1 Data for Student Success March 3, 2010 NCES and MIS Conference “It is about focusing on building a culture of quality data through professional development and web based dynamic inquiries for school improvement.”

2 Creation of Data 4 Student Success Introduction to the Grant Federal Title II Part D of the NCLB Act of 2001 Enhancing Education through Technology Grant awarded through CEPI Awarded to a 3 county partnership - Calhoun, Macomb and Shiawassee Beginning date: January 1, 2007

3 Introduction to the Grant Emphasis on training – 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 many District’s Data? Local districts submit data to the State through the Michigan Student Data System (MSDS), Registry of Education Personnel (REP), etc on intervals throughout the year. In the recent past, data quality was poor because Districts/buildings seldom used their own data. Data Quality has improved significantly as Districts/buildings actually use & understand the purpose/importance of the data. 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

6 Grant Goals Continued 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 Comprehensive Needs Assessment and High Priority Schools

7 Grant Deliverables Local Data Initiatives –Making connections to local data warehouses Local Professional Development –Materials and approach development –Proving ground for scaling up Animated Tutorials available online Dynamic Inquiries –Putting longitudinal State data to work

8 Grant Deliverables continued Train-the-trainers sessions held on the use of data to inform instruction –Launch Events were held at various Intermediate School District (regional based) locations across the State of Michigan. –Involvement of the Michigan Department of Education Field Service consultants –MDE Fall School Improvement Conference and many other presentations at Stakeholder events

9 Grant Budget Categories (Four Years) Professional Development/Training - $2,250,000 Dynamic Inquiry Development - $585,000 Contracted Project Management - $580,000 Local Data Warehouse Initiatives (Years 1 and 2) - $1,250,000

Training began with 3 core partner ISDs: Calhoun Macomb Shiawassee In conjunction with the State of Michigan’s Center for Education Performance and Information Michigan Department of Education

11 Training Includes –Launch Events were held at various Intermediate School District (regional based) locations across the State of Michigan. Training Kits / Materials Binders On-line videos Workshop facilitation tools –Involvement of the Michigan Department of Education –MDE Fall School Improvement Conference and many other presentations at Stakeholder events

12 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 –Administrative Data Quality

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

14 How Did We Perform? What percent and number of our students met proficiency on MEAP in each content area? What percent met advanced? What number of students failed to perform at proficiency? Who are these students? In what subgroups do these students belong?

15 Compared To What? Did we meet our AYP target? What percent of students were moved from one proficiency level to a higher level? How did our performance compare with our district, ISD, and state? Have we made progress over time?

16 MEAP Proficiency - All Students

17 MEAP Proficiency - Statistical Information

18 MEAP Proficiency - Student Drill Down

19 MEAP Proficiency AYP Subgroups

20 MEAP Proficiency Other Subgroups

21 Sub Group Statistical Information

22 Students Near Proficiency Inquiry “What are the demographic characteristics of students who are close to being proficient on a specified test?”

23 Students Near Proficiency - Graph

24 Students Near Proficiency - Drilldown

25 Cohort Proficiency Inquiry “How did students perform on MEAP this year compared to their performance on MEAP last year?”

26 Growth Model Pilot Use performance level change to track students performance from year to year Measure whether students who are not yet proficient are “on track” to becoming proficient within three years Determine that if students are “on track” toward becoming proficient within three years, those students will count toward making AYP even if they are not yet proficient Identify students who are “on track” toward proficiency within three years will apply only to grades 4-8 for ELA and math

27 Cohort Proficiency - Graph

28 Cohort Proficiency - Statistical Information

29 Cohort Proficiency - Drilldown

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

31 Student History Provide student level data from the following datasets: Single Record Student Database, School Code Master, Registry of Education Personnel, Michigan Educational Assessment Program (MEAP), and Mi-ACCESS Sorts student data into multiple areas: Student Identification, Student Attendance and Prior Enrollment, Program Participation, and Achievement History Useful for students transferring to a new school

32 Student History - Identification

33 Student History - Attendance

34 Student History - Participation

35 Student History - Achievement

36 Comparative Item Analysis Inquiry “How did student performance on a strand, GLCE, and item compare to the State?”

37 Comparative Item Analysis - Chart

38 Comparative Item Analysis – Released Items

39 Comparative Item Analysis – Released Items

40 Comparative Item Analysis – Released Items - Students

41 Let’s Investigate the Dynamic Inquiry Tool…

42 Let’s take a tour…

43 Now we have investigated State level achievement data, let’s consider more timely and varied local data

44 Local Warehouse & Assessment Data Allows you to dig deeper and get to root cause Also allows you to monitor and adjust

45 Local Data Warehouse Local Data Warehouse Data is more timely – often uploaded nightly – so monitoring of student achievement and instructional adjustments can be made in the classroom. The State of Michigan is not currently collecting local data (daily updates) in its databases

46 Local Data Warehouse Local warehouse data can include multiple types of data: Achievement: Local Tests, Across Assessments, Grades, Transcript history, state and national test scores, pretests, etc Demographic Enrollment, Attendance, Gender, Migrant, other local district indicators, nutritional eligibility Process: Programs, Response to intervention, special services, parental involvement, progress monitoring, graduation/employment surveys Perception: survey data, failure and achievement aggregate statistics

47 How do Data4SS and local data warehousing tools work together? Together they provide the ability to triangulate data multiple types of data from multiple sources Both provide non-negotiable state data  Data4SS is based on enrollment at time of MEAP  Local warehouse is based on live/current enrollment Local warehouse provides analysis of district required assessments Local warehouse provides analysis of classroom performance data Local warehouse provides frequent systematic monitoring for growth to avoid unexpected results

