirm this theory.

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Walk-Around SurveyWalk-Around Survey

Key learnings

Tools to try

My next steps

Collaborative Inquiry

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

Walk-Around SurveyWalk-Around SurveyCollaborative Inquiry

name name name

name name name

name name name

Key learnings

Tools to try

My next step

Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

LEARNING PARTNERS handout p.27 LEARNING PARTNERS handout p.27

_______________

______________

______________

_______________

Your partner’s name