1. + WISExplore Data Retreat Middle School June 2013 Billie Finco and Sherri Torkelson Day 1

2. 6/25/13 2 Used well, monitoring tools are not just for outside agencies or leaders, but for students, teachers, principals and parents, too. These tools let us know when the students are on the right path and at the right pace. The right data systems illuminate what to fix. They let us know when we need to intervene with training or support or where we need to craft a new solution to innovate. Dr. Jerry Weast

3. + Welcome! Logistics Introductions Materials and Resources Schedule 3 6/25/13

4. + WISExplore Retreat Materials 1. Original WISExplore Retreat materials are posted in LiveBinder. 2. Go to www.livebinders.com/play/play/89 9070 (password: wisexplore) www.livebinders.com/play/play/89 9070 4 6/25/13

5. + Our Objectives Learn how to navigateWISEdash Use WISEdash to analyze school data Be introduced to the WISExplore e-learning Modules Begin the School Improvement Planning process 5 6/25/13

6. + Agenda  Opening Data Inquiry Process WSAS Dashboard Inquiry Growth Dashboard Inquiry Attendance Dashboard Inquiry Access Dashboard Inquiry Plan Next Steps 6/25/13 6 Day OneDay Two

7. + Norms Stay engaged Focus on uncovering/discovering/recovering Use technology respectfully Strive for equity of voice 7 6/25/13

8. + Security and Confidentiality Roles of staff and levels of security 8 6/25/13

9. + Evolution of Data Work in Wisconsin 9 6/25/13

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11. + De 11 6/25/13

12. + Documenting Work During The Retreat In the past … 12

13. + Moving to the Digital Age Using an electronic “Fillable Form” Eventually, the Data Inquiry Process will be embedded within the Data Warehouse for easy use. 13 Note: The current Fillable Form will not function effectively on a MAC computer or iPad.

14. + Using Data to Support Continuous Improvement The “wait” to see how students achieved is too late to make important programmatic and instructional shifts to help students responsively Shift in Federal Requirements External accountability shifts to more “high-stakes” Educator Effectiveness drives ownership of student achievement Shifting data sources allow for more immediate & ongoing access WISEdash Your local interim assessments Ongoing collaborative data work is needed to drive continuous improvement Teacher PLC teams School Improvement teams 14 6/25/13

15. 15 So how do we move the needle on student achievement?

16. + Brief Report Card Walk- Through 6/25/13 16

17. + Reconnect to Your Data Reality Access your school report card online in SAFE Locate the following two documents: 6/25/13

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21. + As you consider last year’s reality… 6/25/13 21 What are you wondering about this year’s data?

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24. 6/25/13 24

25. + As you consider last year’s reality… 6/25/13 25 What are you wondering about this year’s data?

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28. + As you consider last year’s reality… 6/25/13 28 What are you wondering about this year’s data?

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30. + 30 Part A Part B

31. + As you consider last year’s reality… 6/25/13 31 What are you wondering about this year’s data?

32. + 6/25/13 32

33. + As you consider last year’s reality… 6/25/13 33 What are you wondering about this year’s data?

34. + Other data… 6/25/13 34

35. + Here’s Sherri! June 2013 35

36. + Data Retreat Data Inquiry Process June 25, 2013 Data Inquiry Process 36 June 2013Finco & Torkelson

37. + WISExplore Data Inquiry Process The data inquiry process … Always begins with a meaningful question Guides teams in intentional data work by providing focus Can be applied to any data question Leads to identifying possible root causes June 2013 37 Finco & Torkelson

38. + Question Begin data inquiry by posing a question that is important to your school’s continuous improvement June 2013 38 Finco & Torkelson

39. + Pose a data question first because … It provides a clear starting point It is the first step in a process that will lead to clear goals It will connect the data inquiry to the vision It keeps you on track It clarifies your purpose for data navigation The process will be both efficient and effective June 2013 39 Finco & Torkelson

40. + Context for a Data Question Considerations when posing a data question: Past performance Current data results Trends over time Changes in … the school community school programs standards June 2013 40 What do I need to know? Finco & Torkelson

41. + Categories of Data Questions Grade level questions Gap and subgroup questions Change, progress and trend questions General questions Subject area and skill questions Course, program questions Achievement questions Individual student questions June 2013 41 Finco & Torkelson

42. + Shaping Data Inquiry Questions BIG questions… How does achievement compare across levels? June 2013 42 Add “Filter” Language to Focus the Data Inquiry  How does math achievement on the WKCE compare in 7 th and 8 th grades for all students enrolled for a full academic year in Elm Grove Middle School during 2012-13? Add “Related Dashboard” Language for Subgroup Comparisons  How does math achievement on the WKCE compare in 7 th and 8 th grades for students with disabilities as compared with nondisabled students enrolled for a full academic year in Elm Grove Middle School during 2012-13? Finco & Torkelson

