let’s grow some dendrites: understanding and analyzing variation can help!!! prepared for...
TRANSCRIPT
Let’s Grow Some Dendrites: Understanding and Analyzing
Variation Can Help!!!
Prepared for Weatherford ISD
February 21, 2011
Bryan R. Cole Professor Dept. of Educational Administration and Human Resource Development Texas A&M University [email protected] (Copyright - All rights reserved, 2011)
During learning, dendrites sprout more...?
True or false?
Dendrites are major parts of neurons. If a nerve cell dies it doesn't grow back, but existing nerve cells grow branches called dendrites that connect to living nerve cells. When it happens you get your previous level of brain function.
Growing dendrites is a normal process of the learning process. When you learn something new the brain grows dendrites to make nerve connections. As the nerve cells become more connected, your ability to learn improves.
TRUE
During learning, dendrites sprout more spines with receptor sites and particular neurons increase their release of neurotransmitters
What determines how much we learn or what we learn is the extent to which we actively utilize our brains.
The significance of the dendrites in the neo-cortex is that their neural networks are the basis of human intelligence.
MANAGING THE SYSTEM
A superintendent I know spoke for 99 percent of the school districts in America today when he told me that his district had systems to manage money down to the dime, but no systems to manage the learning mission. This is the most critical challenge for school districts to meet.
Larry Lezotte
HIGH PERFORMING LEARNING COMMUNITY
Continuous improvement in education is about
intentionally building teaching and learning and management and
support processes that drive
continuous improvement of student performance
CI is such great process to have in our classes. It has made me work harder. We have goals we have to reach. Doing the graphs has also made sure I stayed focused on what I’m supposed to do.
* Student
LEANDER ISD
JOURNEY TO EXCELLENCE
A High Performing Learning Community
provides the environment to achieve
Performance Excellence and to
optimize Student Achievement
The guiding principle of a High Performing Learning Community is that the purpose of the school is to ensure high levels for all students.
- Will focus the attention and energy of the entire school on learning.
- The frame of reference for all decisions will become, “what is the impact on learning?”
The First (and Biggest) Idea of a High Performing Learning Community
Rick DuFourProfessional Learning Communities
• We can achieve our fundamental purpose of high levels of learning for all students only if we work together.
• We cultivate this collaborative culture through the development of high performing teams focused on organizational learning.
The Second “BIG IDEA” Idea of a High Performing Learning Community
Rick DuFourProfessional Learning Communities
• We assess our organizational and individual effectiveness in helping all students learn at high levels on the basis of results rather that activity.
• We eagerly seek out multiple indicators of student achievement and analyze data to promote continuous improvement.
The Third “BIG IDEA” Idea of a High Performing Learning Community
Rick DuFourProfessional Learning Communities
HIGH PERFORMING LEARNING COMMUNITY
High Performing Learning Community
Management Theory
PERFORMANCE EXCELLENCE MANAGEMENT THEORY
UnderstandingSystems
UnderstandingVariation
UnderstandingPsychology
Theory of Knowledge
EducationalSystem
AREA ELEMENTS
Understanding
Systems
The interdependence of all functions, activities, processes
The need for an aim that is understood by all The need to optimize the system
Understanding
Variation
Deming, W.E.
The stability, capability, and predictability of processes Uncertainty in measurement Special and common causes The consequences of variation for all types of decision making
PERFORMANCE EXCELLENCE MANAGEMENT THEORY
How people interact How people learn How people are motivated How people are affected by change
AREA ELEMENTS
Understanding Psychology
Theory of Knowledge
Prediction as the basis of planning and decision making
Theory as the basis of prediction, and of further questioning and learning The need for operational definitions as a means of stating facts and to facilitate
communicationDeming, W.E
PERFORMANCE EXCELLENCE MANAGEMENT THEORY
AREA ELEMENTS
Organizational Behavior
The dynamics of group behavior
How organizational structure affects behavior
How change occurs in organizations
PERFORMANCE EXCELLENCE MANAGEMENT THEORY
Understanding and managing complex educational systems is fundamentally
about the
processes used and improved
(1) to achieve organizational goals and
(2) for organizational and individual learning.
MANAGING FOR PERFORMANCE EXCELLENCE
A system is a network of interdependent components that work together to try to accomplish the aim of the system.
