using data f or continuous school improvement
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Using Data f or Continuous School Improvement. 2014 Fall CIP Workshops. 4 Types of Data. Type : Demographic Who are we?. Type : Perceptions How do we do business?. Type : School Processes What are our processes?. Type : Student Learning How are our students doing?. Goal 2 SLDS Grant. - PowerPoint PPT PresentationTRANSCRIPT
Using Data for Continuous
School Improvement2014 Fall CIP Workshops
4 Types of Data
Type: PerceptionsHow do we do business?
Type: Student LearningHow are our students doing?
Type: School ProcessesWhat are our processes?
Type: DemographicWho are we?
Goal 2 SLDS GrantProvide a statewide system of professional development training for data analysis that
reaches every district.Tiered Training Delivery
✔
School District Staff
School District Leadership
ESUs and NDE Staff ✔
Statewide Data Cadre ✔
Statewide Data Cadre• ESUs/ESUCC
– Rhonda Jindra – ESU 1
– Mike Danahy – ESU 2
– Marilou Jasnoch – ESU 3
– Annette Weise – ESU 5
– Lenny VerMaas – ESU 6
– Denise O’Brien – ESU 10
– Melissa Engel – ESU 16
– Jeff McQuistan – ESU 17
• NDE– Data, Research, Evaluation
– Russ Masco– Matt Heusman– Rachael LaBounty– Kathy Vetter
– Assessment– John Moon
– Federal Programs– Beth Zillig
– Special Education– Teresa Coontz
– Curriculum– Cory Epler– Tricia Parker-Siemers
– Accreditation and School Improvement– Don Loseke– Sue Anderson
• Higher Ed– Dick Meyer – UNK
Nebraska Data Literacies
What do the data show?
DataComprehension
Why might this be?
DataInterpretation
Did our response produce results?
Evaluation
How should we respond?
Data Use
Data Literacies Format
1.
a.
i.
Concept
Indicators
Literacy
Data Literacieshttp://www.education.ne.gov/DataServices/SLDS_Grant/
Data_Literacies.pdf
Data Use Curriculum
Nebraska Data
Literacies
WHY data analysis/continuous school improvement?
WHAT process/data do we need to engage for school improvement?
HOW do we involve all staff in the process of school improvement?
AGENDA
Tools and resources…
Bernhardt, V.L. (2013)
Data Analysis for Continuous
School Improvement
(Third Edition)
New York, NY: Routledge
BACKGROUND
• Education for the Future – Non-Profit Initiative
• Victoria L. Bernhardt, Exec Director
• California State University, Chico
• Our Mission
• Funded by contracts.
• 17 Books, Conferences, Institutes, Workshop.
• Manage long-term implementation contracts.
• Monthly online meeting series.
Data Analysis for Continuous School Improvement, Third Edition, ……is about inspiring schools and districts to commit to a continuous school improvement framework that will result in improving teaching for every teacher, and improving learning for every student, in one year, through the comprehensive use of data. It is about providing a new definition of improvement, away from compliance, toward a commitment to excellence.
P. 5
HOW MUCH TIME DOES IT TAKE?
It will take one school year
for a school staff to do all
the work described in this
book. If parts of the work are
already done, a staff might
still want to spread out the
work throughout the year.
P. 10
WHY Data Analysis/Continuous
and School Improvement?
What would it take to ensurestudent learning at
every grade level, in every subject area, and with every student group?
WHAT IS THE HARDEST PARTFROM YOUR PERSPECTIVE?
Beliefs that all children can learn. Schools honestly reviewing their data. One vision. One plan to implement the vision. Curriculum, instructional strategies, and
assessments clear and aligned to standards. Staff collaboration and use of data related to standards
implementation. Staff professional development to work differently. Rethinking current structures to avoid add-ons.
THINGS WE KNOW ABOUT DATA USE
For data to be used to impact classroom instruction, there must be structures in place, to—
implement a shared schoolwide vision. help staff review data and discuss
improving processes. have regular, honest collaborations
that cause learning.
Continuous Improvement Cycle
MissionVision
VISION defines the desired or
intended future state of an
organization or enterprise in terms
of its fundamental objectives
relative to key, core areas
(curriculum, inst, assess, environ).
VISION
• Curriculum—What we teach.
• Instruction—How we teach the curriculum.
• Assessment—How we assess learning.
• Environment—How each person treats everyother person.
MISSION succinctly defines the
fundamental purpose of an
organization or an enterprise,
describing why they exist.
FOCUSED ACTS OF IMPROVEMENT
Data Analysis for Continuous
School Improvement Is About
What You Are Evaluating Yourself
Against
“In times of change, learners
inherit the earth, while the learned
find themselves beautifully
equipped to deal with a world that
no longer exists.”
-
Eric Hoffer
Page 14
Where are we now?
How did we get to where we are?
Where do we want to be?
How are we going to get to where we want to be?
Is what we are doing making a difference?
Data Literacy 1What do the data show?
Data Literacy 2Why might that be?
Data Literacy 3How should we respond?
Data Literacy 4Did our response produce results?
Data Literacy 2Why might that be?
Page 14
Data Literacy 1What do the data show?
