1. Using Data for Decision-making Rob Horner, Anne Todd, Steve Newton, Bob Algozzine, Kate Algozzine

2. Main Ideas  Decisions are more likely to be effective and efficient when they are based on data.  The quality of decision-making depends most on the first step (defining the problem to be solved)  Define problems with precision and clarity

3. Main Ideas  Data help us ask the right questions…they do not provide the answers: Use data to Identify problems Refine problems Define the questions that lead to solutions  Data help place the “problem” in the context rather than in the students.

4. Main Idea  The process a team uses to problem solve is important: Roles : Facilitator; Recorder; Data analyst; Active member Organization Agenda; Old business (did we do what we said we would do); New business; Action plan for decisions. What happens BEFORE a meeting What happens DURING a meeting What happen AFTER a meeting

6. Main Ideas  Build “decision systems” not “data systems”  Use data in “decision layers” Is there a problem? (overall rate of ODR) Localize the problem (location, problem behavior, students, time of day) Get specific  Don’t drown in the data  It’s “OK” to be doing well  Be efficient

8. Using Data  Do we have a problem?  Refine the description of the problem?  What behavior, Who, Where, When, Why  Test hypotheses  “I think the problem on the playground is due to Eric”  “ We think the lunch period is too long”  “We believe the end of ‘block schedule” is used poorly”  Define how to monitor if solution is effective

9. Collect and Use and UseData Review Status and Identify Problems Develop and Refine Hypotheses Discuss and Select Solutions Develop and Implement Action Plan Evaluate and Revise Action Plan Problem Solving Foundations Team Initiated Problem Solving (TIPS) Model

10. Identifying problems/issues  What data to monitor ODR per day per month OSS, ISS, Attendance, Teacher report Team Checklist/ SET (are we doing what we planned to do?)  What question to answer Do we have a problem?  What questions to ask of Level, Trend, Peaks How do our data compare with last year? How do our data compare with national/regional norms? How do our data compare with our preferred/expected status?  If a problem is identified, then ask What are the data we need to make a good decision?

11. Total Office Discipline Referrals Total Office Discipline Referrals as of January 10

13. Using Data to Refine Problem Statement  The statement of a problem is important for team- based problem solving.  Everyone must be working on the same problem with the same assumptions.  Problems often are framed in a “Primary” form, that creates concern, but that is not useful for problem- solving.  Frame primary problems based on initial review of data  Use more detailed review of data to build “Solvable Problem Statements.”

14. Solvable Problem Statements (What are the data we need for a decision?)  Solvable 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

15. 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. 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.

16. 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. 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.

17. 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.

18. 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 7 th and 8 th 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).

19. Precise or Primary Statement?  Three 5 th grade boys are name calling and touching girls inappropriately during recess in an apparent attempt to obtain attention and possibly unsophisticated sexual expression.  Boys are engaging in sexual harassment

20. Organizing Data for Decision-making  Compare data across time  Moving from counts to count/month

21. SWIS summary 2008-2009 (Majors Only) 3,410 schools; 1,737,432 students; 1,500,770 ODRs Grade Range Number of Schools Avg. Enrollment per school National Avg. for Major ODRs per 100 students, per school day K-62,162450.34 = about 1 Major ODR every 3 school days, or about 34 every 100 days 6-9602657.85 = a little less than 1 Major ODR per school day, or about 85 every 100 days 9-122158871.27 = more than 1 Major ODR per school day, or about 127 every 100 days K- (8-12) 4314081.06 = about 1 Major ODR per school day, or about 106 every 100 days Newton, J.S., Todd, A.W., Algozzine, K, Horner, R.H. & Algozzine, B. (2009). The Team Initiated Problem Solving (TIPS) Training Manual. Educational and Community Supports, University of Oregon unpublished training manual.

22. Start with the ODR/Day/Month Graph  Use the information in the data to build a narrative that draws the team into problem solving.  Be descriptive  Link local data to national patterns  Tie the data back to local conditions/events.

23. Elementary School 465 students (465/ 100 = 4.6 X.34= 1.56) Our rate of problem behavior has been above the national average for schools our size for 9 of 10 months this year. There has been a decreasing trend since Dec.

