1. Data Teams at Windham Middle School in the context of THE seed pilot
Adapted from materials developed by the Leadership and Learning Center
Presented by Jane Cook

2. What Are Data Teams?
Small grade-level, interdisciplinary or vertical content area teams that examine individual student work generated from standardized and non- standardized Indicators of Academic Growth and Development (IAGDs)
Collaborative, structured, scheduled meetings that focus on the effectiveness of teaching and learning
It is essential that throughout this seminar that you emphasize that Data Teams are SCHEDULED, STRUCTURED and focus on TEACHING AND LEARNING. Plan on repeating this statement often through this seminar.
Data Teams

3. 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.”
(S. White, Beyond the Numbers, 2005, p. 18)
p. 18
Data Teams

4. Illustration of Core Requirements of SEED Teacher Evaluation PILOT
Student Growth and Development (45%)
Whole-school Student Learning Indicators or Student Feedback (5%)
Observations of Performance and Practice (40%)
Peer or Parent Feedback (10%)
Practice Rating (based on Cause Data) (50%)
Outcome Rating
(based on Effects Data) (50%)
All factors are combined to reach each teacher’s final annual
rating (as described in the Connecticut guidelines).
Observations & Surveys
Student Learning Objectives (SLOs)
Adapted from CSDE Teacher Evaluation Orientation, 8/10/12

5. The Data Team Process
Step 1—Collect and chart data AKA “The Treasure Hunt”
Step 2—Analyze strengths and obstacles
Step 3—Establish SMART goals that are Specific, Measurable, Achievable, Relevant/Realistic, and Timely: Set, Review, Revise
Step 4—Select instructional strategies: What effective teaching strategies will adults use to help students achieve SMART goals?
Step 5—Determine results indicators: What measures will we use? How will we know that we have succeeded?
Data Teams

6. HOW The data Team process aligns with setting Student learning objectives IN SEED
Teacher Goal Setting Forms A & B
Step 1: Treasure Hunt
Baseline Data/Background Information
Step 2: Identify strengths and weaknesses
SLO & Rationale for objective
Step 3: Establish SMART goals
Set IAGDs
Step 4: Select instructional strategies
Strategies/Actions to achieve SLOs
Step 5: Identify results indicators
Interim Assessments & Data Collection/Assessment of Progress Toward Achieving the SLO

7. Do Data Teams Really Work?
One district’s story:
80% free and reduced lunch
68% minority student enrollment
40+ languages
(D. Reeves, The Learning Leader, 2006)
Data Teams

8. One district’s story: 7 YEARS OF PROGRESS FROM 1998 to 2005
Elementary Schools
1998
2005
Schools with more than 50% of students proficient in Grade 3
11%
100%
Middle Schools
Schools with more than 50% of students passing English 0%
High Schools
Schools with more than 80% of students passing English Language Arts
17%
D. Reeves, The Learning Leader, 2006

9. Asking the Right Questions
What does student achievement look like (in reading, math, science, writing, foreign language, tech ed, music, art, physical education, health)?
What variables that affect student achievement are within our control?
How do we currently explain our results in student achievement?
START ON THIS SLIDE WITH GRADE 7 - The questions related to student achievement vary. Grade level teachers will ask different questions than a building principal. A literacy coach may ask additional questions. The language support specialist may still ask different questions. Think of this as the construction phase….developing a data mind-set is critical to sustain Data Teams. We need to focus our attention on the needs of our students – and using the data to help us make the best instructional decisions to meet the needs of our students.
Participants will be completing p 13, Developing a Data Mindset. At the end of 7 minutes ask for folks to share in small groups their questions. Allow for a few responses in the large group. Note the similarities between the questions….math, reading, writing, %proficient, %non-proficient, #students needing immediate intervention in reading, math, language. What are the greatest academic needs for the AYP subgroups at schools? What % of identified gifted/talented students are scoring at their potential or scoring below potential/proficiency? What # of students are on the fence – or who could advance to proficient with application of a few more skills, concept understanding?
Data Teams

