The document outlines a learning-centered leadership development program that teaches school leaders to use data-informed decision making. It covers topics such as understanding different types of data, overcoming fears about using data, and establishing a collaborative inquiry process among teachers to analyze data and identify areas for improvement. The goal is for principals and aspiring principals to learn how to utilize data to effectively monitor student achievement and implement evidence-based initiatives to improve learning outcomes.

1. Learning-Centered Leadership Development Program for Practicing and
Aspiring Principals
Western Michigan University
Kalamazoo, MI 49008
A Project funded by the United States Department of Education (USDOE), Washing, DC: 2010
Module 1: Data-Informed Decision-Making

2. INTRODUCTION
• Do you Believe in me? - Dalton Sherman
• Reflections
• Learning Objectives
2

3. Dalton Sherman
http://www.youtube.com/watch?v=HAMLOnSNwzA
3

4. Reflection Upon Dalton Sherman’s Speech
• Do staff in your school believe that all students 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?
4

5. Learning Objectives
As a consequence of participating in this module, participants will:
• Understand and experience the importance of data in a continuous
improvement cycle;
• Utilize a data mining tool D4SS (Data for Student Success, MDE) that will
equip you with an understanding about how to disaggregate student data
and identify learning gaps in students performance by gender, ethnicity,
SES, and learning impairments;
• Learn from other practicing and aspiring school leaders about the effect and
challenges of their evidence-based instructional initiatives; and
• Develop and implement a renewal activity in a high priority content area
that is designed to improve student achievement.
5

6. Conceptual Framework for Date-Informed Decision-Making
• What is Data?
• Putting your Fear on the Table Regarding the Use of Data
• Conceptual Model for Data Use
• Collaborative Inquiry Process
6

7. What is Data?
7

8. Examples of Data
 Numbers
 Opinions
 Observations
 Essays
 Science projects
 Demonstrations
 and …….
8

9. Data Can Answer These Questions
1. How are we doing?
2. Are we serving all students well?
3. In what areas must we improve?
Other Questions?
9

10. Principals’ Perception Regarding the Use of Data
Of Teachers
 Teachers uncomfortable with
data
 Teachers cannot read data
 Data has meaning to classroom
 Do not know what to do with
data
 Data not part of teacher
training
 Lack of knowledge data-
instruction
 No data link to teaching
practices
Of Themselves
 Do not understand data use
 What data do you use
 Teacher collection of data
 Need systematic disaggregation
 Find better assessment tools
 PD for teachers and principals
10

11. Principals’ Perception of
Time Constraints to Analyze Data
 Time to complete tasks
 Data vs. classroom duties
 Limited instructional time
 Time to analyze data
 Time for collaboration
 Time to monitor teacher use
 Time in getting test results
 Time in getting data back
 A year behind-results
 Holistic approach in working
with teachers
11

12. Principals’ Perception of Teacher and Student Issues
 Teacher cooperation in
assessment
 Teacher- team cynicism
 Teachers see data as important
 Teacher-staff cooperation in data
assessment
 Inconsistent teacher collection of
student data
 Student do not take testing
seriously
 A few teachers see testing as a fad
 Utility of data
 No relevance to individual
students
 Results do not reflect current
students
 Needs to make sense
 Students mirror teacher attitude
 Too much student testing
 Teacher buying into data
 Unsure if data use beneficial
 Quality of instruction
 No consistence in teacher use of
tools
12

13. Put Your Fears on the Table
What concerns you most about using data
to make school decisions?
Internal?
External?
Do the following concerns sound familiar? 13

14. “Putting data on the
table will damage union
negotiations.”
Fear of Data
“My questions
about data will
sound silly.”
“Will we get sued if we
look at student data? What
about privacy issues?”
“Can we trust the
data? What if the
numbers are
„cooked‟?”
“If people know the
truth about how our
district is doing, we‟ll
get pummeled.”
14
“I don‟t
understand the
data.”
“People will take the
data out of context to
further their own
agendas.”