43. + Investigate Based on the data inquiry question, navigate the appropriate dashboard to locate the data picture that best answers your question. June 2013 43 Finco & Torkelson

44. + Navigation: Functionality “Must- Knows” Dashboards Filtering and clearing filters Related Dashboards Hovering Opening up student lists Sorting student lists Individual Student Profiles 44  Managing layers  Selecting and copying  Exporting  pdfs  Excel  ms word  Saving to favorites  Recalling favorites June 2013Finco & Torkelson

45. + Clarify Documenting data observations, stating specific data findings, trends and gaps Remembering the Data Inquiry Question, view the data picture to shape the team’s observations Observe the data, refine and state critical data findings with supporting details. June 2013 45 Finco & Torkelson

46. + Data Findings With the Inquiry Data Question in mind, describe the data findings. What do you see in the data picture? What patterns do you observe? June 2013 46 Well-written data findings: Are objective – just facts, not opinions or questions Are precise (include the data) Are statements (complete sentences) understandable by stakeholders Include the test type and subject Include the timeframe for the findings Include the specific group of students Finco & Torkelson

47. + Sample Data Findings Non-Examples of Data Findings Our kids didn’t do well in reading. The special ed students went down. There are so many students in the red – why? Examples of Data Findings Related to the Data Question In 8 th grade Reading on the fall 2012 WSAS, over half (54%) of SwD scored in the minimal category, while only 11% of Students Without Disabilities scored at that same level. 58.5% of Students Without Disabilities were below proficient, compared to 85.7% of Students With Disabilities below proficient in reading in fall 2012 on the same test. 47 June 2013 No! Yes! Finco & Torkelson

48. + Hypothesize Examine your system What practices, policies or procedures might be producing these results? June 2013 48 Finco & Torkelson

49. + Hypotheses of Practice Posing educated guesses about school or classroom practices as possible underlying root causes for critical data findings. June 2013 49 Finco & Torkelson

50. + Posing Hypotheses of Practice June 2013Finco & Torkelson 50 Take quiet time to think about all of the causes for the data pattern. Translate each “cause” into a “hypothesis of practice”. Hypotheses of practice … Are our best educated guesses about what “we” are doing or not doing that could be contributing to the results Are guided by the data findings Are shaped by both perceptions of practices and professional experiences in the school Are guided by research-based practices Never place blame on student or family characteristics, or any other unalterable factors

51. + Example Hypotheses Is it because we have not changed our instructional techniques as our school population has changed? Is it because we have low expectations for our special education students? Is it because we are not using best practices in our literacy instruction? Is it because we need to revamp our curriculum? Is it because we are not teaching our curriculum with fidelity? Is it because our instruction is not engaging students? June 2013Finco & Torkelson 51

52. + Categorizing Your Thinking-- 52 Hypotheses of Practice Classroom Instruction & Assessment Instructional Planning & Preparation Learning Environment Professional Responsibilities Human Resource Leadership Instructional Leadership Organization Management School Climate Collaborative Culture Stakeholder Relationships June 2013Finco & Torkelson

53. + 53 June 2013Finco & Torkelson

54. + 2 small tasks and then a break… Create a data folder for today. June 2013Finco & Torkelson 54 My WISExplore Data Folder 2013-14 Log into WISEdash.

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56. + e Learning Module Finco & Torkelson

57. + E-Learning Modules 1. For learning 24/7 anytime anywhere 2. Eventually posted on the DPI website 3. Long-term evolving project based on dashboards 4. Can be used by … Individual educators for self-paced learning PLC teams Higher education classes 57 June 2013Finco & Torkelson

58. + e-Learning Modules: 1. Overview of WISEdash in LiveBinders under WISExplore Retreat Grounding tab 2. WSAS Inquiry Module in LiveBinders under WSAS Data Inquiry tab 58 June 2013Finco & Torkelson

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60. + WISEdash WSAS Data Inquiry WSAS Dasboard Inquiry 60 June 2013Finco & Torkelson

61. + WSAS Entry Points School Report Card 61 ACHIEVEMENT (Rdng & Math) % of Students at Proficiency Levels GROWTH (K-8 Rdng & Math) Year-to-year change compared to state GAPS (Rdng & Math) Gap closure for subgroups ON-TRACK (Rdng & Math) 3 rd Grade Reading & 8 th Grade Math  Provides focus for Data Inquiry  The state assessment plays a major role in external accountability  Summative assessments can reveal internal achievement issues ◦ Marginalized subgroups ◦ Inadequate progress ◦ Persistent low achievement June 2013Finco & Torkelson

