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

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

School Improvement Process

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

Source: Presentation by Dr. Victoria Bernhardt, April 2007

Current State of Data Collection Student Learning – Statewide assessments – Other standardized assessments – Few common classroom assessments Demographic – Three Years ( , and ) 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

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

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

Building Blocks of Core Data in the Warehouse 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.

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

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.

Subgroup Analysis Which 4 th grade students are not making adequate growth based upon the Fall 2007 and Fall 2008 Reading MEAP? 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

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

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?

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

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

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

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

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

Summer Data Camp – Using State Data to Inform School Improvement Goals – Using School Data to Clarify and Address the Goals Follow up Professional Development – Examining Student Work to Inform Instruction – Using Classroom Data to Monitor Student Progress

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

Assessment Calendar

Definition, Implementation and Timelines for Quality Data Director Roles School YearRoles Principals, Superintendents, and Central Office (Data Coordinators, Technology Support Personnel, and Key Data Team Members) Add: Classroom Teachers Add: Others

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

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

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

StandardTeachersPrincipalsCentral Office (Data Coordinators, Technology Support Personnel, and Office Staff) Superintendent Process: Data- Driven Works with colleagues to use disaggregated data to establish professional learning goals Analyzes with the faculty disaggregated student data to determine school improvement/professional development goals Support administrator and teacher analysis of data Work with the school board, central office staff, and school faculties to establish adult learning priorities Analyzes relevant student data in order to monitor and revise school and classroom improvement strategies Engages teachers, parents, and community members in data-driven decision making Use staff data to design district wide professional development experiences Analyzes relevant staff data to design teacher professional development Desired Outcomes for National Professional Development Standards,

StandardTeachersPrincipalsCentral Office (Data Coordinators, Technology Support Personnel, and Office Staff) SuperintendentSchool Board Content: Quality Teaching Uses various classroom assessment strategies Promotes the use of a variety of classroom assessments Ensures staff implementation of quality instruction Context: Leadership Participates in instructional leadership development Creates a school culture that supports continuous improvement Develop teachers to serve as instructional leaders Ensures improved student achievement is district priority Adopts policies that support ongoing professional learning and continuous improvement Desired Outcomes for National Professional Development Standards,

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

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

Questions?