1. Data Teams Process for New School Leaders
2014 – 2015 SY

2. Objectives:
Develop an understanding of why data teams are essential to the implementation of RTI, PLCs, and Value Added
Describe the characteristics of Data Teams
Go through the Data Teams process
Consider preparatory steps for principals
Review next steps and available support

3. Essential Understandings:
During the 2013 – 2014 SY, APS began a district-wide rollout of Data Teams
Data Analysts have passed rigorous requirements and have become certified to deliver the Data Teams content
It is the expectation that ALL schools have functioning Data Teams in the 2014 – 2015 SY
Ongoing support will be provided

4. Data Teams and RTI: This graphic represents the function of RTI.
Notice how the center of it is “Data Based Decision Making”. This is the work of Data Teams.
Data Teams provide a framework to implement RTI with fidelity.
This graphic represents the function of RTI. Notice how the center of it is “Data Based Decision Making”.
This is the work of “Data Teams”.
The Data Team structure and process helps teachers navigate the unfamiliar territory of learning-focused collaboration. Data Teams provide a framework to implement RTI with fidelity. As the structures and processes are adapted over time to fit the unique needs of each team, student learning soars- and teacher learning does, too. (Peery, 2011)

5. We just started PLCs!!! Is this more?
Data Teams are not new! The model of PLCs that APS adopted is the Dufoor’s model! >>>>Go to next Slide
Dr. Rick DuFour defines a professional learning community (PLC) as “a group of people working interdependently toward the same goal.”1 Interdependence is an essential element because it:
Provides equal access (equity, or universal access) to quality teaching by strengthening each teacher’s practice through collaboration, coaching, and shared planning
Ends teacher isolation (thus reducing burnout)
Helps teachers work smarter by sharing the tasks of analyzing data, creating common assessment tools, and devising other strategies for both students who struggle and those who need more challenge !!!!
Enables teachers on grade-level (interdisciplinary) teams to devise lessons that teach reading and writing across the curriculum
Provides teacher professional growth and job satisfaction through intellectual renewal, new learning, and cultivating leadership2

6. PLCs are who we ARE… Data Teams are what we DO.
Curriculum drives what we teach. Data Teams drive how we teach. They provide us with the directions for teaching and instruction. They streamline the work of professional learning communities and provide a standardized framework for how to do it. So, no matter which teacher students have, they have are provided with access to teams whose work centers around constant changes in their instructional practices to match their students’ needs based upon continuous data.

7. How do Data Teams align with our PLCs?
Data Teams are not new! The work of DTs is embedded in the work of PLCs. It’s not more. It is structured!
The model of PLCs that APS adopted is the Dufoor’s model.
Dr. Rick DuFour defines a professional learning community (PLC) as “a group of people working interdependently toward the same goal.” Interdependence is an essential element because it:
Provides equal access (equity, or universal access) to quality teaching by strengthening each teacher’s practice through collaboration, coaching, and shared planning
End teacher isolation (thus reducing burnout)
Help teachers work smarter by sharing the tasks of analyzing data, creating common assessment tools, and devising research-based instructional strategies for both students who struggle and those who need more challenge

8. What are Data Teams?
Data Teams are small, grade-level, department, course-alike, or organizational teams that examine work generated from a Common Formative Assessment (CFA).
Your teachers, most likely, already have scheduled team meetings look like the ones described here. So, what’s the difference? >>>>Go to next slide
Your teachers, most likely, already have scheduled team meetings that look like the ones described above. So, what’s the difference?

9. Here’s the difference . . .
Data Teams adhere to:
Continuous improvement cycles
Examine patterns and trends
Establish specific timelines, roles, and responsibilities to facilitate analysis that results in action.”

10. Data Teams Process The Data Teams Process for Results
This is the process of Data Teams work inside the PLCs Structured, not more!
Step 1:
Step 2:
Collect and Chart Data
Analyze Data and Prioritize Needs
Data
Teams
Process
Step 6:
Step 3:
Monitor and Evaluate Results
Set SMART Goals
Step 5:
Step 4:
Determine Results Indicators
Select Common Instructional Strategies

11. Connection to Student Growth (Value-Added)
Value-added information allows educators to better identify what is working well and areas for improvement to help students. Value-added data provides important diagnostic information not previously available with traditional achievement reporting and allows educators to assess the impact their programs and practices have on student learning.
Data Teams work in its highest fidelity is data driven, strategic, and continuously monitored to make mid-course changes to provide equitable access to achievement for ALL students at ALL schools.
No more random acts! Systemic, standardized, effective practices emerge from purposeful data driven work of PLCs.

