Harnessing the Power of Data: Effecting Improvement in Schools

1. Harnessing the Power of Data: Effecting Improvement in Schools Please answer our survey at www.zellerandassociates.com/IL-TCE. It’s “zeller and associates” as all one word with “dot com”… Forward slash… And “IL-TCE” must be in CAPS!

2. Harnessing the Power of Data: Effecting Improvement in Schools Dr. Leslie R. Wilson Gerry Zeller February 2008

3. Agenda • Introductions • Protocols • Survey Report • Illustrative Story • The Process • Tools • Work • More Tools • Reflection and Evaluation

4. Protocols for this Academy

5. Survey Results • Why are you here? • Your School Improvement Involvement • Your Expertise

6. Know and be able to do • School Improvement = Problem Solving • Problem Solving = Process • District, School, and Classroom Levels

7. Story

8. Area of Concern

9. Targeted Concern There were approximately 15 students out of total student population of 1,000 that were dropping out of school when they reached their 16th birthday.

10. Generate Hypotheses: Answer the “Why?”

11. Hypotheses Generated • Young men leave school. • Low income students do not value education. • Students who get discipline referrals want out. • The lower the GPA, the more likely one is to walk. • Parents didn’t finish high school, children won’t. • Single parent families and similar find it difficult to shepherd students through school. • These students come to us with poor test scores. • These students earn too few credits. • These students are frequently tardy or leave early.

12. Determine and Collect Needed Data Gender SES Discipline Referrals GPA Parental Background Family Makeup Entry Test Scores Credits Attendance

13. Analyze Data Gender No SES: No Discipline Referrals: Yes GPA: No Parental Background: No Family Makeup: No Entry Test Scores: No Credits: Yes Attendance: Yes

14. Develop Solution Criteria • Diploma/GED (Rigor) • On Campus Program (Relationships) • Quick Successes (Relevance) • Non-traditional School Day (Relevance) • In-depth Knowledge of/Caring for Students (Relationships)

15. Investigate Solutions • Brainstorm: What ideas do we have? • SBR: What does research suggest? • Best Practice: What are other schools doing?

16. Select Best Match to Criteria: Late School • Diploma/GED • On Campus Program • Quick Successes • Non-traditional School Day • In-depth Knowledge of/Caring for Students

17. Implement Solution • Human Resources • A Multiple Certified Person • U of MO HS Extension • Material/Facility Resources • Classroom/Computer Lab • Policies/Procedures • Ineligible for Interscholastics • Application and Interview

18. Communicate Solution Constituencies • Formally • Informally

19. Monitor Progress

20. Monitor Progress • Method • Attendance • Credits • BehavioralContracts • Frequency • Responsibility

23. Holcomb, Edie L.

24. Bernhardt, Victoria

25. What Does Your DIP/SIP Model Look Like?

26. IIRC • Access e-Plan • Review District Data • State Report Card • Special Education Profile • Multiple Measures • PISA • TIMMS

27. District Improvement

28. School Improvement

29. State Report Card

30. Special Educ. Profile

32. IIRC Review School Data Top Comparables CPS School Profile Bernhardt’s Perception Surveys Local School Data Summative Assessment Data Behavioral Trend Data 32

33. Interactive Illinois Report Card: School • Performance Over Time • By Grade Level • By Subject Area • By Cohort • Comparison To Others • Scatter Plots • Compare Tool

34. CPS Profile

35. High School “Perfect” Resources • College Board • http://www.collegeboard.com/splash • ACT • http://www.act.org/

36. Do We Have the Whole Picture?

37. Generate Hypotheses • Brainstorm (Bias and Consensus) • Eighth graders cannot do Math. • Eighth grade teachers cannot teach Math. • We do not use the appropriate eighth grade textbook. • The test is too hard. • Correlations • Steven D. Levitt and Stephen J. Dubner • Reading and Writing

38. Determine Needed Data • Sources of Data • Holcomb, Edie L.: Workbook, p. 5 • Bernhardt, Victoria L. Data Analysis for Continuous School Improvement. Larchmont, NY: Eye on Education, 2004. • ---. Using data to improve student learning in school districts. Larchmont, NY: Eye on Education, 2006.

39. On-line Survey Tools • Survey Monkey http://www.surveymonkey.com/ • Zoomerang http://info.zoomerang.com/ • Profiler Pro http://www.profilerpro.com/

40. Bernhardt Perception SurveysSocial – Emotional Measures • When I am at school, I feel:

41. Discipline Referral Tracking System (SIS/PBIS) • Student • Infraction • Reporter • Location • Day/Date • Time • Disposition • Etc.

42. Analyze Data

43. The Five “W’s” • Who’s Learning, Who’s Not? • Why? • What’s Being Learned, What’s Not? • Why? • What Are You Going To Do About It?

44. See, Say, So • What do you SEE in the data? • What do you want to SAY about the data? • And SO what’s next? Dr. Jay Linksman

45. 3 Whats • What? • So what? • Now what? Rick Prestley

46. Your Questions

47. The Five “Gets” • Get Data • Get Them Electronic • Get Them Disaggregated • Get Them Graphical • Get Talking (Bias and Consensus)

48. Get It Graphical: 1869

49. Get It Graphical: 2009 http://www.wordle.net/

50. SI/PS Tools