Rate of Improvement Calculation and Decision Making

1
Rate of Improvement Calculation and Decision
Making

• Caitlin S. Flinn, EdS, NCSP
• Andrew E. McCrea, MS, NCSP

2
Why were here

• While there exists a wealth of convincing
  research supporting the implementation of a
  response-to-intervention (RtI) framework, there
  are many questions yet to be empirically
  answered.
• Within multi-tiered model of assessment and
  instruction/intervention, how do we know whether
  a student is learning?

3
Measuring Learning

• Class tests
• Quizzes
• Assignment/homework completion and accuracy
• Ask students questions in class
• Grades/report cards
• State/local assessments
• Universal screening, benchmark assessments
• Progress monitoring

4
With Progress Monitoring Data

• How do we know if a student is learning?
• Look at the data points
• Where are they on the graph?
• Are the data points getting closer to the goal
  or benchmark?
• Is there a way to measure growth?
• Make an aimline toward goal
• Look to see where data points are compared to
  aimline
• Calculate Rate of Improvement (RoI)

5
Todays Objectives

• Explain what RoI is, why it is important, and how
  to compute it.
• Establish that Simple Linear Regression should be
  the standardized procedure for calculating RoI.
• Discuss how to use RoI within a problem
  solving/school improvement model.

6
RoI Definition

• Rate of Improvement can be described
  algebraically as the slope of a line
• Slope is defined as the vertical change over the
  horizontal change on a Cartesian plane. (x-axis
  and y-axis graph)
• Also called Rise over run
• Formula m (y2 - y1) / (x2 - x1)
• Describes the steepness of a line (Gall Gall,
  2007)

7
RoI Definition

• Finding a students RoI is determining the
  students learning
• Creating a line that fits the data points, a
  trendline
• To find that line, we use
• Linear regression
• Ordinary Least Squares

8
How does Rate of Improvement Fit into the Larger
Context?
9
School Improvement/Comprehensive School Reform
Response to Intervention
Dual Discrepancy Level Growth
Rate of Improvement
10
School Improvement/ Comprehensive School Reform

• Grade level content expectations (ELA, math,
  science, social studies, etc.).
• Work toward these expectations through classroom
  instruction.
• Understand impact of instruction through
  assessment.

11
Assessment

• Formative Assessments/High Stakes Tests
• Does student have command of content expectation
  (standard)?
• Universal Screening using CBM
• Does student have basic skills appropriate for
  age/grade?

12
Assessment

• Q For students who are not proficient on grade
  level content standards, do they have the basic
  reading/writing/math skills necessary?
• A Look at Universal Screening if above
  criteria, intervention geared toward content
  standard, if below criteria, intervention geared
  toward basic skill.

13
Progress Monitoring

• Frequent measurement of knowledge to inform our
  understanding of the impact of instruction/interve
  ntion.
• Measures of basic skills (CBM) have demonstrated
  reliability validity (see table at
  www.rti4success.org).

14
Classroom Instruction (Content Expectations)
Measure Impact (Test)
Proficient!
Non Proficient
Content Need?
Basic Skill Need?
Use Diagnostic Test to Differentiate
Intervention Progress Monitor With CBM
Intervention Progress Monitor
If CBM is Appropriate Measure
Rate of Improvement
15
So

• Rate of Improvement (RoI) is how we understand
  student growth (learning).
• RoI is reliable and valid (psychometrically
  speaking) for use with CBM data.
• RoI is best used when we have CBM data, most
  often when dealing with basic skills in
  reading/writing/math.
• RoI can be applied to other data (like behavior)
  with confidence too!
• RoI is not yet tested on typical Tier I formative
  classroom data.

