The Data Coach's Guide to Improving Learning for All Students
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Katherine E. Stiles. The Data Coach's Guide to Improving Learning for All Students
The Data Coach’s Guide to Improving Learning for All Students
Contents Book
Contents. CD-ROM
Foreword Unleashing the Power of Collaborative Inquiry
Acknowledgments
Publisher’s Acknowledgments
About the Authors
Introduction
Catch the Spirit of Success
How This Book Came About
Purpose of the Book
Audience
Assumptions
Assumption 1:
Assumption 2:
Assumption 3:
Assumption 4:
Assumption 5:
Assumption 6:
A Word About Our Language
How to Use This Book. Organization of the Book
Chapter 1:
Chapter 2:
Chapters 3, 4, 5, 6, and 7:
Chapter 8:
CD-ROM
Icon Guide
Multiple Entry Points: It’s Not as Linear as It Looks!
1 The Power of Collaborative Inquiry
Bridging the Data Gap
Collaborative Inquiry Is the Bridge
Our Theory of Action: Building the Bridge Between Data and Results
The Using Data Process: A Framework for Collaborative Inquiry
What’s Unique About the Using Data Process?
Building Leadership and Capacity
Emphasis on Cultural Proficiency and Equity
Focus on Building a Data Culture and Collaborative Relationships
Long-Term and Short-Cycle Improvement
Look Before You Leap
Student Learning Improves in Schools Implementing the Using Data Process
Schools Build High-Performing Using Data Cultures
2 Getting Organized for Collaborative Inquiry
Make Collaborative Inquiry an Integral Part of the School Operation and Improvement Initiatives
Build Stakeholder Support
Assess and Take Steps to Strengthen a Collaborative Culture
Select, Prepare, and Empower Data Coaches
The Data Coach Role
Criteria for Selecting Data Coaches
Developing Data Coaches
Organize Data Teams
What Do Data Teams Do?
Clarifying Roles and Responsibilities
Create Time for Collaboration
Ensure Timely Access to Robust Data Sources: The Democratization of Data
Summary
3 Building the Foundation
Foundational Tools
Standard Procedures for Data Team Meetings
Preparing for Data Team Meetings: Processes
Preparing for Data Team Meetings: Materials
What Is Task 1?
Background Information for Data Coaches. What Are the Basics of Successful Data Teams?
How Do Data Teams Establish Collaborative Norms?
The Seven Norms of Collaboration
Four Agreements of Courageous Conversations1
What Is the Using Data Process of Collaborative Inquiry?
What Are the Challenges and Pitfalls Inherent in This Task? Unresolved Personal and Informational Concerns
The Data Team Has Not Clarified Its Relationship With Other Initiatives
Data Team Members Are Impatient With the Process and Want to Get Right to the Data
Resources
Major Activities for Task 1
Preparing for the Task
Materials Preparation
Data Coach Notes. Activity 1.1: Connect as a Team
Activity 1.2: Introduce the Using Data Process of Collaborative Inquiry
Activity 1.3: Clarify the Data Team’s Purpose and Roles and Responsibilities
Activity 1.4: Establish Collaborative Norms
What Is Task 2?
Background Information for Data Coaches. What Are Demographic Data and What Can You Learn From Them?
What Do We Mean by Race/Ethnicity, Economic Status, Educational Status, Language, and Mobility? Race/Ethnicity
Economic Status
Educational Status
Language
Mobility
Other
How Accurate Are Demographic Data?
What Do I Do If the Data Team Veers Into Culturally Destructive or Blind Conversations?
What Is Dialogue Versus Discussion? How Will We Use These Two Ways of Talking on Our Data Team?
What Is Data-Driven Dialogue?
Phase 1: Predict
Phase 2: Go Visual
Phase 3: Observe
Phase 4: Infer/Question
Resources
Major Activities for Task 2
Preparing for the Task
Materials Preparation
Data Preparation
Data Inventory for Task 2. School/District Demographic Data About Students, Teachers, and Community
Data Coach Notes. Activity 2.1: Create the Body of Our School
Activity 2.2: Engage in Data-Driven Dialogue With Demographic Data
Activity 2.3: Reflect on Program Elements Matrix (PEM) of a Using Data School Culture
Activity 2.4: Synthesize Learning
What Is Task 3?