A system must have an aim. Without an aim, there is no system…optimization is the process of orchestrating the effort of all components toward achievement of the aim (Deming, 1993).
MANAGING SYSTEMS
The aim of the system is collaboratively developed / identified by the stakeholders in the system and then it is the responsibility of management to manage the system to optimize the achievement of the aim.
MANAGING SYSTEMS
Aggie MotorcycleAggie Motorcycle System
If you don’t know who your customer/client is ---
You don’t know what your job is!
Understanding Systems and Processes from Your “Helicopter”
“Helicopter management” is about
- understanding systems and processes
through
- seeing relationships and
- observing/analyzing patterns of organizational and individual behavior over
time.
Systems are developed in order to achieve goals and outcomes that are too complex or interdependent to achieve individually.
MANAGING SYSTEMS
The first step to managing for performance excellence is to
understand the system.
MANAGING FOR PERFORMANCE EXCELLENCE
WHAT CREATES RESULTS IN COMPLEX SYSTEMS?
System
System Design Factors
Human Performance
Factors
Interaction Between System
Design and Human Performance
Factors
MANAGING THE SYSTEM
is the “fuel” for understanding
and managing systems and
processes and the foundation for
continuous improvement.
ORGANIZATIONAL LEARNING
MANAGING THE SYSTEM
SystemDesign Interaction
HumanPerformance
Organizational Structure
Policies
Systems
Processes
Infrastructure
Governance
Systems
Processes
Culture
Knowledge
Skills
Training
Experience
Attitude
Organizational Learning/DevelopmentIndividualProfessionalDevelopment
80-95% of Results5-20%
of Results
All systems left unattended will find their state of disorganization (the principle of entropy). This may also be referred to the
“random capacity of the system”.
If you have problems in a system/organization and you want to grow the system/organization,
then you are also going to grow the problems!!
which results from increased complexity in unmanaged systems.
MANAGING SYSTEMS
PERFORMANCE EXCELLENCE MANAGEMENT THEORY
UnderstandingSystems
UnderstandingVariation
UnderstandingPsychology
Theory of Knowledge
EducationalSystem
PERFORMANCE EXCELLENCE MANAGEMENT THEORY
UnderstandingSystems
UnderstandingVariation
UnderstandingPsychology
Theory of Knowledge
EducationalSystem
A critical component of understanding the system is
understanding and managing the variation
in the system.
MANAGING FOR PERFORMANCE EXCELLENCE
THE CHALLENGE OF MANAGING VARIATION IN EDUCATION
When intentionally designed systems are not in
place, the results you get are primarily a function of the
unmanaged variation in the system.
Therefore, any improvements you make will be a
result of random acts of improvement which will
have little, if any, systemic impact.
UNDERSTANDING VARIATION
Variation
You better learn to “mess” with it
because
it is always “messing” with you!!!!
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MANAGING VARIATION
Variation is a natural phenomenon and exists in everything -- people, services, programs, outputs, achievement.
In applying the theory of variation, educational leaders must understand that management of processes and people requires
knowledge of the effect of the system
on the performance of people and the
capability of the process
to achieve desired results.
MANAGING VARIATION
• Variation will exit in point in time measures as well as longitudinal measures.
• Variation may be attributed to common causes and special causes.
• Variation within a stable system allows prediction.
THE CHALLENGE OF MANAGING VARIATION IN EDUCATION
Unlike business, the greatest amount of variation occurs at its most critical process -
the teaching/learning interaction between teacher and student
CONTROL CHART
Is simply a run chart with statistically determined upper and possibly lower lines drawn on either side of the process average. The upper is known as the Upper Control Limit and the lower as Lower Control Limit.
When to use: When you need to discover how much variability in a process is due to random variation and how much is due to unique events/individual actions in order to determine whether a process is in statistical control.
THE CHALLENGE OF MANAGING VARIATION IN EDUCATION
When intentionally designed systems are not in
place, the results you get are primarily a function of the
unmanaged variation in the system.
Therefore, any improvements you make will be a
result of random acts of improvement which will
have little, if any, systemic impact.
CONTROL CHART
What is a control chart?
Control charts are used to identify and distinguish the common and special causes of variation in a process or a system.
• Special causes of variation are those causes that are outside the control of the system or are abberations of the system.
• Common causes of variation are those causes that result from the design and day-to-day routine operation of the system.
