Creative ways to use data: A toolkit for schools

Susan Barrett [email protected]

Objectives

 Review why and how to use discipline data  Provide examples of how CCPS schools use various forms of data to monitor the effectiveness of PBIS  Highlight and demonstrate templates utilized to share information with staff and PBS teams  Determine what barriers to learning we have  Complete an activity to help plan for data-based decision making

Data

IS NOT:  A scary or “four letter” word  Should not intimidate us  Just numbers IS:  Powerful when used to discuss discipline  Empowering when used by school teams  Reviewed frequently to determine areas of strength and weakness

Scenarios

 You work at an elementary school with 400 students. Upon reviewing data at the end of the year you find that your school had 20 suspensions.

 You work at a high school with 1000 students. You have a total of 100 days of suspension during the school year.

Scenarios

 You work in a middle school of 650 students. Last school year there were 100 referrals.

 You work at an elementary school of 450 students. Last year there were 800 referrals

What impact does it have?

 Think about each of the scenarios

         Administrators Teachers Staff Students Parents School Climate Interventions Support Services needed Academic Achievement

Impact

Improving Decision-Making

Solution

From

Problem

To

Problem Problem Solving Information Solution

Why Collect Discipline Data?

 Decision making  What decisions do you make?

 What data do you need to make these decisions?

 Professional Accountability  Decisions made with data (information) are more likely to be (a) implemented, and (b) effective

From primary to precise

 Primary statements are vague and leave us with more questions than answers  Precise statements include information about 5 “Wh” questions:  What is the problem and how often is it happening?

 Where is it happening  Who is engaging in the behavior?

 When is the problem most likely to occur?

 Why is the problem sustaining?

From primary to precise: An example

 Primary statement:  “There is too much fighting at our school”  Precise statement  There were 30 more ODRs for aggression on the playground than last year, and these are most likely to occur from 12:00-12:30 during fifth grade’s recess because there is a large number of students, and the aggression is related to getting access to the new playground equipment. “

From primary to precise: An example



Primary statement:

 “ODRs during December were higher than any month”  

Precise statement:

Minor disrespect and disruption are increasing and are most likely to occur during the last 15-minutes of our classes when students are engaged in independent seat work. This pattern is most common in 7 th and 8 th grades, involve many students, and appears to be maintained by work avoidance/escape. Attention may also be a function of the behavior we’re not sure.

Effective Data Systems

 The data are accurate and valid  The data are very easy to collect (1% of staff time)  Data are presented in picture (graph) format  Data are current (no more than 48 hours old)  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.

Data Collection



The “Big 5”

 Average referrals per day per month  Location  Problem behavior  Student  Time

Summarize the “Big 5”

 Is there a problem?

 If no, what will we do to sustain our efforts?

 If yes, is problem definable or do we need more information?

 Next steps  How will we know if it’s working?

 Where will we review the data?

Steps to Problem-Solving

       Define the problem(s)  Analyze the data Define the outcomes and data sources for measuring the outcomes Consider 2-3 options that might work Evaluate each option  Is it safe?

  Is it doable?

Will it work?

 Which option will give us the smallest change for the biggest outcome?

Choose an option to try Determine the timeframe to evaluate effectiveness Evaluate effectiveness by using the data    Is it worth continuing?

Try a different option?

Re-define the problem?

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 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?

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.

 An example (hallways)

5:1 Ratio of tickets to referrals

 Our data tells us that we should be giving 5 positives to each corrective response  How is that measured?

 Number of coupons versus number of referrals.

