1. Using Data for Continuous School Improvement

2. Goal 2 SLDS Grant Provide a statewide system of professional development training for data analysis that reaches every district. Tiered Training Delivery ✔ School District Staff School District Leadership ESUs and NDE Staff ✔ Statewide Data Cadre ✔

3. Statewide Data Cadre ESUs/ESUCC – Rhonda Jindra – ESU 1 – Mike Danahy – ESU 2 – Marilou Jasnoch – ESU 3 – Annette Weise – ESU 5 – Lenny VerMaas – ESU 6 – Denise O’Brien – ESU 10 – Melissa Engel – ESU 16 – Jeff McQuistan – ESU 17 NDE – Data, Research, Evaluation –Russ Masco –Matt Heusman –Rachael LaBounty –Kathy Vetter – Assessment –John Moon – Federal Programs –Beth Zillig – Special Education –Teresa Coontz – Curriculum –Cory Epler –Tricia Parker-Siemers – Accreditation and School Improvement –Don Loseke –Sue Anderson Higher Ed – Dick Meyer – UNK

4. Nebraska Data Literacies What do the data show? Data Comprehension Why might this be? Data Interpretation Did our response produce results? Evaluation How should we respond? Data Use

5. Data Literacies Format 1. a. i. Concept Indicators Literacy

6. Data Literacies http://www.education.ne.gov/DataServices/SLDS_ Grant/Data_Literacies.pdf

7. Nebraska Data Literacies and Their Relationship to the Continuous School Improvement Process

8. Nebraska Data Literacies What do the data show? Data Comprehension Why might this be? Data Interpretation Did our response produce results? Evaluation How should we respond? Data Use

10. Data Use Curriculum Nebraska Data Literacies

11. Bernhardt, V.L. (2013) Data Analysis for Continuous School Improvement (Third Edition) New York, NY: Routledge

12. Page 14

13. Where are we now? How did we get to where we are? Where do we want to be? How are we going to get to where we want to be? Is what we are doing making a difference? Data Literacy 1 What do the data show? Data Literacy 2 Why might that be? Data Literacy 3 How should we respond? Data Literacy 4 Did our response produce results? Data Literacy 2 Why might that be? Page 14

14. FOCUSED ACTS OF IMPROVEMENT

15. Data Analysis for Continuous School Improvement Is About What You Are Evaluating Yourself Against

16. Data Literacy 1 What do the data show?

17. Data Literacy 2 Why might that be?

18. Data Literacy 2 Why might that be?

19. Data Literacy 3 How should we respond?

20. Data Literacy 4 Did our response produce results?

21. IMPORTANT NOTES Continuous School Improvement describes the work that schools do, linking the essential elementsContinuous School Improvement describes the work that schools do, linking the essential elements Continuous School Improvement is a process of evidence, engagement, and artifactsContinuous School Improvement is a process of evidence, engagement, and artifacts

22. A PROCESS OF EVIDENCE, ENGAGEMENT, AND ARTIFACTS Evidence: Data to inform and drive a logical progression of next steps. Engagement: Bringing staff together to inform improvement through the use of data, moving from personality driven to systemic and systematic. Artifacts: The documentation of your improvement efforts.

24. RANDOM ACTS OF IMPROVEMENT

25. FOCUSED ACTS OF IMPROVEMENT

26. Four Types of Data

27. Data Literacy 1 What do the data show?

28. Page 17

29. Demographic Data

30. Page 17

31.  Describe the context of the school and school district.  Help us understand all other numbers.  Are used for disaggregating other types of data.  Describe our system and leadership. DEMOGRAPHICS ARE IMPORTANT DATA

32.  Enrollment  Gender  Ethnicity / Race  Attendance (Absences)  Expulsions  Suspensions DEMOGRAPHICS

33.  Language Proficiency  Indicators of Poverty  Special Needs/Exceptionality  IEP (Yes/No)  Drop-Out/Graduation Rates  Program Enrollment DEMOGRAPHICS (Continued)

