1. Where are We Now? Data Literacy Deb Wragge, ddwragge@esu8.orgddwragge@esu8.org Kathy Vetter, kathy.vetter@nebraska.gov kathy.vetter@nebraska.gov

2. Agenda Welcome – Kahoot - https://kahoot.ithttps://kahoot.it Why Data Analysis? The 4 Types of Data What Do the Data Show?

3. http://oadataliteraciestrng.weebly.com/ Weebly Site

4. 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 ✔

5. NDE Data, Research, & Evaluation Data Cadre –Professional development effort between NDE and the ESUCC in direct partnership with the Education for the Future – Non-Profit Initiative Victoria L. Bernhardt, Exec Director California State University, Chico –ESU Professional Coordinators and the NE educators from across the state were trained and certified by Education for the Future, July 2014 in order to put this statewide initiative into play. –All Training materials and Handouts are available at the NDE website - http://www.education.ne.gov/DataServices/SLDS_Grant/Data_Cadr e.html http://www.education.ne.gov/DataServices/SLDS_Grant/Data_Cadr e.html

6. Omaha Archdiocesan Data Trainings Sessions – Rural Principal Sessions Metro Principal Sessions Session 1 Tuesday, February 17, 2015; 9-3 LLC, Norfolk Friday, February 20, 20;, 9- 3 St. Robert’s Parish Hall, 11900 Pacific St., Omaha Session 2 Tuesday, March 24, 2015; 9-3 Sacred Heart Parish Center, Norfolk Monday, March 23, 2015; 9-3 St. Robert’s Parish Hall 11900 Pacific St. Omaha Session 3 Tuesday, April 14, 2015; 10-12 LLC, Norfolk Thursday, April 16, 2015; 10-12 TBA

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

8. WHY Data Analysis?

9. WHY Data Analysis/Continuous and School Improvement?

10. Consensus is reached when everyone in the group can buy into, or live with the decision without feeling compromised in any way. Michael Doyle and David Staug How to Make Meetings Work: The Interaction Method (1993)

11. Consensus is…  Finding a proposal acceptable enough that all members can support it; no member opposes it. Consensus is not….  A unanimous vote – a consensus may not represent everyone’s first priorities  A majority vote – in a majority vote, only the majority gets something with which they are happy, while those in the minority may get something they don’t want at all.  Everyone is totally satisfied. Consensus requires… Time to discuss ideas. The active participation of all group members. Good communication skills (listening, clarification, conflict resolution, and facilitation for both discussion and dialogue). Creative thinking and open-mindedness.

12. What would it take to ensure student learning at every grade level, in every subject area, and with every student group?

13. WHAT IS THE HARDEST PART FROM YOUR PERSPECTIVE?  Beliefs that all children can learn.  Schools honestly reviewing their data.  One vision.  One plan to implement the vision.  Curriculum, instructional strategies, and assessments clear and aligned to standards.  Staff collaboration and use of data related to standards implementation.  Staff professional development to work differently.  Rethinking current structures to avoid add-ons.

14. THINGS WE KNOW ABOUT DATA USE For data to be used to impact classroom instruction, there must be structures in place, to—  implement a shared schoolwide vision.  help staff review data and discuss improving processes.  have regular, honest collaborations that cause learning.

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

17. 4 Types of Data Type: Perceptions How do we do business? Type: Student Learning How are our students doing? Type: School Processes What are our processes? Type: Demographic Who are we?

18. Frayer Model Definition -Facts/Characteristics - Term: Examples -Nonexamples -

19. Frayer Model Definition -Facts/Characteristics - Term: Examples -Nonexamples - Facts, figures, and other evidence collected in an experiment or study Record data carefully Permanent account of what happened in and experiment or study Descriptions Measurements Data tables and graphs Problems Inferences Hypothesis Data

20. Frayer Model

21. 4 Types of Data Activity At your table you will “Number Off” 1-4 Your number identifies which group and Type of Data your group is assigned – 1.Demographic – use pgs 28-31 2.Perceptual – use pgs 42-44 3.Student Achievement (Learning) – use pgs 52-57 4.School Processes – use pgs 80-85 Using the Frayer Model, each group will create a poster for their assignment Post your Frayer Model to the wall Please post all the Demographic posters together, the Perceptual posters together, and so on Select a spokesperson from your group to “share” your poster with the whole group

22. Page 14

24. What do the data show?

25. RANDOM ACTS OF IMPROVEMENT

26. FOCUSED ACTS OF IMPROVEMENT

27. COMPLIANCE VERSUS COMMITMENT Bernhardt, V.L. (2013). Data Analysis for Continuous School Improvement. Third Edition. New York, NY: Routledge. Page 4. Reproducible. Page 4

28. Evidence

30. What do the data show?

31. “Study the past if you would like to define the future.” - Confucius

33. DATA STUDY TOOL StrengthsChallenges Implications for the continuous school improvement plan. Other data... http://goo.gl/6rXlrU

34.  STRENGTHS: Something positive that can be seen in the data. Often leverage for improving a challenge.  CHALLENGES: Data that imply something might need attention, a potential undesirable result, or something out of a school’s control. DEFINITIONS

35.  IMPLICATIONS FOR THE SCHOOL IMPROVEMENT PLAN are placeholders until all the data are analyzed. Implications are thoughts to not forget to address in the school improvement plan. Implications most often result from CHALLENGES. DEFINITIONS

36. Individually, then in Small Groups, you will complete the Data Study Tool Questions, considering - Strengths, Challenges, Implications, Other Data Needed YOUR TASK IS TO ANALYZE THE DATA

37. Small Groups for Data Review & Analysis NeSA Reading – 3-11:NeSA Math – 3-11: NeSA Science – 5, 8, 11:MAP Reading – 3-11 & 3-6 DIBELS: MAP Math – 3-11:MAP for Primary Grades & K-2 DIBELS: Demographics:Perceptual: Developmental AssetsSWIS

38. DATA STUDY TOOL StrengthsChallenges Implications for the continuous school improvement plan. Other data... http://goo.gl/6rXlrU

39. 1 2 3 4 5 6 7 8 9 10 11 12 Demographic Perceptual Programs & Processes Student Achievement Back of the Room Front of the Room

40. Small Groups for Data Review & Analysis Somewhere Elementary School Data Using Appendix F pgs 265-334 Demographics – pgs 265-296 Perceptions – pgs 297-321 EnrollmentEthnicityStudent Questionnaire Results MobilityAttendanceStaff Questionnaire Results English Language Learners Free/Reduced Lunch Status Staff Open-ended Responses RetentionsSpecial EducationParent Questionnaire Responses BehaviorThe StaffParent Open-ended Responses Student Learning – pgs 322-334 Processes and Programs – pgs 81-87 English Language Arts CST (California Standards Test: Includes Reading and Language Usage) Instructional Processes, Organizational Processes, Administrative Processes, CSIP Processes & Programs Mathematics CST

41. STRENGTHS CHALLENGES IMPLICATIONS

42. AGGREGATING IMPLICATIONS Intersections

43. WHAT DID YOU LEARN?

44. “Education is learning what you didn’t even know you didn’t know.” - Daniel J. Boorstin

45. Exit Survey – http://goo.gl/forms/Kbb4hJQhGG