62. + Navigation Demo 62 June 2013Finco & Torkelson

63. + Navigation: Functionality “Must- Knows” Dashboards Filtering and clearing filters Related Dashboards Hovering Opening up student lists Sorting student lists Individual Student Profiles 63  Managing layers  Selecting and copying  Exporting  pdfs  Excel  ms word  Saving to favorites  Recalling favorites June 2013Finco & Torkelson

64. + Thinking Ahead about “Drilling-Down” Because WISEdash allows you to “drill down” to specific students, think ahead about the role that the WSAS plays in planning for students. State assessments are external indicators of internal work. Compare state assessment summative results with local assessment results – use caution when comparing fall (WKCE) data with spring local data. Understand that the current WSAS will be implemented for one more year – in fall 2013. Therefore, these analyses help prepare staff for the upcoming new era with the Smarter Balanced Assessment in 2013-15. Respect confidentiality. Save data in a secure data folder. 64 Current WSAS (WKCE & WAA) Future SBAC Local Assessment s Local data should predict external assessment results June 2013Finco & Torkelson

65. + 65 June 2013Finco & Torkelson

66. + Navigation: Functionality “Must- Knows” Dashboards Filtering and clearing filters Related Dashboards Hovering Opening up student lists Sorting student lists Individual Student Profiles 66  Managing layers  Selecting and copying  Exporting  pdfs  Excel  ms word  Saving to favorites  Recalling favorites June 2013Finco & Torkelson

67. + Thinking Ahead about “Drilling-Down” Because WISEdash allows you to “drill down” to specific students, think ahead about the role that the WSAS plays in planning for students. Respect confidentiality. Save data in a secure data folder. 67 Student Lists Will you need lists of … Students who are at minimal or basic levels? Students who achieve at advanced levels? Students who took the WAA? Student Profiles Will you need individual student data such as … Standards Performance Index (SPI) “skill” data in reading or math? The primary disability and educational environment? The language proficiency level? June 2013Finco & Torkelson

68. + Demo – Drilling Down into WSAS Data Example: Student List of math “minimal” WSAS students in a school 68 June 2013Finco & Torkelson

69. + Demo – Drilling Down to the Student Profile Example: Note yellow “tabs” for further details for a student Provides entire WSAS test history Note SPI scores are “extrapolated” percent correct scores. Sort to discover lower scoring skill areas. 69 June 2013Finco & Torkelson

70. + WSAS Data Inquiry – Get Started Go to your Report Card questions and choose 1 related to student achievement in math or reading. We will use this question to apply the data inquiry process to the WSAS dashboard. 70 June 2013Finco & Torkelson

71. + Documenting Your Work- Introducing the Fillable Form The WISExplore “Fillable Form” is designed to help you document the data inquiry process. To use the Fillable Form, you must use a PC rather than an iPad, or MAC platform. Also, the Fillable Form is not compatible with Google Docs. June 2013 71 Finco & Torkelson

72. + The Fillable Form Leads the team through the Data Inquiry Process Guides thinking Keeps the team on task 72 June 2013Finco & Torkelson

73. + We’ll Use the Fillable Form to Guide our Data Inquiry Process QUESTION Enter your data question and select the data source INVESTIGATE Enter WISEdash. Navigate and apply filters to find the data picture that answers the data question. CLARIFY Document data findings. Fill in the areas indicated on the Fillable Form. HYPOTHESIZE Pose and categorize hypotheses of teacher and leadership practices related to the data findings. 73 June 2013Finco & Torkelson

74. + How Do I Find the Fillable Form? Replaces the large flip charts and miscellaneous notes previously used at Data Retreats LiveBinder: go to www.livebinders.com/play/ play/899070 (password: wisexplore) www.livebinders.com/play/ play/899070 Open up the Fillable Form 74 June 2013Finco & Torkelson

75. + WSAS Question Topics Which question will you start with? 75 Common WSAS Data Inquiry Topics (Rdng/Math) Our Data Inquiry Question  Achievement by grade level  Achievement by grade level - trends over time  Inconsistencies in grade level achievement  Achievement differences between comparisons (gaps)  Gap Trends by Gender  Gap Trends by Disability  Gap Trends by Race/Ethnicity  Gap Trends by ELL Status  Gap Trends by Economic Status June 2013Finco & Torkelson

76. + Question What meaningful question have you determined from the examination of your report card? June 2013 76 Finco & Torkelson