16
RoI is usually applied to

• Tier One students in the early grades at risk for
  academic failure (low green kids).
• Tier Two Three Intervention Groups.
• Special Education Students (and IEP goals)
• Students with Behavior Plans

17
RoI Foundations

• Deno, 1985
• Curriculum-based measurement
• General outcome measures
• Technically adequate
• Short
• Standardized
• Repeatable
• Sensitive to change

18
RoI Foundations

• Fuchs Fuchs, 1998
• Hallmark components of Response to Intervention
• Ongoing formative assessment
• Identifying non-responding students
• Treatment fidelity of instruction
• Dual discrepancy model
• One standard deviation from typically performing
  peers in level and rate

19
RoI Foundations

• Ardoin Christ, 2008
• Slope for benchmarks (3x per year)
• More growth from fall to winter than winter to
  spring
• Might be helpful to use RoI for fall to winter
• And a separate RoI for winter to spring

20
RoI Foundations

• Fuchs, Fuchs, Walz, Germann, 1993
• Typical weekly growth rates in oral reading
  fluency and digits correct
• Needed growth to remediate skills
• Students who had 1.5 to 2.0 times the slope of
  typically performing peers were able to close the
  achievement gap in a reasonable amount of time

21
RoI Foundations

• Deno, Fuchs, Marston, Shin, 2001
• Slope of frequently non-responsive children
  approximated slope of children already identified
  as having a specific learning disability

22
How many data points?

• 10 data points are a minimum requirement for a
  reliable trendline (Gall Gall, 2007)
• Is that reasonable and realistic?
• How does that affect the frequency of
  administering progress monitoring probes?
• How does that affect our ability to make
  instructional decisions for students?

23
How can we show RoI?

• Speeches that included visuals, especially in
  color, improved recall of information (Vogel,
  Dickson, Lehman, 1990)
• Seeing is believing.
• Useful for communicating large amounts of
  information quickly
• A picture is worth a thousand words.
• Transcends language barriers (Karwowski, 2006)
• Responsibility for accurate graphical
  representations of data

24
Skills for Which We Compute RoI

• Reading
• Oral Reading Fluency
• Word Use Fluency
• Reading Comprehension
• MAZE
• Retell
• Early Literacy Skills
• Initial Sound
• Letter Naming
• Letter Sound
• Phoneme Segmentation
• Nonsense Word
• Spelling
• Written Expression
• Behavior

• Math
• Math Computation
• Math Facts
• Early Numeracy
• Oral Counting
• Missing Number
• Number Identification
• Quantity Discrimination

25
Guidelines?

• Visual inspection of slope
• Multiple interpretations
• Instructional services
• Need for explicit guidelines

26
Ongoing Research

• RoI for instructional decisions is not a perfect
  process
• Research is currently addressing sources of
  error
• Christ, 2006 standard error of measurement for
  slope
• Ardoin Christ, 2009 passage difficulty and
  variability
• Jenkin, Graff, Miglioretti, 2009 frequency of
  progress monitoring

27
Future Considerations

• Questions yet to be empirically answered
• What parameters of RoI indicate a lack of RtI?
• How does standard error of measurement play into
  using RoI for instructional decision making?
• How does RoI vary between standard protocol
  interventions?
• How does this apply to non-English speaking
  populations?

28
How is RoI Calculated? Which way is best?
29
Multiple Methods for Calculating Growth

• Visual Inspection Approaches
• Eye Ball Approach
• Split Middle Approach
• Tukey Method
• Quantitative Approaches
• Last point minus First point Approach
• Split Middle Tukey plus
• Linear Regression Approach

30
The Visual Inspection Approaches
31
Eye Ball Approach
32
Split Middle Approach

• Drawing through the two points obtained from the
  median data values and the median days when the
  data are divided into two sections
• (Shinn, Good, Stein, 1989).

33
Split Middle
X(14)
X (9)
X(9)
34
Tukey Method

• Divide scores into 3 equal groups
• Divide groups with vertical lines
• In 1st and 3rd groups, find median data point and
  median week and mark with an X
• Draw line between two Xs
• (Fuchs, et. al., 2005. Summer Institute Student
  progress monitoring for math. http//www.studentpr
  ogress.org/library/training.asp)

35
Tukey Method
X(14)
X(8)
36
The Quantitative Approaches
37
Last minus First

• Iris Center last probe score minus first probe
  score over last administration period minus first
  administration period.
• Y2-Y1/X2-X1 RoI
• http//iris.peabody.vanderbilt.edu/resources.html

38
Last minus First
39
Split Middle Plus
X(14)
X(9)
(14-9)/80.63
40
Tukey Method Plus
X(14)
X(8)
(14-8)/80.75
41
Linear Regression
42
RoI Consistency?
Any Method of Visual Inspection ???
Last minus First 0.75
Split Middle Plus 0.63
Tukey Plus 0.75
Linear Regression 1.10
43
RoI Consistency?