MANAGING VARIATION
There are two mistakes that are frequently made as a result of misunderstanding variation –
• To react to an outcome as if it came from a special cause, when actually it came from common causes of variation.
• To treat an outcome as if it came from common causes of variation, when actually it came from a special cause.
CONTROL CHARTS
Are an excellent way to display variation
Help to locate problem areas
Are effective ways to understand how a process is working
Will help to identify common and special causes of variation
CONTROL CHARTS
Help determine what kind of action to take
Help prevent over and under reaction
Determine if the next steps are to remove special cause of variation in the process or improve the system using PDSA
CONTROL CHART
When you need to discover how much variability in a process/system is due to common cause (random/routine) variation and how much is due to special cause (unique events/individual actions) in order to determine whether a process is in statistical control and to identify the appropriate intervention.
Class Average on Algebra 1 Tests
55
UCL
AVG
LCL60
65
70
75
80
85
90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1918
Cla
ss A
vera
ge
Test
Percentage of Students Going on to Higher Level Math Classes
36
34
32
30
28
26
24
22
20
96 97 98 99 00 01 02 03 04 05 06
UCL
AVG
LCL
Per
cent
age
of S
tude
nts
Year
SIX SIGMA QUALITY
-00 +00
0 1 2 543 6-6 -5 -2-3-4 -1
68.26%
95.46%
99.73%
99.9937%
99.999943%
99.9999998%
VARIATION
• If the data used in preparing the control chart all plot within the limits, and no unusual patterns are noted, we can infer that the system is stable, and therefore predictable.
• All variability is due to common causes that were present throughout the time when the data were collected. If the points fall outside of the limits, then the system is not stable -- special causes are present that are making the system change with time.
VARIATION
• If we have a stable system, as evidenced by a control chart, and then we make a deliberate change to the system, the control chart will tell if the change has had a real effect on the system. This will show up as points plotting outside of the control limits, or unusual patterns in the data.
VARIATION
• If we have a stable system, as evidenced by a control chart, and then at a later time the chart shows points plotting outside the control limits or unusual patterns in the data, this is evidence that the system has changed -- even though we may not know what caused the change.
WISD TAKS (April 2010) Grade 6 Reading by Lexile
1133 Average Lexile Score 1491 Upper Control Limit (+2 sigma) 33 Number of students 774 Lower Control Limit (-2 sigma)
Sub GroupData
Sub GroupData
Sub GroupData
1 735 26 1225 51 1225
2 775 27 1225 52 1225
3 1200 28 810 53 855
4 1050 29 1225 54 1015
5 1095 30 1200 55 1350
6 1350 31 1225 56 1200
7 1350 32 1200 57 1350
8 1050 33 1350 58 1050
9 1200 34 1225 59 1095
10 985 35 1350 60 955
11 1095 36 1350 61 1200
12 790 37 1350 62 1200
13 1225 38 1095 63 1200
14 1015 39 1225 64 1200
15 1350 40 1350 65 1350
16 955 41 1225 66 1350
17 1050 42 1200 67 1350
18 1350 43 855 68 1350
19 715 44 1350 69 1015
20 1225 45 1225 70 1350
21 1350 46 645 71 925
22 1095 47 1200 72
23 1015 48 1225 73
24 1350 49 955 74
25 925 50 1350 75
WISD TAKS (April 2010) Grade 6 Reading by LexileFor One Campus
WISD TAKS (April 2010) Grade 6 Reading by LexileFor One Campus
1151 Average Lexile Score 1517 Upper Control Limit (+2 sigma) 71 Number of students 785 Lower Control Limit (-2 sigma)
1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071
0
200
400
600
800
1000
1200
1400
1600
1800
Sub GroupData
Sub GroupData
Sub GroupData
1 79.88 26 57.25 51 76.59
2 79.46 27 60.63 52 68.8
3 73.03 28 55.38 53 67.02
4 73.9 29 59.38 54 71.94
5 66.3 30 53.63 55 74.23
6 81.68 31 63.61 56 74.62
7 73.84 32 57.25 57 84.62
8 79.73 33 60.63 58 75.77
