1. Administrative Leadership Data Teams Professional Development
Kathleen Wilson
St. Paul Public Schools PK-12 Math Supervisor
Thursday, August 13, 2015

2. Goals Learn strategies for setting Data Teams up for success
Experience Data Team process and reflect
Problem solve challenging scenarios related to Data Team
Plan for your Data Teams
Answer your questions

3. My Journey
– attended Dufour conf. & began PLC with Harding HS math dept
– building data coach & math teacher
– PLC data coach & started 7 period day
– PLC data coach & part math teacher
– REA district-wide data coach; LLC Data Team certified
– SPPS Math Supervisor
– rolled out math common assessments district wide

4. Write 1 idea per post-it note
Reflect Back
As you reflect on PLCs and Data Teams in ,
what was most successful for you (or your school)?
what was most challenging for you (or your school)?
Write 1 idea per post-it note

5. Dufour’s Four Critical Questions Leadership and Learning Center
PLCs and Data
Dufour’s Four Critical Questions
Leadership and Learning Center
6 Step Data Teams Cycle
1. What are students supposed to know and be able to do?  
Priority Standards
2. How do we know when our students have learned?
Common Formative Assessment
3. How do we respond when students haven't learned?
Select Instructional Practices
4. How do we respond when students already know?
Differentiation
Step 1 Collect and chart data
Step 2 Analyze data and prioritize
needs
Step 3 Set a SMART Goal
Step 4 Select Common
Instructional Strategies
Step 5 Determine Result
Indicators
Step 6 Monitor and Evaluate
Results
PLCs 101
PLCs 201

6. Where are they mentioned?

8. Critical to PLCs
Soft Skills are critical to having high functioning PLCs using the Data Teams process.
Opportunities for growth:
Trust Activity
Norms Creation and Setting
Group Roles
Monitoring Trust & Norms & Roles

9. Trust Activity
Trust Quotes
Think, Pair, Share

10. Individual – Partner – Group 1 question at a time

11. Norms of Collaboration
Pausing
Paraphrasing
Putting Inquiry at Center
Probing
Placing Ideas on the Table
Paying Attention to Self & Others
Presuming Positive Intentions

12. Group Norm Creation
To ensure all individuals have the opportunity to contribute
To increase productivity and effectiveness
To facilitate achievement of goals
Activity:
Each person write 5 ideas – 1 per post-it
Behaviors you consider ideal for a high functioning group

13. Assign Your Group Roles
Facilitator / Data Team Leader
Recorder
Focus Monitor
Timekeeper
Common Assessment Editor
Data Technician / Wall Curator
Engaged Participant
Assign Your Group Roles
& Write on Name Tent

14. Monitoring Trust & Norms & Roles
Discuss what we will do when our actions are not aligned to agreements?
Check-in at end using Plus/Delta tally +
What went well today?
What do we need to work on?

15. Data Teams
This seminar was designed by The Leadership and Learning Center to help schools develop and use a structured approach to improve teaching, learning, and leadership. It is the vehicle teams use to have structured conversations around data, analysis, goals, strategies, and monitoring. The focus is on RESULTS.
Recommended resources:
Leaders Make it Happen, Besser & McNulty
A Guide to Effective Data Team Meetings, Peery
DT: The Big Picture, –editor, multiple authors
Data Team Success Stories, K. Anderson, editor
Organize Your Data Team Now ,Besser
Results, Schmoker
Results Fieldbook, Schmoker
Results Now, Schmoker
Professional Learning Communities at Work, DuFour & Eaker
Complementary Leadership and Learning Center Seminars:
Data Teams at the School Level
Data Teams at the District Level
The seminar is divided into four parts to be delivered in one day:
Part 1 – Introduction to DT, Powerpoint slides 1-16, training manual pages 1-12
Part 2 – Foundation, Powerpoint slides 17-49, training manual pages 13-38
Part 3 – DT Meeting simulation
Part 4 – Implementation and Sustainability (separate Ppt)
Information about The Leadership and Learning Center, the authors, the objectives and the agenda are included in the first part of the training manual.

16. Quotes
Choose a quote that connects for you as you think about implementing Data Teams
Why did you choose this quote?

17. The Leadership/Learning Matrix
Lucky
High results, low understanding of antecedents
Replication of success unlikely
Leading
High results, high understanding of antecedents
Replication of success likely
Losing Ground
Low results, low understanding of antecedents
Replication of failure likely
Learning
Low results, high understanding of antecedents
Replication of mistakes unlikely
Effects/Results Data
Refer to Learning Leader, Douglas Reeves, ch. 8 for additional information
“Labeling a condition is helpful only if we can follow up that work with interpretation of the data and application of the research in a meaningful way that will lead not only to improved professional practices, but to improved student achievement.” Reeves p. 133
Teach cause/effect through the explanation of each quadrant
Application-independent activity in training manual:
Creating Leadership Maps
-participants will first list teaching/leadership strategies and in the next column add student achievement indicators. This serves as a planning tool for creating their maps
-participants then plot their data on the leadership map in the training manual
-reflection questions ask folks to identify patterns, outliers and inferences
Leadership/Teaching Practices

19. Video Clip – working hard

20. Core Message – TM, p.3 – matches slide
The process is structured so the focus is on the student/learning and the teacher/teaching.
When you read this, what is a word that stands out for you?
Continuous – data teams meet with a specific cycle using the entire process every 2-4 weeks. The end result is the improvement of teaching and learning, but the process is cyclical
Inspires and empowers – teachers own the process, focus, and the goal. When the focus in on “results,” teachers embrace this powerful structure.
Data Teams are a model for continuous, collaborative action that inspires and empowers professionals to improve teaching, learning, and leadership for all.

