Creative ways to use data: A toolkit for schools Melissa Leahy, Ph.D. Kimberly Muniz, M.A./CAS, NCSP Carroll County Public Schools Maryland

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

Carroll County  Carroll County has 135,000 residents  CCPS is the 9 th largest school system in the state 41 schools 28,000 students Rural/suburban demographics  CCPS began implementing PBS in 2001 Started with 1 school Currently 14 schools (34%) implement PBS. Three special programs use PBS.

Status of PBS in CCPS  14 schools (34%) implement PBS (8 elementary, 5 middle and 1 high school).  3 alternative programs use PBS  4 schools will attend new team training in July  10 schools use SWIS for data collection  Ongoing coaching and support has been and will continue to be provided for all schools implementing PBIS

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

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

Improving Decision-Making Problem Solution From To Problem Solving Solution Information

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.

 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. Effective Data Systems

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 QuarterK12345Total One Two Overall

Ratio of Tickets: Referrals

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

Triangle of Student Referrals: August/September 2005

Triangle of Student Referrals: April 2006

Cost-Benefit Analysis

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 1-5% 5-10% 80-90% Below grade level 6+ referrals Approaching grade level 2-5 referrals On or above grade level 0-1 referral 1-5% Classroom Performance: State-Wide Assessment: Basic Borderline Proficient or Advanced Discipline:

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  Staff Meeting template  Template for pacing conference data  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      “Without data, you’re just another person with an opinion”- Unknown