77. + Enter the Data Question Type the meaningful data question in the top section of the Data Inquiry Process Form. June 2013 77 Finco & Torkelson

78. + Selecting the Data Source Using the Fillable Form, select the WISEdash dashboard needed to explore the data question – for this section, we will use the WSAS WISEdash dashboard. June 2013 78 Finco & Torkelson

79. + Investigate Based on the data inquiry question, navigate the WSAS dashboard to locate the data picture that best answers your question. June 2013 79 Finco & Torkelson

80. + Investigation Steps: Saving a Data Picture. Find the data picture that best answers your data inquiry question. When you find the picture, save it on the Fillable Form. To save the picture … Copy and paste (easiest) Use a “snipping tool” or similar tool Export to pdf and save the picture in a folder Export to Excel and copy the picture 80 June 2013Finco & Torkelson

81. + Clarify Documenting data observations, stating specific data findings, trends and gaps Remembering the Data Inquiry Question, view the data picture to shape the team’s observations Observe the data, refine and state critical data findings with supporting details. June 2013 81 Finco & Torkelson

82. + List the Data Findings With the Inquiry Data Question in mind, describe the data findings. What do you see in the data picture? What patterns do you observe? June 2013 82 Well-written data findings: Are objective – just facts, not opinions or questions Are precise (include the data) Are statements (complete sentences) understandable by stakeholders Include the test type and subject Include the timeframe for the findings Include the specific group of students Finco & Torkelson

83. + Sample Data Findings June 2013 83 Non-Examples of Data Findings Our kids didn’t do well in reading. The special ed students went down. There are so many students in the red – why? Examples of Data Findings Related to the Data Question In 8 th grade Reading on the fall 2012 WSAS, over half (54%) of SwD scored in the minimal category, while only 11% of Students Without Disabilities scored at that same level. 58.5% of Students Without Disabilities were below proficient, compared to 85.7% of Students With Disabilities below proficient in reading in fall 2012 on the same test. No! Yes! Finco & Torkelson

84. + Identify the Data Findings 84 June 2013Finco & Torkelson

85. + Criticality June 2013 85 How do you determine which data findings are most critical to target for improvement? Some critical patterns and trends to note: 1.Persistent low achievement over time 2.Declining achievement over time 3.Increasing or persistent gaps between groups of students Finco & Torkelson

86. + Identify the Most Critical 86 June 2013Finco & Torkelson

87. + Consider Trends and Patterns 87 June 2013Finco & Torkelson

88. + Any Other Data Findings? 88 June 2013Finco & Torkelson

89. + Prioritize High Priority. Very critical. Data findings show persistently low results, unacceptable gaps or declining performance. Medium Priority. Serious, but not especially critical. Some concerns in the results due to inconsistencies, mediocre performance or slow progress. Low Priority. Not critical. Data patterns should be monitored. 89 June 2013Finco & Torkelson

90. + Prioritize 90 June 2013Finco & Torkelson

91. + Hypothesize Examine the system What practices, policies or procedures might be producing these results? June 2013 91 Finco & Torkelson

92. + Hypothesize Getting to Root Causes 92 June 2013Finco & Torkelson

93. + Hypotheses of Practice Posing educated guesses about school or classroom practices as possible underlying root causes for critical data findings. June 2013 93 Finco & Torkelson

94. + Posing Hypotheses: Focus on the Data Picture Pose the question … What is it that we are doing or not doing that may have contributed to this picture? June 2013 94 Finco & Torkelson

95. + Importance of Hypotheses of Practice Thoughtful posing of hypotheses of practice will help educators determine alterable school variables that impact results. Identifying these variables will be a vital step to the proposed strategies in the School Improvement Plan. June 2013 95 We have a problem. Hypotheses of Practice Proposed Strategies Desired Results Finco & Torkelson

96. + Posing Hypotheses of Practice Take quiet time to think about all of the causes for the data pattern. Translate each “cause” into a “hypothesis of practice”. Hypotheses of practice … Are our best educated guesses about what “we” are doing or not doing that could be contributing to the results Are guided by the data findings Are shaped by both perceptions of practices and professional experiences in the school Are guided by research-based practices Never place blame on student or family characteristics, or any other unalterable factors June 2013 96 Finco & Torkelson

97. + Categorizing Our Thinking-- 97 Hypotheses of Practice Classroom Instruction & Assessment Instructional Planning & Preparation Learning Environment Professional Responsibilities Human Resource Leadership Instructional Leadership Organization Management School Climate Collaborative Culture Stakeholder Relationships June 2013Finco & Torkelson

