WELCOME MATES!

Data ConferenceSearching for Data Treasures

Setting a CourseMeet the ShipmatesShip RulesFollow a Data Driven Dialogue

“Easy Pickin’s” on the beach“Worth Digging for – Hidden Treasures”“Legends and Tales – Sunken Ships”

Analysis – Which sunken ship to explore?Set GoalsCreate a Treasure Map“Sail through the Port”

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The Crew

SHIP’S LOGData Conference

April 3, 2012

ESU 7 Crew_____Sue Oppliger ________________Dave Perkins________________Beth Kabes _________________Deb Wragge_________________Barb Friesth_________________Candy Conradt__________

Meeting the Shipmates

Before we sail:What should we know about the band of

pirates?

• Introductions– Name, position, school or organization – Your interactions/responsibilities with data

(Continuous Improvement Team, data team, newbee…)

NormsNorms are the standards of behavior by which we agree to operate while we are in this group.

Norms are a set of guidelines that a team establishes to shape the interactions of team members with each other.

What “BUGS” you?• What bugs you when you attend different

meetings?

• Record your thoughts on the sticky notes.

• Use one sticky note per idea.

• Be ready to share.

What makes a meeting go well?

Ships Rules

• Prepare to lift anchor – work together as the ship sails through uncharted waters

• Record your travels in the “Ship’s Log” – Listen to mates ideas– Ask questions to clarify

• Be mindful of time• Yo Ho Ho and a bottle of…. Have fun and learn! • Silence your cell phones…reception is poor at sea!

The Purpose: Improving Student Learning

The Process: Reflective

Collaboration

The Power: Importance of Data

Plan for Continuous Improvement

Nebraska Continuous

Improvement Model

•Where are we now as a school building?

•Where do we want to go?

•How will we get there?

•How will we know when we get there?

•How will we sustain the effort?

We use data to determine…

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Analyzing Data Patterns

Broad Indicators

More Detailed Results

Norm-Referenced Assessments

State Writing Assessment

State Standards Assessments

Disaggregation of Data

If we believe that all students can achieve, then any subgroup we

choose should have similar achievement and results.

Disaggregation allows us to:

• see if we are meeting the goals of our school; • identify subgroups that are not responding as well to

school process as other subgroups;• understand why a subgroup is not responding and

begin searching for a different process so that all students can learn; and

• meet requirements for school improvement.

Identify Subgroups

Identify Subgroups• FRL--free and reduced lunch• ELL--English language learners• Special education• Ethnic minorities• Migrant students• Male/female• Students in your school for less than 2 years• Time spent on a bus route• Coming from different elementary schools• Others factors which might cause students to

perform different than expected

Ground Rules for Participating in a Data Retreat

• No blaming students

• No blaming teachers

• Data is JUST information

• Use data for instructional purposes

• “De-emotionalize” data

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I pledge to hold confidential and private any information regarding individual students shared during

this retreat.

I will respect the use of data as a tool to facilitate the improvement of

student learning.

Pledge of Confidentiality

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What we DISCUSS in this room, stays in this room.

What we LEARN in this room, may be shared.

Pledge of Confidentiality

Phase I - Predictions

Phase I - Before You See the Data

• Hear and honor all assumptions– I assume ….– I predict ….– I wonder ….– My questions/expectations are influenced by …– Some possibilities for learning that this data may

present ….

Shipmate to Shipmate

• Equal voice• Make shared meaning of data• Replace hunches and feelings with facts• Examine patterns and trends of performance

indicators• Generate “root-cause” discussions - move

from identifying symptoms to possible causes

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We assume…

We predict…

We wonder…

Data Driven Dialogue - Predictions

Easy Pickin’s

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“Easy Pickin’s” – What data sources do we currently have access to?

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Phase II—Observations

Phase II - Just the Facts

Because – Therefore It seems - However

• Use these sentence starters:

– I observe that ….– Some patterns/trends

that I notice ….– I can count ….– I am surprised that I see

….

Phase II - Examine the data

http://www.education.ne.gov/State of the Schools Report and Data Reporting System

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I observe that…

Some patterns/trends that I notice…

I can count…

I am surprised that we see…

Data Driven Dialogue - ObservationsBecause… Therefore… It seems… However…

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We observe that…

Some patterns/trends that we notice…

We can count…

We’re surprised that we see…

Data Driven Dialogue - ObservationsBecause… Therefore… It seems… However…

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Our questions/expectations are influenced by…

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Phase III - Inferences

Phase III - Inferences

• I believe that the data suggest …. Because …

• Additional data that would help me verify/confirm my explanations is …..