9 59.23 34 55.38 59 65.38
10 64 35 59.38 60 79
11 55.56 36 53.63 61
12 74.59 37 76.73 62
13 86.13 38 75.55 63
14 85.77 39 77.86 64
15 84.12 40 66.39 65
16 76.65 41 63.27 66
17 75.48 42 71.92 67
18 84.83 43 79.27 68
19 83.34 44 80.23 69
20 85 45 67.32 70
21 79.95 46 67.18 71
22 82.79 47 55.28 72
23 71.75 48 78.01 73
24 85.1 49 81.72 74
25 63.75 50 73.06 75
WISD TAKS Release – All Objectives % Mastery
WISD TAKS Release – All Objectives
71.56 Average % Mastery 84.4 Upper Control Limit (+2 sigma) 60 Number of students 58.7 Lower Control Limit (-2 sigma) x = Objective 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
0
10
20
30
40
50
60
70
80
90
100
x
x
xx
x
x
x
x
xx
Tchr A Tchr C Tchr D Tchr E Tchr F Tchr G Tchr H Tchr I Tchr JTchr B
MANAGING VARIATION
Benefits of a Stable Process …Process performance is predictable
Costs and quality are predictable
Rational basis for planning
Effects of changes in the process are measured with greater speed and accuracy
Special cause effects can be differentiated from common cause effects more easily
MANAGING VARIATION
Losses from misinterpretation of data …Blaming people for system problems
Spending money needlessly
Wasting time looking for explanations of symptoms
Working on the wrong problem
Taking other actions when doing nothing may be better
PLAN, DO, STUDY, ACT
PLAN
Define the
SystemAssess Current
Situation
Analyze Causes
Develop Improvement
Theory
DO
Try Out Improvement
Theory
STUDYStudy the Results
ACT
Standardize
Improvements
Plan for
Continuous
Improvem
ent
This cycle contains four major activities:
Plan: Develop a plan for improvement
Do: Implement the plan.
Study: Evaluate the impact according to specific criteria.
Act: Adjust strategies to better meet criteria
THE SCHOOL IMPROVEMENT CYCLE
The PDSA is a continuous improvement process thatbegins with (P) planning an improved process, (D)implementing the new process, (S) studying the results of the new process through the collectionof data, (A) acting on this information to makeadjustments to the process for improvement, thenthe cycle is repeated.
THE SCHOOL IMPROVEMENT CYCLE
* Data are the key to continuous improvement.
* When we "Plan," we must use data to provide insight and focus for our goals. Data patterns reveal strengths and weaknesses in the system and provide excellent direction.
* When we "Do," we collect data that will tell us the impact of our strategies.
* Through collaborative reflection, we "Study" the feedback offered by our data
and begin to understand when to stay the course and when to make changes.
* Then we "Act" to refine our strategies.
Grade
Non-Special Causes
Total Population
Special Causes
Non-Special Cause
ReferralsTotal #
Referrals
Special Cause
Referrals
K 280 292 12 112 220 108
1 277 288 11 74 161 87
2 246 254 8 133 266 133
3 244 251 7 99 219 120
4 235 249 14 135 298 163
5 222 235 13 142 301 159
6 230 245 15 488 792 304
7 232 248 16 322 617 295
8 215 228 13 564 795 231
9 266 282 16 363 557 194
10 236 250 14 209 365 156
11 220 232 12 265 417 152
12 217 234 17 85 199 114
K 1 2 3 4 5 6 7 8 9 10
11 12
Num
ber
of R
efer
rals
Grade Level
K 1 2 3 4 5 6 7 8 9 10 11 12
Grade Level
Tota
l Num
ber
of S
tude
nts
K 1 2 3 4 5 6 7 8 9 10 11 12
Grade Level(% of total students/# of students)
% o
f R
efe
rrals
Discipline Referral AnalysisSpecial Causes
(PEIMS Reports from 8/15 to 2/15 ONLY)
(3.89/11)
(3.21/8)
(2.77/7)
(5.76/14)
(5.48/13)
(5.65/15)
(5.05/16)
(5.73/13)
(6.00/16)
(5.80/14)
(5.17/12)
(5.78/17)
62
40
5054 54 53
38
48
29
34
43
36
58
(6.81/15)
“In my opinion, I would definitely recommend CI to other teachers. I think it has been really fun and is a great learning process for kids to experience. I hope that one day every child will get to do CI.”
Student
WEATHERFORD ISD
“…teach, challenge and inspire each student in a safe, nurturing
environment to succeed in the global community.”