Hillsdale County’s Data Director Initiative: ‘Growing Our Warehouse’ Implementation Plan

1. Hillsdale County’sData Director Initiative:‘Growing Our Warehouse’Implementation Plan August 4, 2010 Pat Dillon and William Yearling Hillsdale ISD Jennifer DeGrie and Stan Masters Lenawee ISD

3. Professional learning around • analyzing State data • analyzing school data • analyzing student work • monitoring classroom achievement

4. School Improvement Process

5. What We Already Know About Using Data Analysis to Change Instructional Practices • Zellmer (1997) • Without specific expectations and professional development, teachers will simply ignore data and continue to use the same instructional practices. • Price-Baugh (1997) • Without data regarding alignment to State standards, teachers will not supplement their use of instructional resources to help students meet the standards. • Moss-Mitchell (1998) • Student learning increases across socio-economic and gender predictors when coupled with: • filling in gaps between tests and instructional materials • supervision to monitor curriculum alignment • focused professional development • public copies of aligned district curriculum • the involvement of school principals Source: English and Steffy, (2001), Deep Curriculum Alignment, pp. 91-97.

6. Source: Presentation by Dr. Victoria Bernhardt, April 2007

7. Current State of Data Collection • Student Learning • Statewide assessments • Other standardized assessments • Few common classroom assessments • Demographic • Three Years (2007-2008, 2008-2009 and 2009 2010) of data School Processes • Little data on programs, curriculum maps, and School improvement efforts • Perceptions • Data on stakeholder beliefs and feelings taken from self-reported needs on EXPLORE and PLAN

8. Future State of Data Collection • Student Learning • Use of statewide assessments for diagnostic purposes • Creation of common, summative assessments for end-of-unit and end-of course purposes • Move toward standards-based grading and use of formative assessment strategies • Demographic • Development of historical demographics from using data from former student management systems • Collection of teacher, parent, and community demographic data, especially environmental data sets • School Processes • Numeric and alpha data collected on school instructional and management programs, curriculum maps, and school improvement plans • Perceptions • Stakeholder beliefs and feelings surveys/inventories disaggregated by student and teacher data

9. Talking Points for the Purpose of Implementing a Data Warehouse in Hillsdale County Schools WE MUST UTILIZE AN INQUIRY APPROACH TO DATA ANALYSIS WE MUST USE MULTIPLE SOURCES OF DATA We need a data warehouse for our 21st century schools WE MUST FOCUS ON DATA TO INCREASE STUDENT ACHIEVEMENT

10. Data Warehousing

11. Data is pulled from (student information system) and then imported into Data Director (the warehouse). Demographics – Student demographic information Teachers – Teacher name and building information Courses – Courses in the Master Schedule Rosters - Makes students visible in warehouse “Garbage in/Garbage out” is our warning! The point of entry into the student management system is crucial. The warehouse does not “create” or “fix” data, just reports on the data. Building Blocks of Core Data in the Warehouse

12. Factors Affecting Components Quality DataCulture Importance Roles Technology Policies & Regulations Timeliness Hardware Security Standards & Guidelines Software Network Utility Data Entry Environment Timelines & Calendars Accuracy Training & Professional Development More Effective Decision-Making Data Steward Superintendent Achieving AYP (Adequate Yearly Progress) Other Board Member Teacher Office Staff Program Funding Principal Technology Support Staff

13. What can we do to help develop a data-driven culture? Ask questions about your district’s involvement in the data warehouse initiative Think of questions regarding student achievement that can be answered with data.

14. Which 4thgrade students are not making adequate growth based upon the Fall 2007 and Fall 2008ReadingMEAP? Subgroup Analysis • The Criteria: • Title in the form of a question • Identify the type(s) of data • Demographic, student learning, school processes, and/or perceptions • Identify the source and year(s) of data

15. Strategies to Build a Data Director Culture • Professional development each month at the principal meetings • Additional trainings for local leaders with identified data warehousing roles • Calendar of reporting results to inform local leadership • Development of each district’s curriculum alignment with State standards, • Professional development around • student learning summative assessments • use and analysis of multiple measures of data • standards-based grading and reporting • formative assessment strategies • studying student work through collaborative inquiry

16. Review the following data roles, starting with the principal. • What data roles/responsibilities do you have? • Reviewing your list of trained professional staff: • What data roles/responsibilities will these people play? • What roles still need to be filled?