Number of RRR Tickets

Quarter

One Two

K 1 2 3 4 5

306 289 278 236 110 193 678 526 423 278 147 191

Total 1412 2243

Overall 984 815 701 514 257 384

3655

Ratio of Tickets: Referrals

6.0

5.0

4.0

3.0

2.0

1.0

0.0

S ep te m be r O ct ob er N ove m be r D ece m be r Ja nu ar y F eb ru ar y M ar ch A pr il M ay Ju ne T ot al

Triangle of Student Referrals

Intensive, Individual Interventions  Individual Students  Assessment-based  High Intensity Targeted Group Interventions  Some Students (at-risk)  High Efficiency  Rapid Response Universal Interventions  All Students  Preventive, proactive 1-5% 1-5% 5-10% 5-10% 80-90% 80-90% 6+ referrals 2-5 referrals 0-1 referral

Triangle of Student Referrals: August/September 2005

 Individual Students  Assessment-based  Intense, durable procedures Targeted Group Interventions  Some Students (at-risk)  High Efficiency  Rapid Response Universal Interventions  All Settings  All Students,  Preventive, proactive 1-5% 10-15% 80-90% 07% % 03% 90% more referrals referral Students with 0 referrals

Triangle of Student Referrals: April 2006

Theory Actual data Intensive, Individual Interventions  Individual Students  Assessment-based  Intense, durable procedures Targeted Group Interventions  Some Students (at-risk)  High Efficiency  Rapid Response 1-5% 10-15% 3% 4% Students with 2 or more referrals Students with 1 referral Universal Interventions  All Settings  All Students,  Preventive, proactive 80-90% 93% Students with 0 referrals

Cost Benefit Analysis

Minutes Hours Days Student 6300 105 13 Staff 14175 236 30 Number of referrals Q1 and Q2 2005-2006 548 Number of referrals Q1 and Q2 2006-2007 233 Average # of minutes student is out of class due to referral Average # of minutes administrator needs to 20 45

30000 25000 20000 15000 10000 5000 0 10960 24660 La st Ye ar 4660 10485 Th is Y ea r Student Minutes Admin Minutes 6300 Tim e Re ga ine d 14175

Cost-Benefit Analysis

COST/BENEFIT ANALYSIS WORKSHEET Enter info below

School name

Robert Moton Elementary School

Number of referrals November 2005 Number of referrals April 2006

132 61 3000 2500 2000 1500 1000 500 0 2640 660 6

Average # of minutes student is out of class due to referral Average # minutes staff need to process referral

22 5 2 1 0 6 5 4 3 1220 305 1 3 1420 1 355

Other data to consider

 Is our attendance rate improving?

 Is our achievement data improving?

 How many students are on the honor roll?

 Are state tests scores improving?

 What is our graduation rate?

 How many students are taking AP courses?

What else does the data tell you?

 Is there a problem on  Bus  Cafeteria  Hallways  If you have been implementing for many years, are you still seeing the same results?

 Are older students still motivated by the same incentives?

Next Steps

 Comparing academic and behavior data

State-Wide Assessment:

Basic

Classroom Performance:

Below grade level 1-5% 1-5%

Discipline:

6+ referrals Borderline Approaching grade level 5-10% 5-10% 2-5 referrals Proficient or Advanced On or above grade level 80-90% 80-90% 0-1 referral

What is the academic/behavior connection in your school?

 What information do you need to answer this question?

 What types of data do you currently use?

 How often? Is it working?

 What would make it better?

 What are your goals when you leave to return to your building?

Templates

 Excel data template  Cost-Benefit Analysis Worksheet

Discipline Data: Essential Questions

Staff have questions regarding effective discipline strategies How do you collect data?

What data do you use?

What do we do with the data?

When do you know you have a problem?

How often do you look at your data? How often is discipline data shared with staff?

Discipline Data is collected to answer questions What information do you already have?

Attendance, suspension, office referrals, achievement scores, tardies, timeout/support room referrals What are the critical discipline issues in your building?

Who, What, How Often, When, Where?

Discipline Data: Essential Questions

Design intervention to target concern How do you know what invention is needed?

How many students contribute to your referrals? Are referrals coming from one grade, classroom, or area?

Measure success What do we measure?

How do we measure "it"?

How often do we measure "it"?

How do we know when we have success?

How do we know when we need to make changes?

Who do we share it with?

How do we share it?

Resources

 www.pbis.org

 www.swis.org

 www.pbssurveys.org

 www.pbismaryland.org

“Without data, you’re just another person with an opinion”- Unknown