1. Assessment Leadership October, 2012

2. Agenda Welcome and Norms Ice Breaker Activity Galileo Celebration Colorado Growth Model Data Driven Dialogue Lunch Data Ethics Survey The Future of Assessment in Colorado Learning Resource Center In Alpine Achievement Plans for sharing new information

3. Outcomes for today Participants will… … apply knowledge of the Colorado Growth Model in order to have an “elevator conversation”. … utilize the data driven dialogue process to analyze student growth data … critique test preparation practices and data uses … recognize the changes in the state assessment program … draw conclusions about how the PARCC Assessment will effect instructional practice … plan how to share new information with staff members

4. Norms of Collaboration 1. Pausing Pausing before responding or asking a question allows time for thinking and enhances dialogue, discussion, and decision-making. 2. Paraphrasing Using a paraphrase starter that is comfortable for you – “So…” or “As you are…” or “You’re thinking…” – and following the starter with an efficient paraphrase assists members of the group in hearing and understanding one another as they converse and make decisions. 3. Posing Questions Two intentions of posing questions are to explore and to specify thinking. Questions may be posed to explore perceptions, assumptions, and interpretations, and to invite others to inquire into their thinking. For example, “What might be some conjectures you are exploring?” Use focusing questions such as, “Which students, specifically?” or “What might be an example of that?” to increase the clarity and precision of group members’ thinking. Inquire into others’ ideas before advocating one’s own. 4. Putting Ideas on the Table Ideas are the heart of meaningful dialogue and discussion. Label the intention of your comments. For example: “Here is one idea…” or “One thought I have is…” or “Here is a possible approach…” or “Another consideration might be…”. 5. Providing Data Providing data, both qualitative and quantitative, in a variety of forms supports group members in constructing shared understanding from their work. Data have no meaning beyond that which we make of them; shared meaning develops from collaboratively exploring, analyzing, and interpreting data. 6. Paying Attention to Self and Others Meaningful dialogue and discussion are facilitated when each group member is conscious of self and of others, and is aware of what (s)he is saying and how it is said as well as how others are responding. This includes paying attention to learning styles when planning, facilitating, and participating in group meetings and conversations. 7. Presuming Positive Intentions Assuming that others’ intentions are positive promotes and facilitates meaningful dialogue and discussion, and prevents unintentional put-downs. Using positive intentions in speech is one manifestation of this norm.

5. Icebreaker Head to the song sign that best describes how your school year is going so far Cablecar (Over my Head) You’ve Got a Friend Eight Days a Week Bright Side of the Road Explain why you picked that song to the group Pick a reporter who will report to the other groups

6. Celebrate!!!

7. Colorado Growth Model

8. A person on an elevator asks you what the Colorado Growth Model is… you have 2 minutes to explain it. Make sure to include: What is a matched cohort? How is student growth measured? How is grade level, school, and district growth determined? What is a median growth percentile? What is an adequate growth percentile? How does a student or school achieve adequate growth?

9. Break!

10. Data Driven Dialogue What predictions do we have about our data? What observations and trends appear from the data? What are our top priorities from the data? What are our root causes?

11. Data Driven Dialogue Step 1- Predict PredictionsAssumptions I predict girls will have Girls are more higher growth than boys engaged in school

12. Data Driven Dialogue Step 2- Explore The purpose: Generate priority observations or fact statements about the data that reflect the best thinking of the group. The steps include: 1.Interact with the data (highlighting, creating graphical representations, reorganizing) 2.Look for patterns, trends, things that pop out 3.Brainstorm a list of facts (observations) 4.Prioritize observations 5.Turn observations into priority performance challenges Avoid: Statements that use the word “because” or that attempt to identify the causes of data trends. Sentence starters: It appears... I see that... It seems... The data shows...

13. Data Driven Dialogue Step 2- Explore Data Sources in Alpine Achievement o Executive Summary- grade level data o Disaggregation report by grade level- growth data for sub groups o Custom Spreadsheets shared by Tori Teague

14. Data Driven Dialogue Step 2- Explore At your Table… Share the trends you saw in your data Share the Priority Performance Challenges o Summary of the data where there are challenges o For example: Persistent low performance among English Language Learners in reading across all standards and grades. For the past three years, English Language Learners have had median growth percentiles below 30 in all content areas, substantially below the minimum state expectation of 55.

15. Data Driven Dialogue Step 3- Explain The Purpose: Generate theories of causation, keeping multiple voices in the dialogue. Deepen thinking to get to the best explanations and identify additional data to use to validate the best theories. The steps include: 1.Generate questions about observations 2.Brainstorm explanations 3.Categorize/classify brainstormed explanations 4.Narrow (based on criteria) 5.Prioritize 6.Get to root causes 7.Validate with other data Guiding Questions: What explains our observations about out data? What might have caused the patterns we see in the data? Is this our best thinking? How can we narrow our explanations? What additional data sources will we explore to validate our explanation?

16. Data Driven Dialogue Step 3- Explain A cause is a “root cause” if: 1.The problem would not have occurred if the cause had not been present 2.The problem will not reoccur if the cause is dissolved 3.Correction of the cause will not lead to the same or similar problems  The school should have control over the root cause 

17. Data Driven Dialogue Step 3- Explain Root Cause Examples Non-Examples Student attributes (poverty level) Parent education & involvement Student motivation Why Non-Examples? Schools do not have control over these causes

18. Data Driven Dialogue Step 3- Explain Getting to Root Cause The “5 Whys” Protocol (Explanation) Proposed Cause:____________ 1.Why?4. Why? Because…. Because…. 2.Why?5. Why? Because…. Because…. 3.Why? Because….

19. Sharing the Work One person from each table will share… Trends Explanations Root causes

20. Lunch!

21. Data Ethics Survey Fill out the data ethics survey independently with a yes/ no and explanation if needed When everyone at your table is finished, discuss the correct answer We will discuss any disagreements as a group.