Data Literacy 2Why might that be?
Data Literacy 2Why might that be?
Data Literacy 3How should we respond?
Data Literacy 4Did our response produce results?
IMPORTANT NOTES
• Continuous School Improvement
describes the work that schools do,
linking the essential elements
• Continuous School Improvement is
a process of evidence, engagement,
and artifacts
A PROCESS OF EVIDENCE, ENGAGEMENT, AND ARTIFACTS
Evidence:
• Data to inform and drive a logical progression of
next steps.Engagement:
• Bringing staff together to inform improvement
through the use of data, moving from personality
driven to systemic and systematic.
Artifacts:
• The documentation of your improvement efforts.
RANDOM ACTS OF IMPROVEMENT
Where are we now?
How did we get to where we are?
Where do we want to be?
How are we going to get to where we want to be?
Is what we are doing making a difference?
Data Literacy 1What do the data show?
Data Literacy 2Why might that be?
Data Literacy 3How should we respond?
Data Literacy 4Did our response produce results?
Data Literacy 2Why might that be?
Page 14
FOCUSED ACTS OF IMPROVEMENT
COMPLIANCE
VERSUS
COMMITMENT
Bernhardt, V.L. (2013). Data Analysis for
Continuous School Improvement. Third Edition.
New York, NY: Routledge. Page 4. Reproducible.
Page 4
Evidence
Data Literacy 1What do the data show?
“Study the past if you would
like to define the future.”
- Confucius
Page 17
Page 17
Describe the context of the schooland school district.
Help us understand all other numbers.
Are used for disaggregatingother types of data.
Describe our system and leadership.
DEMOGRAPHICS AREIMPORTANT DATA
Enrollment
Gender
Ethnicity / Race
Attendance (Absences)
Expulsions
Suspensions
DEMOGRAPHICS
Language Proficiency
Indicators of Poverty
Special Needs/Exceptionality
IEP (Yes/No)
Drop-Out/Graduation Rates
Program Enrollment
DEMOGRAPHICS (Continued)
WHAT STUDENT DEMOGRAPHIC DATA ELEMENTS CHANGE WHEN LEADERSHIP CHANGES?
Enrollment
Gender
Ethnicity/Race
Attendance(Absences)
Expulsions
Suspensions
Language Proficiency
Indicators of Poverty
Special Needs/ Exceptionality
IEP (Yes/No)
Drop-Out / Graduation Rates
Program Enrollment
School and Teaching Assignment
Qualifications
Years of Teaching/At this School
Gender, Ethnicity
Additional Professional
Development
STAFF DEMOGRAPHICS
Page 17
Help us understand whatstudents, staff, and parents are perceiving about the learning environment.
We cannot act different from what we value, believe, perceive.
PERCEPTIONS AREIMPORTANT DATA
Student, Staff, Parent,Alumni Questionnaires
Observations
Focus Groups
PERCEPTIONS INCLUDE
PERCEPTIONS
What do you suppose students
say is the #1 “thing” that has to
be in place in order for them to
learn?
Page 17
Know what students are learning.
Understand what we are teaching.
Determine which studentsneed extra help.
STUDENT LEARNING AREIMPORTANT DATA
STUDENT LEARNINGDATA INCLUDE
Diagnostic Assessments(Universal Screeners)
Classroom Assessments
Formative Assessments(Progress Monitoring)
Summative Assessments(High Stakes Tests, End of Course)
Defined:
Pages54-57
What happens when learning
organizations react solely to the
measures used for compliance
and accountability?
STUDENT LEARNING AREIMPORTANT DATA
Page 17
Schools are perfectly designed to
get the results they are getting now.
If schools want different results,
they must measure and then change
their processes to create the
results they really want.
SCHOOL PROCESSES
SCHOOL PROCESSES
Processes include…
Actions, changes, functions that bring about a desired result
Curriculum, instructional strategies, assessment, programs, interventions …
The way we work.
Tell us about the waywe work.
Tell us how we get theresults we are getting.
Help us know if we have instructional coherence.
SCHOOL PROCESSES AREIMPORTANT DATA
SCHOOL PROCESSES DEFINITIONS
INSTRUCTIONAL: The techniques and strategies that teachers use in the learning environment.
ORGANIZATIONAL: Thosestructures the school puts in placeto implement the vision.
ADMINISTRATIVE: Elements about schooling that we count, such as class sizes.
CONTINUOUS SCHOOL IMPROVEMENT: The structures and elements that help schools continuously improve their systems.
PROGRAMS: Programs are planned series of activities and processes, with specific goals.
SCHOOL PROCESSES DEFINITIONS
Data Profile
Demographic Data
ENGAGEMENTAppendix F Page 265-296
Data Literacy 1What do the data show?
STUDY
QUESTIONS
Demographic
Data
Strengths Challenges
Implications for the continuous school improvement plan.
Other data . . .
Page 348
STRENGTHS: Something positivethat can be seen in the data. Oftenleverage for improving a challenge.
CHALLENGES: Data that implysomething might need attention,a potential undesirable result,or something out of a school’s control.
