Using Data f or Continuous School Improvement

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