RTI Data Analysis

1. RTI Data Analysis Making Sense of it All

2. Quick review of RTI Core Components

3. Universal Screening • Tiered Interventions • Research-based Practices • Fidelity Monitoring • Data-based Decision Making

4. Tier I—Meets the needs of 80-85% of students • Tier II—Is provided to the remaining 15 to 20% of students and meets the needs of all but about 5 to 8% (when provided in conjunction with Tier I) • Tier III—Is provided to the Tier II “non-responders”

5. Data-based Decision Making • Universal Screening— • FIRST--Identifies systemic problems • Instructional • curricular • THEN--Identifies individual student problems

6. One of the most common mistakes in analyzing RTI data analysis is skipping the first step

7. Universal Screening Data Analysis

8. What are you looking for? • Is Tier I working? • How can you tell? • Do you have any curriculum problems? • Do you have any instructional problems?

9. Is Tier I Working?

10. Yes!

11. Is Tier I Working?

12. No.

13. Is the problem with the curriculum or is it with instruction? • How can you tell?

14. Using CBM to Identify Curriculum Problems

15. AIMSweb

16. District-wide Screening Results

17. Universal Screening Interpreting Results Between and Within Campuses

19. Classroom Problem Campus Problem

20. Selecting Universal Screening Tools

22. Tier II RTI at the Individual Student Level

23. Quick Review of Problem-Solving Method

24. Collect Data/ Identify the Problem Review/ Revise the Plan Develop the Intervention Monitor Progress Implement the Intervention

25. Collect Data and Define the Problem • The team uses data to • analyze the problem • develop a hypothesis about the core deficit or reason for the problem

26. Define the Problem • Concrete, Measurable • usually stated as the difference between the student’s performance and a benchmark standard • Collect baseline data re: behavior or performance • Data must be high quality because this is what you will analyze later

27. 5 Components of Data-Driven Instruction • good baseline data, • measurable instructional goals, • frequent formative assessment, • professional learning communities, and • focused instructional interventions.

28. Teachers and administrators have access to lots of data. How are you using it? • You must have high quality data because…

29. More is not always better And not all data is worth analyzing

30. GIGO

31. How do you select a Progress Monitoring tool that meets your needs?

32. High Quality Academic Data • 6 Characteristics of Effective Progress Monitoring Systems • Adapted from Fuchs and Fuchs, 1999

37. Curriculum Based Measurement • CBM is one form of Progress Monitoring with a growing research base. • CBA vs CBM • Research and application dates to the 70’s (Deno) • First used to assess progress toward IEP goals • More research in reading than math • Is it a General Outcome Measure? • Does NOT assess fluency for the sake of fluency!

38. Why not use end of unit tests? • Susie is a 4th grader referred for special education evaluation because she is getting further behind her peers. She is in a Tier 2 reading intervention, working at second grade level.

39. Data from End of Unit Tests

41. BUT WHAT ABOUT TAKS ?

42. Do not make me bring out my friend…

43. Do not despair

44. Colorado (Shaw & Shaw, 2002) Florida (Buck & Torgeson, 2003; Castillo, Torgeson, Powell-Smith & Al Otaiba, 2003) Illinois (Sibley, Biwer, & Hesch, 2001) Michigan (McGlinchey & Hixson, 2004) Minnesota (Hintze & Silberglitt, 2005) North Carolina (Barger, 2003) Oregon (Crawford, Tindal & Stieber, 2001) Washington (Stage & Jacobson) Research on CBM Oral Reading Fluency and Performance on Statewide Assessments

45. Reading • Average correlation between CBM ORF and performance on state assessments was in the .60 to .75 range

46. Math • Helwig, Anderson, and Tindal, 2002 • CBM math probe—48 problems including both computation and problem solving, untimed • Predicted which students would meet the state math standards with 87% accuracy

47. CBM—Math Computation (25 mixed operation problems) Positive Predictive Power .85 for Spring administration CBM—Math Concepts (18 or 24 problems) Positive Predictive Power .88 to .91 for Spring administration MathShapiro, Keler, Lutz, Santoro, & Hintze, 2006—CBM & state assessment (PSSA)

48. Set a Goal • Use baseline data and results of problem analysis to set a measurable goal for the student • Goal must be • Challenging • Attainable

49. Specific, Measurable, Attainable, Results-Oriented, and Time-Bound. Example: The percentage of third grade students scoring 2100 or higher on the state mathematics test will increase from 64% in Spring 2008 to 82% in Spring 2009. Focus areas for improvement Number sense Computation Measurement SMART Goals

50. Match an intervention to the student’s deficit • Check the research supporting the proposed intervention to verify: • The effect size is adequate (does the intervention result in large enough improvement to allow for goal attainment?) • The duration is appropriate (will the intervention result in improvement within the timeline established for goal attainment?) • www.ies.ed.gov (What Works Clearinghouse)