Background Information for Data Coaches. What Is Culture?
What Is Cultural Proficiency and What Does It Have to Do With Data?
What Are the Five Guiding Principles of Culturally Proficient Leadership?
What Is the Cultural Proficiency Continuum?
How Will I Use the Cultural Proficiency Continuum With My Data Team?
How Can I Make Dialogue About Race/Ethnicity, Class, Gender, and Culture Safe, If Not Comfortable?
Start With the Personal
Reinforce Collaborative Norms
Build Foundational Knowledge
Carefully Structure Dialogue
What If I as the Data Coach Do Not Feel Culturally Proficient Myself?
Resources
Major Activities for Task 3
Preparing for the Task
Materials Preparation
Data Coach Notes. Activity 3.1: Share My Culture2
Activity 3.2: Dialogue on the Principles of Culturally Proficient Leadership
Activity 3.3: Explore the Cultural Proficiency Continuum
What Is Task 4?
Background Information for Data Coaches. What Are Core Values and Why Are They So Important?
What Is Equity?
What Are the Three Critical Questions About Student Learning?
Exactly What Is It We Want All Our Students to Learn?
How Do We Know If Students Have Learned?
What Will We Do If Students Don’t Learn?
What Is a Vision?
How Do I Keep the Work the Team Does in Task 4 Alive Throughout the Process?
Resources
Major Activities for Task 4
Preparing for the Task
Materials Preparation
Data Coach Notes. Activity 4.1: Engage With Four Variations on “All Students Can Learn”
Activity 4.2: Craft Core Values About Student Learning and Equity
Activity 4.3: Consider Three Key Questions About Student Learning
Activity 4.4: Envision Curriculum and Instruction, Equity, and Critical Supports
4 Identifying a Student-Learning Problem
Gather Data Needed for Tasks 5–12
State CRT Assessment Data (for Tasks 6–10)
Benchmark Common Assessments or Other Local Student-Learning Data (for Task 11)
Student Work (for Task 10)
What Is Task 5?
Background Information for Data Coaches. What Are Data?
What Is Data Literacy? What Are Collaborative Inquiry Knowledge and Skills? Why Are They Important?
What Are t he Different Types of Data and How Often Are They Analyzed?
What Is “Drilling Down” Into Student-Learning Data?
What Are the Different Types of Standardized Student-Learning Data? Why Does the Using Data Process Emphasize CRT Data?
What Are the Key Data Terms That Data Team Members Should Know and Be Able to Use?
What Are High-Capacity and Low-Capacity Data Use?
What Are the Principles of Effective Data Use?
What Are Some Data “Safety Regulations” to Guide the Use of Data?
What Are Some of the Best Practices for Displaying and Communicating the Data and Findings?
What Are the Challenges and Pitfalls Inherent in This Task? Data Coach Lacks Confidence in His or Her Data Literacy
Statistics Can Be Intimidating!
Matching Data-Literacy Activities With Needs
Resources
Major Activities for Task 5: Data Coach’s Planning Matrix
Preparing for the Task
Materials Preparation
Data Preparation
Data Coach Notes. Activity 5.1: Assess Our Data Literacy
Activity 5.2: Engage With the Data Pyramid and Drill-Down
Activity 5.3: Compare High-and Low-Capacity Data Use
Activity 5.4: Explore Data Principles and Safety Regulations
Activity 5.5: Mix and Match Data Literacy Tools to Learning Goals
Activity 5.6: Reassess Our Data Literacy
What Is Task 6?
Background Information for Data Coaches. What Will I Want to Know About the State CRT Test and Results?
Performance Levels and Cut Points
Test Blueprints
What Are Trend Data?
What Can We Learn From Aggregated Data Over Time?
What Are Some Cautions When Examining Aggregated State CRT Data?
Sampling Error—Different Students! Different Tests!
Sample Sizes
Measurement Error
Cultural Bias
What Are “Meaningful Differences”?