10. Data Worth Collecting: Have a Purpose
How do we use data to inform instruction and improve student achievement?
How do we determine which data are the most important to use, analyze, or review?
In the absence of data, what is used as a basis for instructional decisions?
assessment – data: assessments generate data, but the issue is that not all assessment data is valuable causing the question to be raised about the purpose of assessing. Too many teachers still believe that assessments are for the express purpose of generating a grade rather than for lesson planning and teaching clarification. This is usually one of the biggest ah-has for participants.) This is a great place to use the Results Fieldbook for excerpts related to Data Teams.
Teacher teams examine the teaching learning cycle for clarification about what actions they need to take at specific times usually to answer the question: Have students learned that which was just taught? Are the concepts mastered or just memorized for the moment? This is one of the biggest differences between formative and summative assessments.
Data Teams

11. Two Types of Data
“In the context of schools, the essence of holistic accountability is that we must consider not only the effect variable—test scores—but also the cause variables—the indicators in teaching, curriculum, parental involvement, leadership decisions, and a host of other factors that influence student achievement.”
(D. Reeves, Accountability for Learning, 2004)
What information have educators used to make decisions. The need for accurate information is substantiated in Accountability In Action (AIA) Chapter 4 – Accountability as a Decision Making Tool. Note particularly the information about ‘Fact-Free Debates’.
As teachers prepare for instruction, on what basis and with what information do they decide that the lesson is what students need? How do they know that what they are planning to teach is exactly what students need?
Which assessments do we need to use to provide the data on which to base lesson plans, group for specific intervention and differentiation, to know when to move on to new curriculum and concepts or to spend more time going deeper with a variety of instructional tools in order to support learning and increasing student achievement.
Data Teams

12. Two Types of Data
Effect Data: Student achievement results from various measurements, both standardized and non- standardized – Related to SEED Outcome Rating
Cause Data: Information based on actions of the adults in the system – Related to SEED Practice Rating
Data Teams

13. Effect Data (AKA STUDENT ACHIEVEMENT DATA)
What types
of effect data
are you collecting
and using?
What other data
do you need
to analyze?
How does this
effect data
answer your
questions about
student achievement?
Effect Data (AKA STUDENT ACHIEVEMENT DATA)
Allow participants just a few minutes to record effect data on p 17. If teams are present, they can work on this application together. The point is that students are generally ‘overtested’ yet under-assessed. Dr. Douglas Reeves.
An example of assessments most schools have in place:
K screening
Primary reading comprehension
Primary reading fluency
Decoding and vocabulary
Math readiness
Common end of year math
Common end of year reading
Quarterly writing assessment – building/departments
Weekly writing – grade level teams/departments
Data Teams

14. Cause Data (aka Adult Actions)
How do you use this
cause data
to change
instructional strategies?
How does this
support your
school or team
goals and focus?
What types of cause data are you collecting?
Cause Data (aka Adult Actions)
Collecting the cause data provides insight and possible explanations for the effect data. Without the cause data, the effect data is not as useful. For strategic teaching and leadership to occur, the generation of cause data to monitor is vital. Cause data are those ‘actions of the adults’ that influence student achievement. Samples include:
The number of minutes of daily writing, reading, math instruction
The number of common end of course assessments administered
The % of teachers who have had specific professional development in the area of Data Teams
The % of principals who collect and monitor Data Team information
The frequency of Data Team meetings
The number of teachers who use the prescribed curriculum on a regular basis
The number of teachers who use the district recommended pacing chart on a regular basis
The frequency of teams that examine reading, writing and math data results and establish new differentiated learning/instructional groups for specific concepts and skills
The number of effective teaching strategies selected for specific support of key concepts and skills
The number of teachers who know and understand the effective teaching techniques
The number of meetings held for the purpose of making student achievement decisions where decision makers have relevant data
The number of teachers who have developed performance assessments for units of study
Data Teams