• If we are not all using the same model to compute
  RoI, we continue to have the same problems as
  past models, where under one approach a student
  meets SLD criteria, but under a different
  approach, the student does not.
• Hypothetically, if the RoI cut-off was 0.65 or
  0.95, different approaches would come to
  different conclusions on the same student.

44
RoI Consistency?

• Last minus First (Iris Center) and Linear
  Regression (Shinn, etc.) only quantitative
  methods discussed in CBM literature.
• Study of 37 at risk 2nd graders

Difference in RoI b/w LmF LR Methods Difference in RoI b/w LmF LR Methods
Whole Year 0.26 WCPM
Fall 0.31 WCPM
Spring 0.24 WCPM
McCrea (2010) Unpublished data McCrea (2010) Unpublished data
45
Technical Adequacy

• Without a consensus on how to compute RoI, we
  risk falling short of having technical adequacy
  within our model.

46
So, Which RoI Method is Best?
47
Literature shows that Linear Regression is Best
Practice

• Students daily test scoreswere entered into a
  computer programThe data analysis program
  generated slopes of improvement for each level
  using an Ordinary-Least Squares procedure (Hayes,
  1973) and the line of best fit.
• This procedure has been demonstrated to represent
  CBM achievement data validly within individual
  treatment phases (Marston, 1988 Shinn, Good,
  Stein, in press Stein, 1987).
• Shinn, Gleason, Tindal, 1989

48
Growth (RoI) Research using Linear Regression

• Christ, T. J. (2006). Short-term estimates of
  growth using curriculum based measurement of oral
  reading fluency Estimating standard error of the
  slope to construct confidence intervals. School
  Psychology Review, 35, 128-133.
• Deno, S. L., Fuchs, L. S., Marston, D., Shin,
  J. (2001). Using curriculum based measurement to
  establish growth standards for students with
  learning disabilities. School Psychology Review,
  30, 507-524.
• Good, R. H. (1990). Forecasting accuracy of slope
  estimates for reading curriculum based
  measurement Empirical evidence. Behavioral
  Assessment, 12, 179-193.
• Fuchs, L. S., Fuchs, D., Hamlett, C. L., Walz, L.
  Germann, G. (1993). Formative evaluation of
  academic progress How much growth can we expect?
  School Psychology Review, 22, 27-48.

49
Growth (RoI) Researchusing Linear Regression

• Jenkins, J. R., Graff, J. J., Miglioretti, D.L.
  (2009). Estimating reading growth using
  intermittent CBM progress monitoring. Exceptional
  Children, 75, 151-163.
• Shinn, M. R., Gleason, M. M., Tindal, G.
  (1989). Varying the difficulty of testing
  materials Implications for curriculum-based
  measurement. The Journal of Special Education,
  23, 223-233.
• Shinn, M. R., Good, R. H., Stein, S. (1989).
  Summarizing trend in student achievement A
  comparison of methods. School Psychology Review,
  18, 356-370.

50
So, Why Are There So Many Other RoI Models?

• Ease of application
• Focus on Yes/No to goal acquisition, not degree
  of growth
• How many of us want to calculate OLS Linear
  Regression formulas (or even remember how)?

51
Pros and Cons of Each Approach
Pros Cons
Eye Ball Easy Understandable Subjective
Split Middle Tukey No software needed Compare to Aim/Goal line Yes/No to goal acquisition No statistic provided, no idea of the degree of growth
52
Pros and Cons of Each Approach
Pros Cons
Last minus First Provides a growth statistic Easy to compute Does not consider all data points, only two
Split Middle Tukey Plus Considers all data points. Easy to compute No support for plus part of methodology
Linear Regression All data points Best Practice Calculating the statistic
53
An Easy and Applicable Solution
54
Get Out Your Laptops!

