We now have data. What do we do next?

1. We now have data.What do we do next?

2. DATA . . .“Policy and Practice” Dr. Doug Christensen Commissioner, Nebraska Department of Education

3. We Have . . . • Created a “lofty” place for data • Making data a “commodity” • systems are being designed to “create” data • Data is now a product • can be packaged • can be marketed • can be sold

4. Commoditizing Data • Creates new language . . . New questions • “Data driven” . . . • What does the data say?

5. Few Paying Attention To • Credability of data • Accuracy of data • Prinicples of data collection and use

6. “He who has the data, rules . . . the _________”

7. Wheatley • “. . . We increasingly depend on numbers to know how we are doing for virtually everything.” • “. . . The measures define what is meaningful rather than letting the greater meaning of the work define the measures.” • “As the focus narrows, people disconnect from any larger purpose and only do what is required of them.”

8. Wheatley • “. . . Dethrone measurement from its godly position, . . .” • “. . . Offer measurement a new job – that of helpful servant.” Margaret Wheatley (1999)

9. The purpose of data is information. . . . To “inform”

10. Data informs . . . • Does not “tell” • Does not “conclude” • Does not “drive”

11. Inform • The public . . . about value of the public enterprise (how well it is doing) • The educators . . . to energize and inform the process of continuous improvement

12. Three Dimensions of Accountability • Collecting appropriate, valid and useful data • Reporting data in understandable ways to our public and stakeholders • Usingthe data to inform continuous improvement

13. Accountability is a policy of “information” . . . data is the tool Data

14. Data • More than test or assessment results • Data includes information about • processes • inputs • contexts • capacities

15. Major policy question . . . • Where does data come from? • Outside? • Inside? • Neither? • Both?

16. Rocket to Moon • Off course 95% of the time • Instruments that fed data to rocket engines and navigation systems were on board the rocket • Not remote • Not on the ground

17. Data about student learning • Must come from • Assessments of learning (not tests) • Assessments of • Processes • Inputs • Contexts • Capacities

18. Assessments of Learning • Tests • Demonstrations • Performances • Portfolios (over time) • Observations

19. Test data is far too narrow to inform.

20. Here is our point . . .

21. Data from classroom (point of activity) tends to create change by informing the engagement of the key players.

22. Data from the classroom empowers • Informs individuals about what doing and how well • Provides feedback for self direction • Provides feedback to system re supports needed

23. Primary goal of data is empowering the learner . . . • Learn what is expected • Learn to self determine level beyond expectations • Learn beyond content • Learn how to learn and direct own learning

24. Data should inform key education decisions and decision-makers . . . • Who is learning? What? • Who is not learning? What? • What do we do about both?

25. Data should inform key policy questions Excellence – How well . . .? How much . . .? Equity – For whom?

26. What is a “good” school? • Overall achievement is high • Subgroup achievement mirrors the whole group • Both trend lines are moving upward • Gaps are narrowing

27. Data Should InformAlignment and Rationality District Aims and Goals School Aims and Goals Programs, Practices

28. Data should inform . . . • Strengths • Areas of concern • Possible strategies

29. Policy Pitfalls of Data • Indicators Outcomes • About process not technology • Data threatens . . . • People • Conventional wisdom • Current authority • Does not save time • Bad data = bad decision-making

30. Einstein Not everything that can be counted counts, and not everything that counts can be counted.

31. We now have data.What do we do next? Dr. Pat Roschewski Statewide Assessment Director, NE Department of Education

32. The Continuous Improvement Model The “No Fear” Model Data, Data, Data Where are we? What should be our goals?

33. Organizing For Data Analysis Who Should be Involved In . . . • Collecting Data • Displaying Data • Analyzing Data • Sharing Results of Data • Other Local Uses of Data Involve All – 100% have a voice

34. Organizing For Data Analysis Grouping for Involvement • Grade levels • Departments • Subject Areas • Staff, Students, Parents, Support Staff, Community Members, Board Members

35. Organizing For Data Analysis When? • Pre-service, early out, late starts, mid-year, summer, common planning times How Often? • Frequency • Multiple Sessions Format • Big Picture – District Data – 1st session • Building, grade level disaggregated – 2nd session • By standard by student – 3rd and subsequent sessions

36. Understanding Data Basics • Data Sources • Data for Specific Purposes • Clarification of Data Types

39. GroundRules • No blaming students • No blaming teachers • Data is just information • Use data for instructional purposes • “De-emotionalize” data

40. Analyzing Data • What do these data show? • Factual Information Why might this be? • Hypotheses How should we respond? • Planning for action

41. Question One:What do these data show us?(Factual Information) • How many students are involved? • How many students met the standards? • How many students are in each proficiency level? • How great are the differences in grade levels? • What stands out in the data? Reading _____________________________________________________________________________________________________________________________________ Math _____________________________________________________________________________________________________________________________________

42. Question Two:Why Might this be?(Hypotheses) • Does the assessment measure what we teach? Why/why not? • How does the timing of assessment impact the outcomes? • What trends do we see in the data? Why? • Skill strengths? Weaknesses? • What differences are there in grade level or sub-groups? Why? Reading _____________________________________________________________________________________________________________________________________ Math _____________________________________________________________________________________________________________________________________

43. Question Three:How should we respond?(Planning for Action) 1. How do we match instruction to skill needs? Do we have the skill as a staff to do that? 2. How can we obtain the knowledge of instructional strategies for all staff? 3. In what ways do we offer remediation or acceleration? In classroom, summer, flexible grouping, curricular adjustment? 4. How can we effectively monitor, support, and evaluate classroom effectiveness? Reading _____________________________________________________________________________________________________________________________________ Math _____________________________________________________________________________________________________________________________________

44. We now have data.What do we do next? David D. Hamm Superintendent, Plainview Public Schools

45. Questions to Ponder Why is it important to be able to produce evidence of what the school achieves for its students? Is accountability a matter of compliance or responsibility?

46. When playing golf, which shot is the most important?

47. A Data ‘Rich’ Environment • Board of Education • Administrative Team • Leadership Team • Teachers • Community • Students How does ‘this’ benefit kids? Live it every day!!!

48. It all starts with data ‘mining.’ • Define the essential questions that matter most to your school system. • Describe the types of evidence you will need in order to answer the questions. • Identify the measures that will be needed to collect the necessary evidence.

49. We now have data.What do we do next? Jan K. Hoegh Statewide Assessment, NE Department of Education

50. Good ‘old’ days versus Good ‘new’ days TELL me! SHOW me!