DEFINITIONS
IMPLICATIONS FOR THE
SCHOOL IMPROVEMENT PLAN
are placeholders until all the data are
analyzed. Implications are thoughts
to not forget to address in the school
improvement plan. Implications
most often result from CHALLENGES.
DEFINITIONS
List other demographic data you would like to have in your data profile.
Make sure your data profile describes your uniqueness and provides the information you need to monitor your system.
OTHER DEMOGRAPHICS
LETS SEE WHAT IT LOOKS LIKE
Pages 265-334
• Individually review the
data to identify strengths,
challenges, implications
for planning, and further
data needed.
• Write your findings on
the Demographic Data
handout.
DEMOGRAPHIC DATA PP 265-296
Answer Questions—
Strengths, Challenges,Implications, OtherDemographic Data.
1. Independently
2. Merge to Whole Group
3. Write combined findings on Poster Paper
ANALYZING THE DATA
WHAT ARE
THE BENEFITS OF
THIS APPROACH?
DEMOGRAPHIC DATA PP 265-296CASE STUDY Demographic Data
5 Divisions
1. Enrollment: Pages 265-273
2. Mobility: P. 273, Attendance: P. 274, ELL: P. 275, & FRL: P. 276
3. Special Education: P. 277-284
4. Retention: PP. 276-277, Pre-Referral Team: PP. 285-286, Staff: Pages 294-296
5. Behavior: Pages 287-293
NEXT STEPS
Work with your ESU Staff Developer to
• Engage with your district/school data
• Analyze demographic, perceptual, student
learning, and school process data
• Understand the common and systemic
implications of strengths and challenges from
all four data types
• Solve challenges using data
DATA INVENTORIES - APPENDIX B
Pages 205-217
Next Steps….
Aggregating Implications for
Planning Across All Areas of Data
Review implications across data. Look for commonalities.
Create an aggregated list ofimplications for the schoolimprovement plan.
MERGE STRENGTHS, CHALLENGES,AND IMPLICATIONS FOR THE SCHOOL
IMPROVEMENT PLAN
After analyzing all four types of data
AGGREGATING IMPLICATIONS
• Intersections
• Presentation
and
interpretation/en
gagement as a
function of
analysis.Page 17
Page 343
FACILITATION GUIDE
Pages 343-353
“Education is learning what
you didn’t even know you didn’t
know.”
- Daniel J. Boorstin
CONTRIBUTING CAUSES:
Underlying cause or causes
of positive or negative results.
Pages 105-108
Page 106-108
PROBLEM SOLVING CYCLE
EXAMPLE
Not enough students
are proficient in
Mathematics.
IDENTIFY THE PROBLEM
THE PROBLEM-SOLVING CYCLEExample Hunches/Hypotheses Page 106
THE PROBLEM-SOLVING CYCLEExample Hunches/Hypotheses Page 106
What questions do you
need to answer to know
more about the problem
and what data do
you need to gather?
THE PROBLEM-SOLVING CYCLE
THE PROBLEM-SOLVING CYCLEExample Questions/Data Needed Page 107
1. Identify a problem/
undesirable result.
2. List 20 reasons
this problem exists
(from the perspective
of your staff).
THE PROBLEM-SOLVING CYCLE
3. Determine what
questions you need
to answer with data.
4. What data do you
need to gather to
answer the questions?
THE PROBLEM-SOLVING CYCLE
THE PROBLEM-SOLVING CYCLE
Please record
on chart
paper.
P. 357 P. 358
PROBLEM SOLVING CYCLE
Evidence:
• Automatically end up at the 4 circles.
• Focus on the process(es) at the root.Engagement:
• Makes big problems manageable.
• Time savings.
• Key in making the move from
personality driven to systemic and
systematic.
FACILITATION GUIDE
Pages 354-358
Where are we now?
How did we get to where we are?
Where do we want to be?
How are we going to get to where we want to be?
Is what we are doing making a difference?
Data Literacy 1What do the data show?
Data Literacy 2Why might that be?
Data Literacy 3How should we respond?
Data Literacy 4Did our response produce results?
Data Literacy 2Why might that be?
Page 14
Data Literacy 1What do the data show?
Perceptual and DemographicResources available
through NDE
Perceptual Data• Surveys are available for students, parent, staff,
for districts/schools that will work with their ESU staff developer to learn how to analyze the perceptual data
• Districts/schools complete a (revised) form Schools receive links to the surveys
• Schools and ESU staff developer will receive the perceptual survey data
• The data belongs to the districts/schools
Perceptual Data Request FormReturn to ESU Staff Developer
Perceptual Data
• Ability to administer surveys will be available in future years as well
• NDEs capacity to manage the perceptual data surveys is developing
Data Profile - Reports in DRSProfile similar to Bernhardt Appendix F
Continuous Improvement
Data ProfileEnrollment example
Data Profile-Enrollment by Ethnicity
Data ProfileEthnicity Not SPED/ SPED Example
Evaluation & Next Steps with your ESU Staff Developer
https://www.surveymonkey.com/s/dataliteracy
Please complete one survey per district together as a district team
http://www.education.ne.gov/DataServices/SLDS_Grant/
Data_Cadre.html
ResourcesPPT