15. Take Away Your Fear
 You don’t have to be a statistician
 Data are actionable
 Data must be viewed in relationship to something else
 Data should be used to establish a focus of inquiry
15

16. 16
School
Processes
Description of
School Programs
and Processes
Perceptions
Perceptions of
Learning Environment
Values and Beliefs
Attitudes
Observations
Enrollment, Attendance,
Drop-Out Rate
Ethnicity, Gender,
Grade Level
Demographics
Standardized Tests
Norm/Criterion-Referenced Tests
Teacher Observations of Abilities
Authentic Assessments
Student
Learning
Multiple Measures of Data

17. Demographics Perceptions Student Learning
Demographics
- Gender
- Grade
- Teacher
- Age
- Time in Building
- Behavior
- Attendance
- Poverty Level
- Racial/ethnic
- Socioeconomic
- Single Parent
- Siblings in household
- Free/Reduced Lunch
Parental Involvement
- Preparedness
- Transience
-Out of school
experiences
Community Support
- Programs e.g., Head
Start
- Services e.g, FIA
Opportunity to Learn
- Current Offerings
- Extra Curricular Activities
Teacher quality
- Qualifications & Credentials
- Instructional Practices
- Professional Development
- Collective Efficacy
- Learning Communities
- Professional Affiliations
Leadership
- Vision, Mission, Goals
- Staff Engagement &
Perceptions
- Parent Engagement &
Perceptions
- Supervision Practices
- Professional Affiliations
Resource Allocation
- Budget Allocation
- Staffing Patterns
- Professional Development
- Facility Usage/Maintenance
- Technology Distribution
Results Data (Static Data)
- MEAP/MME
- ACT
- AP Testing
- District Benchmark
Assessments
- Standardized Assessments
- Graduation Rate
- Postgraduate Follow-up
Process Data (Real-Time
Data)
- Instructional Strategies
- Classroom Assessments
- Instructional Time on Task
- Behavioral Referrals
- Books
- Writing Samples
- Homework
Assigned/Completed
- Positive Parent Contacts
School Processes
Perception Data
- Student Engagement
- Student motivation
- Student perceptions of
success
- Values
- Beliefs
- Culture
- Attitudes
- Observations
17

18. 1. My school has a written vision that focuses on
student achievement.
Yes No I Don’t Know
6. My school is willing to explore ways to use
data to measure progress.
Yes No I Don’t Know
2. My school has a general awareness about why
data are significant.
Yes No I Don’t Know
7. Everything my school does aligns with our
vision.
Yes No I Don’t Know
3. My school has a mission statement that
reflects core values and beliefs.
Yes No I Don’t Know
8. My school knows that staffs role is using data
to improve student achievement.
Yes No I Don’t Know
4. My school agrees data shows evidence of
progress in achieving student goals.
Yes No I Don’t Know
9. My school uses data to set goals?
Yes No I Don’t Know
5. My school has stated, measurable goals that
are tied to our vision.
Yes No I Don’t Know
10. My school make decisions based on data
based research?
Yes No I Don’t Know
Are We Ready To Use Data More Effectively?
18

19. Types of data
•Input
•Process
•Outcome
•Satisfaction *Set and assess progress toward goals
*Address individual or group needs
*Evaluate effectiveness of practices
*Assess whether client needs are being met
*Reallocate resources in reaction to outcomes
*Enhance processes to improve outcomes
Information Actionable knowledge
District
School
Classroom
SOURCE: Marsh, J. A., Pane, J. F., and Hamilton, L. S. (2006). Making sense of data-driven decision making. Rand Corporation. p. 3.
Conceptual Framework for Data Use
19

20. 20

21. Establishing the Bridge for Student Improvement
Collaborative Inquiry
21

22. Enabling Collaborative Work
• Schools have an abundance of data. There is the propensity of school
officials to relegate the technical work in organizing data to a small group
of individual – i e., principal, principal and select teachers, or data
specialist.
• This responsibility needs to be shared among all teachers, and ideally,
among all members of the school community.
• It is quite apparent that when people are involved in analyzing and
interpreting data collaboratively, they become more invested in the school
improvement efforts that are generated out of those discussions.
• The more people involved in data analysis and interpretation, the more
effective the resulting school improvement efforts will be.
22

23. John Dewey
23
What the best and wisest
parent wants for his own
child, that must the
community want for all of
our children.

