Data Teams Process
for
New School Leaders
2014 – 2015 SY
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
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
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.
We just started PLCs!!!
Is this more?
PLCs are who we ARE…
Data Teams are what we DO.
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
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 that look like
the ones described above.
So, what’s the difference?
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.”
The Data Teams Process for Results
This is the process of Data Teams work inside the PLCs.
Structured, not more!
Step 1:
Collect and
Chart Data
Step 2:
Analyze Data
and Prioritize
Needs
Data
Teams
Process
Step 6:
Monitor and
Evaluate
Results
Step 5:
Determine
Results
Indicators
Step 4:
Select
Common
Instructional
Strategies
Step 3:
Set
SMART
Goals
Connection to Student Growth
(Value-Added)
Connection to Student Growth
• 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 midcourse 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.
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.
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.
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?
Choosing Data Team Leaders
Start thinking
about who you
can select to lead
each of your Data
Teams (DTs).
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.)
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
Your Data Teams Calendar
Start thinking about
the district’s
assessment calendar
(i.e., Computer
Adaptive Assessment)
and your internal
benchmark dates
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.
How will Data Teams receive feedback?
(monitoring for improvement)
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?
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?
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).
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.
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.
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)
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.
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: http://atlantapublicschools.info
Questions?
Let’s Practice the
Data Teams Process
Principles of Decision Making for Results (DMR)
Antecedents
Adult Actions
(Cause Data)
Accountability
* Instructional Strategies
* Administrative Structures
* Conditions for Learning
* Congruence
* Respect for Diversity
* Fairness
* Specificity
* Accuracy
* Universality
* Feedback for
continuous
improvement
Collaboration
Collaboration has
to be built into
every step of data
management and
integrated into
every data-driven
decision.
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.
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.
`
We are a Professional Learning Community.
We do Data Teams.
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)
Four Critical Questions that guide a PLC:
1. What are students supposed to know and be able to do?
COMMON CORE STANDARDS
2. How do we know when our students have learned?
COMMON FORMATIVE ASSESSMENTS
3. How de we respond when students haven't Learned?
INTERVENTION
4. How do we respond when students already know the
content? DIFFERENTIATION
Data Teams Six-Step Process
Step 1:
Collect and
Chart Data
Step 2:
Analyze Data
and Prioritize
Needs
Data
Teams
Process
Step 6:
Monitor and
Evaluate
Results
Step 5:
Determine
Results
Indicators
Step 4:
Select
Common
Instructional
Strategies
Step 3:
Set
SMART
Goals
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.]
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.]
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)
Step 1: Collect & 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)
Step 1: Collect and Chart Data
0
0
0
0%
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0
0
0%
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0
0
0%
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0
0
0%
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0
0
0%
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0
0
0%
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0
0
0%
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0
0
0%
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
0
0%
[Enter Students' Names]
TEAM
0
0
0%
0
0%
0
0%
0
0%
Example on pg. 64 in the Training Manual (TM)
Step 2: Analyze Data and Prioritize Needs
Why?
To identify causes for celebration and to identify
areas of concern
Considerations:




Performance Strengths
Needs (Errors and Misconceptions)
Performance behavior
Inference/Rationale
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
Inference
Students Far to Go But Likely to Become Proficient
Performance Strengths
Inference
Performance Errors and/or Misconceptions
Inference
Intervention Students (Far to Go But Not Likely to Become Proficient)
Performance Strengths
Inference
Performance Errors and/or Misconceptions
Inference
Example on pgs. 65 – 66 in the Training Manual (TM)
Step 3: Set SMART Goals
Why?
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)?
Step 3: Set SMART Goals
Step 3: SMART Goal Statement
0%
Group:
Current
Proficiency:
End of Unit
Date:
Topic:
0%
Projected Goal:
0%
0%
Adjustment:
Assessment
Tool:
Modified Goal:
0%
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)
Assessment
Date:
Step 4: Select Common Instructional Strategies
Why?
Adult Actions will impact student achievement
Strategies are:
Considerations:




 Instructional Strategies
should be the main focus
during the Data Teams
process
Action-oriented
Measurable
Specific
Research-based
 Instructional Strategies
should be research-based
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
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 Intervention Students (Far to Go and Not Likely to Become Proficient)
Identified Need:
Inference:
Selected Instructional Strategy
Learning Environment
Time - Duration of the Teaching
of Specific Concepts and Skills
Example on pgs. 68 – 69 in the Training Manual (TM)
Materials for Teachers and Students
Assignments, Assessments - Where will students be required to use
the strategy?
Step 5: Determine Results Indicators
Why?
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
Step 5: Determine Results Indicators
Step 5: Results Indicators
Name of Students Close to Proficiency
Identified Need:
Inference:
Results
Indicators:
Selected Strategy:
Adult Behaviors:
Student Behaviors:
Look fors in Student Work:
Name of Students Far To Go But Likely to Become Proficient
Identified Need:
Inference:
Results
Indicators:
Selected Strategy:
Adult Behaviors:
Student Behaviors:
Look fors in Student Work:
Name of Intervention Students (Far to Go and Not Likely to Become Proficient)
Identified Need:
Inference:
Results
Indicators:
Selected Strategy:
Adult Behaviors:
Student Behaviors:
Look fors in Student Work:
Example on pgs. 70 – 71 in the Training Manual (TM)
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
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
Example of Step 6 (Monitor and Evaluate Results):
Monitoring Plan Template
Cluster or School
Team
Date
Goal
Targeted Strategies
Has This Strategy Been Implemented?
Not Implemented
Partially Implemented
Implemented Fully
Has This Activity Had Impact?
Yes
No
Dates of Next Monitoring Cycle
Reasons Expected Impact Did or Did Not Occur:
Reasons Implementation Was Incomplete or Did Not Occur?
Evidence of Actual Impact on Instructional Practice and/or Student Learning:
Suggested Adjustments or Recommendations:
Reflections:
Other Relevant Information:
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
The DATA TEAM Meeting Cycle
The Cycle Continues
•
Meeting before instruction (same as Meeting 3)
•
Monitoring Meetings
•
Meeting after instruction (same as Meeting 4)
Questions?
Website Resources
•
Data Teams Resources
http://atlantapublicschools.info
•
Student Growth Percentiles (SGP)
http://www.WhatIsSGP.com
•
R&E Dashboards
http://dashboard.atlantapublicschools.info
•
2014 – 2015 Curriculum Resources
https://my.apsk12.org/ci/sites/Teaching%20and%20Learning/default.aspx
•
RTI Resources
https://my.apsk12.org/ci/sites/Teaching%20and%20Learning/Pages/Response-to-Instruction-and-Intervention.aspx
Additional Support:
Data Teams Refresher Courses will be offered during the
2014-15 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 (johnsonsl@atlanta.k12.ga.us)
West Region – Curtis L. Grier (clgrier@atlanta.k12.ga.us)
South Region – Adrienne T. Johnson (adtjohnson@atlanta.k12.ga.us)
North Region – Vacant (TBD)
CLL – Adam Churney (achurney@atlanta.k12.ga.us)