What More Will I Want to Think About in Terms of Using Data-Driven Dialogue With Aggregated Student-Learning Data?
What Are the Challenges and Pitfalls Inherent in This Task? Moving Too Quickly Into Generating Solutions
Blaming Students
Targeting Bubble Students
Getting Discouraged by Poor Performance
Test Quality
Resources
Major Activities for Task 6
Preparing for the Task
Materials Preparation
Data Preparation
Data Coach Notes. Activity 6.1: Establish Group Roles
Activity 6.2: Facilitate Data-Driven Dialogue With Aggregated Data
Activity 6.3: Closure and Reflection
What Is Task 7?
Background Information for Data Coaches. What Will I Want to Know About Data That Are Disaggregated?
What Are Achievement Gaps?
What Are the Challenges and Pitfalls Inherent in This Task? Blaming Students
Resources
Major Activities for Task 7
Preparing for the Task
Materials Preparation
Data Preparation
Data Coach Notes. Activity 7.1: Facilitate Data-Driven Dialogue With Disaggregated Data
Activity 7.2: Extend the Equity Conversation: Final Word Dialogue
Activity 7.3: Closure and Reflection
What Is Task 8?
Background Information for Data Coaches. What Will I Want to Know and Understand About Strand Data?
Why Do We Continue to Look at Multiple Years of Strand Data and Disaggregate by Student Populations If Possible?
What Kinds of Graphs Are Used to Display Strand Data?
What Are the Challenges and Pitfalls Inherent in This Task? Drawing Conclusions
Blaming Students and/or Teachers
Major Activities for Task 8
Preparing for the Task
Materials Preparation
Data Preparation
Learning Objectives
Strand Areas, Outcomes, and Number of Test Items
Strand-Level Student-Proficiency or Percentage-Correct Data
Data Coach Notes. Activity 8.1: Facilitate Data-Driven Dialogue With Strand Data
Activity 8.2: Closure and Reflection
What Is Task 9?
Background Information for Data Coaches. What Will I Want to Know and Understand About Item-Level Data?
What Are the Four Approaches the Data Team Will Use to Analyze Item-Level Data?
1. Percentage Correct: Looking at Item Data for the Percentage of Students Who Responded Correctly
2. Distractor Patterns: Looking at Multiple-Choice Items to Determine Which Incorrect Answers a High Percentage of Students Are Choosing
3. Open-Response Items Analysis: Analyzing Percentage Proficient or Passing for Short-Answer and Extended-Response Items
4. Task Deconstruction: Looking at Multiple-Choice, Short-Answer, and Extended-Response Test Items to Identify the Knowledge, Skills, and Concepts That Students Should Know and Understand in Order to Respond Correctly to a Test Item
What Are the Challenges and Pitfalls Inherent in This Task? No Access to Item-Level Data or Released Test Items
Test-Item Quality
Data Team Members’ Own Content Knowledge
Keeping the Task Manageable
Resources
Major Activities for Task 9
Preparing for the Task
Materials Preparation
Data Preparation
Data Coach Notes. Activity 9.1: Facilitate Data-Driven Dialogue With Multiple-Choice Item Data: Percentage Correct and Distractor Patterns
Activity 9.2: Facilitate Data-Driven Dialogue With Open-Response Item Data
Activity 9.3: Engage in Task Deconstruction
Activity 9.4: Closure and Reflection
What Is Task 10?
Background Information for Data Coaches. Why Is Student Work So Valuable as a Source of Data?
What Are the Challenges and Pitfalls Inherent in This Task? No Access to State CRT-Generated Student Work
Staying Objective
Sharing Student Work Publicly
Teacher Content Knowledge
Resources
Major Activities for Task 10
Preparing for the Task
Materials Preparation
Data Preparation
State CRT-Generated Student Work
Classroom Student Work
Data Coach Notes. Activity 10.1: Facilitate Data-Driven Dialogue With Student Work
Activity 10.2: Closure and Reflection
What Is Task 11?
Background Information for Data Coaches. What Are Common Assessments and Why Are They So Important?
