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Introduction to Data for Student Success

Data for Student Success: Michigan’s Online Tools Supporting Local Cultures of Quality Data and Improvement Macul, March 19, 2009. “It is about focusing on building a culture of quality data through professional development and web based dynamic inquiries for school improvement.”.

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Introduction to Data for Student Success

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  1. Data for Student Success: Michigan’s Online Tools Supporting LocalCultures of Quality Data and ImprovementMacul, March 19, 2009 “It is about focusing on building a culture of quality data through professional development and web based dynamic inquiries for school improvement.”

  2. Introduction toData for Student Success • Federal Title II Part D of the NCLB Act of 2001 Enhancing Education through Technology Grant awarded through CEPI • Awarded to Calhoun ISD in partnership with Macomb ISD and Shiawassee RESD • Beginning date: January 1, 2007

  3. Introduction to the Grant • Emphasis on teacher quality – at least 25% of funds must be spent on professional development • Focus on high-need LEA partners • Expand the tools and professional development activities to all ISDs across the state

  4. What Happens with your Data Now? • Local districts report to State through SRSD, REP, etc. • District/building seldom sees this data, so quality/purpose/importance of data is unclear to the district/building • Data-based decision making to inform school improvement, a key to increasing student achievement, requires separate, labor intensive effort

  5. Goals of Data for Student Success • Build and bring to scale a program that helps schools develop cultures of quality data in which there are consistent and sustained efforts to: • Focus on existing data that give insight into specific school improvement questions • Validate data provided to the State and used to support school improvement decisions • Identify critical questions whose answers would benefit school districts in decision making to inform instruction • Provide inquiries designed around the critical questions • Provide focused professional development on data-based decision making • Provide a scaffold of support for the CNA and High Priority Schools

  6. Cycle 1 Focus(January – September 2007) • Local Data Initiatives • Making connections to local data warehouses • Local Professional Development • Materials and approach development • Proving ground for scaling up • Animated Tutorials • Prototype Dynamic Inquiries • Putting longitudinal State data to work

  7. Cycle 2 Focus(October 2007 – September 2008) • Scaling Up - Local Initiatives with the Future in Mind • Continued robust support for Calhoun ISD, Macomb ISD and Shiawassee RESD local data warehouses and intensive professional development • Intensive PD support for Branch and Barry ISDs • Pilot of scalable PD with Dynamic Inquiries for Jackson ISD and Gratiot-Isabella ISD • Access to Dynamic Inquiries and “train the trainer” participation for Eastern UP ISD • Animated Tutorials • Dynamic Inquiries • Refine and extend current inquiries • Planning for Predictive Inquiries • Planning for Statewide rollout

  8. Cycle 3 Focus(June 2008 – September 2010) • Implementing Statewide Roll Out of the Data 4SS Professional Development Model to many ISDs (2008-09) • Spring/Summer 2008 • Revision of Data 4SS Professional Development modules and website • Wayne RESA – PD Overview • UP Facilitators Overview • Eastern UP Data 4SS Overview

  9. Cycle 3 Focus • October – December 2008 • Two Launch Events (Escanaba, Flint) • MDE Field Service consultants presentation • Presentation at MDE Fall School Improvement Conference

  10. Training Update ISDs/ESAs that have attended a Launch: • Barry • Bay-Arenac ISD • Branch • Calhoun • COP ESD • Copper Country ISD • Delta-Schoolcraft • Dickinson-Iron • Eastern UP ISD • Eaton ISD • Genesee ISD • Gogebic-Ontonagon • Gratiot-Isabella RESD • Jackson County ISD • Lapeer County ISD • Lenawee ISD • Livingston ESA • Marquette-Alger • Mason-Lake/Oceana ISD • Menominee • Midland County ESA • Oakland Schools ISD • Saginaw ISD • Shiawassee • St. Clair RESA • Traverse Bay Area ISD • Washtenaw ISD • Wayne RESA

  11. Training Update • ISDs/ESAs that registered for Grand Rapids Launch: • Ingham ISD • Kalamazoo RESA • Kent ISD • Macomb • Mecosta/Ocesola

  12. Cycle 3 Focus • December 2008 – September 2010 • Provide support to Cohort ISDs – could be assistance for training sessions, debriefing sessions, and future planning • Provide follow up/support PD • Continuous communication with identified groups • Provide input for Data 4SS revisions and updates • Enhancements – MI-Access, MME, ELPA • Launch 4

  13. Cycle 4 – 6 Focus • March 2009 – September 2012 • Provide train-the-trainer professional development and support for multiple cohorts of current and remaining ISDs/RESAs • Enhance the online dynamic inquiries based on currently identified data sets (MEAP, MME, ELPA, and MIACCESS) and input from participating ISDs. • Modify the professional development model around the enhanced dynamic inquiries and Identify how the ‘School Improvement Planning’ and D4SS inquiry tools could be used to support the CNA and PA25 reporting process. • Continue building a basis for the expansion and sustainability of the work with local and intermediate school district staff and among and between state agencies.

