1. Using ODR Data for Decision ‐ Making

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

3. SWIS Summary 07-08 (Majors Only) 2,717 schools, 1,377,989 students, 1,232,826 ODRs Grade RangeNumber of Schools Mean Enrollment Per School Mean ODRs Per 100 per school day K – 61,756445.35 (sd=.45) (1/300/day) 6 – 9476654.91 (sd=1.40) (1/100/day) 9 – 121779101.05 (sd=1.56) (1/100/day) K – (8 – 12)3084011.01 (sd=1.88) (1/100/day)

4. Calculating the absolute value per 100 studen ts 1. Divide total enrollment by 100 2. Using that total, multiply the per day per 100 student figure from th e 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 throu ghout 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 Problem Absolute – 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 infor mation about the five core “W” question s. – 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 chi ldren. This is creating a sense of disrespect and incivil ity 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 pat tern 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 atte ntion… we are not sure).

11. Precise or Primary Statement? The playground is out of control. The students won’t l isten to anyone and are fighting all the time. Major & minor referrals have increased by 50% durin g lunch time on the playground. The referrals are most ly 4th and 5th graders, and disrespect and aggressive behavior are the highest problem behaviors. Peer attention is the motiv ation.

12. Primary to precise Primary – Kids are noisy in the hallway going to reading. Precise – Many second graders coming from reading are too lou d from room 13 to room 22 and their noise is maintai ned by peer attention. We also have very little adult su pervision 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 behavi or? 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 t han 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 w ay. Continuation of behavior shows that the student is get ting something they want or don’t want. How do we decrease the likelihood that problem beha vior 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 Prevent “Trigger” Define/Teach Reward/Reinforce Withhold Reward Corrective Consequence Data Collection

20. Action Planning Determine Solutions Determine who will do what tasks and the timeline for completion Use meeting agenda with format for problem solving s teps to prompt team. Problem Statement (what, when, where, who) Hypothesis (why) Solutions (prevent, teach, reward, withhold, correct Who?By When?

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”.

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

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

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

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

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

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

31. Signs posted up and down those halls.

32. Taught the rules, everybody did know…

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

34. Ya gotta love that data!

35. Ya gotta use that data!

36. Without a team scanning data…

37. You really ain’t a PBS school!

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

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

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

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

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

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

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

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

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

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

48. Signs posted up and down those halls.

49. Taught the rules, everybody did know…

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

51. Ya gotta love that data!

52. Ya gotta use that data!

53. Without a team scanning data…

54. You really ain’t a PBS school!

55. Without a team scanning data…

56. You really ain’t a PBS school!

57. Without a team scanning data…

58. You really ain’t a PBS school!

59. From Jeff Sprague…. Implementing PBS without data is like Alice in Wonderland talking to the Cheshire Cat …

60. Alice: Which way should I go? Cheshire Cat: That depends on where you are going. Alice: I don’t know where I am going! Cheshire Cat: Then it doesn’t matter which way you go!

61. Love That Data!