The document outlines a presentation on utilizing data analysis in educational settings to enhance student learning and inform teaching practices. It details the process of data analysis, including steps for data collection, reflection, setting priorities, developing an action plan, and collaborative team building. The ultimate goal is to use data effectively to drive improvements in instructional strategies and student performance.

1. Putting Data Analysis to WorkUsing data analysis to answer the questions, “What do the data tell us about our students’ learning and what do we do next?” Presented by: Ginny Huckaba

2. NORMSBe timely, present and participatory

3. Phones on silent

4. Minimize bird walking (M. Hunter)

5. Return from breakGoalsAt the end of this session, participants will:Have knowledge of the process of data analysisBe able to use HIVE site to analyze student performance dataHave templates to use to perform item trend analysesBe able to serve as a resource of information to other educatorsHave developed a plan for taking the knowledge back to colleagues

6. AGENDAWelcome, Introduction, GoalsGroup dynamicsWhat do you already know?What do you want to know (goals)?Using Data to Enhance & Improve Student LearningHIVEItem & Trend AnalysisData analysis-needs assessmentPlanning for Back-home Colleagues/PDClose

7. What is Data Analysis?The breaking (“unwrapping” per Ainsworth) of a whole into its parts and looking for relationships and functions. In the educational setting, it provides clarity for what students must know and be able to do.

8. It is NOT data disaggregation (that stops at the breaking-down stage)

9. Analyzing data requires:

10. looking at data closely and objectively.

11. using it to make improvements.

12. halting the gathering of it if you don’t use it!Analyzing the DataWhat is the function (the purpose) of data analysis?

13. What does “it” measure and how is that information used?

14. What is the best use for assessments? What are the relationships between interim assessment data, progress reports/report cards and criterion-referenced tests?Common reasons for Data Analysis:Improve student learning and achievement

15. Improve teachers’ instruction

16. Provide students with feedback on proficiency

17. Get a common understanding of exemplary performance/work and how to achieve it

18. Measuring program effectiveness

19. Rescuing kids who are falling through the cracks

20. Learn what programs are yielding desired results

21. Getting to the root cause of a problem

22. Accountability

23. Guide curricular revisions/development4 Data Lenses through which to look:Demographics ( sources: test scores, APSCN)Student learning (sources: state, school, class levels)School process data (sources: special programs, finance, transportation, professional learning)Perceptions (public/stakeholders; sources--surveys)

24. Step 1 of Data Analysis: Data Collection(Treasure Hunting)Student assessment data shows what is/was—they do not necessarily tell why

25. “Why” may come from secondary data

26. Teacher training

27. Instructional strategies

28. Student demographics, norms, behaviors

29. Interventions

30. Existing support systemsStep 1: Data CollectionAsk: “Who are our kids and how are they performing on high-stakes assessments?”

31. Find out: “How do the prominent stakeholders feel about our actions/efforts?”

32. Examine: “What programs and processes are in place in our school that meet our students’ needs (or don’t meet them)?”

33. Objectively answer: “How are our students identified for extension, supplemental, and gifted-student programs?”2: Data Analysis(Reflection)Most critical; most complex to organize

34. Student achievement drives change (avoid getting PD cart before the horse)

35. Don’t implement what kids don’t need

36. Look first at the data, then decide on the PD

37. Focus on use of data at the classroom level

38. To be effective, teachers must be provided:

39. Tools

40. Time

41. Leadership’s commitment2: Data Analysis(Reflection)What did we discover from the data:

42. Strengths & Success (celebrate)?

43. Challenges (to be met)?

44. Trends (across subject/grade levels)?3: Set Data-based Priorities(Translation—or “triage!”)Careful scrutiny of each component:

45. What has to be done

46. External factors

47. “Do now” (meet AYP)

48. Rank Ordering (determining most pressing)

49. Based on areas of greatest need

50. Based on existing capacitySet Data-based Priorities(Translation)Moves the data to the instructional level

51. Requires change at the systems level

52. Decisions grounded in:

53. Analyzing test data

54. Implementing measurable changes

55. Studying change data

56. Making decisions from resulting data

57. Evidence: visible changes within schoolsSet Data-based Priorities (Translation)Components (partial listing):

58. Curriculum mapping

59. Content adjustments

60. Instructional adjustments

61. Data-driven instructional design, feedback

62. Formative and summative assessments

63. Goal setting4: GoalsGoals must be:

64. Explicit

65. Focused

66. Make sure all goal statements are designed to be:

67. Specific

68. Measurable

69. Achievable

70. Relevant

71. TimelyGoals—three essentialsSetting

72. Reviewing

73. Revising5: Instructional DesignIs powerful & focused

74. Requires: teacher training and consistent practice in classroom instruction

75. Enhances student achievement

76. Is composed of Instructional Strategies

77. Is NOT: activities, programs, adopted textbooks6: Feedback(Interim measures)Formative (formal and teachermade) and summative assessments (and teachers’ use of them)

78. Use to determine:

79. Proper implementation of instructional design (strategies)

80. Effectiveness (intended effect on student performance taking place)

81. “Is it worth it?”7: Action Plan(It’s Quality, not quantity, that matters)Quality Action Plan Must Haves:

82. Explicitly communicated to staff, parents

83. Administrative backing of implementation and sustainability

84. Leadership follow-up

85. Relentless efforts to maintain focus on the dataThe Process, in a Nutshell:Build the team, then get started

86. Identify the Problem (key indicators for student success/failure)

87. Hypothesize and articulate hunches

88. Identify the data to be examined and gather it

89. Analyze the data (strengths/weaknesses/trends/challenges)

90. Begin developing an Action Plan by:

91. Setting Goals for the plan and implement the plan

92. Evaluate using interim assessments/measures

93. Make decisions for improvement based on evaluation results

94. Put Action Plan to work

95. Start the process all over again—data analysis IS a cycle, NOT a checklist!Building your TeamCollaboratively decide:

96. Who is going to do what work and when?

97. Team roles (not limited to):

98. Lead project

99. Input data

100. Produce reports

101. Maintain data “warehouse”

102. Report the results of analysisQuestions to Ponder:Is time set aside to reflect on actual student work?

103. Is there a process in place whereby reflections and insights are used to make modifications in instructional/school practices?

104. Is there a habit of taking action as a result of patterns/trends that come out of the data?

105. Are there redundant or ineffective practices that need to be eliminated?

106. Is data collection followed by their analyses and changes/improvements?

107. Is the feedback cycle used for continuous improvement of instruction? (evaluation, decisions made based on evaluation results , actions taken because of those decisions, evaluation of resulting actions, etc.)Resources:Bernhardt, V.L. (2004), Data Analysis for Continuous School Improvement. Larchmont, NY: Eye on Education, Inc.

108. Blink, R.J. (2007), Data-Driven Instructional Leadership. Larchmont, NY: Eye on Education, Inc.

109. Love, N. editor (2009), Using Data to Improving Learning for All. Thousands Oaks, CA: Corwin Press

110. White, S.H. (2005), Beyond the Numbers. Englewood, CO: Advanced Learning Press

111. White, S.H. (2005), Show Me the Proof. Englewood, CO: Advanced Learning Press