  14. ISD/ESA Team Roles: Why? • “Schools that explore data and take action collaboratively provide the most fertile soil in which a culture of improvement can take root and flourish.” "The Collaborative Advantage." Educational Leadership Dec/Jan (2009)

  15. ISD/ESA Team Roles: Why? • What is the technology leader's role in helping to create a culture of collaboration? Summary of actual responses from district technology leaders: • Support efforts toward collaboration by attendance and participation.  • Be a part of that culture. The trust factor is critical for the tech leader to be an effective resource.  The tech director needs to be seen and trusted as an educator. • The technology leader's role is to act as an active member of the school's leadership team that models collaboration and creates an environment that supports collaboration for all stakeholders. • Model and promote means to improve collaboration, communication, data access, analysis, and reporting.

  16. PD Modules Using Data to Improve Student Achievement is a series of four modules designed to support principals and school teams in leading school improvement efforts through data-driven instructional decisions. The modules intend to enhance the skills of school leaders to analyze and use their state assessment, school and classroom data to improve student achievement.

  17. Using Data to Improvement Student Achievement Modules • Using State Data to Identify School Improvement Goals • Using School Data to Clarify and Address the Problem  • Examining Student Work to Inform Instruction • Using Classroom Data to Monitor Student Progress For more detailed information please go to www.data4ss.org

  18. Data for Student Success PD Tools • Each professional development module will utilize the following tools: • In depth focus questions to help determine outcomes • Agenda for participants • PowerPoint presentations to guide the workshop • Worksheets for participants • Animated tutorials

  19. Let’s take a tour of the Data4SS Web site www.data4ss.org

  20. Let’s investigate the Dynamic Inquiry Tool… • All data mining efforts must be based on inquiry – asking the right questions, and then asking more questions of the answers in order to make informed decisions. • “Data-driven decision making does not simply require good data; it also requires good decisions.” "The New Stupid." Educational Leadership Dec/Jan (2009) • “The essential-questions approach provides the fuel that drives collaborative analysis.” “Answering the Questions that Count." Educational Leadership Dec/Jan (2009)

  21. Dynamic Inquiry Tool • Interactive inquiries that allow a user to drill down into their student data • Six inquiries based on essential questions aligned with the school improvement process: • MEAP Proficiency • Students Near Proficiency • Comparative Item Analysis • Cohort Proficiency • Student History • Admin Review

  22. Access the Demo Site • Go do www.data4ss.org • Click on Dynamic Inquiries page and access the Data Inquiry Tool • Username: demo_test1 • Password: fall_01 • Examples of the Questions and Inquiries used follow

  23. MEAP Proficiency Inquiry “How did students perform on MEAP tests by content area, strand, and GLCE?”

  24. MEAP Proficiency - All Students

  25. WHY? • Why compare school to district? • Why compare school to ISD? • Why compare school to state? • AYP Targets – Cautions • State average does not mean proficient

  26. MEAP Proficiency - Statistical Information

  27. MEAP Proficiency - Student Drill Down

  28. MEAP ProficiencyAYP Subgroups

  29. MEAP ProficiencyOther Subgroups

  30. Sub Group Statistical Information

  31. Students Near Proficiency Inquiry “What are the demographic characteristics of students who are close to being proficient on a specified test?” “How well did those students perform by strand, GLCE, and comment codes?”

  32. Students Near Proficiency - Graph

  33. Students Near Proficiency - Drilldown

  34. Cohort Proficiency Inquiry “What is the evidence of one year’s growth for one year of instruction?”

  35. Cohort Proficiency - Graph

  36. Cohort Proficiency - Statistical Information

  37. Cohort Proficiency - Drilldown

  38. Student History “What is the complete academic history of an individual or group of students?”

  39. Student History • Provide student level data from SRSD and MEAP • Student data in 4 areas • Student Identification • Student Attendance • Program Participation • Achievement History • Useful when students transfer to a new school because of the ability for district key staff to view or download the data.

  40. Student History – Student Record

  41. Student History - Attendance

  42. Student History - Participation

  43. Student History - Achievement

  44. Comparative Item Analysis Inquiry “How did student performance within a district or building or ISD compare to the State?” • The comparative item analysis inquiry also answers the following question: “How did we do in comparison to the state on items/GLCE in a strand?” • Will help to identify curriculum and teaching areas that may need adjustment

  45. Comparative Item Analysis - Chart

  46. Comparative Item Analysis:Numbers and Operations Detail Graph

  47. Comparative Item AnalysisNumbers and Operations Tabular Results

  48. Comparative Item Analysis – Released Items – Student Data

  49. FERPA Issues • Legally and ethically responsible • Data security – physical and confidential • Paper, electronic, conversations • What do our LEAs know? • How secure/protected is the data? • Do we have confidentiality issues? • Are the annual requirements and notifications in place? • What is the ISD’s role?

  50. FERPA Quiz Group Results • Sample of the results from the Ann Arbor Data4SS Launch event…

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