21. Data Teams have a common focus or common standard, a common formative assessment, and a common scoring guide.
Definitions – TM, p. 4.
Note: The definition of a common formative assessment here is a short-cycle, common formative pre- and post- assessment, meaning that it is given IN COMMON (every member of the team), used in a FORMATIVE manner (to change instruction immediately), and relies on COMMON/calibrated scoring (each team member is scoring the same things the same way).
Guide – give examples as follows:
Common focus (attendance, engagement, discipline)
Common standard (academic content standard, 21st Century Skill, or process standard)
Common formative assessment (short-cycle assessment that measures only the substandard/subskill being taught. Could take the form of selected response, constructed response, or a combination of both)
Common scoring guide (rubric or answer key providing information on the subskill only)
Don’t get sidetracked by questions about this slide. You’ll address standards and assessment in part two.
Application – reflection on TM p.4 or can use this reflection after the next 3 slides.
How is this alike and different from how your PLC operates? Discuss in small groups and be prepared to share.
What difference do you think that makes?

22. that examine work generated from a common formative assessment
Data Teams are small, grade-level, department, course-alike, or organizational teams
that examine work generated from a common formative assessment
in order to drive instruction and improve professional practice
Instructional Data Team Information TM, p. 4. The descriptions of district, school Data Teams, and student-led Data Teams will change slightly. This seminar is focused on instructional Data Teams.
Instruct: give another description to enhance understanding.
Guide – give examples.
TM (speaker’s notes):
Elementary – 3rd grade team of 4 people, focused on reading comprehension- inferences, uses a rubric to measure the forming of inferences, meet every 2 weeks using 5-step process.

23. Data Teams meetings are:
collaborative structured scheduled
that focus on the effectiveness of teaching and learning.
Definitions continued, TM, p.4.
Instruct: give definition/description of meeting.
Guide – give examples. It may be helpful to differentiate the Data Team meeting from grade level/department meetings, cadres or curriculum meetings, and from the multitude of meetings that occur within a school.
Collaborative – 2 or more people. DT leader is also a member of the team. Teams that have more than 5 people are sometimes less effective, but a skillful DT leader can make the difference.
Structured – follow the 5-step process: collect & chart data, analyze data, goals, strategies, results indicators.
Scheduled – recommended that teams use the 5-step formal process every 2-3 weeks (at least once monthly).
Focus on teaching – collect cause data based on us of their instructional strategies
Focus on learning – collect effect data based on common formative assessments
Application – reflection on TM, p.4 if not already done

24. ` We are a Professional Learning Community. We do Data Teams.
PLC s and Data Teams are not competitive practices, and we don’t advocate one over the other. The PLC model provides the foundation. DTs provide the structure, the fuel, and the power behind the PLC. There is much that is similar – in fact, it has been said that the Data Team is simply PLCs on steroids.
We are a Professional Learning Community.
We do Data Teams.

25. Data Teams focus only on Priority Standards.
Priority standards, TM, p
They are explained on the next slide and in the manual.
It is important for teams to realize that they do not focus on all academic standards, Data Teams should only use priority standards – or the standards that are most important, in the Data Teams process.
Supplemental resource:
Power Standards, Ainsworth, Lead and Learn Press, Englewood, CO
Data Teams focus only on Priority Standards.

28. Practice Being a Data Team
Priority standard
Calculate experimental probabilities from experiments; represent them as percents, fractions and decimals between 0 and 1 inclusive. Use experimental probabilities to make predictions when actual probabilities are unknown.
Assessment and rubric
Student work
Grouping student work
Exceeds, Proficient, Partially Proficient, Not Proficient

29. Rubric
Learning Target a: I can calculate experimental probabilities
Exceeds
All 4 parts correct and excellent explanation using math vocab to describe numbers in numerator & denominator
Proficient
#1, 2a, 2b completely correct and correct explanation for 2c
Partially Proficient
2 parts correct
Not Proficient
1 part correct

32. Step 1: Collect and chart data (5% of DT process time)
Key points and considerations:
Step 1 allows you to disaggregate data in order to accelerate all groups of learners.
Step 1 allows you to place a name with the number
Quality disaggregation is a process that allows you to see the “parts” in a system. Through disaggregation you can more easily determine the strengths and needs within a school.
By disaggregating data, you are able to see results of different groups, and it helps you understand if you are headed in the right directions.
The incremental goal of step 1 is to accelerate learners; the ultimate goal is for all learners to reach proficiency levels.