15. Data Should Invite Action
“Data that is collected should be analyzed and used to make improvements (or analyzed to affirm current practices and stay the course).”
(S. White, Beyond the Numbers, 2005, p. 13)
If the data that you are collecting and analyzing is not helping inform your practice, i.e., planning, curriculum, instruction, or assessment, use different data.
- Jane Cook, WMS Data Team Training
p This quote reinforces the fact that data must serve a purpose.
Data Teams

16. The Leadership/Learning Matrix (L2 Matrix)
Effects/Results Data
Lucky
High results
Low understanding of antecedents
Replication of success unlikely
success unlikely
Leading
High understanding of antecedents
Replication of success likely
Losing Ground
Low results
Replication of failure likely
Learning
Replication of mistakes unlikely
Antecedents – Adult Actions/Interventions
Cause Data
Data Teams

17. Data-Driven Decision Making
“Effective analysis of data is a treasure hunt in which leaders and teachers find those professional practices—frequently unrecognized and buried amidst the test data—that can hold the keys to improved performance in the future.”
(D. Reeves, The Leader’s Guide to Standards, 2002)
Last slide for Specials Team & Grade 8 – Start with Slide 18 on Day 2
Data Teams

18. Steps to Create and Sustain Data Teams
Collaborate
Communicate expectations
Form Data Teams
Identify Data Team facilitators
Schedule meetings
Data Team meetings
Principal and Data Team facilitators
Post data and graphs
Create communication system
Data Teams

19. Effective Collaboration
Collaborative
teams
Commitment to
results
Shared beliefs
about student
achievement
Continuous
improvement
Plan, Do, Study,
Act (PDCA)
Total Quality
cycle
Shared
inquiry
Effective
Collaboration
Data Teams

20. What Is Needed for Effective Data Teams?
Effect data (student achievement) and cause data (adult actions)
Authority to use the data for instructional and curricular decisions
Supportive, involved building administrators
Positive attitude
Data Teams

21. Collaboration: The Heart of Data-Driven Decision Making
What is collaboration?
What does collaboration look like?
How do you start collaborating?
How do you create a self-sustaining capacity for a collaborative culture?
Data Teams

22. Communicating Expectations
Do we indeed believe that all kids can learn?
What does this belief look like in our school?
How do we know that all students are learning?
What changes do we need to make to align practices with beliefs?
Do we believe all students can learn? What do we believe about the rate of progress all students should make? What do we believe about our expectations for all students? How do our current practices align with our beliefs? Do we believe that our actions impact student learning? Is there any doubt about the importance of effective teaching practices?
Data Teams

23. DATA TEAM CONFIGURATIONS - Vertical Data TeamS
Middle School Math Team
Grade 6 Math Teachers
Grade 7 Math Teachers
Grade 8 Math Teachers
Middle School: The two most common options –
Grade level teams – discussions focus more on specific students and academic needs not content specific
Vertical content departments – discussions focus more on status of student achievement in content area as well as specific and effective instructional strategies
Data Teams

24. DATA TEAM CONFIGURATIONS – Horizontal Middle school Data Team
Grade 6 Interdisciplinary Team
English/LA Teacher
Math Teacher
Science Teacher
Social Studies Teacher
Support Services staff
Data Teams

25. DATA TEAM CONFIGURATIONS - SpecialS teachers Data Team
Grade 6-8 Specials Teachers Data Team
Art
Music
PE/Health
Tech Ed
Library/Media
DATA TEAM CONFIGURATIONS - SpecialS teachers Data Team
This illustration is but one example of a high or middle school configuration. Note that in addition to the core content teachers of English and math, we also have special education teachers, language support specialists for English Language Learners, and Music and Art.
In some settings, high school departments divided into smaller groups/teams by commonly taught classes or formed by the students they have in common (freshman, seniors, etc.)
Data Teams

26. Team Member Responsibilities
Participate honestly,
Respectfully,
constructively
Assume a role
Come prepared to
meeting
Be punctual
Engage fully
In the process
Data Teams