24. It’s Easy to Get Lost in the Numbers
24
63542
63542
37620
60915
5629098625
8762987620
980098
89365and forget that the numbers represent
the hope and future of real children
with strengths as well as challenges,
each deserving the kind of education we
want for our very own children

25. Bridging the Data Gap
 Imagine two shores with an river in between.
 On one shore are data—the masses of data now
overwhelming schools:
 On the other shore are the aspiration, intention, moral
assurance, and directive to improve student learning and
close repetitive achievement gaps.
 course-taking patterns
 attendance data
 survey data
 and on and on
 graduation rates
 state test data sliced and diced
 local assessments
 demographic data
 dropout rates
25

26. 26
I saw this new
reading program at
the State
conference, let’s
try it, it can’t hurt!
If we put more
resources into
“Bubble Kids” our
scores will
improve
It is evident that
those kids cannot
learn as efficiently
as others
? ? ?

27. 27
Build and identify the parts of a bridge that is needed to get Data to the Results?

28. Collaborative Inquiry Is The Bridge
• Schools know that they have to improve
• But they often do not know how to improve
• Collaborative inquiry is the how
• As collaborative inquiry grows, schools shift away from
traditional data practices and toward those that build a high-
performing culture of data use
• When engaged in collaborative inquiry, Data Teams
investigate the current status of student learning and
instructional practice and search for successes to celebrate and
amplify.
28

29. Setting the Stage for Collaborative Inquiry
Participants’ Activity:
• In this particular activity, participants will discuss the type of
external and internal data they use in their schools. After this
participants will identify trends associated with these
administrations.
• On post- it notes, participants will be asked to make the
following observations and report out in groups the following
questions:
29

30. Group Activity
Question:
1. Is there one particular data type (external or internal) used more often than
the other? If so, why?
2. What decisions are made by the use of external and internal data tools?
Who make these decisions (by data type)?
3. To what extent is there a close relationship between the gaps in student
learning, as identified by the data types, and the initiatives that were
developed?
4. What challenges are you facing implementing the initiative in your school?
5. To what extent is the initiative producing the intended results you
originally sought? How do you know? What data are you using? If you
are not getting the desired results, what are you doing about it?
30

31. Demographics Perceptions Student Learning
Demographics
- Gender
- Grade
- Teacher
- Age
- Time in Building
- Behavior
- Attendance
- Poverty Level
- Racial/ethnic
- Socioeconomic
- Single Parent
- Siblings in household
- Free/Reduced Lunch
Parental Involvement
- Preparedness
- Transience
-Out of school
experiences
Community Support
- Programs e.g., Head
Start
- Services e.g, FIA
Opportunity to Learn
- Current Offerings
- Extra Curricular Activities
Teacher quality
- Qualifications & Credentials
- Instructional Practices
- Professional Development
- Collective Efficacy
- Learning Communities
- Professional Affiliations
Leadership
- Vision, Mission, Goals
- Staff Engagement &
Perceptions
- Parent Engagement &
Perceptions
- Supervision Practices
- Professional Affiliations
Resource Allocation
- Budget Allocation
- Staffing Patterns
- Professional Development
- Facility Usage/Maintenance
- Technology Distribution
Results Data (Static Data)
- MEAP/MME
- ACT
- AP Testing
- District Benchmark
Assessments
- Standardized Assessments
- Graduation Rate
- Postgraduate Follow-up
Process Data (Real-Time
Data)
- Instructional Strategies
- Classroom Assessments
- Instructional Time on Task
- Behavioral Referrals
- Books
- Writing Samples
- Homework
Assigned/Completed
- Positive Parent Contacts
School Processes
Perception Data
- Student Engagement
- Student motivation
- Student perceptions of
success
- Values
- Beliefs
- Culture
- Attitudes
- Observations
31