98. + Add Hypotheses of Practice Insert the new Hypotheses of Practice (HOPs) into the Data Inquiry Process Form (step 3). List specific actions for each HOP that would be needed to make measured improvements. (step 4) For each HOP, select the appropriate category of improvement. (step 5). June 2013 98 Finco & Torkelson

99. + Completion of the Data Inquiry Process Save the data inquiry process form and all related data pictures in the data folder. Note the three final steps that will need to continue after the Data Inquiry Process. June 2013 99 Finco & Torkelson

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101. + What did you learn through this Inquiry Process? 101 Reflecting… June 2013Finco & Torkelson

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103. + Growth Dashboard Inquiry 103103103 Summer 2013Finco & Torkelson

104. + GROWTH Entry Points School Report Card 104 ACHIEVEMENT (Rdng & Math) % of Students at Proficiency Levels GAPS (Rdng & Math) Gap closure for subgroups ON-TRACK (Rdng & Math) 3 rd Grade Reading & 8 th Grade Math  Provides focus for Data Inquiry  The state assessment plays a major role in external accountability  Summative assessments can reveal internal achievement issues ◦ Marginalized subgroups ◦ Inadequate progress ◦ Persistent low achievement GROWTH (K-8 Rdng & Math) Year-to-year change compared to state Summer 2013

105. + GROWTH Question Topics Which questions will be most important to explore? 105 Common GROWTH Data Inquiry Topics (Rdng/Math) Our Data Inquiry Questions  Growth by grade level  Growth Trends and Gaps by Gender  Growth Trends and Gaps by Disability  Growth Trends and Gaps by Race/Ethnicity  Growth Trends and Gaps by ELL Status  Growth Trends and Gaps by Economic Status  Growth Patterns for a List of Students  Growth Patterns for an Individual Student Summer 2013

106. + About the Growth Dashboard Growth data are available only for grades 3-8. “Bubbles” represent student groups—bubble size corresponds to the number of students. The y axis indicates the percent of students achieving at proficient & advanced levels—the higher the “bubble,” the higher the achievement. The x axis indicates the median student growth percentile – or the average amount of change in test performance from one year to the next, as compared to students across Wisconsin. The more the “bubble” is to the right, the greater the growth. 106 x y Summer 2013Finco & Torkelson

107. + Exploring Growth Gaps To analyze growth gaps, select the related dashboards in the left panel. This example shows the difference in both achievement and growth percentile between students with disabilities and students without disabilities. 107 x y SGP Gap Ach. Gap Summer 2013Finco & Torkelson

108. + Thinking Ahead about “Drilling-Down” Because WISEdash allows you to “drill down” to specific students, think head about the role that the WSAS plays in planning for students. State assessments are external indicators of internal work. Compare state assessment summative results with local assessment results – use caution when comparing change in WKCE testing to growth on local measures. Understand that the current WSAS will be implemented for one more year – in fall 2013. Therefore, these analyses help prepare staff for the upcoming new era with the Smarter Balanced Assessment in 2013-15. Respect confidentiality. Save data in a secure data folder. 108 Current WSAS (WKCE & WAA) Future SBAC Local Assessment s Local data should predict external assessment results Summer 2013Finco & Torkelson

109. + 109 Summer 2013Finco & Torkelson

110. + Thinking Ahead about “Drilling-Down” Because WISEdash allows you to “drill down” to specific students, think head about the role that the WSAS Growth plays in planning for students. Respect confidentiality. Save data in a secure data folder. 110110110 Student Lists Will you need lists of … Students who showed lower or higher growth on the WSAS as compared to the state? 0 to 34 th SGP indicates “low growth” 35 to 65 SGP indicates “typical growth” 66 and higher SGP indicates “high growth” Summer 2013 Finco & Torkelson

111. + Demo – Drilling Down into GROWTH Data (student list) Example: Student List of students in the “bubble” selected. Student list is sorted according to the SGP (student growth percentile) column – showing low to high SGPs. This list can be exported to Excel for further analysis. 111 Summer 2013Finco & Torkelson

112. + Demo – Drilling Down to the Student Growth Profile Example: Indicates test performance change from year to year Each SGP shows the degree of growth as compared to students with the same starting point in Wisconsin. Colors correspond to degree of growth. Note the bottom table reveals WKCE cutpoints. Use this data to determine the amount of growth needed to move to a new proficiency level. 112 Summer 2013

113. + Don’t forget about your growth friend MDAT…MDAT… June 2013 113

114. + GROWTH Data Inquiry – Get Started Go to your data inquiry GROWTH question to start. Follow the four steps. 114 Summer 2013Finco & Torkelson

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116. + What did you learn through this Inquiry Process? 116 Reflecting… June 2013Finco & Torkelson

117. June 2013 117