1. USING DATA FOR SCHOOL IMPROVEMENT Charting the Course

2. Phase II - Analyzing the System Step 1. Form the District Leadership Team. Step 2. Collect and Organize Additional Data. Step 3. Conduct a Gap Analysis. Step 4. Formulate a Problem Statement. Step 5. Describe the Ideal State.

3. Data Analogy Think about the data collected on a school district or campus. With a partner, discuss how this data and its analysis are analogous to the “focus” and “zoom” functions on a camera.

4. Data Scenario 1-A Ranking by H. S. Graduation Rate (Class of 2002) 1) New Jersey 2) Iowa 3) Wisconsin 4) North Dakota 5) Minnesota 6) Nebraska 7) Pennsylvania 8) Connecticut What general conclusion can we reach regarding these 8 states?

5. Data Scenario 1-B You have been given a list [Greene & Winters (2005)] with states’ cumulative graduation rates from the class of 2002. Fill in the blanks with the following states’ ranks. Colorado ____ Michigan ____ Iowa ____ Nebraska ____ Massachusetts ____North Dakota ____ Minnesota ____Pennsylvania ____

6. Data Scenario 1-B What do those 8 states have in common? Answer: http://mb2.ecs.org/reports/Report.aspx?id=735 (Education Commission of the States database)

7. Data Scenario 1-B ** Denotes that there are no state mandated course requirements for graduation. Ranking by H. S. Graduation Rate (Class of 2002) 1) New Jersey **2) Iowa 3) Wisconsin **4) North Dakota **5) Minnesota **6) Nebraska **7) Pennsylvania 8) Connecticut

8. Data Scenario 1-C ** Denotes that there are no state mandated course requirements for graduation ^ Denotes that this state has no exit exam requirement for graduation Ranking by H. S. Graduation Rate (Class of 2002) 1) New Jersey ** ^ 2) Iowa ^ 3) Wisconsin ** ^ 4) North Dakota **5) Minnesota ** ^ 6) Nebraska ** ^ 7) Pennsylvania ^ 8) Connecticut

9. Data Scenario 1-D Having this additional information, revisit the state rankings and discuss with others at your table how that knowledge might impact your interpretation of the original state ranking data.

10. Data Scenario 2 Review the “Gummy Bears” scenario. Based on the given data, what is this student’s level of understanding of the concept of multiplication? Discuss with others at your table and be prepared to share with the entire group.

11. What Data? As a table group, use one index card and the guiding questions below. List the types of data needed to guide school improvement efforts. Select a spokesperson to share out. –What data and strategies are necessary for finding out whether the steps that districts or campuses are taking are making a difference for all students? –What data will help you know what needs to change in order to get better results?

12. One measure, by itself, gives useful information. Comprehensive measures used together and over time provide much richer information. Together, these measures can provide a powerful picture that can help us understand the school’s impact on student achievement. These measures, when used together, give schools the information they need to get the results they want. What Data?

13. Student Learning Demographics Perceptions School Processes Bernhardt’s Model of Data Categories Bernhardt, V. (2004). Data analysis for continuous school improvement (2nd ed.). Larchmont, NY: Eye on Education.

14. Dr. Victoria L. Bernhardt Will be at Learning for a “Change” Summit III, sponsored by Region 16 Title I Statewide School Support/Parental Involvement Initiative September 30, 2008 8:30 am – 3:30 pm ESC 16 Workshop Registration Deadline: September 15, 2008 http://www.esc16.net/dept/isserv/title1swi/parental/worksh op%20info08_09.html

15. Student Learning Demographics Perceptions School Processes Examples: Enrollment Attendance Drop-out Rate Ethnicity Gender Grade Level Language Proficiency Bernhardt’s Model of Data Categories

16. Student Learning Demographics Perceptions School Processes Examples: Perceptions of learning environment Values and beliefs Attitudes Observations Bernhardt’s Model of Data Categories

17. Student Learning Demographics Perceptions School Processes Examples: TAKS Norm-referenced tests Teacher observations Authentic assessments Bernhardt’s Model of Data Categories

18. Student Learning Demographics Perceptions School Processes Example: Description of school programs and processes Bernhardt’s Model of Data Categories

19. Although not a data category, time is an important variable that needs to be an integral component with the four data types. Looking at data over time adds a dimension that usually reveals data trends and increases overall understanding of the collected information. Time

20. Data Categories in Real Context You have been provided with an abbreviated AEIS report from ABC Junior High School. Individually, scan through the data with a focus on the types of data categories found and the types of conclusions that can be reached with that data. Discuss the above with your table group. Also, be mindful of what things these data do not tell you. Be prepared to share with the whole group.

21. Intersecting Categories of Data By itself, each category of data provides a limited perspective. A much clearer picture and deeper understanding are developed by merging data from different sources and from different categories.