What Kinds of Common Assessments Are There?
What Does It Mean to Engage in the Drill-Down Into Common or Other District Assessments?
What Are the Challenges and Pitfalls Inherent in This Task? Desire to Move Ahead Faster
Access to Common Assessments
Comparing Apples With Oranges
Resources. Assessment: General
Assessment: Common Assessments
Assessment: Rubrics
Major Activities for Task 11
Preparing for the Task
Materials Preparation
Data Preparation
Additional Data Preparation Guidelines. Aggregated Data
Disaggregated Data
Strand Data
Item-Level Data
Student Work
Data Coach Notes. Activity 11.1: Facilitate Data-Driven Dialogue and Conduct Drill-Down Into Common Assessments and Other Local Student-Learning Data Sources
What Is Task 12?
Background Information for Data Coaches. How Do We Identify the Student-Learning Problem to Address?
What Is a Good Goal Statement?
What Are the Challenges and Pitfalls Inherent in This Task? Scope of the Goal Statement
Resources
Major Activities for Task 12
Preparing for the Task
Materials Preparation
Data Preparation
Data Coach Notes. Activity 12.1: Walk Across the Data
Activity 12.2: Identify and Prioritize Student-Learning Problems
Activity 12.3: Articulate a Draft Student-Learning Goal Statement
Activity 12.4: Plan to Engage Stakeholders and Review School Culture
5 Verifying Causes
Gather Data Needed for Task 14
What Is Task 13?
Background Information for Data Coaches. What Is Cause-and-Effect Analysis?
What Will I Want to Consider and Be Prepared to Do to Facilitate Effective and Respectful Dialogue During This Task?
What Guidelines Should We Use to Inform the Research We Gather?
What Are the Challenges and Pitfalls Inherent in This Task?
Dialogue Suggestions. Discuss undiscussables
Final Word Dialogue (see Toolkit)
Facilitation Suggestions. Scaffold the cause-and-effect analysis by providing the categories
Fishbone redo
Use white-out tape
The Cultural Proficiency Continuum
Use of Research or External Resources. Dialogue about research
Provide examples from reflective practitioners
Invite a skilled facilitator
Resources
Major Activities for Task 13
Preparing for the Task
Materials Preparation
Data Coach Notes. Activity 13.1: Generate Possible Causes
Activity 13.2: Dialogue About Causes and Their Underlying Assumptions
Activity 13.3: Identify Causes for Further Verification
Activity 13.4: Frame Research Questions Based on Causes
What Is Task 14?
Background Information for Data Coaches. What Kinds of Questions Can We Ask to Guide Local Data Collection?
What Are the Challenges and Pitfalls Inherent in This Task? Gathering Local Data
Resources
Major Activities for Task 14
Preparing for the Task
Materials Preparation
Data and Research Preparation
Data Coach Notes. Activity 14.1: Study the Research
Activity 14.2: Frame Questions for Local Data Collection
Activity 14.3: Develop the Local Data Collection Plan
Activity 14.4: Analyze Local Data and Verify Causes
Activity 14.5: Plan for Engaging Stakeholders and Review School Culture
6 Generating Solutions
Gather Data Needed for Tasks 15–17
What Is Task 15?
Background Information for Data Coaches. What Is the “Logic Model” Approach?
Verified Cause
Strategies
Outcomes
Monitoring Tools
Student-Learning Goal
What Are the Challenges and Pitfalls Inherent in This Task?
Resources
Major Activities for Task 15
Preparing for the Task
Materials Preparation
Data Preparation
Data Coach Notes. Activity 15.1: Refine the Student-Learning Goal
Activity 15.2: Build Your Logic Model
Expected Outcomes
Strategies to Address the Verified Cause and Achieve the Student-Learning Goal and Intended Outcomes
Monitoring Tools
What Is Task 16?
Background Information for Data Coaches. What Do We Need to Know About Change as a Process?
What Are the Challenges and Pitfalls Inherent in This Task?
Resources
Major Activities for Task 16
Preparing for the Task
Materials Preparation
Data Preparation
Data Coach Notes. Activity 16.1: Refine the Logic Model: Outcomes and Strategies
Refining the Strategies
Refining the Outcomes
Reviewing the Overall Logic Model
What Is Task 17?
