Data driven decision making
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Data Driven Decision Making. Missouri PBS Summer Institute June 28 & 29, 2006. Purpose. Provide guidelines for using data for team planning Provide guidelines for using data for on-going problem solving Apply guidelines to examples. Improving Decision Making. From . Problem. Solution.

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Data Driven Decision Making

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Data Driven Decision Making

Missouri PBS Summer Institute

June 28 & 29, 2006


Purpose

  • Provide guidelines for using data for team planning

  • Provide guidelines for using data for on-going problem solving

  • Apply guidelines to examples


Improving Decision Making

From

Problem

Solution

To

Solution

Problem

Problem Solving


Key features of data systems that work

  • The data are accurate and valid

  • The data are very easy to collect (1 % of staff time)

  • Data are presented in picture (graph) form

  • Data are used for decision-making

    • The data must be available when decisions need to be made (weekly?)

    • Difference between data needs at a school building and data needs for a district

    • The people who collect the data must see the information used for decision-making.


Why collect discipline data?

  • Decision making

  • Professional accountability

  • Decisions made with data (information) are more likely to be 1) implemented and 2) effective.


What data to collect for decision making?

Use what you have:

  • Attendance

  • Suspensions/Expulsions

  • Vandalism

  • Office discipline referrals/detentions

    • Measure of overall environment. Referrals are affected by 1) student behavior 2) staff behavior and 3) administrative context

    • An under-estimate of what is really happening

    • Office referrals per day per month


When should data be collected?

  • Continuously

  • Data collection should be an embedded part of the school cycle, not something “extra”

  • Data should be summarized prior to meetings of decision-makers

  • Data will be inaccurate and irrelevant unless the people who collect and summarize it see the data used for decision making.


Organizing Data for “active decision making”

  • Counts are good, but not always useful

  • To compare across months use “average office discipline referrals per day per month”


Using Data for On-going Problem Solving

  • Start with the decision, not the data

  • Use data in “decision layers” (Gilbert, 1978)

    • Is there a problem? (overall rate of ODR)

    • Localize the problem

      • (location, problem behavior, students, time of day)

  • Don’t drown in the data

  • It’s “OK” to be doing well

  • Be efficient


Interpreting Office Referral Data: Is there a problem?

  • Absolute level (depending on size of school)

    • Middle, High Schools (1> per day per 100)

    • Elementary Schools (1> per day per 250)

  • Trends

    • Peaks before breaks?

    • Gradual increasing trend across year?

  • Compare levels to last year

    • Improvement?


What systems are problematic?

  • Referrals by problem behavior?

    • What problem behavior is most common?

  • Referrals by location?

    • Are there specific problem locations?

  • Referrals by student?

    • Are there many students receiving referrals or only a small number of students with many referrals?

  • Referrals by time of day?

    • Are there specific times when problems occur?


Designing Solutions

  • If many students are making the same mistake it typically is the system that needs to change, not the students.

  • Teach, monitor and reward before relying on punishment.


Application Exercise

  • What is going well?

  • Do you have a problem?

  • Where?

  • With whom?

  • What other information might you want?

  • Given what you know, what considerations would you have for possible action?


SWIS: School-Wide Information System

  • http://www.swis.org

  • SWIS Readiness Checklist

  • SWIS Compatibility Checklist


Summary

  • Transform data into “information” that is used for decision making

  • Present data within a process of problem solving

    • Use the trouble-shooting tree logic

    • Big Five first (how much, who, what, where, why)

  • Ensure the accuracy and timeliness of data


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