48 Available Data in Macomb ISD’s Warehouse Right Now Achievement Data MEAP MME DIBELS DRA MLPP PLAN EXPLORE ACT End of Course Exams Common Assessment Grades GPA Courses Credits Teachers Student Data Subgroups Lunch Status Special Education Language Ethnicity Discipline/Positive Behavior Support Data Attendance Parental Involvement Process Data Title One Programs Extra Curricular Activities Programming

49 Of all the data sets listed on the previous slide, only MEAP, MME, and summary data for students based on SRSD are available on Data 4SS for analysis And none of that data is very timely…

50 Local Data Warehouse has a Variety of Uses Summary Reports – Annual/semi-annual long- term results, after instruction Monitoring Reports – On-going, check of progress Trend Data – Will help us see progress over time. Evaluative Data – Will help us determine our effectiveness and make predictions. Local Data Warehouse Data is USED by classroom teachers

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52 Data 4SS “The Big Picture” inquiries that allow schools to see their state level data over time and compared to the district, county & state. Allows schools to drill down to the individual student’s performance. Provides a common set of data reports that can be discussed statewide and used for Education YES!, grant applications, etc… Local Warehouse Brings local data sets into the mix (i.e., local assessment, attendance, grades, demographics, programs, etc…) Allows for data triangulation, on-going monitoring & adjustment (nightly updates), comprehensive data analysis & program evaluation. Dynamic data analysis

53 Data 4SS State data loaded Pre-Built Reports (MEAP, MME, Mi-Access) Comprehensive Needs Assessment (CAN) including student enrollment, and grade level and subgroup analysis PA 25 reporting Compare for local, district, isd, and state data Basic student level data Cannot LOAD own data Local Warehouse Data Director Student information system data based on State ID code (unique identifier code) Load ANY and ALL assessment data including ELPA, DIBELS, ITBS, Teranova, Data for individual Teachers Prebuilt reports Custom Reporting Ability to share exam, assessment, and reports across consortium Create Exams and instrument State, national and District standards Student profile reports Transcripts and grades

54 Macomb’s Data Director Local Initiative Preschool children ~ K-12 ~and Graduates loaded in the local data warehouse Quick and Easy to learn and USE Data Coaches are a CRITICAL component of our initiative Data / Leadership Teams are essential for district and building success in using data to inform instruction

55 Macomb’s Data Director Local Initiative Implementation and Re-implementation Adoption County-wide and extending through the Regional Data Initiative Grant On-going training and integration of training into as many professional development offerings as possible Professional development on creating a culture of Quality data using NCES materials

56 Macomb’s Data Director Local Initiative All finding the real meaning of a “common assessment” Participation in State initiatives for end-of-course exams and jurying released items for use in the item bank Attention to security requirements and relentless adherence to FERPA and “open online CA60s” Teaching professionals driving the need for commonality with support from Administration

57 Macomb’s Data Director Local Initiative Stretching Professional development offerings to be available literally morning, noon, and night and …. On-line. Finding and Proving answers and instructional techniques through the use of data and turning that data into actionable information

58 Macomb’s Data Director Local Initiative Example

59 Macomb’s Data Director Local Initiative Screen shot example from dAta director will go here

60 Macomb’s Data Director Local Initiative

Michigan Literacy Progress Profile Standards created and adopted by teachers 61

Professional Development for School Improvement Teams A Manual for Data Inquiry and Access 62

63 Data 4SS + Local Warehouse Student Success

64 Four Professional Development Modules on Using Data to Improve Student Achievement

65 PD Modules 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.

66 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

67 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

68 Future Plans

69 How Does DATA 4SS Fit with “The Bigger Picture” Michigan’s Regional Data Warehouse Initiative Michigan’s Longitudinal Data System Michigan’s Education Data Portal

70 School Improvement Planning Process Do Implement Plan Monitor Plan Evaluate Plan Plan Develop Action Plan Study Analyze Data Set Goals & Measurable Objectives Research Best Practice Student Achievement Gather Getting Ready Collect Data Build Profile

71 The School Improvement Process Getting Ready Collect Data Build Profile School Data Profile School Process Profile Analyze Data School Data Analysis School Process Analysis Summary Report Set Goals & Measurable Objectives Research Best Practice Develop Action Plans Implement Plan Monitor Plan Evaluate Plan Comprehensive Needs Assessment School Improvement Plan

72 School Summary Report What are the challenges identified in your comprehensive needs assessment (including the School Data Analysis and the School Process Analysis)? What are the root causes for the gaps? Greatest needs Greatest challenges Additional information or data Study the School Summary

73 Comprehensive Needs Assessment School Improvement Plan Annual Education Report Consolidated Grant funds utilized to support challenge areas (LEA Planning Cycle) leads to MAKING CONNECTIONS - THE BIG PICTURE Where are we now? Where do we want to go and how are we going to get there? How did we do? Do: Requirements

74 Most Difficult Question? –SUSTAINABILITY of the software applications both State and Local –How can we afford these important tools amidst the cuts and threats to education budgets –We search out local funding, grant funding, private funding, pop machine money, donations, PTOs, and grant funding ….. To do the right thing…..

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76 Questions???

77 Data for Student Success Key Contact Information Andrew Henry – Data 4SS Project Director Steve Brodeur – Data 4 SS Project Coordinator Mary Gehrig, Assistant Superintendent, Calhoun ISD Mike Oswalt, Assistant Superintendent, Calhoun ISD Becky Rocho, Assistant Superintendent, Calhoun ISD Mark Cummins, CIO/Assistant Superintendent, Macomb ISD Kristina Martin, Director Management Technology, Macomb ISD Dave Schulte, Assistant Superintendent, Shiawasee RESD Kathy Miller, Director Instructional Services, Shiawasee RESD Michigan Department of Education And CEPI – Michigan’s Center for Education Performance Information