34. Step 2: Analyze data and prioritize needs (30% of DT process time)
Key points and considerations:
Analysis is designed to identify strengths and urgent needs.
Analysis should identify successful practices for celebration, replication and/or generalization.
Analysis should involve more than numbers, it should be a direct examination of student work.
Analysis should identify specific areas of focus that, when addressed, will take the learner to the next level of performance.
Prioritization must be deliberate and thoughtful.
Prioritization allows us to respond in depth to urgent needs.
Celebrate the strengths.

36. Step 3: Set, review, and revise incremental SMART goals (5% of DT process time)
Key points and considerations:
Goals allow you to analyze, monitor, and adjust professional practice.
Data Teams set incremental goals.
Teams revisit step 1 when determining the desired state, paying close attention to students in the categories of “close to proficiency” and “far to go”.
What are the ramifications if the goal is changed to reflect a higher or lower outcome?
Is it possible to reset the goal higher? If so, is it achievable?
Is the time frame to short, just right, or too long?

37. STEP 3: SMART Goal
The % of ______ (grade level) scoring proficient or higher in ________( what standard) will increase from ___% to ___% measured using ____________ (what assessment) by ______ (date).

38. Step 4: Select common instructional strategies (30% of DT process time)
Key points and considerations:
There must be a direct link between the identified need (step 2) and the selected research-based strategy.
Strategies are actions of adults that impact student cognition.
Researched-based instructional strategies should include actions to enhance student achievement
Researched-based instructional strategies should include actions that provide active involvement of students in learning
Response to Intervention (RTI) with inferences – differentiate based on 4 groups of students.
Not only do strategies demand active involvement of students, they are designed to deliberately address the identified need.
Only select strategies that teachers are responsible for.

39. Instructional Strategies
Similarities/Differences
Summarizing and Note-taking
Nonlinguistic Representation
Cooperative Learning
Higher level questioning
Compare/Contrast
Non-verbal Communication
Reinforcing Effort and Providing Recognition
Homework and Practice
Setting Objectives and Providing Feedback
Generating and Testing Hypotheses
Cues, Questions, and Advance Organizer

40. Vocabulary Strategies
Strategies and Tools Word Sorts (open and
closed)
Frayer Model
List-Group-Label
Semantic Feature Analysis
Three-Column Word Chart
Four Corners
Concept Definition Map
Word Analysis (word parts)

41. Frayer Model

43. Step 5: Determine results indicators (30% of DT process time)
Key points and considerations:
“When this strategy or these strategies are implemented, we expect to see the following evidence … and students will be able to…”
“If we do _____, then we expect to see _______ in student achievement.”
Results indicators serve as a monitoring tool for teams and allow teams to make mid-course corrections before administering the post assessment
Results indicators serve as the “picture of progress” between the pre- and post- assessments.
Results indicators add intentionality by bringing specific adult and student actions to the forefront
Need to be concrete because it will help teachers be aware and intentional

45. Step 6: Monitoring and evaluating results
Key points and considerations:
Monitoring allows educators to reflect on their professional practice
Monitoring allows teams to make mid-course corrections.
Monitoring allows teams to celebrate results on a continuous basis.
Monitoring is critical component of a continuous improvement cycle.

48. Monitoring Trust & Norms
Check-in using Plus/Delta tally +
What went well today?
What do we need to work on?

49. 3 Big Ideas & Questions ?s

50. Data Team Scenarios Activity: Data Team Troubleshooting
Choose 3 scenarios that you would like to discuss
Discuss with table group
Troubleshoot as a room

51. Team Planning Time Communicate expectations Form Data Teams
Appoint Data Team leaders
Schedule Data Team leader & administrator meetings
Create complete assessment calendar
Map out Data Team cycles on calendar

52. Scheduling Building Master Schedule Meeting Schedule
Grade level teams (elementary)
Course teams (secondary)
Department teams (secondary)
Support or elective teachers
Meeting Schedule
Every other week
Expectations / outline of meetings

53. Data Team Leaders Essential to successful implementation
Need support and personalized PD
What do Data Team Leaders Look Like?
Believe in all kids and in all teachers
Are respected leaders
Are resilient
Understands standards and assessments
Understands researched based strategies
Understands facilitation skills and strategies

54. What does this mean for my site?
Next Steps
What does this mean for my site?

56. Tight and Loose Meeting Times
Tight all sites have an agreed upon meeting time in which all licensed staff participate in the data team process at the same time for one hour twice a month
Loose sites determine the agreed upon meeting time

57. Tight and Loose Training
Tight All principals will train their site on the data team process by October 15
Loose Sites will determine when training will occur

58. Tight and Loose Principal Role
Tight All Principals will be involved in the data team process
Loose Principals will facilitate a data team or be a member of a data team

59. Tight and Loose Specialists
Tight All specialists will be assigned a data team
Loose Site decides if specialists join grade/department/course alike data teams or join other specialist at the site