12. Connection to Student Growth
Value-Added
Data Teams
VA information allows educators to better identify what is working well and areas for improvement to help students.
VA data provides important diagnostic information not previously available with traditional achievement reporting.
VA data allows educators to assess the impact their programs and practices have on student learning.
DTs work, in its highest fidelity, is data-driven, strategic, and continuously monitored to make mid-course changes to provide equitable access to achievement for ALL students at ALL schools.
With DTs, there are NO more random acts!
Systemic, standardized, effective practices emerge from purposeful data driven work of PLCs.
Value-added information allows educators to better identify what is working well and areas for improvement to help students. Value-added data provides important diagnostic information not previously available with traditional achievement reporting and allows educators to assess the impact their programs and practices have on student learning.
Data Teams work in its highest fidelity is data driven, strategic, and continuously monitored to make mid-course changes to provide equitable access to achievement for ALL students at ALL schools.
No more random acts! Systemic, standardized, effective practices emerge from purposeful data driven work of PLCs.

13. OUR Data Gathering Exercise
Data Analysts visited schools and learned a lot about how we are currently using data in APS.
We know our school culture, we’ve done our observations, we have seen some great practices in place, but we have also seen pockets where some important components are missing (namely instructional strategies).
We have been asked by stakeholders in schools to ensure there is a consistent, standardized DTs process in place to make sure all of our kids, no matter where they attend school in APS, attend a school where a functioning DT is in place.
Data Analysts visited schools and learned a lot about how we are currently using data.
We know our school culture, we’ve done our observations, we have seen some great practices in place, but we have also seen pockets where some important components are missing (namely instructional strategies).
We have been asked by people in schools to ensure there is a consistent, standardized DTs process in place to make sure all of our kids, no matter where they attend school in APS, attend a school where a functioning DT is in place

14. Considerations for Principals
There are some key components principals will have to consider to make the roll-out of Data Teams a success in their schools.
Data Analysts visited schools and learned a lot about how we are currently using data.
We know our school culture, we’ve done our observations, we have seen some great practices in place, but we have also seen pockets where some important components are missing (namely instructional strategies).
We have been asked by people in schools to ensure there is a consistent, standardized DTs process in place to make sure all of our kids, no matter where they attend school in APS, attend a school where a functioning DT is in place

15. Formation of the TEAMS
Start thinking about how these teams might be formed in your schools.
This will impact your Master Scheduling
(which must be conducive to support Data Teams meetings)
How will the teams be organized to best improve student learning?

16. Choosing Data Team Leaders
Start thinking about who you can select to lead each of your Data Teams (DTs).

17. Characteristics of Data Team Leaders
Knows instruction
Organized
Is not currently tapped for multiple obligations in the school
Shows leadership potential
Should NOT be someone who is in a leadership position already (coach, assistant principal, department chair, etc.)
*Good Listener believe that
Effective facilitator of dialogue
Must Sincerely believe that students can achieve at high levels with support from adults
Must be willing to challenge view of peers
Must be well informed about instructional strategies
DT Leader sets agenda, meeting times and places

18. Characteristics of Data Team Leaders
Good Listener
Effective facilitator of dialogue
Must Sincerely believe that students can achieve at high levels with support from adults
Must be willing to challenge the views of peers
Must be well informed about instructional strategies
*Good Listener believe that
Effective facilitator of dialogue
Must Sincerely believe that students can achieve at high levels with support from adults
Must be willing to challenge view of peers
Must be well informed about instructional strategies
DT Leader sets agenda, meeting times and places

19. Your Data Teams Calendar
Start thinking about the district’s assessment calendar (i.e., Computer Adaptive Assessment) and your internal benchmark dates

20. A word about assessments
Schools should be administering pre and post Common Formative Assessments (CFAs) for each unit of instruction.
Note: C&I has created Pacing Guides for all content areas, and there will be two Formative Assessment Specialists to support this work.
Assessments must be aligned to CCGPS.
Appropriate levels of DOK must be considered.
We’re in talks with Curriculum and Instruction & Testing and Assessment to determine if some of the school support with Common Formative Assessments (CFAs) will come from their shops.
The IMS will play an active role in support of housing and posting CFAs.

