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

Data Teams Processfor

New School Leaders

2014 – 2015 SY

• 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

Objectives:

• 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

Essential Understandings:

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.

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

How do Data Teams align with our PLCs?

Data Teams are small, grade-level, department, course-alike, or

organizational teams that examine work generated from a Common Formative Assessment (CFA).

What are Data Teams?

Your teachers, most likely, already have scheduled team meetings that look like

the ones described above. So, what’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.”

Here’s the difference . . .

The Data Teams Process for Results

Step 1:Collect and Chart Data

Step 2:Analyze Data and Prioritize

Needs

Step 3:Set

SMARTGoals

Step 4:Select

Common Instructional Strategies

Step 5:Determine

Results Indicators

Step 6:Monitor and

Evaluate Results

DataTeams

Process

This is the process of Data Teams work inside the PLCs.

Structured, not more!

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 mid-course changes to provide equitable access to achievement for ALLstudents at ALL schools.

• With DTs, there are NO more random acts!

• Systemic, standardized, effective practices emerge from purposeful data driven work of PLCs.

Data TeamsValue-Added

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.)

• 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

Characteristics of Data Team Leaders

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 assessmentsSchools 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 theData Teams Process

Principles of Decision Making for Results (DMR)

Antecedents

CollaborationAccountability

Adult Actions (Cause Data)

* Instructional Strategies* Administrative Structures* Conditions for Learning

* 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.

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.

`• 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)

We are a PLC. We do Data Teams.

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

INTERVENTION

DIFFERENTIATION

Four Critical Questions that guide a PLC:

Data Teams Six-Step Process

Step 1:Collect and Chart Data

Step 2:Analyze Data and Prioritize

Needs

Step 3:Set

SMARTGoals

Step 4:Select

Common Instructional Strategies

Step 5:Determine

Results Indicators

Step 6:Monitor and

Evaluate Results

DataTeams

Process

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

Step 1: Collect and Chart Data

Teacher # Students

# Proficien

t and Higher

% Proficien

t 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

# Interven

tion

% Interven

tion

Name of Intervention Students (Far to Go and Not Likely to

Become Proficient)

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

To identify your most critical goals for student achievement for each category of students (e.g., Proficient, Close to Proficient, Intervention, etc.)

Criteria:

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)?

Specific (What exactly will we measure)?

Why?

Step 3: Set SMART Goals

Step 3: SMART Goal Statement

0% 0%

Group:

Topic:

End of Unit

Date:

Assessment

Tool:

Current Proficiency: 0% Projected Goal: 0% Adjustment: Modified Goal: 0%

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)

Step 4: Select Common Instructional Strategies

Why?

Adult Actions will impact student achievement

Considerations: Instructional Strategies

should be the main focus during the Data Teams process

Instructional Strategies should be research-based

Strategies are: Action-oriented Measurable Specific Research-based

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 Materials for Teachers and Students Assignments, Assessments - Where will students be required to use

the strategy?

Step 4: Select Common Instructional Strategies

Example on pgs. 68 – 69 in the Training Manual (TM)

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.

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

• How will you know that the strategies are working? • Look-fors and evidence/artifacts of learning? • What are proficient students able to do successfully?

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

Identified Need: Inference:

Selected Strategy:

Results Indicators:

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:

Selected Strategy:

Results Indicators:

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 TemplateCluster 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:

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 Resourceshttp://atlantapublicschools.info

• Student Growth Percentiles (SGP)http://www.WhatIsSGP.com

• R&E Dashboardshttp://dashboard.atlantapublicschools.info

• 2014 – 2015 Curriculum Resourceshttps://my.apsk12.org/ci/sites/Teaching%20and%20Learning/default.aspx

• RTI Resourceshttps://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 Analystsin the department of Research & Evaluation for School Improvement:

• East Region – Stacey L. Johnson ([email protected])• West Region – Curtis L. Grier ([email protected])• South Region – Adrienne T. Johnson ([email protected])• North Region – Vacant (TBD)• CLL – Adam Churney ([email protected])