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2	The Wallace Foundation 28th largest private foundation and is a legacy of DeWitt and Lila Acheson Wallace A major force in education Grant initiatives designed to increase student achievement though more effective leadership Michigan was 1 of 15 states that received a grant from the Wallace Foundation WMU was the only university funded out of the 15 states The purpose of the grant is to promote creative, effective working dynamics between local leaders and state policy makers.
3	The Michigan-WMU Initiative The Michigan project is designed to improve student achievement by increasing principal leadership skills through the development of a statewide model for data driven decision-making.
4	Michigan Coalition of Educational Leadership Michigan Department of Education and the Governor’s Office Michigan Association of School Boards (MASB) Michigan Association of School Administrators (MASA) Michigan Association of Secondary School Principals (MASSP) Michigan Elementary and Middle School Principal Association (MEMSPA) Michigan Institute for Educational Management (MIEM) Eastern Michigan University Central Michigan University Western Michigan University Michigan Leadership Improvement Framework (MI-LIFE)
5	Why Data? Data can take the politics out of a program, district analysis, and resource allocation.
6	Questions We would like participants to formulate three critical questions that should be asked to promote student learning. What are questions that board member should be asking? What are questions that every superintendent should be asking?
7	Are Students Learning? Do teachers, administrators and board members understand "why students are learning and why they are not learning? " Some Issues: curriculum alignment instructional related students attendance discipline teacher attitude related (student cannot learn a district or building issue (K-12).
8	Principals’ Perception Regarding the Use of Data Do not understand data use What data do you use Teacher collection of data Need systematic disaggregation Find better assessment tools PD for teachers and principals Teachers uncomfortable with data Teachers cannot read data Data has meaning to classroom Do not know what to do with data Data not part of teacher training Lack of knowledge data-instruction No data link to teaching practices
9	Principals’ Perception of Time Constraints to Analyze Data Time to analyze Time in getting test results Time in getting data back District slow data return, A year behind-results Time to complete tasks Data vs. classroom duties Limited instructional time Time to analyze data Time for collaboration Holistic approach in working with teachers Time to monitor teacher use Time for collaboration
10	Principals’ Perception of Teacher and Student Issues Results do not reflect current students Needs to make sense Students mirror teacher attitude Too much student testing Teacher buying into data Unsure if data use beneficial Quality of instruction No consistence in teacher use of tools Teacher cooperation in assessment Teacher- team cynicism Teachers see data as important Teacher-staff cooperation in data assessment Consistent teacher collection of data Student do not take testing seriously A few teachers see testing as a fad Utility of data No relevance to individual students
11	Principals’ Perception Regarding Data Overflow and Other Barriers Personnel shortages Inadequate technology Too much data Not user friendly Data piecemeal Time needed to disaggregate data Streamline data Need data warehouse Placing data in useful forms Inability to track progress Returned reports incomplete Mobile community State consistency Overemphasis on state tests Teachers assume responsible for student achievement Low student expectations
12	Why Deal With Data? School leaders and policy-makers everywhere are talking about data and why they are important for improving our schools. It makes good sense. How can you measure whether the performance policies you approve as a school board member are effective if you don’t look at the numbers? But why are the numbers significant? What do they tell us about our schools? In what ways have school boards used data successfully? What are the consequences of ignoring data?
13	Put Your Fears on the Table What concerns you most about using data to make school policy decisions?
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15	"How are we doing" How are we doing? Are we serving all students well? In what areas must we improve?
16	"1" 1. How are we doing? This question focuses on the most important part of your work — creating high-quality public schools. It requires boards to examine student performance with the help of indicators such as test scores, the number of Advanced Placement students, attendance rates, dropout rates, student mobility and other measures.
17	"2" 2. Are we serving all students well? Today’s schools enroll students with very different learning needs: special education students, gifted and talented students, speakers of other languages. Schools are more racially and ethnically diverse than ever before. By looking at data that examine how subgroups of students are performing, you quickly learn who is excelling, who is falling behind and why. Armed with this information, school board members and staff can develop a plan to ensure that no student is left behind.
18	"3" 3. In what areas must we improve? After examining data with administrators and staff, you may find gaps in learning that point to poor leadership, outdated curriculum, ineffective instructional practices, limited budgets or little parental involvement. Data can help you learn more about your district’s strengths and weaknesses so you can address problems head-on.
19	"Data are to goals what..." Data are to goals what signposts are to travelers; data are not end points, but data are essential to reaching them . . . The signpost on the road to school improvement. Thus data and feedback are interchangeable. (Schmoker,1999, p. 30).
