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Making Data Driven Decisions: Cut Points, Curve Analysis, and Odd Balls Robert Rosenthal, David Lillenstein, Jason Pedersen, Laura Lent, Richard Hall,

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Presentation on theme: "Making Data Driven Decisions: Cut Points, Curve Analysis, and Odd Balls Robert Rosenthal, David Lillenstein, Jason Pedersen, Laura Lent, Richard Hall,"— Presentation transcript:

1 Making Data Driven Decisions: Cut Points, Curve Analysis, and Odd Balls Robert Rosenthal, David Lillenstein, Jason Pedersen, Laura Lent, Richard Hall, Joe Kovaleski, and Edward Shapiro

2 Agenda To hear how decisions are made regarding intervention and evaluation in schools from all over Pennsylvania that implement Response to Intervention and Instruction models. To learn about some outcomes as a result of these decision strategies.

3 Overlook Elementary K-6 Enrollment460 % Free/Reduced25% % Minority45% % Proficient on Reading PSSA77% % Proficient on Math PSSA84% 4 th Year with RtII

4 RtI Level Curriculum Component Grade Level K – Tier 1 Treasures (Macmillon/McGraw Hill) XX First 2 yrs- Houghton Mifflin X X Compass Learning X X Tier 2 First 2 yrs Breakthrough to Literacy Treasures Leveled Reader X X Soar to Success X Tier 3 Fundations X First 2 yrs Breakthrough to Literacy Wilson Reading X Triumphs (Macmillon/McGraw Hill) X X Instructional Programs

5 Grade Level Team Meetings Examine data every 6 weeks Include all data on excel spreadsheet Use DIBELS Prog monitoring charts Calculate slope (rate of progress) Generally follow DIBELS recommended Instructional levels Must present data to not follow recommended levels

6 Data examined at Team Meetings Universal screening (DIBELS) Unit (curriculum) test scores Unit (curriculum) weekly assessment 4-Sight scores (3 times) PSSA (annual state assessment) Rate of progress (slope of PM data) Length of time at a tier level Instructional program at T2&3 Behavior infractions

7 Tier Assignment Decisions First look at DIBELS recommendation K-2 Then examine Unit/Weekly test scores For students in T2 or 3: Sub-groups decoding/fluency/comp 3-6 Then examine Unit/Weekly tests, PSSA, 4-Sight Sub-group fluency/decoding/comprehension/writing

8 Making Sub-Groups Every 6 Weeks, Once Tier Level Decision is Made: Group by high vs low Group by decoding vs fluency vs comp Group by Program (Fundations)

9 K-2 nd Teacher Perceptions-What Influences Tier Placement

10 3 rd -6 th Teacher Perceptions- What Influences Tier Placement

11 Percent of Time We Followed DIBELS Instructional Recommendations

12 When Didnt Follow Inst Rec 43 times (10% of total students) we gave more support than indicated 31 times (8%) we gave less support Reasons: Unit Test scores Behavior/Emotional Issues (gave more) Borderline- look at other data Not a fluency problem (gave more) A fluke (gave less- other indicators ok) Resources- group when similar

13 Decision to Evaluate Rate of progress is below target and typical rate (unless not fluency prob) History of failure in curriculum In targeted instructional support for at least 6 months with multiple data- driven changes using research-proven techniques and programs PM shows significantly below peers BB or B on PSSAs

14 Teacher Perceptions: What Influences Decision to Evaluate

15 Eligibility Decisions LEA decided to use discrepancy Augment ER with RtI data Slope scores can help support decision (especially when ½ target rate) Helps in making recommendations Type and quantity of program Sometimes data is conflictual : Used to be: Discrepancy rules Now any sign of success makes it difficult Always helps with ED classification

16 Evaluations Across Years

17 Placements Across Years

18 Average T/A: Differences between Referral Sources

19 State Testing Across Years

20 Conclusions Must include special ed students Teachers need more training We see a reduction in testing, with school referrals being more accurate Now at Team meetings staff dont ask about evaluations, they ask about interventions Must continually remind staff to look at data to make decisions- we need to move them to less support more often


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