1. Getting Started: Implementing Data Teams at your school. Presented by: Lanakila Elementary Pat Anbe, Linell Dilwith, Lei Hall

2. About the school…

3. Multi-year School Demographic Comparison SY 09-10SY 10-11SY 11-12% Change from last year Enrollment40340944940 (9.8%) Students Receiving Free/Reduced-cost lunch 275 (68.2%)285 (69.7%)318 (70.8%)33 (11.6%) Students in Special Education programs 31 (7.7%)24 (5.9%)25 (5.6%)1 (4.2%) Students with limited English proficiency 129 (32.0%)111 (27.1%)108 (24.1%)-3 (-2.7%) Retrieved(SSIR 2010, Retrieved 11/22/10) (LDS, Retrieved 12/14/10) (LDS, Retrieved 9/2011) % change = [(SY11- 12) –(SY10-11)] / (SY10-11) D Demographic Data School Accountability Trend Report 3

4. School Subgroup Profile Trends A Achievement Data

5. ARCH >Proficiency Math and Reading > By Grade & Year 5 Students in grades 3-5 continue to perform well above state benchmarks with 82% proficient in reading and 86% proficient in math. We attribute this to the successful implementation of a SPED/ELL inclusion program, systematic data analysis, use of formative instruction, increased use of technology in the classroom with mobile labs and Promethean boards, ongoing interventions provided for struggling learners in reading and math and an increase of instruction in non-fiction writing across content areas. A Achievement Data

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7. Why Data Teams at Lanakila? 1. STUDENT BENEFITS- -Diverse population of students -growing free/reduced lunch population- up to 71%. -- -current practices worked BUT how were we going to address those remaining 15% of students not meeting proficiency? How were we going to get to the next level?

8. 2. TEACHER BENEFITS -provides teachers with TIME and effective tool that helps streamline the process for data analysis and gives a structure for action planning and monitoring results 3. COMPLIANCE -required in the State’s Strategic Plan, Goal 1 -KMR Given (schools will use Data Teams and close the achievement gap)

9. So we decided to attend…. Data Teams Summit in Nov 2011 If we are going to learn this, we are going to learn it the right way In attendance- GLCs, responsible for leading implementation in their grade levels with admin support

10. Our Goal Learn, understand and be able to implement a clear, realistic, consistent system at the school level that will impact student achievement

11. Upon our return…. How would we share this critical information with the entire faculty? Team created a power point overview of the Summit to share Principal created sense of urgency to implement Data Teams, set clear expectations and shared a timeline

13. Why must we do Data Teams? We know the implementation of DT benefit STUDENTS -helps teachers identify exceeding/proficient students and plan for targeted instruction -helps teachers identify struggling students and plan for instruction We know the implementation of DT benefit TEACHERS -provides teachers with an effective tool that helps streamline the process for data analysis and gives a structure for action planning and monitoring results COMPLIANCE -required in the State’s Strategic Plan, Goal 1 -KMR Given ( schools will use Data Teams and close the achievement gap)

14. What are Data Teams? Data Teams are a model for continuous, collaborative action that inspires and empowers professionals to improve teaching, learning and leadership for all.

15. Data Teams are… Small grade-level or department teams that examine individual student work generated from Common Formative Assessments Collaborative, structured, scheduled meetings that focus on the effectiveness of teaching and learning

16. Data Team Actions “Data Teams adhere to continuous improvement cycles, examine patterns and trends, and establish specific timelines, roles, and responsibilities to facilitate analysis that results in action.” White, 2005

17. Data Team Expectations & Norms When? Weekly articulation time How long should this take? 45 minutes (you have 90) What materials do I bring? Data, assessments, computer, forms etc. What should be completed before I get there? Step 1 in the 6 step process Where? I building Participation… ALL….own all students!