22. Student Learning Demographics Demographic/ Student Learning Data Tells us: whether specific groups of students are learning at the same level or not Bernhardt’s Model of Data Categories

23. Perceptions School Processes School Process/Perceptions Data Tell us: if student perceptions about a school program are favorable Adapted from: Bernhardt, V. (2004). Data analysis for continuous school improvement (2nd ed.). Larchmont, NY: Eye on Education. Bernhardt’s Model of Data Categories

24. Your Turn…

25. Each table has been provided with sets of 4 circles each. The circles represent Bernhardt’s 4 data categories. With a partner, choose any two of the circles and place them in such a way that they illustrate an intersection of the two data types. Make up a real life educational context of data that would represent that intersection. Be prepared to share with the whole group. Working with Data Categories

26. Data Scenario 1: Are students who are most satisfied with school (group A) being taught differently from students who are not satisfied with school (group B), and who are these students? Discuss at your tables: What data categories are needed? What could be some examples that model the question and data scenario above? Working with Data Categories

27. Demographics Perceptions School Processes (which students) (satisfaction) (how taught) Bernhardt’s Model of Data Categories

28. Data Scenario 1: Data that is the intersection of school processes, perceptions, and demographics … Based on your discussion, how could this type of information inform your district or campus improvement plan? Discuss this with a partner at your table. Working with Data Categories

29. Data scenario 2: What are the differences in student learning results, based on who the students are and how they are taught algebra? Discuss at your tables: What data categories are needed? What could be some examples that model the question and data scenario above? Working with Data Categories

30. Student Learning Demographics School Processes (which students) (how taught) (algebra achievement) Bernhardt’s Model of Data Categories

31. Data Scenario 2 : Data that is the intersection of school processes, student learning, and demographics … Again, based on your discussion, how could this type of information inform your district or campus improvement plan? Discuss this with a partner at your table. Working with Data Categories

32. Revisit Data Category List Find your index card and review the data category list that you developed earlier. Discuss with others at your table any additional types and/or combinations of data that you utilize in your work and how useful/informative those data are. Be prepared to share with the whole group.

33. Beyond Raw Data Raw data alone, even from different sources and from different categories is insufficient. How that data is organized, analyzed, and presented is critical.

34. It is critically important to graph the data in such a way that everyone on staff can understand the information in the same way. Graphs allow us to see the same information at the same time. It is important to make the graph as clear as possible for no misinterpretations. It is crucial to label the graphs and to pay attention to the scale and baselines of the graphs. Presenting Data

35. Data Presentation Example

36. Original Data Presentation

37. Same Data, Two Perspectives

39. Questions About Where We Are Data We Have on Hand Data We Need to Collect Is the same cohort of students improving in math achievement from year to year? Grades 3–8 state test Benchmark tests Demographic data Test scores from year-to-year on same cohort Student Achievement Data Organization

40. Questions About Where We Are Data We Have on Hand Data We Need to Collect At what grade level does parent involvement start to decrease? Why do some parents stay involved? Rosters of parent attendance at parent/teacher conferences in grades K–8 Year-to-year comparison of rosters Parent perceptions of conferences at critical junctures Parent Involvement Data Organization

41. School Support Teams and Data At your table group, discuss the following: 1) How might the SST encourage districts and schools to collect data? 2) How might the SST help districts and schools analyze and use data?

42. Using Data to Analyze the System Tools and processes used in the Working Systemically approach

43. SEDL — Working Systemically As part of a long-term collaboration with the Dana Center and districts in several states, SEDL developed processes and tools for data collection and organization. Refer to the handout Alignment Summary of District and Campus Data SEDL. (2005). Working systemically to increase student achievement in reading and mathematics. Austin, TX: Author.

44. Example from a District’s Plan

45. Perception Data from Surveys, Conversations, and Focus Groups

46. SEDL Data Collection Tools Working Systemically Survey: TXCC offers this survey tool on-line. Campuses may choose a the mathematics or reading surveys depending on their AYP data and needs. (Revised survey will be generic for content area.) TXCC will assist by providing data graphs and report. Contact your TXCC support staff, who will send the report(s) to you. NOTE: The survey Web site is currently being revised.

47. Working Systemically Survey Go to http://txcc.sedl.org/survey/ws/ And follow the prompts. To go to a specific survey, you may use these links: Mathematics  http://survey.sedl.47.dll/s/1g6 http://survey.sedl.47.dll/s/1g6 Reading  http://survey.sedl.org/efm/wsb.dll/s/1g7 http://survey.sedl.org/efm/wsb.dll/s/1g7

48. Example of WS Survey Data

49. Example of WS Survey Graph

50. Ten questions are provided for district and school leader conversations. Information can be added to the Alignment Summary. Conversations With Leaders

51. One or more focus groups may be held to reflect on the ten questions for teachers. Feedback can be added to the Alignment Summary. Focus Groups With Teachers