• Open Microsoft Excel

I love ROI
55
Graphing RoIFor Individual Students

• Programming Microsoft Excel to Graph Rate of
  Improvement
• Fall to Winter

56
Setting Up Your Spreadsheet

• In cell A1, type 3rd Grade ORF
• In cell A2, type First Semester
• In cell A3, type School Week
• In cell A4, type Benchmark
• In cell A5, type the Students Name (Swiper
  Example)

57
Labeling School Weeks

• Starting with cell B3, type numbers 1 through 18
  going across row 3 (horizontal).
• Numbers 1 through 18 represent the number of the
  school week.
• You will end with week 18 in cell S3.

58
Labeling Dates

• Note You may choose to enter the date of that
  school week across row 2 to easily identify the
  school week.

59
Entering Benchmarks(3rd Grade ORF)

• In cell B4, type 77. This is your fall benchmark.

• In cell S4, type 92. This is your winter
  benchmark.

60
Entering Student Data (Sample)

• Enter the following numbers, going across row 5,
  under corresponding week numbers.
• Week 1 41
• Week 8 62
• Week 9 63
• Week 10 75
• Week 11 64

• Week 12 80
• Week 13 83
• Week 14 83
• Week 15 56
• Week 17 104
• Week 18 74

61
CAUTION

• If a student was not assessed during a certain
  week, leave that cell blank
• Do not enter a score of Zero (0) it will be
  calculated into the trendline and interpreted as
  the student having read zero words correct per
  minute during that week.

62
Graphing the Data

• Highlight cells A4 and A5 through S4 and S5
• Follow Excel 2003 or Excel 2007 directions from
  here

63
Graphing the Data

• Excel 2003
• Across the top of your worksheet, click on
  Insert
• In that drop-down menu, click on Chart

• Excel 2007
• Click Insert
• Find the icon for Line
• Click the arrow below Line

64
Graphing the Data

• Excel 2003
• A Chart Wizard window will appear

• Excel 2007
• 6 graphics appear

65
Graphing the Data

• Excel 2003
• Choose Line
• Choose Line with markers

• Excel 2007
• Choose Line with markers

66
Graphing the Data

• Excel 2003
• Data Range tab
• Columns

• Excel 2007
• Your graph appears

67
Graphing the Data

• Excel 2003
• Chart Title
• School Week X Axis
• WPM Y Axis

• Excel 2007
• Change your labels by right clicking on the graph

68
Graphing the Data

• Excel 2003
• Choose where you want your graph

• Excel 2007
• Your graph was automatically put into your data
  spreadsheet

69
Graphing the Trendline

• Excel 2003
• Right click on any of the student data points

• Excel 2007

70
Graphing the Trendline

• Excel 2003
• Choose Linear

• Excel 2007

71
Graphing the Trendline

• Excel 2003
• Choose Custom and check box next to Display
  equation on chart

• Excel 2007

72
Graphing the Trendline

• Clicking on the equation highlights a box around
  it
• Clicking on the box allows you to move it to a
  place where you can see it better

73
Graphing the Trendline

• You can repeat the same procedure to have a
  trendline for the benchmark data points
• Suggestion label the trendline Expected ROI
• Move this equation under the first

74
Individual Student GraphFall to Winter
75
Individual Student Graph

• The equation indicates the slope, or rate of
  improvement.
• The number, or coefficient, before "x" is the
  average improvement, which in this case is the
  average number of words per minute per week
  gained by the student.

76
Individual Student Graph

• The rate of improvement, or trendline, is
  calculated using a linear regression, a simple
  equation of least squares.
• To add additional progress monitoring/benchmark
  scores once youve already created a graph, enter
  additional scores in Row 5 in the corresponding
  school week.

77
Individual Student Graph

• The slope can change depending on which week
  (where) you put the benchmark scores on your
  chart.
• Enter benchmark scores based on when your school
  administers their benchmark assessments for the
  most accurate depiction of expected student
  progress.

78
Programming ExcelFirst Semester

• Calculating Needed RoI
• Calculating Benchmark RoI
• Calculating Students Actual RoI

79
Quick Definitions

• Needed RoI
• The rate of improvement needed to catch up to
  the next benchmark.
• Benchmark RoI
• The rate of improvement of typically performing
  peers according to the norms
• Students Actual RoI
• Based on the available data points, this is the
  students actual rate of improvement per week

80
Calculating Needed RoI

• In cell T3, type Needed RoI
• Click on cell T5
• In the fx line (at top of sheet) type this
  formula ((S4-B5)/18)
• Then hit enter
• Your result should read 2.83333...
• This formula simply subtracts the students
  actual beginning of year (BOY) benchmark from the
  expected middle of year (MOY) benchmark, then
  dividing by 18 for the first 18 weeks (1st
  semester).