24. Elementary School 1000 Students (1000/100 =10 X.34= 3.4) The rate of problem behavior has been at or below the national average schools our size for 6 of 10 months. The past 4 months have been below the national average

25. Middle School 765 students (765/100 = 7.6 X.85= 6.46) The rate of problem behavior has been at or below the national average schools our size for 9 of 10 months. The past 8 months have been below the national average with a decreasing trend

26. Describe the narrative for this school

30. Using Data to Build Precision  Given that we know we have a problem  What problem behaviors  Where are they occurring  When are they occurring  Who is involved  Why do they keep happening

31. What questions to ask about the patterns of problem behaviors? 1. Do we have one problem behavior situations or more than one? 2. Do we have many problem behaviors or just a few problem behaviors? 3. Do we have clusters of problem behaviors? 4. What school wide expectations do we need to re-teach?

32. Data should allow asking the right question… not supplying the answer.  If many referrals in class  Which classes?  Which students?  When?  If many referrals in cafeteria  Which students?  What times? (beginning or end of lunch period?)  What problem behaviors?

33. Disrespect is our most frequent problem behavior. We also have incidents of fighting and harassment What are next questions? Who, When, Why?

34. Our most frequent problem behavior is disrespect, followed by inappropriate language, disruption and tardy What are next questions? Who, When, Why?

35. We have many instances of disrespect, aggression/fighting. technology violations, tardy, harassment, lying, skipping, and inappropriate language

36. What questions to ask about Referrals by Location  Where are the problems occurring?  Are there problems in many locations, clusters of locations, or one location?

37. Many problem behaviors in class Many problem behaviors in unstructured settings (hall, playground, parking lot, bathroom)

38. Many problems in the cafeteria, hallway, common area, class, bathroom. Where is the ‘unknown’ location?

39. What questions to ask about Referrals by Time  When are the problem behaviors occurring?  How do those times match with the daily activities?  How does this information match up to Referrals by Location?

40. Most problems are occurring between 9:45- 10:45. Other problematic times are 8-8:45 and 11:30.

41. Most problems are occurring at noon

42. Many problems at 12:15, 7:45-8:30, 10:00-10:45

43. What questions to ask about Referrals by Student  What proportion of students has 0-1 ODR?  What proportion of students has 2-5 ODRs?  What proportion of students has 6+ ODRs?  Do we have systems of support that increase student success?

44. Student # 121 needs individualized support. 8 students are likely candidates for some type of Tier II support. 87% of our students have received 0-1 ODR

45. 14 students are likely candidates for some type of Tier II support. Student #119 needs individualized support

46. We have 11 students who are likely candidates for some type of Tier II support 93% of our students have received no more than one ODR

47. What questions to ask about Referrals by Perceived Motivation  What is perceived as maintaining the problem behavior?  Are there one or more perceptions?

48. The problem behaviors are most likely maintained by task avoidance and peer avoidance. We have many incidents with unknown motivation

49. Problem behaviors appear to be maintained by peer and adult attention

50. Problem behaviors appear to be maintained by escaping adult attention

51. Using the TIPS Minutes to guide Problem Solving 1. Write the precision problem statement in the left hand column below 2. Define a goal and write it in the right hand column

52. 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?

53. Solution Development Prevention Teaching Reward Extinction Corrective Consequence Data Collection

54. Trevor Test Middle School 565 students Grades 6,7,8

55. Trevor Test Middle School Is there a problem? If so, what is it?

56. Trevor Test Middle School 11/01/2007 through 01/31/2008 (last 3 mos.)

57. Precise Problem Statement & Hypothesis Development  Many students from all grade levels are engaging in disruption, inappropriate language and harassment in cafeteria and hallway during lunch, and the behavior is maintained by peer attention  A smaller number of students engage in skipping and noncompliance/defiance in classes, (mostly in rooms 13, 14 and 18), and these behaviors appear to be maintained by escape.

58. Solution Development Prevention Teaching Reward Extinction Corrective Consequence Data Collection

59. Solution Development: For disruption in hall and cafeteria Prevention *Teach behavioral expectations in cafeteria *Maintain current lunch schedule, but shift classes to balance numbers. Teaching Reward Establish “Friday Five”: Extra 5 min of lunch on Friday for five good days. Extinction Encourage all students to work for “Friday Five”… make reward for problem behavior less likely Corrective Consequence Active supervision, and continued early consequence (ODR) Data Collection Maintain ODR record and supervisor weekly report

60. Summary  Establish information systems for decision- making… not data systems for reporting.  Use data to identify problems  Use data to clarify/refine problems  Use knowledge of context and content to build solutions.  Use data to monitor impact of solutions.