Data Use Professional

Development Series301

www.ride.ri.gov

www.wirelessgeneration.com

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Welcome back!

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Days 4, 5, and 6

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Today• Welcome• Implementation

Progress• Data Conversations• Inference Validation

BREAK• Data Analysis

Questions• Correlation/Causation

LUNCH• Triangulation/

Intersection Analysis• Implementation

Planning• Adaptive Work and

Collaborative Structures

BREAK• Revisit Data

Conversations: Conversations with parents

• Implementation Planning

• Wrap Up/Evaluations

Day 5: On-Site VisitPossible activities for the Data Analysis Coach are:

• Collaboration time with the SDLT and/or school and district leaders.

• Observing Communities in Practice or Data Team meetings.

• Model/review Turn Key Activities.

• Analyze classroom data with classroom teachers.

• Model low stakes data conversation.

• Access NARS (NECAP Analysis and Reporting System) and other RIDE resources online.

Day 6: Partial list of topics

• Action

Research and

Expanding

Circles of Data

Use

• Data

Conversations

with Students

• Aggregate

Data and Sub-

Populations

• Intersection

Analysis

Data Use 301Day 4 Agenda

• Welcome• Implementation Progress• Data Conversations• Inference Validation

BREAK• Data Analysis Questions• Correlation/Causation

LUNCH• Triangulation/Intersection

Analysis• Implementation Planning• Adaptive Work and

Collaborative StructuresBREAK

• Revisit Data Conversations: Conversations with parents

• Implementation Planning• Wrap Up/Evaluations

ObjectivesBy the end of Day 4, SDLTs will be able to:

Identify challenges and successes of their data use implementation.

Engage in Data Conversations with colleagues and parents using Positive Presumptions.

Employ various data analysis techniques such as root cause analysis, triangulation, and assessing correlation, as applicable; and consider effort/impact on student learning when prioritizing action.

Articulate questions appropriate to various data sources and types.

Articulate a plan for ongoing data use implementation.

 

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Implementation Progress

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Implementation Progress

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• How many educators have you implemented with?

• What has surprised you the most about implementing this work at your school?

• What has been the biggest challenge?

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Data Conversations

Three types of Data Conversations:

• Gathering Information

• Guiding Improvement

• Finding Solutions

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Data Conversations

Which of the three types of Data Conversations did you have most frequently?

What challenges did you encounter?

• Gathering Information

• Guiding Improvement

• Finding Solutions

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Inference Validation

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Effort/Impact

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Summary

• Implementation of the work looks different at different schools.

• As educators develop more facility with data use, they will apply strategies and protocols situationally.

• It is important to have educators think through factors like effort and impact on student learning, when prioritizing where to take action.

Data Analysis Questions

Correlation/Causation

LUNCH

Triangulation

Data set 1 • Hypothesis

Refined Hypothesis

“Triangulation” is the process of using multiple data sources to address a particular question or problem and using evidence from each source to illuminate or temper evidence from the other sources. It also can be thought of as using each data source to test and confirm evidence from the other sources in order to arrive at well-justified conclusions about students’ learning needs.

-IES Practice Guide: Using Student Achievement Data

to Support Instructional Decision Making

TriangulationData set 

1• Hypothesis

Data set 2

• Refined Hypothesis

Data set 3

• Refined Hypothesis

Data set 4

• Refined Hypothesis

Implementation Planning

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Adaptive Change and Collaborative Structures

• In general, what is the meeting topic?

• What questions are being asked?

• How can we help structure questions so that more data can be brought in to answer them?

Data Sources and Types

• What are the general questions we should be asking of all data sets?

• What are the questions unique to specific data sets or data types?

Summary

• The purpose of Triangulation is to use additional data to illuminate or temper evidence from another data source.

• Understanding the best questions to ask of various data sources can help facilitate productive data meetings and Data Conversations.

Data Conversations

with Parents

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• Involve thinking through what you really want to know, and what assumptions you are making before you ask a question.

• Presume a positive result has already taken place; so you ask a question with this assumption already in mind.

• Presuming positive intent is not the same as “being positive.”

Positive Presumptions

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Implementation Planning

On-Site Visit

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Day 5: On-Site VisitPossible activities for the Data Analysis Coach are:

• Collaboration time with the SDLT and/or school and district leaders.

• Observing Communities in Practice or Data Team meetings.

• Model/review Turn Key Activities. • Analyze classroom data with classroom

teachers. • Model low stakes data conversation. • Access NARS (NECAP Analysis and

Reporting System) and other RIDE resources online.

Day 6

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Some of the topics to be covered Day 6:

• Action Research and

Expanding Circles of Data Use

• Data Conversations with

Students

• Aggregate Data and Sub-

Populations

• Intersection Analysis

Wrap Up

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