Download presentation

Presentation is loading. Please wait.

Published byNathan Ellis Modified over 2 years ago

1
R T I Progress Monitoring Module 6 RI RTI Initiative 2007

2
R T I Topics of Session 1.Progress Monitoring Assessment 2.Importance of graphs 3.Decision Rules: Performance Level 4.Setting Goals 5.Decision Rules: Response rate to instruction

3
R T I Assessment in a RTI Model Progress Monitoring District/School Level (Benchmarking/Screening) To screen and identify students who are at- risk and in need of interventions All students Three times a year All areas At grade-level Intervention Level To monitor progress of individual students and determine rate of improvement and need for adaptation of intervention Students who are not achieving benchmarks (PLP, IEP) Weekly, biweekly, monthly assessments In area of need At instructional level

4
R T I Activity One What can this data tell you? TURN AND TALK

5
R T I Decision Making: Performance Levels Who needs intervention support? 3 ways –Percentiles –Cut Points –Discrepancy ratio

6
R T I Requires a Larger Normative Data Base, Preferably Benchmark Data < 25th At Risk, Consider Problem-Solving at the Group Level <10th Potential Severe Problem, Consider Individual Problem Solving Percentiles

7
R T I A number which represents the point at which scores can be divided into different groups (for example does not meet, meets, and exceeds expectations) for decision-making purposes. May be based on research (e.g., a correlation between scoring at or above a certain level on a CBM or DIBELS task and future academic success) or expectation (e.g., grades at C or above, no more than 3 office referrals). Cut Scores

8
R T I How to calculate: 1.Sample 5-7 Students or Whole Class, Grade 2.Figure Median and Graph 3.Divide by 2 and Graph 4.Students Who Perform Below the Line May Need Problem Solving Discrepancy Ratios

9
R T I Can Compute… Peer Median Target Student Median = Discrepancy of 3.6x

10
R T I Generally speaking… -A student who is 1.5x discrepant from his/her peers may benefit from intensive group interventions. –A student who is 2-2.5x discrepant from his/her peers is appropriate for individualized problem- solving and intensive intervention resources may be appropriate. Example: Jessica is 2.1x discrepant from peers on the Math CBM and may benefit from intensive interventions in math. Data-based decisions on performance level enables team to make decisions about levels of support and resource from the start.

11
R T I Who needs intervention support? 2x discrepant median

12
R T I Data-Based Decisions 1.Performance Level Gaps in Performance PLP Not at Grade Level Special Education Significant Discrepancy 2.Progress Monitoring Rate of Learning Trend in performance Response to Instruction

13
R T I 2. Rate of Learning Why? –Is what we are doing working?.... intervene early –Better able to predict student success at meeting goals –Better able to identify who needs more intensive instruction

14
R T I Measure of Progress…rate of learning Curriculum-Based Assessment Portfolio Work Samples Mastery Measurement Curriculum-Based Measurement

15
R T I Progress Monitoring CBMs Are assessments to monitor progress indicators importantAre designed to serve as indicators of general reading/math achievement. CBM doesnt measure everything, but measures the important things. Standardized tests standard way.Are Standardized tests to be given, scored, and interpreted in a standard way. researchedAre researched with respect to psychometric properties to ensure accurate measures of learning. SensitiveShort PeriodsAre Sensitive to improvement in Short Periods of time. as short as possibledo ability.Designed to be as short as possible to ensure its do ability. linked to decision makingAre linked to decision making for promoting positive achievement and Problem-Solving

16
R T I Word Identification Fluency Student:Student: Date: ____________________Date: ____________________ Class: __________________________Class: __________________________ Correct Items:_____ Total Items Attempted:______Correct Items:_____ Total Items Attempted:______ asfromhowany oldthinkherhas couldputsomeask flygoingtakeonce himletoverhad againroundthenwhen walkjustanmay afterbywherelive

17
R T I Taken from Fuchs, L. S., Hamlett, C. A., & Fuchs, D. (1998). Monitoring Basic Skills Progress: Basic Math Computation (2nd ed.). [computer program]. Austin, TX: ProEd. Available: from edinc.com Math Computation

18
R T I Concepts and Applications Sample page from a three- page test for Grade 2 Math Concepts and Applications –From Monitoring Basic Skills Progress

19
R T I Vocabulary Matching To assess learning in the content areas Middle and High School TERMSANSWERDEFINITIONS Mastery measurementA. Assessments that are direct observations and recording of a student's performance in the local curriculum Curriculum-based assessmentB. Standardized assessment that compares a student with a specified reference group. Norm-referenced testC. An assessment based on a collection of student work over time D. Assessments that measure students ability to meet short- term instructional objectives