21. How will Data Teams receive feedback? (monitoring for improvement)
Guiding Questions on next slide

22. Guiding Questions
How will Data Teams communicate their minutes to you? (i.e., SharePoint)
How will you ensure that the adult actions (cause data) truly lead to improved student achievement?
How will you ensure that the assessments are being used to inform instruction?
How will you provide specific feedback to your data teams?
What will your presence mean for the team?

23. Finally…let’s weed the garden!
Think about this:
If we implement Data Teams with FIDELITY, what might we be able to remove from the work load of our teachers?
Will there be any duplication of meetings or processes that are currently in place?

24. FAQs
Is the term “Data Team” necessary? Can we just continue saying PLCs?
The term Data Team is a copyrighted term created by the Leadership and Learning Center, whose program we purchased and are implementing. It must be used. You can also use PLCs/Data Teams (DTs).

25. FAQs
Will there be more training on PLCs before we are required to implement Data Teams?
Data Teams are the structured, strategic work of PLCs. It is not separate work. It will streamline the work of PLCs.

26. FAQs
What will the support look like in the schools? Who do we call for support?
Your assigned Data Analyst will be available to support the analysis and interpretation of the data.
Content Coordinators will support with aligning instructional strategies.
Formative Assessment Specialists will support with creating the common formative assessments (CFAs).
The RTI Coordinator will support with interventions and tiered instruction.
Departments are currently working together to map out further support.

27. FAQs Will Data Team Leaders receive a stipend?
No, Data Team leaders are simply the leaders of the teams chosen by principals or the respective teams.
Data Team leaders demonstrate the characteristics necessary to sustain the Data Team work. (No Administrative Duties)
Departments are currently working together to map out further support.

28. FAQs
Will there be more training dates available for new principals and new staff.
The next training is on July 30, 2014, for new school leaders.
We are currently looking at the district calendar to determine what dates can be offered to train incoming leaders and newly hired or previously unavailable staff during the upcoming school year.
However, out goal is to train the teams that you choose to send to the initial training well enough for them to transfer their knowledge your staff.

29. FAQs Are there forms to support monitoring?
The training includes forms that can be adjusted for your teams in your manuals.
Also, all forms and an electronic Data Teams tracking tool can be located at the R&E website:
However, we are looking for more dates and training locations for July, and possibly August.

30. Questions?

31. Let’s Practice the Data Teams Process

32. Adult Actions (Cause Data)
Principles of Decision Making for Results (DMR)
Antecedents
Adult Actions (Cause Data)
* Instructional Strategies
* Administrative Structures
* Conditions for Learning
Accountability
Collaboration
* Congruence
* Respect for Diversity
* Fairness
* Specificity
* Accuracy
* Universality
* Feedback for continuous improvement
Collaboration has to be built into every step of data management and integrated into every data-driven decision.

33. Data Teams
Data Teams is a six-step process that allows you to examine student data at the micro level (classroom practitioner level).
Data Teams provide a structure for teachers to specifically identify areas of student need and collaboratively decide on the best instructional approach in response to those needs.

34. Data Teams Definitions:
Data Teams use common standards, generate common formative assessments (CFAs), and use common scoring guides to monitor and analyze student performance.
Data Teams are small, grade-level, department, course, content, or organizational teams that examine work generated from a common formative assessment (CFA) in order to drive instruction and improve professional practice.
Data Teams have scheduled, collaborative, structured meetings that concentrate on the effectiveness of teaching and learning.

35. ` We are a Professional Learning Community. We do Data Teams.
Note: do not change the size or position of the photo.
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. See the DuFours’ work if you have more questions about PLCs. An excellent website is allthingsplc.info/ (maintained by Solution Tree).
We are a Professional Learning Community.
We do Data Teams.

