Data Teams
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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).

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Data teams

  • Data Teams


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

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.


Data teams

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We are a Professional Learning Community.

We do Data Teams.


Data teams

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

INTERVENTION

DIFFERENTIATION


Data teams

Data Teams Six-Step Process

Step 2:

Step 1:

Collect and Chart Data

Analyze Data and Prioritize Needs

Data

Teams

Process

Step 6:

Step 3:

Monitor and Evaluate Results

SetSMARTGoals

Step 5:

Step 4:

Determine Results Indicators

Select Common Instructional Strategies


Data teams

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.]


Data teams

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


Data teams

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)


Data teams

Step 1: Collect & Chart Data


Data teams

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


Data teams

Step 2: Analyze Data and Prioritize Needs


Data teams

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?)


Data teams

Step 3: Set SMART Goals


Data teams

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


Data teams

Step 4: Select Common Instructional Strategies


Data teams

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


Data teams

Step 5: Determine Results Indicators


Data teams

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


Data teams

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


Data teams

Example of Step 6 (Monitor and Evaluate Results):


Data teams

The DATA TEAM Meeting Cycle

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


Data teams

The DATA TEAM Meeting Cycle

The Cycle Continues

  • Meeting before instruction (same as Meeting 3)

  • Monitoring Meetings

  • Meeting after instruction (same as Meeting 4)


Data teams

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])


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