1. Groton Data Day Accountability, Performance, and Balanced Assessments Facilitated by: Neal Capone District Data Coordinator CNYRIC

2. Agenda Grounding – Spring Synectic Data Literacy – Accountability and Assessment 3-8 ELA/Math Collaborative Learning Cycle – Score Trend Comparison – Cohort Trend and Subgroup Performance Balanced Assessment – Rick Stiggens – Self-Evaluation

3. The Region Serviced by the CNYRIC

4. Data Flow SIS (Student Management System) PD Data System IEP Direct NutriKids/ Transfinder Level 2 Repository (SED) Data Warehouse (Level 1) Level 1 Container COGNOS DataMentor nySTART

5. Synectic What are some popular Spring Activities?

6. SYNECTIC Data Analysis is like … because... Syn (bring together) Ectic (diverse elements)

7. Grounding Exercise Name Position Share your Synectic

8. “Using data effectively does not mean getting good at crunching numbers. It means getting good at working together to gain insights from student-assessment results and to use the insights to improve instruction.” - Kathryn Boudett, Elizabeth City, & Richard Murnane, “When 19 Heads Are Better Than One,” Education Week, December 7, 2005.

13. Word Splash Work with a partner to define as many terms as you can on the Word Splash

14. Data Warehouse SIRS NYSSIS Continuous Enrollment Performance Index AYP AMO Effective AMO NYSAA Participation Rate Accountability Subgroups Safe Harbor BEDS NYSESLAT Accountability Cohort AVR Graduation Cohort COGNOS Differentiated Accountability Triangulating Data Word Splash Sampling Principle Summative Assessment Formative Assessment Scale Score --- Raw Score Standards-Referenced Test

15. Calculation of the Performance Index (PI) Elementary-Middle Levels: PI = [(number of continuously enrolled tested students scoring at Levels 2, 3, and 4 + the number scoring at Levels 3 and 4) ÷ number of continuously enrolled tested students] X 100 Secondary Level: PI = [(number of cohort members scoring at Levels 2, 3, and 4 + the number scoring at Levels 3 and 4) ÷ number of cohort members] X 100 A Performance Index (PI) is a value from 0 to 200 that is assigned to an accountability group, indicating how that group performed on a required State test (or approved alternative) in English language arts, mathematics, or science. PIs are determined using the following equations:

16. Level 1: 5 students Level 2: 15 students Level 3: 45 students Level 4: 10 students PI = (15+45+10) + (45 + 10) 75 PI = 167 X 100

17. Measure/PurposeCohort UsedStandard/AMO Subgroup Accountability Performance All grade 3-8 students) or designated ungraded students) reported in the repository as continuously enrolled (one-year continuous enrollment = enrolled BEDS day through assessment dates) English: PI of 167 Math: PI of 152 Science; PI of 100 30 or more students for ELA or Math Participation Rate All grade 3-8 students (or designated ungraded students) reported in the repository as enrolled during assessment administration and make-up dates ELA and Math: 95% Science: 80% for “all students” 40 or more students for ELA or Math 2010-2011 Elementary/Middle Level Accountability

18. 2010-2011 High School Accountability Measure/PurposeCohort UsedStandard/AMO Subgroup Accountability English and Math Performance 2007 Accountability Cohort (one-year continuous enrollment in fourth year of HS = enrolled BEDS day through June 30, 2011) English: PI of 183 Math: PI of 180 30 or more students for ELA or Math English and Math Participation All students reported in State Repository as enrolled in grade 12 on June 30, 2011 and students who graduated between July 1, 2010 and June 30, 2011 95%40 or more students Graduation Rate 2006 Graduation-Rate Cohort (five months’ enrollment) including transfers to GED 80% for “all students”

19. An Effective AMO is the lowest PI that an accountability group of a given size can achieve in a subject for the group’s PI not to be considered significantly different from the AMO for that subject. If an accountability group's PI equals or exceeds the Effective AMO, the group is considered to have made AYP. Effective AMOs Further information about confidence intervals and Effective AMOs is available at: http://www.emsc.nysed.gov/irts/school-accountability/confidence-intervals.htm

20. 2010–11 Safe Harbor Calculation for ELA and Math Safe Harbor is an alternate means to demonstrate AYP for accountability groups whose PI is less than their Effective AMO. The Safe Harbor Target calculation for ELA and Math for 2010-11 using the 2009-10 PI is: Safe Harbor Target = {2009-10 PI} + [(200 – {2009-10PI})  0.10]* For a group to make safe harbor in English or math, it must meet its Safe Harbor Target and also meet the science (at the elementary/middle level) or graduation rate (at the secondary level) qualification for safe harbor. To qualify at the elementary/middle level, the group must make the State Standard or its Progress Target in science in grades 4 and/or 8. At the secondary level, it must make the State Standard or its Progress Target for graduation rate.

