WELCOME MATES!

1. WELCOME MATES! Data Conference Searching for Data Treasures

2. Setting a Course Meet the Shipmates Ship Rules Follow a Data Driven Dialogue “Easy Pickin’s” on the beach “Worth Digging for – Hidden Treasures” “Legends and Tales – Sunken Ships” Analysis – Which sunken ship to explore? Set Goals Create a Treasure Map “Sail through the Port”

3. The Crew

4. SHIP’S LOG ESU 7Crew _____Sue Oppliger ___________ _____Dave Perkins___________ _____Beth Kabes ____________ _____Deb Wragge____________ _____Barb Friesth____________ _____Candy Conradt__________ Data Conference April 3, 2012

5. Meeting the Shipmates Before we sail: What should we know about the band of pirates? • Introductions • Name, position, school or organization • Your interactions/responsibilities with data (Continuous Improvement Team, data team, newbee…)

6. Norms Norms are the standards of behavior by which we agree to operate while we are in this group. Norms are a set of guidelines that a team establishes to shape the interactions of team members with each other.

7. What “BUGS” you? • What bugs you when you attend different meetings? • Record your thoughts on the sticky notes. • Use one sticky note per idea. • Be ready to share. What makes a meeting go well?

8. Ships Rules • Prepare to lift anchor – work together as the ship sails through uncharted waters • Record your travels in the “Ship’s Log” • Listen to mates ideas • Ask questions to clarify • Be mindful of time • Yo Ho Ho and a bottle of…. Have fun and learn! • Silence your cell phones…reception is poor at sea!

9. The Purpose: Improving Student Learning The Process: Reflective Collaboration The Power: Importance of Data

10. Nebraska Continuous Improvement Model Plan for Continuous Improvement

11. We use data to determine… • Where are we now as a school building? • Where do we want to go? • How will we get there? • How will we know when we get there? • How will we sustain the effort?

13. Analyzing Data Patterns Broad Indicators Norm-Referenced Assessments State Writing Assessment State Standards Assessments More Detailed Results

14. Disaggregation of Data If we believe that all students can achieve, then any subgroup we choose should have similar achievement and results.

15. Disaggregation allows us to: • see if we are meeting the goals of our school; • identify subgroups that are not responding as well to school process as other subgroups; • understand why a subgroup is not responding and begin searching for a different process so that all students can learn; and • meet requirements for school improvement.

16. Identify Subgroups

17. Identify Subgroups • FRL--free and reduced lunch • ELL--English language learners • Special education • Ethnic minorities • Migrant students • Male/female • Students in your school for less than 2 years • Time spent on a bus route • Coming from different elementary schools • Others factors which might cause students to perform different than expected

18. Ground Rules for Participating in a Data Retreat • No blaming students • No blaming teachers • Data is JUST information • Use data for instructional purposes • “De-emotionalize” data

19. Pledge of Confidentiality I pledge to hold confidential and private any information regarding individual students shared during this retreat. I will respect the use of data as a tool to facilitate the improvement of student learning.

20. Pledge of Confidentiality • What we DISCUSS in this room, stays in this room. • What we LEARN in this room, may be shared.

22. Phase I - Predictions

23. Phase I - Before You See the Data • Hear and honor all assumptions • I assume …. • I predict …. • I wonder …. • My questions/expectations are influenced by … • Some possibilities for learning that this data may present ….

24. Shipmate to Shipmate • Equal voice • Make shared meaning of data • Replace hunches and feelings with facts • Examine patterns and trends of performance indicators • Generate “root-cause” discussions - move from identifying symptoms to possible causes

25. Data Driven Dialogue - Predictions We assume… We predict… We wonder… A pirate’s life for me

26. Easy Pickin’s

27. “Easy Pickin’s” – What data sources do we currently have access to? * * * * *** * Clues on the Beach

28. Phase II—Observations

29. Phase II - Just the Facts Because – Therefore It seems - However • Use these sentence starters: • I observe that …. • Some patterns/trends that I notice …. • I can count …. • I am surprised that I see ….

30. Phase II - Examine the data http://www.education.ne.gov/ State of the Schools Report and Data Reporting System

31. Data Driven Dialogue - Observations Because… Therefore… It seems… However… I observe that… Some patterns/trends that I notice… I can count… I am surprised that we see…

32. Data Driven Dialogue - Observations Because… Therefore… It seems… However… We observe that… Some patterns/trends that we notice… We can count… We’re surprised that we see…

33. Our questions/expectations are influenced by… * * * * *** *

34. Phase III - Inferences

35. Phase III - Inferences • I believe that the data suggest …. Because … • Additional data that would help me verify/confirm my explanations is …..

36. Phase III - Inferences • I think the following are appropriate solutions/responses that address the needs implied by the data …. • Additional data that would help guide implementation of the solutions/responses and determine if they are working ….

37. Phase III - Inferences • Create your inferences

38. Data Driven Dialogue- Inferences We believe the data suggests… because… Additional data that would help us verify/confirm our explanations are… We think the following are appropriate solutions/responses that address the needs implied in the data… Hold your course, mate

39. Additional data that would help guide implementation of the solutions/responses and determine if we are working… Do your “Easy Pickin's” support your assumptions you are making? Are there additional assumptions which surface at each measure? (SOS, DRS, etc.)

40. Levels of Data Analysis Step 10 – Intersection of 4 measures overtime Step 9 – Intersection of 4 measures Step 8 – Intersection of 3 measures overtime Step 7 – Intersection of 3 measures Step 6 – Intersection of 2 types of measures overtime Step 5 – Intersection of 2 types of measures Step 4 – Two or more variables within measures overtime Step 3 – Two or more variable within same area Step 2 – Snapshots overtime Step 1 – Snapshots Bernhart, V. L. (2004). Data Analysis for Continuous School Improvement (2nd ed.) Larchmont, NY: Eye on Education, Inc.

41. "Worth Looking for" – "Digging for hidden treasures" What did you find, and what more do you need to know about individual students? * * * * * * * Avast there, matey

42. Reflection… • What is something you picked up on the shore? • What do you want to dig deeper for?

43. Our "Ah Ha" from the day * * * * * * Where does your allegiance lie?

44. Legends and Tales

45. Analysis of Data “What does the data tell us about our strengths and challenges, especially as it relates to student achievement and programs/resources which support the learning?”

49. Analysis of Data • Observe the data patterns • Discuss what is observed • Write data findings under the graphs JUST the FACTS!

50. Graphing - "Legends & Tales”…"Sunken Ships" * * * * * * “Walk the plank!”