1. Data Teams
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2. Seminar Overview Part One: Introduction
Part Two: Building the foundation
Part Three: The Data Team process
Part Four: Creating and sustaining Data Teams
See page 6
Data Teams

3. Data Teams Part One Introduction Data Teams
This is a placeholder slide – merely to let you know that we are moving to a different part of the seminar.
Data Teams

4. What Are Data Teams?
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
There must be commonalities among the data teams.
Structured and scheduled-need all the pieces.
If you schedule it, they will come to support the process.
Think about your audience.
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

5. 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

6. Learning Objectives Understand and experience the Data Team process
Create an action plan to implement the Data Team process
Data Teams

7. The Data Team Process Step 1—Collect and chart data
Step 2—Analyze strengths and obstacles
Step 3—Establish goals: set, review, revise
Step 4—Select instructional strategies
Step 5—Determine results indicators
10-15 minutes-give a quick definition; go to the flow chart on p. 8 in manual- have audience put stars next to the portions that they already have in place; put question marks next to parts that are not solid; have them do this independently; then do a quick pair-share with their shoulder buddy; open the pdf for the flow chart-highlight what is important
Conclude this with research-additional that supports- results fieldbook-bring an example and show where it has been done
What are data teams/ why do we have them/ what is embedded in a data team/ discuss the actual process/ give examples of where it has been done- information is also in the training manual
See page 8
Data Teams

8. 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)
See page 9
Data Teams

9. Elementary Schools, Then and Now
1998:
Schools with more than 50% of students proficient in Grade 3 English: 11%
2005:
Schools with more than 50% of students proficient in Grade 3 English: 100%
Data Teams

10. Middle Schools, Then and Now
1998:
Schools with more than 50% of students passing English: 0%
2005:
Schools with more than 50% of students passing English: 100%
Data Teams

11. High Schools, Then and Now
1998:
Schools with more than 80% of students passing English Language Arts: 17%
2005:
Schools with more than 80% of students passing English Language Arts: 100%
Data Teams

12. Building the Foundation
Data Teams
Part Two
Building the Foundation
This is a placeholder slide – merely to let you know that we are moving to a different part of the seminar. We finished the introduction – and now this section is critical to help folks understand the relationship between the actions of the adults (the cause variables) and the impact these actions have on student achievement.
Data Teams

13. Building the Foundation
Be simple in definition of effect data; plant the seed for cause data
Common focus, common standard, common formative assessment-using data on a continuous basis- every four weeks for CFAs
See page 12
Data Teams

14. Asking the Right Questions
What does student achievement look like (in reading, math, science, writing, foreign language)?
What variables that affect student achievement are within your control?
How do you currently explain your results in student achievement?
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?
See page 13
Data Teams

15. Data Worth Collecting Have a Purpose
How do you use data to inform instruction and improve student achievement?
How do you 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.
See page 15
Data Teams

16. Two Types of Data
Effect Data: Student achievement results from various measurements
Cause Data: Information based on actions of the adults in the system
Jump from this slide 16 to slide 21
See page 16
Data Teams

17. 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

18. Effect Data How do these effect data What types answer your
questions about
student achievement?
What types
of effect data
are you collecting
and using?
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
What other data
do you need
to analyze?
See page 17
Data Teams

19. 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)
p This quote reinforces the fact that data must serve a purpose.
See page 18
Data Teams

20. Cause Data What types of cause data are you collecting?
Do you use these
cause data
to change
instructional strategies?
How do these
cause data
support your
school or team
goals and focus?
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
See pages 18-19
Data Teams

21. The Leadership/Learning Matrix (L2 Matrix)
Effects/Results Data
Lucky
High results, low understanding of antecedents
Replication of success unlikely
Leading
High results, high understanding of antecedents
Replication of success likely
Losing Ground
Low results, low understanding of antecedents
Replication of failure likely
Learning
Low results, high understanding of antecedents
Replication of mistakes unlikely
Antecedents/Cause Data
See page 20
Data Teams

