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Data Driven Dialogue: Facilitating Collaborative Inquiry

Data Driven Dialogue: Facilitating Collaborative Inquiry. Developed by Bruce Wellman & Laura Lipton. Focusing Questions for School Improvement H/O p. 4. What do we talk about around here? How do we talk? •structures •protocols •norms •consciousness.

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Data Driven Dialogue: Facilitating Collaborative Inquiry

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  1. Data Driven Dialogue:Facilitating Collaborative Inquiry Developed by Bruce Wellman & Laura Lipton Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  2. Focusing Questions for School ImprovementH/O p. 4 • What do we talk about around here? • How do we talk? •structures •protocols •norms •consciousness What don’t we talk about around here? Why don’t we talk about what we don’t talk about….? Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  3. Four TensionsH/O p. 4 Task Process Comfort Discomfort Collaboration Autonomy Decision Dialogue Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  4. Outcomes • To develop practical structures for using data to focus a group’s attention and energy. • To understand and apply a three-phase model for guiding data-driven dialogue. • To extend a repertoire of tools for mediating productive group learning, planning and problem solving. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  5. Visual Synectics School Improvement is like….. Page 113 Because……. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  6. Crafting the Container - Pg. 12 • Starting the Conversation • Structuring the Conversation • Sustaining Thinking in the Conversation Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  7. Driving Forces - Pg. 2 Shifting From Shifting to • A teaching focus • A learning focus • Teaching as private • Teaching as collaborative practice practice • School improvement as • School improvement as an option a requirement • Accountability • Responsibility Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  8. Driving Forces - Pg. 5 Accountability: Answerable, From the Latin, cunter, compter -- to compute and From Old French, acunter, accomputare - to count up, to reckon. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  9. Driving Forces - Pg. 5 Responsibility: A duty, an obligation, to promise in return, capable of making moral or rational decisions on one’s own and therefore answerable for one’s behavior. From Latin. Respondere, to respond, obligation. ability Response - Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  10. Driving Forces - Pg. 2 Shifting From Shifting to • A teaching focus • A learning focus • Teaching as private • Teaching as collaborative practice practice • School improvement as • School improvement as an option a requirement • Accountability • Responsibility Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  11. TODAY • Welcome • Guiding Assumptions • Developing Dialogue • Sources of Data • Organizing and Integrating Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  12. Meeting Partners - Handout Pg. 21 Your Partner’s Name DATA GROUP MEDIA CHART PERSON Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  13. Facilitation Tip Page 13 Skilled facilitators offer the • ‘What’ • ‘Why’ • ‘How’ of strategies and protocols Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  14. Think, WRITE, Pair Share One assumption I have about helping groups work with data… Join your Partner and share Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  15. BREAK Please return at 10:50 Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  16. Say Something Choose a partner Read silently to the designated stopping point. When each partner is ready, stop and “say something”. *the ‘something’ might be a question, a brief summary, a key point, an interesting idea or personal connection. Continue the process until you have completed the selection. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  17. Guiding Assumptions pg. xi • Data have no meaning • Knowledge is both a personal and social construction • There is a reciprocal influence between the culture of the workplace and the thinking and behavior of its members • Understanding should precede planning • Cycles of inquiry, experimentation and reflection accelerate continuous growth and learning • Norms of data-driven collaborative inquiry generate continuous improvements in student learning Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  18. Processing the Strategy • What are some of the ways that the structure of the strategy influenced you? • What are some ways that paired interaction influenced you? Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  19. Guiding Assumptions • Data have no meaning • Knowledge is both a personal and social construction • There is a reciprocal influence between the culture of the workplace and the thinking and behavior of its members • Understanding should precede planning • Cycles of inquiry, experimentation and reflection accelerate continuous growth and learning • Norms of data-driven collaborative inquiry generate continuous improvements in student learning Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  20. Guiding Assumptions With your partner, choose one guiding assumption that caught your attention and be prepared to share some of your connections. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  21. Guiding Assumptions • Data have no meaning • Knowledge is both a personal and social construction • There is a reciprocal influence between the culture of the workplace and the thinking and behavior of its members • Understanding should precede planning • Cycles of inquiry, experimentation and reflection accelerate continuous growth and learning • Norms of data-driven collaborative inquiry generate continuous improvements in student learning Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  22. First Turn/ Last Turn • Read 38-40 individually. Highlight 2-3 items. • In turn – share one of your items – but do not comment on it. -- The First Turn • Group members comment in round-robin order about the item (with no cross-talk). • The initial person who names the item then shares his or her thinking about the item and gets – The Last Turn.---------------------------------------------------------------------- • Repeat the pattern around the table. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  23. First Turn/ Last Turn 1. How is this strategy a scaffold for dialogue? 2. How might this strategy contribute to group development? Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  24. Matchbook Definitions - Pg. 106 Craft a “Matchbook Definition” of Dialogue Approximately 8-12 words in length. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  25. LUNCH Please return at 1:00 Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  26. Managing Decisions - Pg. 67 • Group size • Length of time • Degree of structure Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  27. Processing the Morning Join your Partner and share Reflect on the morning select a “Most Important Point” Listen carefully to each other and be prepared to share your partner’s “MIP”. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  28. Pg. 41 DIALOGUE ----- DISCUSSION thinking holistically thinking analytically making connections making distinctions surfacing and inquiring surfacing and inquiring into assumptions into assumptions developing shared developing agreement meaning on action seeking understanding seeking decisions Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  29. Related Words •Incision • Precision • Recision • Decision Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  30. Decide -- Related Words With your partner, generate other wordsthat end in the suffix cide. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  31. Application With a neighbor, generate some examples from your work in which engaging a group in purposeful dialogue would be the most effective choice. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  32. Round the Room & Back Again • List one source of data that you use to improve student learning • Without taking notes, move round the room and share your data source and mentally catalogue the sources of others. • Return to home-base and write down the ideas you can remember. • Pool your lists and make the longest discrete list that is possible Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  33. Sources of Data - H/O Pg. 5 DATAQUANTITATIVEQUALITATIVE Student Performance Data Program Data Community Data Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  34. Process What are some patterns you are noticing? What might you add/ refine? What data “speaks” to which audiences? Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  35. “Not everything that counts can be counted. And not everything that can be counted, counts.” - Albert Einstein Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  36. Informational Altitudes TIMMS, I.B., I.A.E.P., PISA International National State/Province District School/C.R. Terra Nova, CTBS, ITBS, Stanford 9, SAT, ACT Standards-Driven Assessments, End of Course Exams Rubrics, Scales and Checklists District-wide Assessments Publishers’ Tests IRI/Running Records Reading Conferences Think Alouds Portfolios Teacher-made tests Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  37. BREAK Please return at 2:45 Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  38. For Tomorrow Read pages 9-17 Identify 5 significant ideas Use Page 7 of the handout to record your ideas Be ready to share your thinking Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  39. Choosing Which Data to CollectThree Ways to Frame a Problem Observation:Many of our students do not turn in their math homework. Question:What is the relationship between our current math homework assignments and our students’ performance? HYPOTHESIS:Student math performance would improve if homework assignments offered more authentic tasks and real-world applications. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  40. CHOOSING WHICH DATA TO COLLECT Identify something in your own work setting that you are interested in knowing more about. 1. Develop an observation, a question, and an hypothesis. 2. Decide which of the three potential ways of framing the issue might be the most productive way to engage some group that you have in mind. 3. Determine at least three data sources you might “tap” to explore your issue. Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

  41. Organizing and Integrating Table Groups: Craft a “One-Word Summary” of this day. Be prepared to share: • your word, the ‘what’ • your reasons for choosing, the ‘why’ • your spokesperson, the ‘who’ Data-Driven Dialogue - Copyright 2006 – MiraVia LLC – All rights reserved

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