81
Calculating Benchmark RoI

• In cell U3, type Benchmark RoI
• Click on cell U4
• In the fx line (at top of sheet) type this
  formula SLOPE(B4S4,B3S3)
• Then hit enter
• Your result should read 0.8825...
• This formula considers 18 weeks of benchmark data
  and provides an average growth or change per week.

82
Calculating Student Actual RoI

• Click on cell U5
• In the fx line (at top of sheet) type this
  formula SLOPE(B5S5,B3S3)
• Then hit enter
• Your result should read 2.5137...
• This formula considers 18 weeks of student data
  and provides an average growth or change per week.

83
Graphing RoIFor Individual Students

• Programming Microsoft Excel to Graph Rate of
  Improvement
• Winter to Spring

84
Setting Up Your Spreadsheet

• In cell A1, type 3rd Grade ORF
• In cell A2, type Second Semester
• In cell A3, type School Week
• In cell A4, type Benchmark
• In cell A5, type the Students Name (Swiper
  Example)

85
Labeling School Weeks

• Starting with cell B3, type numbers 1 through 18
  going across row 3 (horizontal).
• Numbers 1 through 18 represent the number of the
  school week.
• You will end with week 18 in cell S3.

86
Labeling Dates

• Note You may choose to enter the date of that
  school week across row 2 to easily identify the
  school week.

87
Entering Benchmarks(3rd Grade ORF)

• In cell B4, type 92. This is your fall benchmark.

• In cell S4, type 110. This is your winter
  benchmark.

88
Entering Student Data (Sample)

• Enter the following numbers, going across row 5,
  under corresponding week numbers.
• Week 1 74
• Week 3 85
• Week 4 89
• Week 5 69
• Week 6 85

• Week 7 96
• Week 8 90
• Week 9 84
• Week 10 106
• Week 11 94
• Week 15 100

89
CAUTION

• If a student was not assessed during a certain
  week, what do you put in that cell?
• Why?

90
Graphing the Data

• Highlight cells A4 and A5 through S4 and S5
• Follow Excel 2003 or Excel 2007 directions from
  here

91
Graphing the Data

• Excel 2003
• Across the top of your worksheet, click on
  Insert
• In that drop-down menu, click on Chart

• Excel 2007
• Click Insert
• Find the icon for Line
• Click the arrow below Line

92
Graphing the Data

• Excel 2003
• A Chart Wizard window will appear

• Excel 2007
• 6 graphics appear

93
Graphing the Data

• Excel 2003
• Choose Line
• Choose Line with markers

• Excel 2007
• Choose Line with markers

94
Graphing the Data

• Excel 2003
• Data Range tab
• Columns

• Excel 2007
• Your graph appears

95
Graphing the Data

• Excel 2003
• Chart Title
• School Week X Axis
• WPM Y Axis

• Excel 2007
• Change your labels by right clicking on the graph

96
Graphing the Data

• Excel 2003
• Choose where you want your graph

• Excel 2007
• Your graph was automatically put into your data
  spreadsheet

97
Graphing the Trendline

• Excel 2003
• Right click on any of the student data points

• Excel 2007

98
Graphing the Trendline

• Excel 2003
• Choose Linear

• Excel 2007

99
Graphing the Trendline

• Excel 2003
• Choose Custom and check box next to Display
  equation on chart

• Excel 2007

100
Graphing the Trendline

• Clicking on the equation highlights a box around
  it
• Clicking on the box allows you to move it to a
  place where you can see it better

101
Graphing the Trendline

• You can repeat the same procedure to have a
  trendline for the benchmark data points
• Suggestion label the trendline Expected ROI
• Move this equation under the first

102
Individual Student Graph
103
Challenge!

• What was the first equation?
• What is the slope of that equation?
• What was the second equation?
• What is the slope of that equation?
• Describe the achievement gap at the end of the
  school year.