22. Power of Common Assessments
“Schools with the greatest improvements in student achievement consistently used common assessments.”
(D. Reeves, Accountability in Action, 2004)
Incremental assessments must be administered on an ongoing basis in order for teachers to monitor end of year outcomes and which students are making progress and which students must receive what Tom Guskey calls ‘ corrective instruction’ rather than reteaching.
Common assessments also let us know if there are students who at the beginning of a year or unit already know and understand certain key concepts and skills. This is a huge issue especially for gifted students or just high achieving students who mastered certain knowledge and understanding from a variety of sources. Differentiated Instruction and Effective Teaching Strategies are not only about low achieving or slow to achieve students but also include implications and solutions for high achieving students or students who are able to move quickly through material.
Reeves, Accountability For Learning p (also see p.74):
“Schools with the greatest improvements in student achievement consistently used common assessments”
“Common assessments also provide a degree of consistency in teacher expectations that is essential if fairness is our fundamental value. The use of a common assessment for each major discipline allows teachers to have daily discretion and independence while preserving a school wide commitment to equity and consistency of expectations.”
Schmoker, Results Fieldbook, p.14-15
Many secondary schools use end of course assessments. The key is common assessments based on common curriculum. These assessments are essential for dialogue.
“The fastest, most efficient route to common curriculum and to a common dialogue and focus is common assessments.” – Schmoker
Data Teams

23. Common Assessments Provide a degree of consistency
Represent common, agreed-upon expectations
Align with Power Standards
Help identify effective practices for replication
Make data collection possible!
Spend more time here. Use resources to support your delivery. Use the training manual side by side with slides-p. 13, 14, 15, 17, 19, 22, and 23
Make data collection possible- focus here – go back to p. 17 and decide which meet cfa criteria- the audience can see that they do have good stuff in place
See pages 21-23
Data Teams

24. 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)
See page 24
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25. Building the Foundation
Data Teams

26. Data Teams
Part Three
The Data Process
See page 25
Data Teams

27. Data Team Meeting Cycle
Meeting 1: First Ever
Meeting 2: Before Instruction
Meeting 3: Before-Instruction Collaboration
Meeting 4: After-Instruction Collaboration
Alternate meetings
See pages 26-35
Data Teams

28. The Data Team Process Collect and chart data
Analyze strengths and obstacles
Establish goals: set, review, revise
Select instructional strategies
Determine results indicators
MOCK MEETING
In step 1, enter the number of students that actually took the assessment and the actual number of students proficient or higher. Translate these numbers into percentages.
Go to the last data step by completing the totals for each category.
When looking at student work, we can enter the names of those students close to proficiency and those students that need intensive intervention. In the pre-assessment, we expect to see relatively low numbers of students proficient.
In step 2, we analyze strengths and obstacles of student work. In advance of the meeting, teachers score student work and begin the analysis of strengths and obstacles. When examining obstacles, don’t be side-tracked with ‘excuses’ that deviate from a teacher’s circle of influence such as students didn’t do homework, student didn’t apply their skills, students missed too many classes, students didn’t pay attention. Focus on concepts that are lacking rather than getting lost in what the students failed to do.
In step 3, teams either have a goal (and are meeting to discuss progress towards the goal) or set a baseline goal. The goal is calculated by looking at the percentage of students proficient and then adding to that percentage those students that are likely to become proficient during the instructional period.
In Step 4, we identify the instructional strategies that will have the greatest impact on student achievement. Instructional strategies are define as: The specific actions implemented by the teacher during instruction that cause students’ cognition to immediately increase.
Examples: Teachers will use a graphic organizer to help students apply the writing process as it relates to short-constructed responses in math; Teachers will help students identify key differences between applications and processes of math procedures through comparing and classifying. Linda Darling Hammond says the best way to improve student achievement is to improve instruction.
In step 5, we identify results indicators. When teachers implement the effective strategies, then what will improve for students? This helps us identify the outcomes of the strategies and their impact on student learning.
Strategies (step 4) and indicators (step 5) are intricately linked and are the cause/effect relationship of the Data Team plan. When teachers to this___________ then students will be able to do
this______________.
Examples/Model:
By focusing on graphic organizers and focused practice/homework, more students:
Will be proficient/meet goal
Will understand how to identify specific components of the problem
Will be able to compose a short-constructed response that is to the point and includes important points and explanation
Do not be tempted to skip this step as it is truly vital in achieving goals through a viable process.
See pages 36-48
Data Teams

