1. Unified Improvement Planning Update Leadership Council St. Vrain Valley Schools 9/6/12

2. Agenda School Performance Framework Changes Overview of Unified Improvement Planning What’s New Deadlines Reminders Suggested School Timeline Resources/Support Review of Data Driven Dialogue

3. Federal Accountability Requirements Colorado received a waiver from some federal ESEA accountability requirements 2/2012. AYP is now determined by School Performance Frameworks SPFs had to change to meet Federal requirements Schools make AYP if they make Performance (includes Distinction too) or Improvement on SPF Always one year behind (AYP determined by last year’s SPF) What this means to us…we do not have any Title 1 school on Title 1 Improvement

4. SPF Performance Indicators

5. New Measures and Metrics Indicator: Student Academic Growth – Sub-Indicator: English Language – Measure: CELApro – Metrics: Median Student Growth Percentile, Median Adequate Growth Percentile (interpreted differently) Indicator: Postsecondary and Workforce Readiness – Sub-Indicator: Graduation Rate – Measure/Metrics: Disaggregated 4,5,6,7 year graduation rates

6. CELApro Growth Adequate Growth – the growth needed for a student to get to the target level of proficiency

7. SPF Revisions Discuss in table groups: How did the SPF revisions affect your report? What did this mean for your data?

8. The Unified Improvement Plan involves the processes of: Preparing to Plan Identifying Trends and Prioritizing Performance Challenges Identifying Root Causes Setting Performance Targets Identifying Major Improvement Strategies Monitoring Progress

10. What’s New? UIP Handbook – revised, really good resource, takes you step-by-step through the process UIP Template Changes – Sections I & II – changes so matches new SPF and waiver changes – Section III – Narrative on Data Analysis and Root Cause – several changes – Section IV – Action Planning Dates added to timeline to reflect two year plan – Addendums required – Title I, Turnaround

11. Section III – Narrative on Data Analysis and Root Cause Changes Prior Year’s Targets (p. 5, UIP template on blog) – Brief reflection on why targets met or not Data Narrative – (p. 7) – Description of School Setting and Process for Data Analysis – Review Current Performance – Trend Analysis – Priority Performance Challenges – Root Cause Analysis

12. Data Narrative Read the steps for a data narrative on p. 7 – Put a by the parts that you did last year – Underline the new parts For example: Provide a brief description of the school to set the context for readers. – Discuss the parts you underlined with your table group

13. Important Additions to Quality Criteria (blog) Data Narrative – Description of impacts of previous improvement efforts (rationale for either continuing improvement efforts or changing course) – Expanded criteria to provide rationale for prioritization of certain performance challenges (why are they a priority) Trends – effects of improvement efforts should be included in the overall trend analysis Priority Performance Challenges – Clarity around the magnitude of the performance challenge

14. UIP Handbook (blog) Helpful Sections Required data list and availability – p. 5-6, all of this data is Alpine or I sent it Suggested data – p. 7 Performance Indicators, Measures, Metrics, and Expectations – p. 8-10 Description of narrative - #1-7 on p. 11-12 Trend statements & examples of notable trends – p. 14-15 Priority Performance Challenge Examples – p. 17 Planning terminology – p. 28-37

15. Priority Performance Challenges UIP p. 6 UIP Handbook p. 15-18 Summary of the Data Trends Example… – 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. Non-example… – No differentiation in math instruction when student learning needs are varied

16. SVVSD Timeline for School Accreditation and Plan Submission Turnaround, Priority Improvement, Title 1 on Corrective Action – Dec. 1 – turn into Area Assistant Superintendent for review and feedback – Jan. 7 – with revisions completed turn into Area Assistant Superintendent – March 30 th – submit revisions from State Review Panel feedback to CDE (not Title 1 schools) Other Schools – March 1 st – to Area Assistant Superintendent – April 8 th – with revisions completed turn into Area Assistant Superintendent  All Plans must be reviewed by School Accountability Committees before submitting to CDE

17. Parent Involvement – Improvement, Priority Improvement, Turnaround Written notice to parents – (given within 30 days of plan assignment) – Type of plan and the performance results – Timeline for developing and adopting the required plan – Date, time and location of a public hearing held by the school principals to review the plan prior to adoption (must be at least 30 days after the written notice) St. Vrain Valley Schools- – I will send out a form letter to be printed in your newsletter or sent home to parents – Public hearing ideas (most likely in February or March)

18. Reminders Need only 1 target for all four performance indicators where a school is not meeting/exceeding – Academic Achievement – Academic Growth – Academic Growth Gaps – Post-secondary Workforce Readiness Need to set targets for every Priority Performance Challenge (therefore, need only 4 Priority Performance Challenges)

19. Reminders Need at least three years of data and data besides TCAP Try to focus on one area…at the most two in Elementary – Example…math in achievement, growth, and growth gaps Major Improvement Strategies - need 1-2 (at the most 3) All language needs to be consistent throughout the plan…example - list root causes exactly as written in each part

20. Reminders Root Cause – The problem would not have occurred if the cause had not been present – The problem would not reoccur if the cause was corrected – Correction of the cause would not lead to the same or similar problems Is in the school’s power to fix…not student attributes (SES, lack of parent involvement, ELL) Example – Math vocabulary not taught consistently and/or with best- practice vocabulary instruction strategies.