21. 21 Phase Diagnostic Differentiated Accountability Model Category CORRECTIVE ACTIONIMPROVEMENTRESTRUCTURING CURRICULUM AUDITSCHOOL QUALITY REVIEW ASSIGNMENT OF Joint Intervention Team and Distinguished Educator FOCUSEDCOMPBASICFOCUSEDCOMPREHENSIVEFOCUSEDCOMP SURR Intensity of Intervention FAILED AYP 2 YEARS Plan/Intervention CORRECTIVE ACTION PLAN & IMPLEMENTATION OF CURRICULUM AUDIT IMPROVEMENT PLAN CREATE AND IMPLEMENT External personnel to revise and assist school implement the most rigorous plan or, as necessary, PHASE-OUT /CLOSURE Oversight & Support SED provides TA to districts: sustaining greater latitude and more responsibility for addressing schools SED empowers districts: gives them the support and assistance necessary to take primary responsibility for developing and implementing improvement strategies SED & its agents work in direct partnership with the district

22. Student Management System (SIS) Special Education Package (IEP Direct) Active Enrollment Program Services (LEP, CTE, Summer School, Poverty, Free and Reduced) Demographics Program Start and End Dates Process Log Disability Code NYSAA Eligibility 504 Safety Net Eligibility AIS 209 Code (RTIm) Title I 0286 Code (RTIm) At Data Warehouse Refresh Disability Code Monthly DW Refresh Nightly Centris Sync Demographics Data Warehouse (Level 1) Level 1 Container

23. Data Warehouse SIRS NYSSIS Continuous Enrollment Performance Index AYP AMO Effective AMO NYSAA Participation Rate Accountability Subgroups Safe Harbor BEDS NYSESLAT Accountability Cohort AVR Graduation Cohort COGNOS Differentiated Accountability Triangulating Data Word Splash Sampling Principle Summative Assessment Formative Assessment Scale Score --- Raw Score Standards-Referenced Test

24. District Report Card

25. Managing Modeling Mediating Monitoring Data-Driven Dialogue The Collaborative Learning Cycle

26. "He uses statistics as a drunken man uses lamp-posts... Andrew Lang (1844-1912) In reference to an individual who misuses data: …for support rather than illumination."

27. Data-Driven Dialogue The Collaborative Learning Cycle Activating and Engaging Managing Modeling Mediating Monitoring What are some predictionsWhat are some predictions we are making? we are making? With what assumptions areWith what assumptions are we entering? we entering? What are some questionsWhat are some questions we are asking? we are asking? What are some possibilitiesWhat are some possibilities for learning that this for learning that this experience presents to us? experience presents to us?

28. What is a prediction you made? What might be some assumptions that influenced your prediction?

29. Data-Driven Dialogue The Collaborative Learning Cycle Managing Modeling Mediating Monitoring Exploring and Discovering What important points seem to “pop out”?What important points seem to “pop out”? What are some patterns, categories, or trends that are emerging?What are some patterns, categories, or trends that are emerging? What seems to be surprising or unexpected?What seems to be surprising or unexpected? What are some things we have not yet explored?What are some things we have not yet explored?

30. Principles of Data-Driven Dialogue Importance of Predictions Conscious Curiosity Purposeful Uncertainty Visually Vibrant Information Third Point

31. Data-Driven Dialogue The Collaborative Learning Cycle Managing Modeling Mediating Monitoring Exploring and Discovering What important points seem to “pop out”?What important points seem to “pop out”? What are some patterns, categories, or trends that are emerging?What are some patterns, categories, or trends that are emerging? What seems to be surprising or unexpected?What seems to be surprising or unexpected? What are some things we have not yet explored?What are some things we have not yet explored?

32. Data-Driven Dialogue The Collaborative Learning Cycle Managing Modeling Mediating Monitoring Organizing and Integrating What inferences/ explanations/ conclusions might we draw?What inferences/ explanations/ conclusions might we draw? What additional data sources might we explore to verify our explanations?What additional data sources might we explore to verify our explanations?-------------------------------------------------- What are some solutions we might explore... ?What are some solutions we might explore... ? What data will we need to collect to guide implementation?What data will we need to collect to guide implementation?

33. “My team has created a very innovative solution, but we’re still looking for a problem to go with it.”

34.  Curriculum  Instructional methods and materials  Teacher knowledge and skills  Student readiness  Infrastructure Causal Arenas

35. Theories of Causation Observation: record three possible theories of causation re: your observation 1. 2. 3. Circle one theory. In this space, record at least three sources of data you could use to confirm this theory.

36. Data-Driven Dialogue The Collaborative Learning Cycle Managing Modeling Mediating Monitoring Organizing and Integrating What inferences/ explanations/ conclusions might we draw?What inferences/ explanations/ conclusions might we draw? What additional data sources might we explore to verify our explanations?What additional data sources might we explore to verify our explanations?-------------------------------------------------- What are some solutions we might explore... ?What are some solutions we might explore... ? What data will we need to collect to guide implementation?What data will we need to collect to guide implementation?

37. Time to Share Share ONE observation Share ONE theory of causation Share additional data sources that you would want to explore to confirm or disprove your theory

38. Balanced Assessment