29. Data Team Meeting Activity: Participate in Data Team meeting
See pages 36-48
Data Teams

30. Data Team Meeting Feedback
Observations
What did you learn about the Data Team process?
After-Instruction Collaboration –
(see pages 49-55)
Meeting 3: (After focused instruction)
Review norms/roles; complete steps:
1. Discuss post-assessment and post-instruction data
2. Analyze student work
3. Goals – Were the goals met, exceeded, not met? Either move on, or revise goal(s).
4. If new goal is set, then determine instructional strategies and results indicators OR
5.Compare current results with results indicators. Were the results identified in the meeting weeks ago achieved? Exceeded? (Reminder: these are cause data.)
Data Teams

31. Creating and Sustaining Data Teams
Part Four
Creating and Sustaining Data Teams
Cut down to an hour and a half-power- hour and a half for entire mock meeting
Implementation and sustainability
When you deliver it depends if your audience is implementing data teams or sustaining them
Communicate expectations- bring in Reeves- alignment of vision and action-accountability
Architectural leadership
Sets the tone for data teams in schools and districts
There needs to be a clear purpose
Form data teams- get creative but have commonality in all things-focus, standard, assessment tand scoring guide; have recommendations about their structure
Schedule meetings- page 64
Data team leaders *****- this should be at the top of your list- this person is VITAL- effective teams have effective leaders- roles and responsibilities and characteristics-think about sustainability
Options for data teams on page 68-9; not about who is available- they should match the description
P. 70- activity if time
Scheduling- time- we don’t have it- but you cannot argue with the impact that data teams have on teaching and learning- make time!!!!-
Allenton- how can you use the time that you have?---dig into current schedules
Pp slides on data walls- display results of teaching and learning- sharing and celebrating results
Tell the story behind the numbers-connect the narrative with the numbers
How are you going to communicate your results and what will it look like
MOST IMPORTANT- COMMUNICATE EXPECTATIONS AND DATA TEAM LEADERS
See page 57
Data Teams

32. Steps to Create and Sustain Data Teams
Collaborate
Communicate expectations
Form Data Teams
Identify Data Team leaders
Schedule meetings
Data Team meetings
Principal and Data Team leaders
Post data and graphs
Create communication system
See pages 58-59
Data Teams

33. Effective Collaboration
Collaborative
teams
Plan, Do, Study,
Act cycle
Shared beliefs
about student
achievement
Effective
Collaboration
Continuous
improvement
Shared
inquiry
Commitment to
results
See pages 60-61
Data Teams

34. What Is Needed for Effective Data Teams?
Effect data and cause data
Authority to use the data for instructional and curricular decisions
Supportive, involved building administrators
Positive attitude
See page 62
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35. 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

36. Communicating Expectations
Do we indeed believe that all kids can learn?
What does this belief look like in your school?
How do you know that all students are learning?
What changes do you 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

37. Data Team Configurations
Vertical alignment
Horizontal alignment
Specialist arrangement
Combination
See page 63
Data Teams

38. Vertical Data Team See page 63 Data Teams
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
See page 63
Data Teams

