unified improvement planning new principal uip meetings 2014-15

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  • Unified Improvement PlanningNew Principal UIP Meetings2014-15

  • AgendaResources (blog, people)Unified Improvement Plan with Populated DataUnified Improvement Planning ProcessExample Plans and Plan CriteriaPerformance FrameworksData AnalysisRoot Cause IdentificationTarget SettingAction Planning

  • Colorado Unified Planning Template for SchoolsMajor Sections:

    Summary Information About the School

    Improvement Plan Information

    Narrative on Data Analysis and Root Cause Identification

    Action Plan

  • Timeline for School Accreditation and Plan SubmissionUIP Handbook p. 48

  • SVVSD Timeline for School Accreditation and Plan SubmissionTurnaround and Priority Improvement Dec. 1 turn into Area Assistant Superintendent and Tori Teague for review and feedbackJan. 2 with revisions completed turn into Tori TeagueMarch 30th submit revisions from State Review Panel feedback to CDEOther SchoolsMarch 1st to Area Assistant SuperintendentApril 1st with revisions completed turn into Tori Teague

    All Plans must be reviewed by School Accountability Committees, District, Turnaround, and Priority Improvement plans must be reviewed by District Accountability/Accreditation Committee and Board of Education

  • Planning TerminologyUIP Handbook, p. 30-46

    Review each of the terms listedTerms:Performance IndicatorMeasureMetricRoot CauseMajor Improvement StrategyAction StepInterim MeasureImplementation Benchmark

  • Unified Improvement Plan (UIP)Section I: Summary InformationExamine section 1Mark sections with a that you need more clarification onDiscuss with a partnerWhat data surprised you?What data are you most proud of?At initial glance, what is an area of weakness?Questions

  • Unified Improvement Plan (UIP)Section II: Improvement Plan InformationAdditional Information about the School Most schools will not answer yes to any If you are not sure ask(usually Regina)

    Improvement Plan InformationState Accreditation (most schools)If not sure ask(Regina)

  • Section III. Narrative on Data Analysis and Root Cause IdentificationData Narrative do last or as you go

    Progress Monitoring of Prior Years Performance Targets

    Step 1 Review Current Performance Step 2 - Identify Notable TrendsStep 3 Prioritize Performance ChallengesStep 4 Determine Root Causes

  • Example Plans and Criteriahttp://blogs.stvrain.k12.co.us/aciExamplesUIP Quality Criteria (School Level)

    Read Data Narrative Section of Quality Criteria:Place a where you have questionsDiscuss with a partner

  • Section III, Step 1:Review Current Performance and Identify Trends

    Read Step 1 & 2 p. 11-16 in UIP Handbook

    Make a list of data your school has available for school improvement planning

    What questions can your data answer?

  • Gather and Organize DataRequired reports: www.schoolview.orgSchool Performance FrameworkGrowth Summary ReportPost Secondary Readiness DataOther Local DataMust use more sources of data (Galileo, PALS, SRI, Dibels, etc.)Must consider at least three years of data

  • Data Sources in our DistrictSchoolview.org reports listed in previous slide

    Alpine Achievement Colorado Assessments - TCAP, CoAlt, CO-ACT, Colorado Growth Model, ACCESS for ELLsData Warehouse Galileo, SRI, iReady, PALS, AP, DIBELS, Theme Tests and many morePlans APAS - READ, Literacy, RtI, ALP, 504, ELLInfinite Campus

  • Section III, Step 3 Prioritize Performance ChallengesUse Data Driven Dialogue to identify data trends, priority performance challenges, and root causes

    Priority Performance Challenges are a summary of the data trends (examples on p. 16 in UIP Handbook) For the past years, English Language Learners (making up 15% of the student population) have had median growth percentiles below 30 in math, substantially below the minimum state expectation of 55

  • Section III, Step 3 Prioritize Performance Challenges

    Data Driven Dialogue

    Step 1 Predict (Activate & Engage)Step 2 Explore (Explore & Discover)Step 3 Explain (Organize & Integrate)Step 4 Take Action

  • 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:

    Clarify the questions that can be answered by the dataMake predictions about dataIdentify assumptions behind each predictionPrediction 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?

  • Step One (Chart Paper) (Data Driven Dialogue) PredictionsAssumptions

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

  • 1000OverallGrade 4Grade 5BoysGirlsFRLNonFRLELLnonELLIEPnonIEPTCAP Growth Percentile

  • 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:

    Interact with the data (highlighting, creating graphical representations, reorganizing)Look for patterns, trends, things that pop outBrainstorm a list of facts (observations)Prioritize observationsTurn 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 . . .

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

  • UIP - Section III:Analyze Trends in the Data and Identify Performance Challenges

    After completion with staff of Data Driven Dialogue steps for Predict and Explore to

    Identify areas of strengthIdentify areas of needPrioritize needs

    the first two columns (trends and priority performance challenges) of the data analysis worksheet on p. 6 can be filled out

  • How good is good enough?State Performance Indicators:School and District Performance FrameworksState expectations defined for each performance indicator

    Federal Performance Indicators:

  • Trends and Priority Performance ChallengesTrends must include at least 3 years of data.

    Priority Performance Challenges must be identified for every performance indicator for which school performance did not meet state or federal expectations:AchievementGrowthGrowth GapsPost Secondary/Workforce Readiness

  • Trends and Priority Performance ChallengesRead p. 4-5 of UIP School Quality Criteria

    Put where you have questionsDiscuss with partnerwhat surprises you?

  • 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:Generate questions about observations Brainstorm explanationsCategorize/classify brainstormed explanationsNarrow (based on criteria)PrioritizeGet to root causesValidate 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?

  • Step 3: Explain Hints (Data Driven Dialogue)

    Help groups stay open to multiple interpretations of whydevelop multiple theories of causation

    Separate the generation of theories of causation from theories of action (do not go to action steps in this step)

  • UIP Section III, Step 4 Root Cause Analysis

    A cause is a root cause if:The problem would not have occurred if the cause had not been presentThe problem will not reoccur if the cause is dissolvedCorrection of the cause will not lead to the same or similar problems***the school should have control over the root cause

  • Steps in Root Cause AnalysisGenerating explanations (brainstorm)Categorize/classify explanationsNarrow (eliminate explanations over which you have no control)PrioritizeGet to root causeValidate with other data

  • Non-examples of Root CauseStudent attributes (poverty level)Student motivation

    Brainstorm a few ideas with your table team of explanations that might appear to be root causes but dont qualify

  • Root Cause ExamplesThe 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 develop