Data Driven Dialogue

Data Driven Dialogue This protocol leads a team to begin by thinking of questions about student achievement. National School Reform Faculty Data Driven Dialogue Article from ASCD, “Answering Questions That Count” December 2008/January 2009, pages 18-24. Links to documents are on my web site

Getting Started All participants have equal voice. This will assist groups in making shared meaning of data. Helps to replace hunches and feelings with data-based facts. Examines patterns and trends of performance indicators. Generate “root-cause” discussions that move from identifying symptoms to possible causes of student performance.

Overview Phase I—Predictions Phase II—Observations Surfacing perspectives, beliefs, assumptions, predictions, possibilities, questions and expectations. Phase II—Observations Analyzing the data for patterns, trends, surprises, and new questions that jump out. Phase III—Inferences Generating hypotheses, inferring, explaining and drawing conclusions. Defining new actions, interactions, and implementation plan.

Phase 1-Before You See the Data Activate prior knowledge, surface assumptions, and make predictions to create a readiness to examine and discus 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 ….

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? 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?

How do student grades correlate with state assessment results and other measures? What percent of the students improved, stayed the same or declined from last years achievement? Are students making sufficient grade-to-grade progress? How many of the lower performing students in grade 4 are still lower performing students in grade 5. What is the variation in students’ scores within each course or grade.

“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.” P 18 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. 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

Phase II—Just the Facts The terms; because, therefore, it seems, and however may not be used. Use these sentence starters I observe that …. Some patterns/trends that I notice …. I can count …. I am surprised that I see ….

Phase III—Inferences I believe that the data suggest …. Because … Additional data that would help me verify/confirm my explanations is ….. 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 ….

Considerations What changes or improvements can we make? Todd Whitaker—What Great Teachers Do Differently. http://www.youtube.com/watch?v=VXCl2fMsdTU&feature=related

Time 8:30-8:45 Introduction 8:45-9:15 Phase 1 Predictions 9:15-10:15 Phase 2 Just the facts. Looking at the data 10:15-11:15 Phase 3 Inferences 11:15-11:45 Report out and next steps

Lets Get Started Open a word document and come up with 5 essential questions.

Phase 1-Before You See the Data Activate prior knowledge, surface assumptions, and make predictions to create a readiness to examine and discus 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 ….

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? 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?

How do student grades correlate with state assessment results and other measures? What percent of the students improved, stayed the same or declined from last years achievement? Are students making sufficient grade-to-grade progress? How many of the lower performing students in grade 4 are still lower performing students in grade 5. What is the variation in students’ scores within each course or grade.

Phase 2 Just the Facts Looking at Data

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.

Because learning neither takes place in isolation, nor only at school, multiple measures must be considered and used to understand the multifaceted world of school from the perspective of everyone involved. Types of information that assist with planning for and sustaining systemic school improvement include: Demographics Perceptions Student Learning School Processes One measure, by itself, gives useful information. Comprehensive measures used together and over time provide much richer information. Together, these measures can provide a powerful picture that can help us understand the school’s impact on student achievement. These measures, when used together, give schools the information they need to get the results they want. Let’s look at each measure separately. Note: This graphic is on: Page 11 of Using Data to Improve Student Learning in Elementary Schools Book, Page 48 of The School Portfolio Toolkit Book, Page 15 of the Data Analysis Book, and Page 28 of The School Portfolio Book

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

Pledge of Confidentiality 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 What we DISCUSS in this room, stays in this room. What we LEARN in this room, may be shared.

Phase 2—Just the Facts Add at least 3 bullets to each question that you generated in Phase 1.

Phase II—Just the Facts The terms; because, therefore, it seems, and however may not be used. Use these sentence starters I observe that …. Some patterns/trends that I notice …. I can count …. I am surprised that I see ….

Phase 3 Inferences

Phase III—Inferences I believe that the data suggest …. Because … Additional data that would help me verify/confirm my explanations is ….. 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 ….

Report Out