1. Jennifer Borigoli Supporting Effective Data Teams jenniferb@lciltd.org 716-574-6682 Twitter: datadiva jenniferb@lciltd.org

2. Essential Question: Is there a best way for schools to support the use of data?

3. My personal essential question: Can we reduce learning to a number?

9. Do you honestly want to know what X exactly is? Is your life going to be improved by momentarily knowing what x is? No. Absolutely not. This whole problem is a conspiracy against hardworking American students. Let me tell you, solving for X right now is not going to stop the recession. It fact, it’s not going to do anything. And another thing. When have you ever had to know what is X is in your long esteemed professional career? Exactly. This is a futile attempt for “educators” in this district to boast of their student’s success rate. I am going to go the rest of my life not knowing what X is. Because what is X when you really think about it? A letter, the spot, two lines crossing each other. I don’t think anyone will ever really know what X truly is because the essence of X is beyond our brain potential. In conclusion, Harry S. Truman’s middle name was just the letter S, not an actual name. Now that is a letter that’s actually being utilized. See, you learned something, and it was not because of this logarithm. The End.

10. Guiding Question 1: What do good data do?

11. Data are... “the compelling evidence that grounds conclusions in actual results, not speculation” (Love, 2009) “Information output by a sensing device or organ that includes both useful and irrelevant information” (Webster) Regardless of definition, data – by themselves – have no meaning

12. In other words… Data are pieces of evidence that can provide you more information about your school’s curriculum, teaching and student learning

13. Data =

14. John Tukey (Statistician) “Far better an approximate answer to the right question, than the exact answer to the wrong question, which can always be made precise.” “There are no data that can be displayed in a pie chart, that cannot be displayed BETTER in some other type of chart.”

15. Albert Einstein “Not everything that can be counted counts, and not everything that counts can be counted.” Is this a friendly universe?

16. Sherlock Holmes (Sir Arthur Conan Doyle) “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” Who committed the crime?

17. Victoria Bernhardt Based on whom we have as students, how they prefer to learn, and what programs they are in, are all students learning at the same rate?

18. Many data teams... Where did we fail?

19. It begins (and ends) with questions

20. Mindfulness and Making Change Creating new categories/Labeling and relabeling Being open to new information Being aware and seeking more than one perspective Focus on process rather than preoccupation with outcome Communities for Learning 2011

22. To what extent are the data we collect and use aligned with what we value in teacher and student learning?

24. ARCS Page 3 Alignment Representation Culture Sustainability ARCS activates, supports, deepens and/or mends a school’s ability to become a self-sustaining, self-improving, empowered learning organization.

25. Representation Whose perspectives and voices do we include when we determine what students know and can do?

26. Sustainability What data are we using to measure our ability to become the school we want to have?

27. culture involves… What people value What they do What they produce

28. Culture and the Dispositions of Practice

29. The Dispositions of Practice are abiding tendencies whose development will support and deepen a school culture that embraces an intelligent use of data

30. Dispositions Focus individuals and the school on things that matter Assess our growth as individuals and as a school Improve how we communicate, what we do and what we produce

31. LCI, Ltd. (2009) Collegiality Involves having members: learn with and from others act on the belief that learning and working with others increases their expertise produce work that results from engaging in collaborative learning and problem solving

32. Related data question What could you do to ensure that your conversations about data enable you to truly learn with and from each other?

33. Data literate Data shy

34. “Less than 20% of teacher preparation programs contain higher level or advanced courses in assessment design or instructional data analysis.” Inside Higher Education, April 2009

35. Supporting Collegiality Protocols –Round-Robin –First Word, Last Word –Say Something Ground Rules –“Everyone has an equal voice, regardless of title or background knowledge” Language –What inferences might we draw? –What are some conclusions we might explore?

36. LCI, Ltd. (2009) Commitment to Expertise Involves having members: refining and expanding their knowledge and skills disseminating their knowledge and expertise within and outside the school engaging in learning and work that addresses school or other organizational needs

37. Related data question What protocols are in place to ensure that all educators in our community become proficient data users?

38. LCI, Ltd. (2009) Commitment to Reflection Looks like members: thinking about their thinking and learning to set goals, assess and understand themselves, their work and their school sharing their thinking to develop and evaluate it

39. Related data question When and how do teachers reflect upon the data they collect?

40. LCI, Ltd. (2009) Commitment to Understanding Involves members: pursuing questions and developing their understanding of ideas, concepts, etc. using research and evidence accessing multiple perspectives

41. Related data question What guiding questions frame our data collection, analysis, and plan implementation?

42. LCI, Ltd. (2009) Courage and Initiative Involves: discussing uncomfortable topics or issues, including own values and questions accepting the discomfort that stems from the need to change seeking or accepting new or unfamiliar roles, responsibilities or challenges

43. Related data question Do we collect the data that are useful or do we collect data that are easy to collect?

44. LCI, Ltd. (2009) Intellectual Perseverance Involves members: considering ideas or questions for a period of time to improve their work revising and revisiting work and thinking to improve it and to reach high standards withholding the need to finish work before it’s the best that it can be

45. Related data question What role do multiple measures play in confirming or challenging findings from one data source?

46. Am I healthy?

47. Text to 2 3 Collegial ity Expertis e Reflectio n Understandin g Courage and Initiative Intellectual Perseverance 1 - 5106627106681106687106693106694106695 6 to 10 10736410736510738610741217413107449 more than 10 107364107365107386107412107413107449

48. Quantitative Analysis

49. Protocols #1: High, Medium, Low Analysis #2: The Tuning Protocol #3: Standards in Practice™ #4: Wows and Wonders

50. Here’s What! Specific facts or information (data) So What? Interpretatio n of the data Now What? Implications/ New questions, data sources

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