36. ` We are a PLC. We do Data Teams.
PLC s and Data Teams are not competitive practices. 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
See the DuFours’ work if you have more questions about PLCs An excellent website is allthingsplc.info/ (maintained by Solution Tree)
Note: do not change the size or position of the photo.
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. See the DuFours’ work if you have more questions about PLCs. An excellent website is allthingsplc.info/ (maintained by Solution Tree).

37. Four Critical Questions that guide a PLC:
1. What are students supposed to know and be able to do? 2. How do we know when our students have learned? 3. How de we respond when students haven't Learned? 4. How do we respond when students already know the content?
COMMON CORE STANDARDS
COMMON FORMATIVE ASSESSMENTS
Data Teams provides the framework for teachers to be able to respond to questions 3 and 4 and for analyzing the results of question 2. Lead to action!
INTERVENTION
DIFFERENTIATION

38. Data Teams Process Data Teams Six-Step Process Step 1: Step 2:
Collect and Chart Data
Analyze Data and Prioritize Needs
Data
Teams
Process
Step 6:
Step 3:
Monitor and Evaluate Results
Set SMART Goals
Step 5:
Step 4:
Determine Results Indicators
Select Common Instructional Strategies

39. The DATA TEAM Meeting Cycle
Meeting 1: First Ever
Understand the purpose of Data Teams and their alignment with the beliefs of the school
Understand the purpose of Data Teams
Understand the six-step Data Teams process
[Note: The actions of Meetings 1 & 2 can occur at the same time if time permits.]

40. The DATA TEAM Meeting Cycle
Meeting 2: Before Instruction
Meet with the Team to determine the roles, responsibilities, and commitments
Determine the common standards or areas of student learning on which the Data Team will focus first
Create the short-cycle, common formative pre-assessment to measure a small chunk of learning
Identify the date to administer the pre-assessment
[Note: The actions of Meetings 1 & 2 can occur at the same time if time permits.]

41. The DATA TEAM Meeting Cycle
Meeting 3: Before-Instruction Collaboration
Analyze the pre-assessment results
Follow the Six-Step Data Teams process
(Note: Examples of the Six-Step Data Teams process follows on the next 6 slides)

42. Step 1: Collect & Chart Data
Step 1: Collect and Chart Data
Teacher
# Students
# Proficient and Higher
% Proficient and Higher
# Close to Proficiency
% Close to Proficiency
Name of Students Close to Proficiency
# Far to Go But Likely to Become Proficient
% Far to Go But Likely to Become Proficient
Name of Students Far To Go But Likely to Become Proficient
# Intervention
% Intervention
Name of Intervention Students (Far to Go and Not Likely to Become Proficient) 0%
[Enter Students' Names]
TEAM
Example on pg. 64 in the Training Manual (TM)

43. Why? Considerations: Step 2: Analyze Data and Prioritize Needs
To identify causes for celebration and to identify areas of concern
Considerations:
Performance Strengths
Needs (Errors and Misconceptions)
Performance behavior
Inference/Rationale

44. Step 2: Analyze Data and Prioritize Needs
Step 2: Analysis - Identify Strengths and Performance Errors or Misconceptions
Identify the prioritized need for each group of students by placing a 1 in the column next to that need.
Students Close to Proficiency
Performance Strengths
Inference
Performance Errors and/or Misconceptions
Students Far to Go But Likely to Become Proficient
Intervention Students (Far to Go But Not Likely to Become Proficient)
Example on pgs. 65 – 66 in the Training Manual (TM)

45. Step 3: Set SMART Goals Why? Criteria:
To identify your most critical goals for student achievement for each category of students (e.g., Proficient, Close to Proficient, Intervention, etc.)
Criteria:
Specific (What exactly will we measure)?
Measurable (How will we measure it)?
Achievable (Is this a reasonable goal)?
Relevant (Are goals aligned with the CIP)?
Timely (Does each goal have a defined timeframe)?