Background Information for Data Coaches
Why Is Monitoring So Important?
How Do I Help the Data Team Understand Their Role and See the Difference Between Program Monitoring, on the One Hand, and Program Evaluation and Rigorous Research on the Other?
In Task 15, We Identified Several Tools for Monitoring—What More Will We Want to Think About to Finalize Our Plan? Preimplementation and Postimplementation Data
Quantitative and Qualitative Data
Objective and Subjective Data
Diverse Voices
Monitoring Tools
What Are the Challenges and Pitfalls Inherent in This Task? A Manageable Monitoring Plan
Resources
Major Activities for Task 17
Preparing for the Task
Data Preparation
Materials Preparation
Data Coach Notes. Activity 17.1: Expand Our Repertoire of Monitoring Tools
Activity 17.2: Refine the Monitoring Plan
Activity 17.3: School Culture Review
7 Implementing, Monitoring, and Achieving Results
Gather Data Needed for Tasks 18–19
What Is Task 18?
Background Information for Data Coaches. What Role Does the Data Team Play at This Point?
What Are the Key Areas That the Data Coach Focuses On to Support Implementation of the Action Plan?
Practices
People
Policies
Processes
Are There Tools and Models to Help the Data Team Guide Implementation?
What Are the Challenges and Pitfalls Inherent in This Task?
Resources
Major Activities for Task 18
Preparing for the Task
Materials Preparation
Data Preparation
Data Coach Notes. Activity 18.1: The Big Picture of Change: Action Planning
Activity 18.2: Monitoring: Action/Reflection Cycle
Check-in (10 minutes)
Review monitoring data (10 minutes)
Develop criteria for analyzing evidence (10 minutes)
Data-Driven Dialogue (30 minutes)
Draw conclusions (30 minutes)
Next steps (15 minutes)
What Is Task 19?
Background Information for Data Coaches
How Can We Share and Celebrate Our Successes?
Major Activities for Task 19
Preparing for the Task
Materials Preparation
Data Coach Notes. Activity 19.1: Celebrate and Share Results
Activity 19.2: Adjust and Renew Collaborative Inquiry
Activity 19.3: School Culture Review
8 Clark County, Nevada Collaborative Inquiry in Action
Clark County Organizes for Collaborative Inquiry
The Role of the Super Data Coach
Implementing the Using Data Process
Lessons Learned
Katz Elementary School: Problem Solving About Problem Solving. Building the Foundation
Involving the Whole Faculty
Project Facilitator’s Commentary
Principal’s Commentary
Identifying a Student-Learning Problem
Project Facilitator’s Commentary
Principal’s Commentary
Verifying Causes
Examining Student Work
Collecting Local Data About Curriculum Implementation
Project Facilitator’s Commentary
Principal’s Commentary
Generating Solutions
Project Facilitator’s Commentary
Principal’s Commentary
Taking Action and Celebrating Results
Project Facilitator’s Commentary
Principal’s Commentary
Summary Reflections
Appendix A: Student Work on “Addworm” Task
Appendix B: Teacher Observations of Student Working ON “Addworm” Task
CD-ROM Toolkit Guide
Sample Tool From CD-ROM Toolkit. Introducing the Logic Model. Time
Materials
Purpose
Overview
Audience
Use
Advance Preparation
Procedure
References
Index
Отрывок из книги
To Our Families
Foreword by
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It is easy to get swept away in the data-driven mania provoked by federal and state education accountability policies, where data can sometimes seem to be an end in themselves. But test results, lists of “failing” schools, bar graphs, tables, proficiency levels, even student work do nothing by themselves to improve teaching unless they spark powerful dialogue and changes in practice. For example, it doesn’t take hours of data analysis to discover that students struggle with solving nonroutine mathematics problems or reading informational text. But talking about and learning more and more about what to do about those problems does take time and is where teams gain momentum for instructional improvement.
Questions like the following merit as much time in Data Team meetings as does the actual data analysis:
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