20	Traditional and Data-Driven Decision Making
21	Is Your District Ready Use Data More Effectively?
22	Why Pay Attention to Data? Reliable and up-to-date data help you:
23	Multiple Measures of Data
24	Multiple Measures of Data If the purpose of school is to ensure that all students learn, What data will help schools understand if they are effectively carrying out their purpose? What data analyses will help schools know if all students are learning?
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28	Demographic Data Demographic data provide descriptive information about the school community, such as enrollment, attendance, grade level, ethnicity, gender, and native language. Demographic data are the part of our educational system over which we have no control. We can observe trends and glean information for purposes of prediction and planning. Demographic data give us a glimpse of the system and how the school organizes its system.
29	Perceptions Data Perceptions data help us understand what students, parents, staff, and others think about the learning environment. Perceptions can be gathered through questionnaires, interviews, focus groups, and/or observations. Perceptions are important because peoples' actions reflect what they believe, perceive, or think about different topics. Perceptions data can also tell us what is possible.
30	Student Learning Data Student Learning describes the outcomes of our educational system in terms of standardized test results, grade point averages, standards assessments, and authentic assessments. Schools often use a variety of student learning measurements separately, sometimes without thinking about how these measurements are interrelated. Schools normally think of multiple measures as looking only at different measures of student learning, rather than including demographics, perceptions, and school processes.
31	School Processes Data School Processes define what we are doing to help students learn: how we group, teach, and assess students. School processes include programs, instruction and assessment strategies, and other classroom practices. To change the results schools are getting, teachers and school personnel must document these processes and align them with the results they are getting in order to understand what to improve to get different results, and to share their successes with others.
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35	DATA-INFORMED DECISION-MAKING: Introduction This document is developed to address the data needs embedded in the Michigan School Improvement Framework. The illustrated data points and data analyses are just examples. More summative than formative. While the discussion questions in Michigan School Improvement Framework lead to more formative evaluation, the data source and the data analysis suggested in this document are more summative. Decision-oriented. The examples of data points and data analyses are decision-oriented. A decision could be made after each data analysis. Need-based. In a continuum from (a) an open-ended tool kit, to (b) an inquiry process, and to (c) a model of data points and analyses, we choose to focus on (c) because we feel it is important to provide concrete examples for principals and other school personnel. Catering to a wide range of audience. This document is intended for educators in various capacities. This document could also be used as a curriculum guide on data-informed decision-making by universities’ educational leadership programs and professional associations’ professional development.
36	Strand I: Teaching for Learning
37	Strand I: Teaching for Learning
38	Strand I: Teaching for Learning
39	Strand I: Teaching for Learning
40	Strand I: Teaching for Learning
41	Strand I: Teaching for Learning
42	Strand II: Leadership
43	Strand II: Leadership
44	Strand II: Leadership
45	Strand II: Leadership
46	Strand II: Leadership
47	Strand II: Leadership
48	Strand III: Personnel & Professional Development
49	Strand III: Personnel & Professional Development
50	Strand III: Personnel & Professional Development
51	Strand III: Personnel & Professional Development
52	Strand III: Personnel & Professional Development
53	Strand IV: School & Community Relations
54	Strand IV: School & Community Relations
55	Strand IV: School & Community Relations
56	Strand IV: School & Community Relations
57	Strand V: Data & Information Management
58	Strand V: Data & Information Management
59	Strand V: Data & Information Management
60	Strand V: Data & Information Management
61	Strand V: Data & Information Management
62	Strand V: Data & Information Management
63	Policy Examples Data Collection/Assessment The data collected by the District in order to assist each local school improvement committee may include, but not be limited to: student standardized test scores, dropout rates, student/staff ratios, grade point averages, demographic and societal data, career/employment data, vandalism, student attendance rates, student discipline, and others as the committee may need. The collection of the data should involve input from parent(s)/guardian(s), staff, students, and other community members. Confidentiality regarding personally identifiable information shall be maintained at all times by all members of the local school improvement committee.
64	"Wallace Foundation Website" Wallace Foundation Website www.wallacefoundation.org Western Michigan University-Wallace Grant Website www.wmich.edu/wallacegrant Michigan Department of Education School Improvement Framework Document http://www.michigan.gov/mde/0,1607,7-140-28753_38959---,00.html
65	"Dr" Dr. Van Cooley Dr. Jianping Shen Dr. Patricia Reeves Dr. Walter Burt Dr. J. Mark Rainey Department of Educational Leadership, Research and Technology College of Education Western Michigan University 269-387-3896