27. Roles of Data Team Members
Recorder:
Takes minutes
Distributes to Data Team leader, colleagues, administrators
Facilitator/Focus Monitor:
Reminds members of tasks and purpose
Refocuses dialogue on processes and agenda items
Timekeeper:
Follows time frames allocated on the agenda
Informs group of time frames during dialogue
Engaged Participant:
Listens
Questions
Contributes
Commits
It is important for all members of the team to assume a role. This will contribute to a stronger sense of team with all members making contributions. This also alleviates additional responsibilities placed on the Data Team leader. Roles should be defined at the first meeting with all members of the team having a clear understanding of each role. Team members can commit to a role per month, semester, year. It is important to actually record who has each responsibility for each meeting.
Data Teams

28. Data Team Leaders Are not expected to: Serve as pseudo-administrators
Shoulder the responsibilities of the whole team
Address peers and colleagues who do not want to cooperate
Evaluate colleagues’ performance
It must be made very clear that Data Team leaders are not administrators. Some teachers may be reluctant to serve as a Data Team leader because they may fear that they are required to wear a ‘different hat’ which would possibly cause their relationship among their peers to change. Data Team leaders are not administrators and will not formally evaluate their colleagues performance. They are not personally responsible if their colleagues do not cooperate. Data Team leaders facilitate the meeting process.
Data Teams

29. Data Team Leaders Reflect on the needs of the staff and/or their team
Work collaboratively to overcome obstacles
Data Teams

30. Data Team Leader and Principal Debriefs
Meet at least monthly to discuss
Achievement gaps
Successes and challenges
Progress monitoring
Assessment schedules
Intervention needs
Resources
Team needs
Principals usually keep these meetings short and they are a time when the principal can become informed about Data Team processes. Usually Data Team leaders are eager to share results with principal because it represents effective instruction. Most principals have a three-ring binder with dividers for each separate data team. He/she simply three whole punches the meeting agenda and minutes and inserts it into the book.
Principal and Data Team leader meetings should be long enough to address current concerns. It may be how to facilitate meetings and the need for support and motivation, it may be to discuss how to help a staff member move forward, use of materials, curriculum, gaps that persist, certain students, moving closer to AYP and evidence to support success, schedules, etc.
Principals must have the ability on a moment’s notice to pull out a binder, pull a tab indicating a team’s minutes and agendas, identify strategies used, link those strategies to walk-through observations, etc. These meetings are also a time for all the Data Team leaders to support each other and the principal as well.
Data Teams

31. Lessons from the geese
Fact
lesson
1: As each goose flaps its wings, it creates an “uplift” for the birds that follow.   By flying in a “V” formation, the whole flock has 71% greater flying range than if each bird flew alone.
People who share a common direction and sense of community can get where they are going quicker and easier, because they are traveling on the thrust of each other.
Source:

32. Lessons from the geese
Fact
lesson
2: When a goose falls out of formation, it suddenly feels the drag and resistance of flying alone.   It quickly moves back into formation to take advantage of the lifting power of the bird immediately in front of it.
If we have as much sense as a goose, we stay in formation with those headed where we want to go.   We are willing to accept their help and give our help to others.
Source:

33. Lessons from the geese
Fact
lesson
3: When the lead bird tires, it rotates back into the formation to take advantage of the lifting power of the bird immediately in front of it.
It pays to take turns doing the hard tasks and sharing leadership.   As with geese, people are interdependent on each others’ skills, capabilities, and unique arrangement of gifts, talents, or resources.
Source:

34. Lessons from the geese
Fact
lesson
4: The geese flying in formation honk to encourage those up front to keep up their speed.
We need to make sure our honking is encouraging.   In groups where there is encouragement, the production is much greater.   The power of encouragement (to stand by one’s heart or core values and to encourage the heart and core values of others) is the quality of honking we seek.
Source:

35. Lessons from the geese
Fact
lesson
5: When a goose gets sick, wounded, or shot down, two geese drop out of formation and follow it down to help and protect it.   They stay with it until it dies or is able to fly again.   Then, they launch out with another formation to catch up with the flock.
If we have as much sense as geese, we will stand by each other in difficult times as well as when we’re strong.
Source:

36. Burning Questions