46. Step 3: Set SMART Goals Example on pg. 67 in the Training Manual (TM)
Step 3: SMART Goal Statement 0%
Group:
Topic:
End of Unit Date:
Assessment Tool:
Current Proficiency:
Projected Goal:
Adjustment:
Modified Goal:
Assessment Date:
The percentage of students proficient or higher in will increase from 0% to 0% by as measured by a(n) given on .
Example on pg. 67 in the Training Manual (TM)

47. Why? Strategies are: Considerations: Action-oriented Measurable
Step 4: Select Common Instructional Strategies
Why?
Adult Actions will impact student achievement
Strategies are:
Action-oriented
Measurable
Specific
Research-based
Considerations:
Instructional Strategies should be the main focus during the Data Teams process
Instructional Strategies should be research-based

48. Step 4: Select Common Instructional Strategies
Step 4: Select Instructional Strategies
Review the list below and record selected strategies in the chart.
Name of Students Close to Proficiency
Identified Need:
Inference:
Selected Instructional Strategy
Learning Environment
Time - Duration of the Teaching of Specific Concepts and Skills
Materials for Teachers and Students
Assignments, Assessments - Where will students be required to use the strategy?
Name of Students Far To Go But Likely to Become Proficient
Name of Intervention Students (Far to Go and Not Likely to Become Proficient)
Example on pgs. 68 – 69 in the Training Manual (TM)

49. Why? Step 5: Determine Results Indicators Considerations:
To Describe explicit behaviors (both student and adult) we expect to see as a result of implementing the instructional strategies plan.
How will you know that the strategies are working?
Look-fors and evidence/artifacts of learning?
What are proficient students able to do successfully?
Considerations:
Serve as an interim measurement
Used to determine effective implementation of a strategy
Used to determine if strategy is having the desired impact
Used to help determine midcourse corrections

50. Step 5: Determine Results Indicators
Step 5: Results Indicators
Name of Students Close to Proficiency
Identified Need:
Inference:
Selected Strategy:
Results Indicators:
Adult Behaviors:
Student Behaviors:
Look fors in Student Work:
Name of Students Far To Go But Likely to Become Proficient
Name of Intervention Students (Far to Go and Not Likely to Become Proficient)
Example on pgs. 70 – 71 in the Training Manual (TM)

51. The DATA TEAM Meeting Cycle
Monitoring Meetings
Occur between Meeting 3 and Meeting 4
Discuss the strategies. Are they working? Are the strategies having the desired impact on student learning?
Bring student work samples showing evidence of effectiveness of strategies
Make mid-course corrections if necessary
Model the strategies to ensure fidelity of implementation if needed

52. Step 6: Monitor and Evaluate Results
Why?
To engage in a continuous improvement cycle that:
Identifies midcourse corrections where needed
Adjusts strategies to ensure fidelity of implementation

53. Monitoring Plan Template
Example of Step 6 (Monitor and Evaluate Results):
Monitoring Plan Template
Cluster or School
Team
Date
Dates of Next Monitoring Cycle
Goal
Targeted Strategies
Has This Strategy Been Implemented?
Not Implemented Partially Implemented
Implemented Fully
Reasons Implementation Was Incomplete or Did Not Occur?
Has This Activity Had Impact?
Yes No
Reasons Expected Impact Did or Did Not Occur:
Evidence of Actual Impact on Instructional Practice and/or Student Learning:
Suggested Adjustments or Recommendations:
Reflections:
Other Relevant Information:

54. The DATA TEAM Meeting Cycle
Meeting 4: After-Instruction Collaboration
Review Post-Assessment Data from your common formative assessment (CFA)
If the incremental goal was met, create or select the next pre-assessment for the upcoming unit of instruction
If the goal was not met, repeat steps of the Data Teams process

55. The DATA TEAM Meeting Cycle
The Cycle Continues
Meeting before instruction (same as Meeting 3)
Monitoring Meetings
Meeting after instruction (same as Meeting 4)

56. Questions?

57. Website Resources Data Teams Resources
Student Growth Percentiles (SGP)
R&E Dashboards
2014 – 2015 Curriculum Resources
RTI Resources

58. Additional Support:
Data Teams Refresher Courses will be offered during the SY. Check MyPLC for updates and to register.
Always Feel free to Contact your Regional Data Analysts in the department of Research & Evaluation for School Improvement:
East Region – Stacey L. Johnson
West Region – Curtis L. Grier
South Region – Adrienne T. Johnson
North Region – Vacant (TBD)
CLL – Adam Churney