We now have data' What do we do next

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We now have data.What do we do next?
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DATA . . .Policy and Practice
Dr. Doug Christensen Commissioner, Nebraska
Department of Education
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We Have . . .

• Created a lofty place for data
• Making data a commodity
• systems are being designed to create data
• Data is now a product
• can be packaged
• can be marketed
• can be sold

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Commoditizing Data

• Creates new language . . . New questions
• Data driven . . .
• What does the data say?

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Few Paying Attention To

• Credability of data
• Accuracy of data
• Prinicples of data collection and use

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• He who has the data, rules . . . the _________

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Wheatley

• . . . We increasingly depend on numbers to know
  how we are doing for virtually everything.
• . . . The measures define what is meaningful
  rather than letting the greater meaning of the
  work define the measures.
• As the focus narrows, people disconnect from any
  larger purpose and only do what is required of
  them.

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Wheatley

• . . . Dethrone measurement from its godly
  position, . . .
• . . . Offer measurement a new job that of
  helpful servant.
• Margaret Wheatley (1999)

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• The purpose of data is information.
• . . . To inform

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Data informs . . .

• Does not tell
• Does not conclude
• Does not drive

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Inform

• The public . . . about value of the public
  enterprise (how well it is doing)
• The educators . . . to energize and inform the
  process of continuous improvement

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Three Dimensions of Accountability

• Collecting appropriate, valid and useful data
• Reporting data in understandable ways to our
  public and stakeholders
• Using the data to inform continuous improvement

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• Accountability is a policy of information
• . . . data is the tool

Data
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Data

• More than test or assessment results
• Data includes information about
• processes
• inputs
• contexts
• capacities

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Major policy question . . .

• Where does data come from?
• Outside?
• Inside?
• Neither?
• Both?

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Rocket to Moon

• Off course 95 of the time
• Instruments that fed data to rocket engines and
  navigation systems were on board the rocket
• Not remote
• Not on the ground

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Data about student learning

• Must come from
• Assessments of learning (not tests)
• Assessments of
• Processes
• Inputs
• Contexts
• Capacities

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Assessments of Learning

• Tests
• Demonstrations
• Performances
• Portfolios (over time)
• Observations

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• Test data is far too narrow to inform.

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Here is our point . . .
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• Data from classroom (point of activity) tends to
  create change by informing the engagement of the
  key players.

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Data from the classroom empowers

• Informs individuals about what doing and how well
• Provides feedback for self direction
• Provides feedback to system re supports needed

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Primary goal of data is empowering the learner .
. .

• Learn what is expected
• Learn to self determine level beyond expectations
• Learn beyond content
• Learn how to learn and direct own learning

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• Data should inform key education decisions and
  decision-makers . . .
• Who is learning? What?
• Who is not learning? What?
• What do we do about both?

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Data should inform key policy questions

• Excellence
• How well . . .?
• How much . . .?
• Equity
• For whom?

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What is a good school?

• Overall achievement is high
• Subgroup achievement mirrors the whole group
• Both trend lines are moving upward
• Gaps are narrowing

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Data Should InformAlignment and Rationality
District Aims and Goals
School Aims and Goals
Programs, Practices
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Data should inform . . .

• Strengths
• Areas of concern
• Possible strategies

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Policy Pitfalls of Data

• Indicators Outcomes
• About process not technology
• Data threatens . . .
• People
• Conventional wisdom
• Current authority
• Does not save time
• Bad data bad decision-making

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Einstein

• Not everything that can be counted counts, and
  not everything that counts can be counted.

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We now have data.What do we do next?
Dr. Pat Roschewski Statewide Assessment Director,
NE Department of Education
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The Continuous Improvement Model The No Fear
Model
Data, Data, Data Where are we? What should be
our goals?
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Organizing For Data Analysis

• Who Should be Involved In . . .
• Collecting Data
• Displaying Data
• Analyzing Data
• Sharing Results of Data
• Other Local Uses of Data
• Involve All 100 have a voice

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Organizing For Data Analysis

• Grouping for Involvement
• Grade levels
• Departments
• Subject Areas
• Staff, Students, Parents, Support
  Staff, Community Members, Board Members

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Organizing For Data Analysis

• When?
• Pre-service, early out, late starts, mid-year,
  summer, common planning times
• How Often?
• Frequency
• Multiple Sessions
• Format
• Big Picture District Data 1st session
• Building, grade level disaggregated 2nd session
• By standard by student 3rd and subsequent
  sessions

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Understanding Data Basics

• Data Sources
• Data for Specific Purposes
• Clarification of Data Types

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Ground Rules

• No blaming students
• No blaming teachers
• Data is just information
• Use data for instructional purposes
• De-emotionalize data

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Analyzing Data

• What do these data show?
• Factual Information
• Why might this be?
• Hypotheses
• How should we respond?
• Planning for action

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Question One What do these data show
us? (Factual Information)

• How many students are involved?
• How many students met the standards?
• How many students are in each proficiency level?
• How great are the differences in grade levels?
• What stands out in the data?

Reading __________________________________________
__________________________________________________
_________________________________________
Math _____________________________________________
__________________________________________________
______________________________________
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Question Two Why Might this be? (Hypotheses)

• Does the assessment measure what we teach?
  Why/why not?
• How does the timing of assessment impact the
  outcomes?
• What trends do we see in the data? Why?
• Skill strengths? Weaknesses?
• What differences are there in grade level or
  sub-groups? Why?

Reading __________________________________________
__________________________________________________
_________________________________________
Math _____________________________________________
__________________________________________________
______________________________________
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Question Three How should we respond? (Planni
ng for Action)
1. How do we match instruction to skill needs?
Do we have the skill as a staff to do that? 2.
How can we obtain the knowledge of instructional
strategies for all staff? 3. In
what ways do we offer remediation or
acceleration? In classroom, summer, flexible
grouping, curricular adjustment? 4. How can we
effectively monitor, support, and evaluate
classroom effectiveness?
Reading __________________________________________
__________________________________________________
_________________________________________
Math _____________________________________________
__________________________________________________
______________________________________
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We now have data.What do we do next?
David D. Hamm Superintendent, Plainview Public
Schools
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Questions to Ponder

• Why is it important to be able to produce
  evidence of what the school achieves for its
  students?
• Is accountability a matter of compliance or
  responsibility?

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When playing golf, which shot is the most
important?
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A Data Rich Environment

• Board of Education
• Administrative Team
• Leadership Team
• Teachers
• Community
• Students

How does this benefit kids?
Live it every day!!!
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It all starts with data mining.

• Define the essential questions that matter most
  to your school system.
• Describe the types of evidence you will need in
  order to answer the questions.
• Identify the measures that will be needed to
  collect the necessary evidence.

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We now have data.What do we do next?
Jan K. Hoegh Statewide Assessment, NE Department
of Education
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Good old days
versus
Good new days
TELL me!
SHOW me!