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

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Presentation on theme: "Data Teams Process for New School Leaders 2014 – 2015 SY."— Presentation transcript:

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

2 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:

3 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:

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.

5 We just started PLCs!!! Is this more?

6 PLCs are who we ARE… Data Teams are what we DO.

7 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?

8 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?

9 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...

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

11 Connection to Student Growth (Value-Added)

12 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 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.

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.

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.

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.)

18 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

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.

21 How will Data Teams receive feedback? (monitoring for improvement)

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.

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)

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.

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:

30 Questions?

31 Let’s Practice the Data Teams Process

32 Principles of Decision Making for Results (DMR) Antecedents Collaboration Accountability 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.

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.

36 ` 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.

37 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:

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

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) 0000%0 [Enter Students' Names] 00% [Enter Students' Names] 00% [Enter Students' Names] 0000%0 [Enter Students' Names] 00% [Enter Students' Names] 00% [Enter Students' Names] 0000%0 [Enter Students' Names] 00% [Enter Students' Names] 00% [Enter Students' Names] 0000%0 [Enter Students' Names] 00% [Enter Students' Names] 00% [Enter Students' Names] 0000%0 [Enter Students' Names] 00% [Enter Students' Names] 00% [Enter Students' Names] 0000%0 [Enter Students' Names] 00% [Enter Students' Names] 00% [Enter Students' Names] 0000%0 [Enter Students' Names] 00% [Enter Students' Names] 00% [Enter Students' Names] 0000%0 [Enter Students' Names] 00% [Enter Students' Names] 00% [Enter Students' Names] TEAM00 0% Example on pg. 64 in the Training Manual (TM)

43 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

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 StrengthsInference Performance Errors and/or MisconceptionsInference Students Far to Go But Likely to Become Proficient Performance StrengthsInference Performance Errors and/or MisconceptionsInference Intervention Students (Far to Go But Not Likely to Become Proficient) Performance StrengthsInference Performance Errors and/or MisconceptionsInference Example on pgs. 65 – 66 in the Training Manual (TM)

45 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?

46 Step 3: Set SMART Goals Step 3: SMART Goal Statement 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)

47 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

48 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 StrategyLearning 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 StrategyLearning 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 StrategyLearning 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)

49 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?

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 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)

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 Example of Step 6 (Monitor and Evaluate Results): Monitoring Plan Template Cluster or SchoolTeamDateDates of Next Monitoring Cycle GoalTargeted 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 – 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

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


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