32. BREAK
15 Minutes
32

33. Creating A Data Team
A data team is a team that meets regularly to analyze
data and make educational decisions to improve
student achievement.
33

34. THE DATA TEAM PROCESS
1. Collect
and Chart
Data
2. Analyze
Data and
Prioritize
Needs
3. Establish
SMART
Goals
4.Select
Instructional
Strategies
5.
Determine
Results
Indicators
Source: Allison, E. et al.. (2010). Data teams. Lead + Learn Press.: Englewood, CO.
6. Monitor
and
Evaluate
Results

35. Data Teams
The data team members must:
• Be seen as leaders.
• Be willing to learn about data in depth.
• Must have skills in collaboration,
communication, and leadership.
The functions of the data team are:
• To develop expertise on data.
• To share data information with staff members
of the schools.
• To assist is setting up support systems at the
schools.
• To create and complete action plans based on
the data.
The data team members need:
• Information about systems to support data
based decision making.
• Training in the problem solving process.
Data team members should be expected to:
• Meet regularly as a team to develop a plan to
establish using data to improve student
performance.
• Have conversations about student
achievement.
• Show examples of successful schools.
• Set up a system that supports the collection
and use of student data.
• Help staff members understand how to use
student data to guide decision making.
• Work to secure commitment from staff
35

36. D4SS
Data Analysis Activity
36
http://www.data4ss.org
User Name: demo_test1
Pass Word: fall_01

37. Data Narrative Statements Criteria
Data Narrative Statements are objective statements of FACT about
the school data
They:
1. Represent student achievement, demographics, school programs,
school processes, and stakeholder perceptions
2. Communicate a SINGLE idea
3. Are clear and concise – written in sentences or phrases
4. Describe the data; they do not evaluate the data!
5. MUST stand alone; they do not require the data source to
accompany them in order to be understandable.
37

38. Data Narrative Statements
Do they meet criteria from Previous Slide?
Narrative Statement 1 2 3 4 5
1- Spring 2010 Math Assessment shows that our girls do
slightly better than the boys.
2- The Spring 2010 Math Assessment shows that 20.5% of
our 11th grades students were proficient and 79.5% were
not.
3- The Spring 2010 Math Assessment shows that we really
need a new math series.
4- In 2009-2010 21.4 % of all our students taking the Math
Assessment are proficient; while 20.5% of our 11th graders
are proficient and 33.3% of our 12th graders are proficient.
5- Parents do not like the math program.
38

39. Year: _________ Building: _________________________
Which grade level(s) is not meeting the criteria for grade level proficiency?
What do we need to know more about?
%
Proficient
%
Proficient
%
Proficient
AYP Target
(see slide 47
for AYP
Target)
%
Proficient
AYP Target
(see slide 47
for AYP
Target)
Content Area Reading Writing Total ELA ELA Math Math
Overall
Building – All
Students
Grade 3
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8
39
MEAP
Building: Content Analysis

40. MEAP
Overall Building Sub-Group Level Achievement Analysis
Grade
Number
of
Students
%
Proficient
Number
of
Students
%
Proficient
Number
of
Students
% Proficient
AYP
Target
(see
slide 47
for AYP
Target)
Number
of
Students
%
Proficient
AYP
Target
(see
slide
47 for
AYP
Target)
Content Area Reading Reading Writing Writing Total ELA Total ELA ELA Math Math Math
American Indian
or Alaska Native
Black or African
American
Hispanic or
Latino
White
Asian American,
Native Hawaiian
or other Pacific
Islander
Multiracial
Economically
Disadvantaged
Students with
Disabilities
Limited English
Proficient
Non AYP-
Migrant 40

