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Turning Data into Information

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Is there a relationship between attendance and performance? ... Is the demographic make-up of the Barre Town School changing over time? ... – PowerPoint PPT presentation

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Title: Turning Data into Information


1
Turning Data into Information
  • Tommy Walz
  • Rob Stalling
  • Dynamic Landscapes 2008

2
Purpose of this Presentation (Transform data
into information)
  • Pose questions that need thoughtful answers
  • Analyze the process
  • There is more than one correct answer

3
Why Electronic Data?
  • One location
  • Remotely accessible
  • Can be manipulated
  • Grouped
  • Sorted
  • Placed longitudinally
  • Answer What if? or Who is..?

4
Questions We Have Answered
  • Is there a relationship between attendance and
    performance?
  • Is there a relationship between gender and
    performance?

5
Questions We Have Answered
  • How do the kids who have been with us since
    Kindergarten compare with those who have not?
  • Is the demographic make-up of the Barre Town
    School changing over time?
  • Can we forecast enrollments (Cohort Survival
    Ratio)?

6
What Databases do We Have?
  • Staff (AS/400 FMP)
  • Student Management System (Chancery SMS
    MacSchool)
  • Assessments (FMP)
  • Special Ed (Sped Docs - FMP)
  • Health (FMP)
  • Phys Ed (Fitnessgram)
  • Cafeteria (Vendor-owned)
  • Library (Follett Destiny)
  • Inventory (FMP)

7
What Databases do YOU Have?
8
What Data?Examples
  • Lunch (SES)
  • Age, gender, DOB, grade level, teacher
  • Assessment scores
  • Disabilities
  • Health Screenings, Immunizations, Chronic
    conditions
  • Staff credentials

9
Data Sources
  • Clerical staff
  • DOE
  • Vendor
  • Cafeteria
  • Teachers
  • Curriculum Director/Curriculum Specialists

10
Where Does it Go?
  • In specialized databases, i.e., SpedDocs?
  • In general databases?
  • In spreadsheets?
  • On the school/SU network?
  • On a web site?
  • In teachers mailboxes?
  • In administrators hands?
  • In kids backpacks?

11
Data Flow The Simple Model
Data Source
Mediator
Target Audience
12
What Data Flow Really Looks Like
13
How Good is the Data?
  • Is it valid?
  • Is it relevant?
  • Is it current?
  • Is it consistent?
  • What am I missing?

14
Data Changes
  • Time sensitivity
  • Changes in eligibility
  • With transient population
  • Changes in services
  • Changes in assessments
  • Changes in demographics

15
Resources Needed
  • Assessment tools
  • Reliable and consistent data sources
  • Hardware
  • Software
  • Security
  • Policy
  • Channels of communication
  • Personnel

16
Who Has Access?And How Much?
  • Clerical staff
  • Teachers
  • Administrators
  • School Board Members
  • Students
  • Parents
  • Public at Large

17
Where can Information Help?
  • Changes in curriculum
  • Changes in services
  • Test scores
  • Graduation rates
  • Climate
  • Fact-check conventional wisdom

18
Measure the Impact
  • Assessments
  • Historical Records and Comparisons
  • Surveys
  • Student Work
  • Anecdotal Feedback

19
Challenges
  • Incomplete data
  • Poorly designed assessments and/or reports
  • Incorrectly recorded data
  • Duplicate records
  • Very transient students
  • Lag time between assessment and scores
  • Disenfranchised staff members
  • When to archive

20
Additional Info
  • http//www.vtcite.org/forum
  • Tommy Walz twalzbsu_at_u61.net
  • Rob Stalling rstalbsu_at_u61.net
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