Adventures in school data

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Adventures in school data

• LEHS Profile Team
• NCA Fall Conference
• October 16, 2002

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How to know if you have a really good reason to
be in this room

• You are in the right place at the right time IF
• You are laboring (in the dark) on your first
  school profile.
• You are unhappily working on a school profile for
  your schools second cycle.
• Your visiting team belittled your school profile.
• You have had your sleep interrupted wondering
  what the expression data-driven school means.

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How well kill this hour

• General description of the profile
• importance to NCA process
• forms of data
• process of data gathering/disaggregating
• finished product
• A brief history of The Mess We Got Ourselves Into
  and How We Fixed It
• What we plan to do next Our ideas about the
  road ahead

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Profile team (Lapeer East HS)

• Larry Goodenow, counselor
• Sharon Namenye, math department
• Karen Rykhus, math department
• Jim Mikus, math department
• Duane Machesney, foreign language department
• Mike Hobolth, assistant principal

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Mommy, where do NCA goals come from?

• The birthing process involves
• Intuition
• Love
• Baseline data

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Why do we need Baseline data?

• Data demonstrates/validates needs in our building
• Connects intuition to a measurable format
• Essential for future comparisons
• Allows for more precise replication

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The Data Pool could include

• Attendance
• MEAP data
• Disciplinary referrals
• Demographics
• District wide surveys
• GPAs

• Senior exit surveys
• School improvement surveys
• Climate survey

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Sources of data

• Central office
• Standard Poors
• CEPI
• Climate surveys
• School improvement surveys
• parents
• teachers
• students

• In-house surveys
• Attendance/grades/discipline
• Administrivia

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Where does all of this stuff go?

• Profile team as data clearinghouse
• Collection
• Disaggregation
• Communication

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Other functions of data team

• Consulting
• assist with design of goals/strategies with NCA
  goal teams
• assist with design of surveys/data collection
• Outcome data collection/ reporting

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The Rule of Three

• Any NCA goal should rely on at least three (3)
  independent sources
• Ideally
• building data pool
• research
• qualitative source (interviews, surveys,
  professional judgment in some form)

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Chlorinating the pool

• School profile is NOT a one-shot deal care and
  maintenance of the pool is a big issue
• Profile team should be as continuous as any NCA
  goal team
• Profile team should be a major component of the
  bridge between NCA cycles

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History of our current cycle First year

• Goals selected intuitively
• Profile formed by steering committee as
  afterthought
• Goals/strategies not drawn from profile data (cf.
  Rule of Three)
• Result intense chastening from visiting team

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History of our current cycle Second year

• New principal
• Staff morale
• Feelings of wasted time from previous year
• Correct questions about role of profile
• Goals decision to stay with goals previously
  set (w/ some exceptions)
• Decision to attempt to continue with current
  cycle instead of going to performance model

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Remedy restructured data team

• Five-member team
• Function clearinghouse for building data
• Process
• Crunch/cook numbers into a usable form for NCA
  teams
• Ongoing conversations w/ teams about they need

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Remedy reverse engineering

• Problem How do we demonstrate that this process
  is data-driven when it really hasnt been?
• Reverse engineering
• respect the professional intuition that created
  the goals/strategies
• fix the foundation

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Other stuff we learned

• Enroll the building administration
• Use common formats
• Make peace with central office
• Make the profile team important to everyone
• Communicate meaningfully with staff
• Build bridges
• Find the geek(s) and make your deal

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Current status in cycle

• (yet another) New principal
• Profile complete
• Goals on target
• Technology
• Responsibility
• Restructuring
• Achievement
• Strategies big plans
• Documentation manageable

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What we will do next

• Get geekier
• Ongoing issues attendance, MEAP, grade
  distribution
• Contracting data jobs expanding the circle
• Movement from descriptive to inference statistics
• Block scheduling
• User groups
• Transition school for next cycle (performance
  accreditation)