41. MEAP
Sub-Group Analysis
Sub-Group Level Achievement: Choose sub-group for analysis
Year ___________ Group: ____________________
Grade
Number of
Students
%
Proficient
Number of
Students
%
Proficient
Number of
Students
%
Proficient
AYP
Target
(see
slide 47
for AYP
Target)
Number of
Students
%
Proficient
AYP
Target
(see
slide 47
for AYP
Target)
Content
Area
Reading Reading Writing Writing Total ELA Total ELA ELA Math Math Math
Building
3
4
5
6
7
8
41

42. Date: _______________________________
Building: ____________________________
Data Team Members: __________________________________________________________________________________
______________________________________________________________________________________________________
__________
42
Narrative Statement:
MEAP
Data Narrative Statements for Sub-Group Analysis

43. Year: _________ Building: _________________________
MME
Building: Content Area Proficiency
In which subject area(s) is your building not meeting the criteria for proficiency?
What do you need to know more about?
%
Proficient
%
Proficient
%
Proficient
AYP Target
(see slide 47
for AYP
Target)
%
Proficient
AYP Target
(see slide 47 for
AYP Target)
Content Area Reading Writing Total ELA ELA Math Math
Overall Building –
All Students
Grade 11
Grade 12
43

44. MME
Overall Building Sub-Group Level Achievement Analysis
Grade
Number
of
Students
%
Proficient
Number
of
Students
%
Proficient
Number
of
Students
% Proficient
AYP
Target
(see
slide 47
for AYP
Target)
Number
of
Students
%
Proficient
AYP
Target
(see
slide
47 for
AYP
Target)
Content Area Reading Reading Writing Writing Total ELA Total ELA ELA Math Math Math
American Indian
or Alaska Native
Black or African
American
Hispanic or
Latino
White
Asian American,
Native Hawaiian
or other Pacific
Islander
Multiracial
Economically
Disadvantaged
Students with
Disabilities
Limited English
Proficient
Non AYP-
Migrant 44

45. MME Sub-Group Analysis
Sub-Group Level Achievement: Choose sub-group for analysis
Year ___________ Group: ____________________
Grade Number of
Students
%
Proficient
Number of
Students
%
Proficient
Number of
Students
%
Proficient
AYP
Target
(see
slide 47
for AYP
Target)
Number of
Students
%
Proficient
AYP
Target
(see
slide 47
for AYP
Target)
Content
Area
Reading Reading Writing Writing Total ELA Total ELA ELA Math Math Math
Building
45

46. Date: _______________________________
Building: ____________________________
Data Team Members: ___________________________________________________________________________________
______________________________________________________________________________________________________
__________
46
Narrative Statement:
MME
Data Narrative Statements for Sub-Group Analysis

47. Michigan Annual
AYP Objectives
47

48. Reflections On Today’s Session
1. What do you remember from today's session (scenes, events, and conversations)?
2. What words are still ringing in your ears?
3. What image captures for you the emotional tone of today's session?
4. What is a key insight from today's session?
5. What name would you call today's session? (Try a poetic title that captures your
responses.)
48

49. End
Session 1
49

50. 50
Continuous
ImprovementData UseCollaborationLeadership
Capacity
TrustCultureEquity

51. PANEL DISCUSSION WITH DATA EXPERTS
Kathryn Parker Boudett
http://bcove.me/v4ccqy6q
Joel Klein
http://bcove.me/byt742or
Rudy Crew
http://bcove.me/emn3jz77
Martha Greenway
http://bcove.me/4cf0bkzq
Aimee Guidera
http://bcove.me/trsldt2h
Dan Katzir
http://bcove.me/gy3wbiac
http://www.edweek.org/ew/section/video-galleries/april10-event-data.html
51

52. SmartGoals
Data
Teams
Summative
Assessments
Perceptual
Demographic
Formative
Assessment
s
Alignment
QuestionsandInquiry
Process
Revised Instructional
Strategies
Revised Instructional
Strategies
Data
Team
s
Data Feedback Model
DistrictWrittenand
TaughtCurriculum
DataIntersection
Analysis

53. 53

54. 54

55. 55

56. 56

57. 57

58. 58

59. 59

60. 60

61. 61

62. 62