17. Sample Principals’ Meeting Agenda Purpose: Ongoing, work-embedded professional development using DataDirector Objectives developing questions for customized DataDirector reports identifying roles for data team members identifying professional development for spring, summer, and fall of 2009 Procedures PowerPoint slides as prompt for dialogue DataDirector for reports Data Team lists

18. Action Plans • Plan out how you will meet with your learning team to identify and support data roles • Plan out how you will collect and analyze additional data • Who can support you? • HCISD – creates permissions, upload data from OEAA, supports templates for uniform field headers • HCISD – dialogue about field headers for data collection, supports new reports, refine reports, dialogue about instructional decisions • HCISD consultants – support for the content areas, professional development, and use of inquiry to build reports dialogue about field headers for data collection, supports new reports, refine reports, dialogue about instructional decisions

19. Strategies to Build a Data Director Culture • Professional development each month at the principal meetings, supported by local superintendents • Additional trainings for local leaders with identified data warehousing roles • Calendar of reporting results to inform local leadership • Development of each district’s curriculum alignment with State standards, • frontloaded from State standards • backloaded from statewide assessment results • Professional development around • student learning summative assessments • use and analysis of multiple measures of data • standards-based grading and reporting • formative assessment strategies • studying student work through collaborative inquiry

20. Source: Presentation by Dr. Victoria Bernhardt, April 2007 Let’s find out why some processes are successful and why some processes lead to failure Year Two and Three Purposes Year One Purposes Mostly student learning Typically mostly student learning disaggregated Most oftenstudent learninganddemographic. Longitudinal,student learning, demographics,school processes, and perceptions All plus statistics from research. All plus financial

21. Big Picture… 2009 2010 • Overview of Data Director • Principals, Superintendents, “Data Coordinators” • Fall 2010 • --- Basics of Data Director District Data Teams • Winter 2011 --- Assessment Creation District Data Teams • Spring 2011 --- Putting It All Together District Data Teams • Summer 2011 --- Data Camp (two days) District Data Teams Open to Classroom Teachers

22. Summer Data Camp • Using State Data to Inform School Improvement Goals • Using School Data to Clarify and Address the Goals • Follow up Professional Development • 2010-2011 • Examining Student Work to Inform Instruction • Using Classroom Data to Monitor Student Progress

23. Strategies to Build a Data Director Culture • Professional development each month at the principal meetings, supported by local superintendents • Additional trainings for local leaders with identified data warehousing roles • Calendar of reporting results to inform local leadership • Development of each district’s curriculum alignment with State standards, • frontloaded from State standards • backloaded from statewide assessment results • Professional development around • student learning summative assessments • use and analysis of multiple measures of data • standards-based grading and reporting • formative assessment strategies • studying student work through collaborative inquiry

24. Assessment Calendar

25. Strategies to Build a Data Director Culture • Professional development each month at the principal meetings, supported by local superintendents • Additional trainings for local leaders with identified data warehousing roles • Calendar of reporting results to inform local leadership • Development of each district’s curriculum alignment with State standards, • frontloaded from State standards • backloaded from statewide assessment results Professional development around • student learning summative assessments • use and analysis of multiple measures of data • standards-based grading and reporting • formative assessment strategies • studying student work through collaborative inquiry

26. Curriculum Alignment • frontloaded Curriculum Mapping • Implementation of Michigan Merit Curriculum • TECH Center offerings • Personal Curriculum • Implementation of Science GLCE and Social Studies GLCE/HSCE • backloaded • Analysis of standardized test results • Develop SIP goals

27. Strategies to Build a Data Director Culture • Professional development each month at the principal meetings, supported by local superintendents • Additional trainings for local leaders with identified data warehousing roles • Calendar of reporting results to inform local leadership • Development of each district’s curriculum alignment with State standards, • frontloaded from State standards • backloaded from statewide assessment results • Professional development around • student learning summative assessments • use and analysis of multiple measures of data • standards-based grading and reporting • formative assessment strategies • studying student work through collaborative inquiry

28. Desired Outcomes for National Professional Development Standards, 2008-2011

29. Desired Outcomes for National Professional Development Standards, 2008-2011

30. Lessons Learned from Others • Go slow to go deep to go fast • Create a working environment that demonstrates collaboration between technology and instruction • Support necessary capacity for data roles

31. HCISD Data Leads • William Yearling • InformationalTechnology • “Data in” • Identification, collection, and organization of data • Pat Dillon • General Education Director • “Data out” • Creation and analysis of data-driven reports

32. Questions?