Download
1 / 49
490 likes | 630 Views
Using Data for Decision Making. Rob Horner, George Sugai, Anne Todd, Teri Lewis-Palmer Marilyn Nersesian, Jim Watson. Goals. Define purposes of data collection Provide guidelines for using data for team planning Provide guidelines for using data for on-going problem solving
E N D
Using Data for Decision Making Rob Horner, George Sugai, Anne Todd, Teri Lewis-Palmer Marilyn Nersesian, Jim Watson
Goals • Define purposes of data collection • Provide guidelines for using data for team planning • Provide guidelines for using data for on-going problem solving • Apply to examples
Assumptions • School has team focused on school-wide behavior support. • Team has an action plan • Team meets regularly (weekly, every two weeks) • Team has access to information about student behavior
Why Collect Discipline Information? • Decision making • Professional Accountability • Decisions made with data (information) are more likely to (a) be implemented, and (b) be effective
Effective Behavior Support Supporting Decision Making Supporting Staff Behavior INFORMATION SYSTEMS PRACTICES Supporting Student Behavior
Improving Decision-Making Solution Problem From Problem Solving Solution Problem To Information
Key features of data systems that work. • The data are accurate • The data are very easy to collect (1% of staff time) • 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 versus data needs for a district • The people who collect the data must see the information used for decision-making.
What data to collect for decision-making? • USE WHAT YOU HAVE • Office Discipline Referrals/Detentions • Measure of overall environment. Referrals are affected by (a) student behavior, (b) staff behavior, (c) administrative context • An under-estimate of what is really happening • Office Referrals per Day per Month • Attendance • Suspensions/Expulsions • Vandalism
Office Discipline Referral Processes/Form • Coherent system in place to collect office discipline referral data • Faculty and staff agree on categories • Faculty and staff agree on process • Office Discipline Referral Form include needed information • Name, date, time • Staff • Problem Behavior • Location
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 (e.g. weekly) • Data will be inaccurate and irrelevant unless the people who collect and summarize it see the data used for decision-making.
School-Wide Systems Non Classroom Setting Systems Classroom Systems Individual Student Support Systems Using Office Discipline Referrals for Team Planning
Sugai, Sprague, Horner & Walker, in press • 11 elementary schools, 9 middle schools • For the 9 Middle Schools • Number of students: Mean = 635 (204-1194) • Office Dis Referrals: Mean = 1535 (136-3520) • Referrals per student: Mean = 2.4 • Referrals per school day : Mean = 8.6 • % students with at least 10 referrals = 5.4% • % of referrals from top 5% of students = 40%
Focus on School-Wide System if: • More than 35% of students receive 1 or more referrals • Average referrals per student is greater than 2.5
Focus on Non-Classroom Systems if • More than 35% of referrals come from non-classroom settings • More than 15% of students who receive a referral are referred from non-classroom settings.
Focus on Classroom Systems if • More than 50% of referrals are from classroom settings. • More than 40% of referrals come from less than 10% of the classrooms.
Focus on Individual Student Systems • Targeted Group Interventions • If 10 or more students have 10+ referrals • Example (check-in, check-out BEP) • Targeted Individual Interventions • Fewer than 10 students • Intense, individualized support • Wrap Around • Personal Futures Planning • Functional Assessment
Using Data for On-Going Problem Solving • Start with the decisions not the data • Use data in “decision layers” • Is there a problem? • What “system(s)” are problematic • What individuals (individual units) are problematic? • Don’t drown in the data • It’s “OK” to be doing well • Be efficient
The Decisions/Decision Questions • Initial Self-Assessment • Where to focus “investment” energy/time • On-Going Assessment/Planning • Is the action plan working? Should we change? • Decision: Maintain, Modify, Terminate • What is the problem? Where should we focus? • Decision: Allocation of time, money, skills • Do we understand the problem? • What is the smallest effort that will produce the biggest effect?
Is there a problem? • Office Referrals per Day per Month • Attendance • Faculty Reports
Interpreting Office Referral Data:Is there a problem? • Absolute level (depending on size of school) • Middle Schools (>6) • Elementary Schools (>1.5) • 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 behaviors are 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?
Combining Information • Is there a problem? • What data did you use? • What systems are problematic? • Where do you need to focus? • The next level of information needed • What information is NOT needed?
What Individuals/Specific Units are problematic? Detailed Data Sources • Individual student data • Direct observation • Faculty/Staff report
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.