39. Horizontal Data Team
See page 63
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40. Specialist Data Team See page 63 Data Teams
This illustration is but one example of a high 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 setting, high school departments divided into smaller groups/teams by commonly taught classes or formed by the students they have in common (freshman, seniors, etc.)
See page 63
Data Teams

41. Form Data Teams What will Data Teams look like at your school?
How will they be formed?
How will you identify your Data Team Leaders?
See page 64
Data Teams

42. Team Member Responsibilities
Come prepared to
meeting
Assume a role
Participate honestly,
respectfully,
constructively
Be punctual
Engage fully
In the process
See page 65
Data Teams

43. Roles of Data Team Members
Recorder:
Takes minutes
Distributes to Data Team leader, colleagues, administrators
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.
See page 66
Data Teams

44. Data Technician
Data must be submitted to the data collector by the identified date
Simple form should be created and used; may be electronic
Data should be placed in clear, simple graphs
Graphs should be distributed to all members of the team as well as administrators
This is a separate role from the Data Team leader. This person should be familiar with data and be able to create displays that are readable. This person distributes the forms to record assessment information, communicates timelines (data is usually submitted 3-5 days before Data Team meeting. This allows technician to crunch the numbers, create displays (graphs) and distribute the data to team leaders and team members.
Many schools are using an electronic format which does this instantly. Remember, the data technician will be crunching the numbers for the entire school. At larger schools, this position is sometimes split between two people. Many Data Technicians receive compensation through time, money, professional development resources, etc. In some cases someone is already designated as the ‘data person’.
See pages 66-67
Data Teams

45. Data Team Leaders Who they are? What makes them effective?
What are they responsible for?
Data Teams leaders can volunteer, can be appointed by the building principal, can be agreed upon by the team, or any combination thereof.
Appointing Data Team leaders may result in some Data Team leaders who don’t really want the position and the responsibility that accompanies the position.
Volunteer Data Team leaders may result in the same teacher-leaders serving in another leadership position. Principals must be aware of building leadership capacity in the teacher ranks and spreading the opportunities to as many staff members as possible.
Principals should examine many options before deciding who will serve as a Data Team leader which includes giving the department/team the option of selecting their own Data Team leader.
To provide a level of consistency, Data Team leaders should commit to serving at least one full academic year. In some instances, Data Team leaders may be asked to serve for two consecutive school years.
See pages 68-69
Data Teams

46. Data Team Leaders Are not expected to: Serve as pseudo-administrators
Shoulder the responsibilities of the whole team or department
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.
See page 69
Data Teams

47. Data Team Leaders Reflect on your needs as a staff or team
What qualities will a successful Data Team leader possess?
Overcoming obstacles
See pages 70-71
Data Teams

48. Frequency and Length of Data Team Meetings
Varies: Weekly to once a month
Shortest (45 minutes) to longest (120 minutes)
Schools that realize the greatest shift to a data culture scheduled meetings once a week!
Some ways that schools have found the time for Data Team meetings:
Special events for students (assemblies, special projects, etc.); Double specials (PE, Music, Art) schedule for Data Team meeting day; After school; Reorganization of professional development days; Year-Round School may schedule meetings on transition day; Formal meetings scheduled once per month; Informal meetings held when off-track teacher returns to school
Shortened day – early release; Late start; Substitutes to relieve teacher teams for minutes on a rotating basis; Reassignment of non-classroom staff for covering regular classes
Caution: Team meetings that are too short and do not allow all 5 steps of the Data Team process tend to frustrate members and/or rush important steps! To be efficient and effective, Data Team meetings must follow the structures format. The format should be student focused, simple, allow for discussion time, but not feel rushed. Some Data Team meetings are 45 to minutes while others are minutes. The first few Data Team meetings may be longer, but then once teachers are prepared, know what to expect and are in the ‘hang of it’ then meetings progress as planned.
Data Teams

