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.

Data Teams

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.

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We are a Professional Learning Community.

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

Data

Teams

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.]


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


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 DataStep 1: Collect and Chart Data

Teacher#

Students

# Profici

ent and

Higher

% Profici

ent 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 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%

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 NeedsStep 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



Intervention Students (Far to Go But Not Likely to Become Proficient)



Step 3: Set SMART Goals

Why?To identify your most critical goals for student achievement for each category of students (e.g., 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% 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 X% to Y% by _____as measured by a(n) ______ given on________ .

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

Strategies are: Action-oriented Measurable Specific Research-based

Step 4: Select Instructional Strategies Review the list below and record selected strategies in the chart.


Identified Need: Inference:

Selected Instructional Strategy Learning EnvironmentTime - 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)




Step 4: Select Common Instructional Strategies

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 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 IndicatorsStep 5: Results Indicators



Selected Strategy:

Results Indicators:

Adult Behaviors:

Student Behaviors:

Look fors in Student Work:

Name of Students Far To Go But Likely to Become Proficient


Selected Strategy:

Results Indicators:

Adult Behaviors:

Student Behaviors:


Name of Intervention Students (Far to Go and Not Likely to Become Proficient)


Selected Strategy:

Results Indicators:

Adult Behaviors:

Student Behaviors:



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:


Meeting 4: After-Instruction Collaboration

• Review Post-Assessment Data

• If the incremental goal was met, create or select the next pre-assessment

• If the goal was not met, repeat steps of the Data Teams process


The Cycle Continues

• Meeting before instruction (same as Meeting 3)

• Monitoring Meetings

• Meeting after instruction (same as Meeting 4)

Additional Support:

Data Teams Refresher Courses will be offered during the 2013-14 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 – Holly Hayes-Morrisey ([email protected])• CLL – Adam Churney ([email protected])

mailto:[email protected]





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.

Documents

student data

purpose of data teams

data teams definitions

chart data step

students names

step data teams process

step process step

intervention students