Phase III - Inferences

• I think the following are appropriate solutions/responses that address the needs implied by the data ….

• Additional data that would help guide implementation of the solutions/responses and determine if they are working ….

Phase III - Inferences

• Create your inferences

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Data Driven Dialogue- InferencesWe believe the data suggests…

because…

Additional data that would help us verify/confirm our explanations are…

We think the following are appropriate solutions/responses that address the needs implied in the data…

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Do your “Easy Pickin's” support your assumptions you are making?

Are there additional assumptions which surface at each measure? (SOS, DRS, etc.)

Additional data that would help guide implementation of the solutions/responses and determine if we are working…

Levels of Data Analysis

Step 10 – Intersection of 4 measures over time

Step 9 – Intersection of 4 measures

Step 8 – Intersection of 3 measures over time

Step 7 – Intersection of 3 measures

Step 6 – Intersection of 2 types of measures over time

Step 5 – Intersection of 2 types of measures

Step 4 – Two or more variables within measures over time

Step 3 – Two or more variable within same area

Step 2 – Snapshots over time

Step 1 – Snapshots

Bernhart, V. L. (2004). Data Analysis for Continuous School Improvement (2nd ed.) Larchmont, NY: Eye on Education, Inc.

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"Worth Looking for" – "Digging for hidden treasures"

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What did you find, and what more do you need to know about individual students?

Reflection…

• What is something you picked up on the shore?

• What do you want to dig deeper for?

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Our "Ah Ha" from the day*

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Legends and Tales

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Analysis of Data

“What does the data tell us about our strengths and challenges, especially as it relates to student achievement and programs/resources which support thelearning?”

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Analysis of Data

• Observe the data patterns

• Discuss what is observed

• Write data findings under the graphs

JUST the FACTS!

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Graphing - "Legends & Tales”…"Sunken Ships"

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Facts To Hypothesis

• What does the data tell us?• Why might this be?• What are our next steps?

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"Which “sunken ship” are you going for?” Facts To Hypothesis

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Tie to School Improvement

Begin with assumptions

Discover the facts

Make inferences/ hypothesis

Create goals

Develop action plans

Goals

Effective team goals will focus on the intended outcome rather than the strategies to achieve the outcome.

SMART Goals

S = Specific M = Measurable A = Attainable

R = Results-based T = Time-bound

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Setting Goals*

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Create Your Treasure Map - Action Plan

Plot your action plan on the treasure map.Include:

•actions•timeline •who is responsible

75STEADY AS SHE GOES!

"Sail through a Port" – Check-Out with SI contactList your plans to continue this course when you return.How and to whom will you communicate the course you have charted?.

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People without information cannot act.

People with information cannot help but act.

-Ken Blanchard

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FAIR SKIES AND SMOOTH SEAS!

Consider

• “When important questions drove the dialogue about school effectiveness, school staff quickly learned how to identify and use different types of data to answer those questions. (Lachat & Smith, 2004)

• Organizing data around essential questions about student performance is a powerful strategy for building data literacy.

Possible Essential Questions

• How do student outcomes differ by demographics, programs, and schools?

• To what extent have specific programs, interventions, and services improved outcomes?

• What is the longitudinal progress of a specific cohort of students?

Possible Essential Questions

• What are the characteristics of students who achieve proficiency and of those who do not?

• Where are we making the most progress in closing achievement gaps?

• How do absence and mobility affect assessment results?

• “Asking questions such as these enables administrators and teachers to focus on what is most important, identify the data they need to address their question, and use the questions as a lens for data analysis and interpretation.”

• Limit the number of questions to no more than five or six crucial questions that get at the heart of what they need to know.

What is Needed?

• Time to look at data, analyze data and ask more questions.

• Time to look at the data rather than time spent creating the graphs and charts.

• Teachers need opportunity and support to plan and implement improvement strategies and then collect data to see if the strategies work.

What is Needed?

• Opportunity to ask questions and then find data to answer the question.

• Data that is sufficiently disaggregated– By broad categories, male, female, economic

status, programs– Combinations of categories ie female and low SES

Yearly cohort group comparison

Cohort group year-by year comparisons of the same students over time

Yearly program comparisons

Year-by-year comparisons of different students at the same grade level

Within-year progress data