49. Frequency of Meetings and Closing the Gap
Characteristics of Gap-Closing Schools
Frequent assessment
Use assessment data to change instructional strategies
Two-thirds of gap closing schools use data SEVERAL TIMES A MONTH
Tackled race issues head-on, including academic and suspension gaps
Source: Stanford University Center for Research, quoted in Education Week, 1/24/04
See page 72
Data Teams

50. Scheduling Data Team Meetings
How do you currently use the time that is available?
How can you use this time more effectively?
Scheduled meetings must take place. Nonscheduled meetings do not take place or at least they are not likely to take place. And once the meetings are scheduled, all members agree to honor the meeting commitments. All dates are submitted to the administrator and a master schedule is distributed. There are no excuses for not attending these meetings since commitments were made. Doctors appointments, etc. must be scheduled at a different time.
See pages 73-74
Data Teams

51. Data Team Leader and Principal Debriefs
Meet at least monthly to discuss
Achievement gaps
Successes and challenges
Progress monitoring
Assessment schedules
Intervention needs
Resources
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.
See page 75
Data Teams

52. Post Data Graphs Make simple graphs to share results: See page 76
Display in halls
Display in classrooms
Include in newsletters
Data Walls
Tell your story
Graphs showing various groups of student achievement may serve as a level of motivation especially when students see that their peers performed at a higher level. It is not recommended that smaller groups or individual students be identified unless student ID numbers are used rather than names. Students may realize a greater level of responsibility if they see their incremental progress and they may be highly capable of setting goals. When students set their own goals with teacher input, they are more likely to reach higher by putting forth more effort. Motivation is an outcome of the use of data with students.
Bar graphs, Pie charts, line graphs are great to display proficient and non-proficient numbers. Also it is recommended that all achievement be linked directly to standards or outcomes or even specific power standards.
See page 76
Data Teams

53. Data Walls: “The Science Fair for Grownups”
Strategies
Actions of the
adults
Analysis
Why are we
getting the
results we are?
Data
State and district
Left panel - include state, district, building, department, grade level, class data. May also include subgroup data, AYP information
Middle panel – strategies (should be researched based)
Right panel - evidence. The ongoing assessment data. This may be monthly, quarterly, etc. The right side should also include a narrative.
Data displays may also include pictures.
Data Teams

54. Sample Data Walls Topic for professional conversations
Located in prominent places
Data Teams

55. Sophisticated Data Analysis At Its Finest
Simple bar graphs
Can be student generated
Data Teams

56. Month-to-Month Focus Updated frequently Data from various sources
Data Teams

57. Month-to-Month Comparisons
These data walls are meaningful to the students as they track their achievement
See page 76
Data Teams

58. Create Communication System
Internal stakeholders
Minutes
Agendas
External stakeholders
Newsletter
School Web site
See page 77
Data Teams

59. Data Team Agendas Components: Results from post-assessment
Strengths and obstacles
Goals
Instructional strategies
Results indicators
In addition, agendas can include future meeting times, dates, locations, what to bring to the meeting, motivating quotes, etc.
See page 78
Data Teams

60. Data Team Minutes Components: Data from assessments (chart)
Strengths and obstacles
Goals
Instructional strategies
Results indicators
Comments or summary
Minutes should represent the essence of the Data Team meeting. During each meeting, an individual team member should take official minutes for the purpose of representing team thinking and team decisions. These minutes can be recorded during the Data Team meeting itself on the computer using the agenda template as the outline and then immediately ed to team members and principal. Minutes also provide a living history of those instructional strategies that have been effective in improving student achievement.
See pages 80-83
Data Teams

61. Implementation Plan Steps to create and sustain Data Teams
How will you implement each step?
When will it happen?
Who is responsible?
What resources will you need?
See pages 84-85
Data Teams

62. Feedback
Please take a few minutes to complete the Feedback Form. Your comments are very important to us and to your district office, as it provides specific information and thoughts to consider for future professional development.
Data Teams

63. Thank You Thank You Center for Performance Assessment (800) 844-6599
Data Teams