19. How are PLCs different from Data Teams? PLCs are WHO we are Data Teams are WHAT we do

20. Critical questions that guide a PLC/Data Team- PLC’s look at: 1. What are students supposed to know and be able to do? 2. How do we know when our students have learned? 3. How do we respond when students haven’t learned? 4. How do we respond when students already know the content? Data Teams look at: 1.Priority Standards 2. Common Formative Assessments 3. Intervention 4. Differentiation

21. How is this different from what we already do? Not very different…we already function as a PLC and we analyze student data regularly Nothing new, just deeper Streamline our current practice

22. Data Team Prerequisites Before you can work through the 6 steps, the team needs 1) a common understanding of the kind of data analyzed in a data team 2) a common starting point.

23. Data Team Prerequisites What kind of data does a data team look at? Data teams look at causes not effects.

24. Data Team Prerequisites Cause Data: information based on the actions of adults in the system. Effect Data: information that shows student achievement from various measures.

25. Data Team Prerequisites Data teams have a Common Starting Point: Common standards (Power Standards) Common formative assessments Common scoring guide

26. Data Team 6 Step Process

27. Common form for collecting data Borrowed from the Leadership & Learning Center

28. Step 1: Collect and Chart Data Data is assembled per each teacher prior to meeting. Data includes student work samples from the assessment being reviewed. Results include number, percentage, and names of students at multiple performance levels (for ex., proficient, close to proficient, need intervention). Data is disaggregated by grade-level standard if multiple standards are included on the assessment.

30. Step 2: Analyze to Prioritize Inferences are based on direct analysis of student work. Team goes beyond labeling the need or the “what” to infer the “why” or root causes. Analysis includes comparison of student work samples to targeted “unwrapped” standards. Strengths and needs identified are within the direct influence of teachers. Strengths and needs are identified for each "performance group.“ Needs are prioritized to reflect those areas that will have largest impact within subject area.

32. Step 3: Set SMART Goals Establish, review, or revise goal(s). Specific: targeted subject area, grade level, and student Measurable area of need is established; assessment to be used is identified. Achievable gains in student learning are based on consideration of current performance. Relevant goal(s) address needs of students and are aligned with School Improvement Plan. Time frame is established for learning to occur, and assessment is given.

34. Step 4: Select Instructional Strategies Strategies directly target the prioritized needs. Team agrees on research-based strategies that will have greatest impact. Strategies chosen will modify teachers’ instructional practice. Strategies describe actions of the adults. Team describes strategies for each performance group, if applicable. Descriptions of strategies are specific enough to allow for replication (i.e., implementation, frequency, duration, resources).

36. Step 5: Determine Results Indicators Results indicators are created for each selected strategy. Indicators describe what the teacher will be doing when the strategy is being implemented. Indicators describe what the students will be doing when the strategy is being implemented. Indicators describe the anticipated change in student performance if the strategy is having the desired impact.

38. Step 6: Monitor and Evaluate Review student work samples that provide evidence of strategy implementation. Describe implementation of the strategy. Adjust strategies if necessary. Support each other in the use of the strategy through specific dialogue, modeling, planning. Check lesson planning and pacing with each other.

39. Roll out plan-

42. Impact on student achievement at Lanakila?

43. 2012 HSA Scores Reading 86% (+4) Math 88% (+2)

44. Lanakila data….

45. Lessons Learned? Not easy…will take time and persistence. Continue talking about the urgency for students.

46. Hurdles and Challenges Teacher skill levels in data analysis, action planning and technology use Lack of knowledge in Instructional Strategies Not complete buy in from lower grades – shift in thinking and approach to data Use of time- re-defining articulation time and creating a menu of items that should be addressed during data session Budget and personnel- finding enough specialty PTTs to teach PE, character education, health, dance, art etc.

47. Things to consider when planning Identify “experts” on your staff to take the lead Provide modeling, guided practice and heavy support Schedule- build time into the day for Data Teams Lanakila- PTTs for weekly specialty rotation Stevenson- half day subs 2x/mo plus 2 Wed meetings Budget- Put aside monies to fund implementation

48. Data Team video sample from Lanakila

49. Questions