21. School Suggested Timeline August/September – – Data Driven Dialogue (with staff) – Target Progress – p. 5 September/October – – Refine Root Cause (with staff) – Description of Trends – p. 6 – Priority Performance Challenges – p. 6 – Data Narrative – p. 7 October/November – – Target Setting Form p. 9 – Action Planning (with staff) – p. 10

22. Data Driven Dialogue Review

23. Gather and Organize Data Required reports: www.schoolview.org (or me)www.schoolview.org – School Performance Framework – Growth Summary Report – Post Secondary Readiness Data Recommended: the use of more sources of data – SRI, Galileo, AP, etc. (elementary should definitely consider primary data like PALS, DIBELS) Must consider at least three years of data

24. Data Sources in our District Schoolview.org – reports listed in previous slide Alpine Achievement – – Colorado Assessments - TCAP, CoAlt, CO-ACT, Colorado Growth Model, CELA, CELA Growth Model – Data Warehouse – Galileo, PALS, AP, DIBELS, SRI, Theme Tests and many more – Plans – Literacy, RtI, ALP, 504 Infinite Campus

25. Section III, Step 2 Analyze Trends in the Data and Identify Priority Needs Data Driven Dialogue  Step 1 – Predict (Activate & Engage)  Step 2 – Explore (Explore & Discover)  Step 3 – Explain (Organize & Integrate)  Step 4 – Take Action

26. Step One: Predict (Data Driven Dialogue) The purpose: To activate interest and bring out our prior knowledge, preconceptions, and assumptions regarding the data with which we are about to work. Prediction allows dialogue participants to share the frame of reference through which they view the world and lays the foundation for collaborative inquiry. The steps include: 1.Clarify the questions that can be answered by the data 2.Make predictions about data 3.Identify assumptions behind each prediction Prediction Sentence Starters: I predict...I expect to see...I anticipate... Assumption Questions: Why did I make that prediction? What is the thinking behind my prediction? What do I know that leads me to make that prediction? What experiences do I have that are consistent with my prediction?

27. Step One (Chart Paper) (Data Driven Dialogue) PredictionsAssumptions

28. Step One: Predict – Hints (Data Driven Dialogue) Predictions may go fairly quickly at this point because staff members have already seen some of the data Develop assumptions concurrently Groups do not need to agree upon these Give groups a mostly blank data table to help with predictions (so they have some idea of what data they are predicting)

29. 100 0 OverallGrade 4Grade 5BoysGirlsFRLNonFRLELLnonELLIEPnonIEP CSAP Growth Percentile

30. Step Two: Explore (Data Driven Dialogue) 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...

31. Step 2: Explore - Hints (Data Driven Dialogue) It is very important to take the time to really explore the data…remind people to not jump to “because” or “action steps” and to really look at what the data is telling them Give people one piece of data at a time Refine Observations: – In math 58% of 5 th graders were proficient or advanced compared to 52% of 4 th graders. – The ELL population increased from 10% last year to 30% this year.

32. Step Three: Explain (Data Driven Dialogue) 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?

33. Step 3: Explain – Hints (Data Driven Dialogue) Help groups stay open to multiple interpretations of why…develop multiple theories of causation Separate the generation of theories of causation from theories of action (do not go to action steps in this step)

34. UIP Section III, Step 3 Root Cause Analysis 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

35. Steps in Root Cause Analysis 1.Generating explanations (brainstorm) 2.Categorize/classify explanations 3.Narrow (eliminate explanations over which you have no control) 4.Prioritize 5.Get to root cause 6.Validate with other data

36. Non-examples of Root Cause Student attributes (poverty level) Student motivation Brainstorm a few ideas with your table team of explanations that might appear to be root causes but don’t qualify

37. Root Cause Examples The school does not provide additional support/interventions for students performing at the unsatisfactory level Lack of clear expectations for tier 1 instruction in math. Lack of intervention tools and strategies for math. Limited English language development. Inconsistency in instruction in the area of language development. Low expectations for all subgroups. Low expectations for IEP students.

38. Five Why’s (Explanation) 1.Why? Because: 2.Why? Because: 3.Why? Because: 4. Why? Because: 5.Why? Because:

39. 5 Why Example ELL students are not engaged in learning in the core content classes. Why? Because… – Core curriculum is not accessible to ELL students. Why? Because… – ELL students’ English skills are not proficient enough to participate in discussions, ask questions, and comprehend core content. Why? Because… – There is inconsistent English language support for students in core content classes. Why? Because… – Lack of implementation of INSIDE and EDGE ELL curriculum as parallel support for ELL students in core content classes.

40. Step Four: Take Action (Data Driven Dialogue) The Purpose: Prepare to take action based on the data. The critical steps include: 1.Change “observations”/problem statements into goals 2.State the goals as SMART Goals 3.Determine what will indicate that the problem has been solved or the goal(s) have been met. 4.Identify strategies and action steps that will eliminate or correct the “root cause(s)” of the problem 5.Identify what data to track over time to determine if action steps are having the desired effect Cautions Make sure that there is a direct causal link between the goal or solution and the action steps that are being taken. Clearly define what success looks like and measure it. Don’t be afraid to change course if action steps are not having the desired effect.

41. Step 4 - Hints (Data Driven Dialogue) Action plan must be able to eliminate the root cause Action steps must be within the power of the group to implement (budget, capacity, etc.) Action plan should be a commitment to action by the group

42. St. Vrain Resources UIP Handbook ACI (Assessment, Curriculum, Instruction) blog http://blogs.stvrain.k12.co.us/aciblogs.stvrain.k12.co.us/aci New Principals – scheduling sessions Tori Teague, Regina Renaldi, Amy Weed, Mark Mills, Connie Syferd