104
Programming ExcelSecond Semester

• Calculating Needed RoI
• Calculating Benchmark RoI
• Calculating Students Actual RoI

105
Calculating Needed RoI

• In cell T3, type Needed RoI
• Click on cell T5
• In the fx line (at top of sheet) type this
  formula ((S4-B5)/18)
• Then hit enter
• Your result is _____ ?
• This formula simply subtracts the students
  actual middle of year (MOY) benchmark from the
  expected end of year (EOY) benchmark, then
  dividing by 18 for the first 18 weeks (1st
  semester).

106
Calculating Benchmark RoI

• In cell U3, type Benchmark RoI
• Click on cell U4
• In the fx line (at top of sheet) type this
  formula SLOPE(B4S4,B3S3)
• Then hit enter
• Your result should read ____?
• This formula considers 18 weeks of benchmark data
  and provides an average growth or change per week.

107
Calculating Student Actual RoI

• Click on cell U5
• In the fx line (at top of sheet) type this
  formula SLOPE(B5S5,B3S3)
• Then hit enter
• Your result should read 1.89
• This formula considers 18 weeks of student data
  and provides an average growth or change per week.

108
Assuming Linear Growth
Why Graph only 18 Weeks at a Time?

• Finding Curve-linear Growth

109
Non-Educational Example of Curve-linear Growth
110
Academic Example of Curvilinear Growth
111
McCrea, 2010

• Looked at Rate of Improvement in small 2nd grade
  sample
• Found differences in RoI when computed for fall
  and spring
• Ave RoI for fall 1.47 WCPM
• Ave RoI for spring 1.21 WCPM

112
Ardoin Christ, 2008

• Slope for benchmarks (3x per year)
• More growth from fall to winter than winter to
  spring

113
Christ, Yeo, Silberglitt, in press

• Growth across benchmarks (3X per year)
• More growth from fall to winter than winter to
  spring
• Disaggregated special education population

114
Graney, Missall, Martinez, 2009

• Growth across benchmarks (3X per year)
• More growth from winter to spring than fall to
  winter with R-CBM.

115
Fien, Park, Smith, Baker, 2010

• Investigated relationship b/w NWF gains and
  ORF/Comprehension
• Found greater NWF gains in fall than in spring.

116
DIBELS (6th) ORF Change in Criteria
Fall to Winter Winter to Spring
2nd 24 22
3rd 15 18
4th 13 13
5th 11 9
6th 11 5
117
AIMSweb Norms
Based on 50th Percentile Fall to Winter Winter to Spring
1st 18 31
2nd 25 17
3rd 22 15
4th 16 13
5th 17 15
6th 13 12
118
Speculation as to why Differences in RoI within
the Year

• Relax instruction after high stakes testing in
  March/April a PSSA effect.
• Depressed BOY benchmark scores due to summer
  break a rebound effect (Clemens).
• Instructional variables could explain differences
  in Graney (2009) and Ardoin (2008) Christ (in
  press) results (Silberglitt).
• Variability within progress monitoring probes
  (Ardoin Christ, 2008) (Lent).

119
ROI as a Decision Tool

• within a Problem-Solving Model

120
Steps

1. Gather the data
2. Ground the data set goals
3. Interpret the data
4. Figure out how to fit Best Practice into Public
   Education

121
Step 1 Gather Data

• Universal Screening
• Progress Monitoring

122
Common Screenings in PA

• DIBELS
• AIMSweb
• MBSP
• 4Sight
• PSSA

123
Validated Progress Monitoring Tools

• DIBELS
• AIMSweb
• MBSP
• www.studentprogress.org

124
Step 2 Ground the Data

• 1) To what will we compare our student growth
  data?
• 2) How will we set goals?

125
Multiple Ways toLook at Growth

• Needed Growth
• Expected Growth Percent of Expected Growth
• Fuchs et. al. (1993) Table of Realistic and
  Ambitious Growth
• Growth Toward Individual Goal
• Best Practices in Setting Progress Monitoring
  Goals for Academic Skill Improvement (Shapiro,
  2008)

126
Needed Growth

• Difference between students BOY (or MOY) score
  and benchmark score at MOY (or EOY).
• Example MOY ORF 10, EOY benchmark is 40, 18
  weeks of instruction (40-10/181.67). Student
  must gain 1.67 wcpm per week to make EOY
  benchmark.