20
R T I Progress Monitoring National Center on Student Progress Monitoring

21
R T I Progress Monitoring Benefits of Progress Monitoring Parents and students know what is expected Teachers know what is working or not working with their instruction based on data Easy to understand way to show parents progress Teams have comprehensive data on student performance for decision making

22
R T I Setting Goals and Response to Instruction Setting Goals –Benchmarks –Rate of Improvement (ROI) norms –Intra-individual Decision Making Rules: Response rate to instruction –Data Point Rules –Trend Line Rules –Rate of Improvement (ROI) Slope

23
R T I Setting Goals 1.End of the Year Benchmarks GLEs for Reading Fluency –2 nd grade WPM –5 th grade WPM WIF –50 words per minute AIMSweb Math Computation Norms –1 st grade 17 Correct Digits –5th grade 52 Correct Digits

24
R T I Research Norms for Improvement GradeModestReasonableAmbitious 1-21 WRC/week1.5 WRC/week2.0 WRC/week WRC/week1.0 WRC/week1.5 WRC/week **Deno, 2005 READING FLUENCY** GradeReasonableAmbitious CD/Week.5 CD/Week CD/Week1.1 CD/Week *Fuchs, 2006 MATH CALCULATIONS*

25
R T I Intraindividual ROI Weekly rate of improvement in baseline slope calculated from 8 data points (Slope: Difference of highest and lowest/#weeks) Baseline multiplied by 1.5 Product multiplied by number of weeks until end of year Add to students final baseline score to produce end of year goal. Baseline Reading scores: 52, 54, 52, 53, 55, 58, 55, 56 Difference: =5 Divide by number of weeks: 5/8 =.625 (SLOPE) Baseline multiplied by 1.5:.625 × 1.5 =.9375 Number of weeks left (6 weeks):.9375 ×6 =5.6 Add to final baseline score: = 61.6 GOAL 62

26
R T I Carlos – 5 th grader Math Calculation December-January Monitoring 36, 37, 36, 36, 37, 38, 39, 37 What math goal would you set for Carlos for the end of year weeks from now?

27
R T I Carlos – 5 th grader End of Year Benchmarks 52 CD (.77 ROI) National Norms 51 CD (.7 ROI) (38+18*.7) 58 CD (1.1 ROI) (38+18*1.1) Individual ROI 48 CD (.56 ROI) 3/8*1.5 =.56 ROI 38 + (18*.56) What goal would you set for Carlos in math for the end of year?

28
R T I Decision Making: Response rate to instruction 1.Data Points 2.Trend Analysis 3.ROI – Using Tukey to Find Slope

29
R T I Decisions based on data-points Derived from: Fuchs and Fuchs (2006) and Shapiro (2006) 4 scores… Above goal-lineResponding to Instruction … increase if continues for 4 more points Hovering aboutContinue what you are doing ParallelWait for 4 more points… Does it accelerate? Below goal-lineNot Responding to Instruction…Change!

30
R T I Decisions based on trend lines Trend lines based on 6-8 data-points If trend line is steeper than goal line, increase the goal. If trend line is flatter than goal line, revise instruction If trend line equals goal line, make no change at this time. TREND AIMLINE

31
R T I Liz: Did she respond?

32
R T I Matt: Did he respond?

33
R T I Kate: Did she respond?

34
R T I Bob: Did he respond? Trend

35
R T I Tukey Method for Finding Slope Step 1: Divide the data points into three equal sections by drawing two vertical lines. (If the points divide unevenly, then group them approximately.) Step 2: In the first and third sections, find the median data point and median instructional week. Locate the place on the graph where the two values intersect and mark that spot with an X. Step 3: Draw a line through the two Xs and extend that line to the margins of the graph. This represents the trend-line or line of improvement. Step 4: Calculating Slope Third median point – First median point Number of data points – 1

36
R T I Sample to practice Tukey X X slope ROI

37
R T I Practice Interpreting ROI: Determining slope by Tukey method

38

39
R T I Is Carlos responding to the intervention?

40
R T I End of March: 8 Week Check-In X X CHANGE INTERVENTION

41
R T I June: Did Carlos meet the goal? INTERVENTION CHANGE X X

42
R T I When making decisions you can be informed by data or driven by data. TURN AND TALK 1.What is the difference between data-driven and data informing? 2.What are your current practices for decision making? 3.What do you need to do to use data to more effectively meet students' needs?

43
R T I things you learned today 2 things you still have questions about 1 statement about how you feel about PM now

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google