34.  School and Teaching Assignment  Qualifications  Years of Teaching/At this School  Gender, Ethnicity  Additional Professional Development STAFF DEMOGRAPHICS

35. NDE Data Profile – Reports in DRS

36. Data Profile Enrollment example

37. Data Profile-Enrollment by Ethnicity

38. Data Profile Ethnicity Not SPED/ SPED Example

39. Perceptual Data

40. Page 17

41.  Help us understand what students, staff, and parents are perceiving about the learning environment.  We cannot act differently from what we value, believe, perceive. PERCEPTIONS ARE IMPORTANT DATA

42.  Student, Staff, Parent, Alumni Questionnaires  Observations  Focus Groups PERCEPTIONS INCLUDE

43. PERCEPTIONS What do you suppose students say is the #1 “thing” that has to be in place in order for them to learn?

44. Perceptual Data Surveys are available for students, parent, staff, for districts/schools that will work with their ESU staff developer to learn how to analyze the perceptual data Districts/schools complete a (revised) form Schools receive links to the surveys Schools and ESU staff developer will receive the perceptual survey data

45. Perceptual Data Request Form Return to ESU Staff Developer

47. Perceptual Data Ability to administer surveys will be available in future years as well NDEs capacity to manage the perceptual data surveys is developing The data belongs to the districts/schools

48. Student Learning Data

49. Page 17

50.  Know what students are learning.  Understand what we are teaching.  Determine which students need extra help. STUDENT LEARNING ARE IMPORTANT DATA

51. STUDENT LEARNING DATA INCLUDE  Diagnostic Assessments (Universal Screeners)  Classroom Assessments  Formative Assessments (Progress Monitoring)  Summative Assessments (High Stakes Tests, End of Course) Defined: Pages 54-57

53. School Process Data

54. Page 17

55. Schools are perfectly designed to get the results they are getting now. If schools want different results, they must measure and then change their processes to create the results they really want. SCHOOL PROCESSES

56. Processes include…  Actions, changes, functions that bring about a desired result  Curriculum, instructional strategies, assessment, programs, interventions…  The way we work.

57.  Tell us about the way we work.  Tell us how we get the results we are getting.  Help us know if we have instructional coherence. SCHOOL PROCESSES ARE IMPORTANT DATA

58. SCHOOL PROCESSES DEFINITIONS  INSTRUCTIONAL: The techniques and strategies that teachers use in the learning environment.  ORGANIZATIONAL: Those structures the school puts in place to implement the vision.

59.  ADMINISTRATIVE: Elements about schooling that we count, such as class sizes.  CONTINUOUS SCHOOL IMPROVEMENT: The structures and elements that help schools continuously improve their systems.  PROGRAMS: Programs are planned series of activities and processes, with specific goals. SCHOOL PROCESSES DEFINITIONS

60. DATA INVENTORIES - APPENDIX B Pages 265-334

61.  Review implications across data.  Look for commonalities.  Create an aggregated list of implications for the school improvement plan. MERGE STRENGTHS, CHALLENGES, AND IMPLICATIONS FOR THE SCHOOL IMPROVEMENT PLAN After analyzing all four types of data

62. AGGREGATING IMPLICATIONS Intersections Presentation and interpretation/en gagement as a function of analysis. Page 17

63. Page 343

64. NDE Continuous School Improvement 2014-2015 Workshops Sept. 25-26– North Platte October 6-7 – Kearney October 23-24 – Norfolk October 27-28 - Omaha Each school will receive a copy of Data Analysis for Continuous School Improvement

65. Fall CIP Workshops Agenda Why Data Analysis? -What would it take to ensure student learning at every grade level, in every subject area, and with every student group? -Strengths/Challenges/Implications protocol using Demographic Profile Data -Analyze District Data with DRS Reports