127
Expected Growth

• Difference between two benchmarks.
• Example MOY benchmark is 20, EOY benchmark is
  40, expected growth (40-20)/18 weeks of
  instruction 1.11 wcpm per week.

128
Looking at Percent of Expected Growth
Tier I Tier II Tier III
Greater than 150
Between 110 150 Possible LD
Between 95 110 Likely LD
Between 80 95 May Need More May Need More Likely LD
Below 80 Needs More Needs More Likely LD
129
Oral Reading Fluency Adequate Response Table
Realistic Growth Ambitious Growth
1st 2.0 3.0
2nd 1.5 2.0
3rd 1.0 1.5
4th 0.9 1.1
5th 0.5 0.8
130
Digit Fluency Adequate Response Table
Realistic Growth Ambitious Growth
1st 0.3 0.5
2nd 0.3 0.5
3rd 0.3 0.5
4th 0.75 1.2
5th 0.75 1.2
131
If Local Criteria are Not an Option

• Use norms that accompany the measure (DIBELS,
  AIMSweb, etc.).
• Use national norms.

132
Making Decisions Best Practice

• Research has yet to establish a blue print for
  grounding student RoI data.
• At this point, teams should consider multiple
  comparisons when planning and making decisions.

133
Making Decisions Lessons From the Field

• When tracking on grade level, consider an RoI
  that is 100 of expected growth as a minimum
  requirement, consider an RoI that is at or above
  the needed as optimal.
• So, 100 of expected and on par with needed
  become the limits of the range within a student
  should be achieving.

134
Is there an easy way to do all of this?
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137
Access to Spreadsheet Templates

• http//sites.google.com/site/rateofimprovement/hom
  e
• Click on Charts and Graphs.
• Update dates and benchmarks.
• Enter names and benchmark/progress monitoring
  data.

138
What about Students not on Grade Level?
139
Determining Instructional Level

• Independent/Instructional/Frustrational
• Instructional often b/w 40th or 50th percentile
  and 25th percentile.
• Frustrational level below the 25th percentile.
• AIMSweb Survey Level Assessment (SLA).

140
Setting Goals off of Grade Level

• 100 of expected growth not enough.
• Needed growth only gets to instructional level
  benchmark, not grade level.
• Risk of not being ambitious enough.
• Plenty of ideas, but limited research regarding
  Best Practice in goal setting off of grade level.

141
Possible Solution (A)

• Weekly probe at instructional level and compare
  to expected and needed growth rates at
  instructional level.
• Ambitious goal 200 of expected RoI

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Possible Solution (B)

• Weekly probe at instructional level for sensitive
  indicator of growth.
• Monthly probes (give 3, not just 1) at grade
  level to compute RoI.
• Goal based on grade level growth (more than 100
  of expected).

144
Step 3 Interpreting Growth
145
What do we do when we do not get the growth we
want?

• When to make a change in instruction and
  intervention?
• When to consider SLD?

146
When to make a change in instruction and
intervention?

• Enough data points (6 to 10)?
• Less than 100 of expected growth.
• Not on track to make benchmark (needed growth).
• Not on track to reach individual goal.

147
When to consider SLD?

• Continued inadequate response despite
• Fidelity with Tier I instruction and Tier II/III
  intervention.
• Multiple attempts at intervention.
• Individualized Problem-Solving approach.
• Evidence of dual discrepancy

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Three Levels of Examples

• Whole Class
• Small Group
• Individual Student
• - Academic Data
• - Behavior Data

150
Whole Class Example
151
3rd Grade Math Whole Class

• Whos responding?
• Effective math instruction?
• Who needs more?

• N19
• 4 gt 100 growth
• 15 lt 100 growth
• 9 w/ negative growth

152
Small Group Example
153
Intervention Group

• Intervention working for how many?
• Can we assume fidelity of intervention based on
  results?
• Who needs more?

154
Individual Kid Example
155
Individual Kid

• Making growth?
• How much (65 of expected growth).
• Atypical growth across the year (last 3 data
  points).
• Continue? Make a change? Need more data?

156
RoI and Behavior?

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Step 4 Figure out how to fit Best Practice into
Public Education
159
Things to Consider

• Who is At-Risk and needs progress monitoring?
• