1. Data Use Professional Development Series 201 Day 6

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3. Welcome back! 3

4. Days 6, 7, and 8 4 Day 4 Questioning Techniques for Data Conversations Inference Validation Root Cause Analysis Effort/Impact Data Analysis Questions Adaptive Change and Collaboration Data Conversations with Parents Today Welcome/Overview Implementation Progress Correlation/Causation Triangulation BREAK Data and Small Group Differentiation Assessment Literacy LUNCH Aggregate Data Action Research Implementation Planning BREAK Techniques for Data Conversations: Paraphrasing Data Conversations with Students Implementation Planning Wrap Up/Evaluations Day 7: On-Site Visit Agenda to be determined with your coach Day 8: Partial list of topics Creating Assessment Items Applying Action Research Intersection Analysis Revisiting Data Inventory

5. Data Use 201 Day 6 Agenda Welcome/Overview Implementation Progress Correlation/Causation Triangulation BREAK Data and Small Group Differentiation Assessment Literacy LUNCH Aggregate Data Action Research Implementation Planning BREAK Techniques for Data Conversations: Paraphrasing Data Conversations with Students Implementation Planning Wrap Up/Evaluations 5

6. Objectives By the end of Day 6, SDLTs will be able to: Identify impacts of their data use implementation. Examine potential causes for correlation/causation, and triangulate data sources. Evaluate assessment items for alignment to standards and Depth of Knowledge. Engage in Data Conversations with students. Articulate a plan for ongoing data use implementation. 6

7. Implementation Progress 7 What impacts have you seen on teachers’ core instructional practices? What impacts have you seen on school culture? What role did our on-site visit play in advancing the work with teachers? 12345678910

8. Integrating Initiatives 8 How has connecting initiatives been going? How has using data impacted other initiatives? How have other initiatives impacted what you want to learn here?

9. 9 Cycle of Inquiry

10. Correlation/Causation 10

11. Triangulation Data set 1 Hypothesis Data set 2 “Triangulation” is the process of using multiple data sources to address a particular question or problem and using evidence from each source to illuminate or temper evidence from the other sources. It also can be thought of as using each data source to test and confirm evidence from the other sources in order to arrive at well-justified conclusions about students’ learning needs. IES Practice Guide: Using Student Achievement Data to Support Instructional Decision Making 11

12. Triangulation Data set 1 Hypothesis Data set 2 Refined Hypothesis Data set 3 Refined Hypothesis Data set 4 Refined Hypothesis 12

13. Summary Impacts of the work look different at different schools. As educators develop more facility with data use, they will apply data analysis strategies and protocols situationally. 13

14. BREAK 14

15. Cycle of Inquiry 15

16. Data and Small Group Differentiation Types of flexible small groups: Short Term Long Term Spontaneous 16

17. Data and Small Group Differentiation 17

18. EXCEED RTI 18

19. Assessment Literacy Evaluating Assessments Creating Assessments 19

20. Assessment Literacy Summative: Assessment OF learning Formative: Assessment FOR learning Interim: Assessment OF or FOR learning 20

21. Assessment Literacy Dimensions of Formative Assessment: Clearly articulated learning progressions Identified learning goals and success criteria Descriptive feedback Self and peer assessment Collaboration 21

22. Assessment Literacy 22

23. Summary Differentiating for small groups of students can mean flexibly adjusting core instruction for clusters of students within a Cycle of Inquiry. It is important for educators to plan how they will assess student learning while creating their Instructional Action Plan. 23

24. LUNCH 24

25. Evaluating Assessments Alignment to Standards Cognitive Complexity Data to inform instruction 25

26. Evaluating Assessment Items ItemSkill/concept measuredDOKPart/All of standard? What are the themes of Little Women? Determine a theme or central idea of a text. 2Part Write a brief plot summary of Little Women, explaining the themes revealed throughout the text using specific examples from the text. Determine a theme or central idea of a text and how it is conveyed through particular details; provide a summary of the text. 3All RL.6.2 Key Ideas and Details: Determine a theme or central idea of a text and how it is conveyed through particular details; provide a summary of the text distinct from personal opinions or judgments. 26

27. Evaluating Assessment Items Cognitive Complexity: Webb’s Depth of Knowledge Level 1: Recall identify, state, list, define, recognize, use, measure Level 2: Skill/Concept classify, organize, estimate, compare, infer, summarize Level 3: Strategic Thinking generalize, draw a conclusion, support, hypothesize, investigate Level 4: Extended Thinking make connections, synthesize, prove, analyze, design and carry out the project 27

28. Describe one cause of the War of 1812. Describe the similarities between the War of 1812 and the American Civil War. Describe the impact that the War of 1812 and the American Civil War have had on modern day America. Evaluating Assessment Items Cognitive Complexity 28

29. Cycle of Inquiry 29

30. Implementation Planning 30

31. Aggregate Data What is it? “Student performance data reported at the largest, aggregate- group level, such as by grade level and content area for a school, district, or state.” (p. 146) Why is it important? “It paints a broad brush picture of student achievement overall” and helps us “understand how students in their school perform in comparison to students in similar schools.”(p. 146) Love, N., Stiles, K.E., Mundry, S., & DiRanna, K. (2008). The Data Coach’s Guide to Improving Learning for All Students 31

32. Overarching Questions When Examining Aggregate Data Do aggregated groups of students meet or exceed district or state performance levels? -Pockets of success or need within student body Are we able to discern trends over time that would have implications rooted in student learning? -Analyze effectiveness of instruction, curriculum, and resources horizontally and vertically Love, N., Stiles, K.E., Mundry, S., & DiRanna, K. (2008). The Data Coach’s Guide to Improving Learning for All Students Aggregate Data 32

33. Mathematics – Grade 4 Average Scale Scores White Black Gap 30 23 26 24 NAEP Proficient: 249 Aggregate Data and Sub-Populations 33

34. Action Research and Expanding Cycles of Inquiry 34

35. Action Research Practitioner Researcher Cycle of Inquiry Broad area of focus Commitment to documenting progress 35

36. Rhode Island Growth Model 36

37. Implementation Planning 37

38. Summary Assessments should align to standards and include varying levels of Depth of Knowledge. It is important to be prepared for conversations about sub-groups when disaggregating large data sets. Engaging in Action Research is a way to examine broad areas of focus that are important to our school. 38

39. BREAK 39

40. Techniques for Data Conversations Positive Presumptions Paraphrasing 40

41. Principles of Paraphrasing Attend fully Listen with the intention to understand Capture the essence of the message Reflect the essence of voice, tone, and gestures Paraphrase BEFORE asking a question Use the pronoun “you” instead of “I” Center for Cognitive Coaching, 2010 41

42. Data Conversations with Students 42

43. Data Conversations with Students You and Ms. Jackson both teach Antonio. The student is well behaved and is performing well in Ms. Jackson’s class. In your class, Antonio has become disruptive and his performance on weekly quizzes has declined over the last month. What kind of Data Conversation could you have with Antonio? What kinds of questions could you ask? 43

44. Finding Solutions Is the soccer team getting in the way of your school work?versus Gathering Information Did you forget to do your homework again? versus Guiding Improvement Are you going to be ready for the test Friday? versus Presuming Positive Intent 44

45. Student Data Conversations Role Plays Choose a role play card Plan your Data Conversation: – What is your goal for the conversation? – What will you say? Ask questions using Positive Presumptions – Open ended questions to promote thinking and reflection Paraphrase as you go 45

46. Planning for Student Data Conversations Think about Data Conversations that teachers currently have with students. How could these conversations be structured to incorporate more data? How could questioning techniques such as using positive presumptions and open ended questions, and paraphrasing be incorporated more often? 46

47. Implementation Planning 47

48. Day 8 Some of the topics to be covered Day 8: Creating Assessment Items Applying Action Research Intersection Analysis Revisiting Data Inventory Rhode Island Growth Model continued 48

49. Wrap Up 49