Using Data f or Continuous School Improvement

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 • Higher Ed • Dick Meyer – UNK • 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

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

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

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

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

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 elements • Continuous 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. DEMOGRAPHICS AREIMPORTANT DATA • Describe the context of the schooland school district. • Help us understand all other numbers. • Are used for disaggregatingother types of data. • Describe our system and leadership.

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

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

34. STAFF DEMOGRAPHICS • School and Teaching Assignment • Qualifications • Years of Teaching/At this School • Gender, Ethnicity • Additional ProfessionalDevelopment

35. NDE Data Profile – Reports in DRS

36. Data ProfileEnrollment example

37. Data Profile-Enrollment by Ethnicity

38. Data ProfileEthnicity Not SPED/ SPED Example

39. Perceptual Data

40. Page 17

41. PERCEPTIONS AREIMPORTANT DATA • Help us understand whatstudents, staff, and parents are perceiving about the learning environment. • We cannot act differently from what we value, believe, perceive.

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

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 FormReturn 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. STUDENT LEARNING AREIMPORTANT DATA • Know what students are learning. • Understand what we are teaching. • Determine which studentsneed extra help.