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Using ODR Data for Decision‐ Making

Using ODR Data for Decision‐ Making. Questions to Ask. Is there a problem? If so, define the problem with precision: What areas/systems are involved? Are there many students or a few involved? What types of problem behaviors are occurring? When are the behaviors most likely to occur?

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Using ODR Data for Decision‐ Making

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  1. Using ODR Data for Decision‐ Making

  2. Questions to Ask • Is there a problem? • If so, define the problem with precision: • What areas/systems are involved? • Are there many students or a few involved? • What types of problem behaviors are occurring? • When are the behaviors most likely to occur? • Why is the problem occurring? • What solutions might work? • What is the most effective use of our resources to  solve this problem?

  3. SWIS Summary 07-08 (Majors Only)2,717 schools, 1,377,989 students, 1,232,826 ODRs

  4. Calculating the absolute value per 100  students • 1. Divide total enrollment by 100 • 2. Using that total, multiply the per day per 100 student  figure from the SWIS Summary data to get the absolute  value per 100 students • – An elementary school of: • 500 students: 5 X .35 per day = 1.75 ODR per day • 350 students: 3.5 X .35 per day = 1.22 ODR per day • - A middle school of: • 500 students        5 X .91 per day = 4.55 ODRs per day • – A high school of • 1200 students: 12 X  1.05 per day = 12.6 ODRs per day • 3. Calculate this with your school teams and use this number  throughout the year for problem solving

  5. OR • For Primary Schools, ODRS should be less than 1 per day per month for every 250 students • For Secondary Schools, ODRs should be less than 1 per day per month for every 200 students

  6. Is There A ProblemAbsolute – Trend - Compare • http://www.swis.org • Look at Average Referrals Per Day Per Month

  7. Precision Problem Statements (What are the data we need for a decision?) • Precise problem statements include  information about the five core “W”  questions. • – What is problem, and how often is it happening • – Where is it happening • – Who is engaged in the behavior • – When the problem is most likely • – Why the problem is sustaining

  8. Primary versus Precision Statements • Primary Statements • Too Many Referrals • September has more suspensions than last year. • Gang behavior is increasing. • The cafeteria is out of control. • Student disrespect is out of control. • Precision Statements • There are more ODRs for aggression on the playground than last year, and these are most likely to occur during first recess, with a large number of students, and the aggression is related to getting access to the new playground equipment.

  9. Precise or Primary Statement? • Children are using inappropriate language  with a high frequency in the presence of both  adults and other children.  This is creating a  sense of disrespect and incivility in the school. • James D. is hitting others in the cafeteria  during lunch, and his hitting is maintained by  peer attention.

  10. Precise or Primary Statement? • ODRs during December are higher than in any other month. • Minor disrespect and disruption are increasing over time,  and are most likely during the last 15 minutes of our block  periods when students are engaged in independent seat  work.  This pattern is most common in 7th and 8th grades,  involves many students, and appears to be maintained by  escape from work (but may also be maintained by peer  attention… we are not sure).

  11. Precise or Primary Statement? • The playground is out of control. The students  won’t listen to anyone and are fighting all the  time. • Major & minor referrals have increased by  50% during lunch time on the playground. The  referrals are mostly 4th and 5th graders, and  disrespect and aggressive behavior are the  highest problem behaviors. Peer attention is  the motivation.

  12. Primary to precise • Primary – Kids are noisy in the hallway going to reading. • Precise – Many second graders coming from reading are too  loud from room 13 to room 22 and their noise is  maintained by peer attention. We also have very  little adult supervision available.

  13. What are the data you are most likely to need to  move from a Primary to a Precise statement? • What problem behaviors are most common? – ODR per Problem Behavior • Where are problem behaviors most likely? – ODR per Location • When are problem behaviors most likely? – ODR per time of day • Who is engaged in problem behavior? – ODR per student • Why are problem behaviors sustaining? – Team hypothesis

  14. Building Solutions • Areas to think about • Prevention • Teaching • Recognition • Extinction • Consequences • Progress Monitoring & Evaluation

  15. Using Data to Build Solutions • Prevention: How can we avoid the problem context? • Who, When, Where • Schedule change, curriculum change, etc • Teaching: How can we define, teach, and monitor what we want? • Teach appropriate behavior • Use problem behavior as negative example • Recognition: How can we build in systematic reward for desired  behavior • Extinction: How can we prevent problem behavior from being  rewarded? • Consequences: What are efficient, consistent consequences for  problem behavior? • How will we collect and use data to evaluate  • (a) implementation  fidelity, and  • (b) impact on student outcomes?

  16. Things to avoid when building solutions • Avoid going to consequences first. • Look for multiple components to the solution • Most problems that are worth solving require  more than one strategy…. Don’t look for the silver  bullet.

  17. Withholding Reward/Reinforcement “Extinction” • Element that is most missed in Behavior Plans • Decrease the pay off for students doing it the  wrong way. • Continuation of behavior shows that the  student is getting something they want or don’t want. • How do we decrease the likelihood that  problem behavior gets paid off?

  18. Solutions –Generic Strategies • Prevent–Remove or alter “trigger” for problem behavior. • Define/Teach–Define behavioral expectations; provide demonstration/instruction in expected behavior (alternative to problem behavior). • Reward/reinforce–The expected/alternative behavior when it occurs; prompt for it, as necessary. • Withhold reward/reinforcement–For the problem behavior, if possible (“Extinction”). • Use non-rewarding/non-reinforcing corrective consequences–When problem behavior occurs. • Collect additional data–Collect if needed to gain more information before developing hypothesis/solution pair; also use to monitor success of implemented solutions.

  19. Create Plan

  20. Action Planning • Determine Solutions • Determine who will do what tasks and the  timeline for completion • Use meeting agenda with format for problem  solving steps to prompt team.

  21. K-12 Decision Rules • K-12: More than 30% of referrals occur in a specific area of the school: re-teach specific common area behavior expectations, acknowledge/reward positive behavior, & correct inappropriate behavior immediately. • K-12: More than 40% of referrals occur in classrooms: re-teach classroom expectations, increase professional development in classroom management strategies, and/or revisit CORE instruction in specific classrooms. • K-12: More than 30% of referrals for similar reasons (e.g., aggression & fighting), re-teach school rules specific to that area and acknowledge positive behavior.

  22. What if School-Wide Systems Aren’t Enough? Guidelines for providing support for the “High Flyers”.

  23. Love That Data! By Scott Perry Linn Benton Lincoln ESD

  24. You gotta think about data every time you start to look at your school.

  25. Collect that data and use that data as a tool!

  26. Hall Data • Class Data • Data From the Lunchroom • Playground • Breezeway • Even in the Restroom

  27. Without that data it just won’t happen it’s true!

  28. There was a school I knew, said they had it all…

  29. Signs posted up and down those halls.

  30. Taught the rules, everybody did know…

  31. But never looked at data so they never did grow!

  32. Ya gotta love that data!

  33. Ya gotta use that data!

  34. Without a team scanning data…

  35. You really ain’t a PBS school!

  36. Ahhhh data: If it can't be counted… does it exist? George Sugai

  37. Let the data lead you to the right “questions to ask” not the answers. Rob Horner

  38. Data need not be a four letter word! Anne Todd

  39. Let decisions drive the data you collect. Rob Horner

  40. When I die, I want a graph on my tombstone… and in my obituary! George Sugai

  41. You gotta think about data every time you start to look at your school.

  42. Collect that data and use that data as a tool!

  43. Hall Data • Class Data • Data From the Lunchroom • Playground • Breezeway • Even in the Restroom

  44. Without that data it just won’t happen it’s true!

  45. There was a school I knew, said they had it all…

  46. Signs posted up and down those halls.

  47. Taught the rules, everybody did know…

  48. But never looked at data so they never did grow!

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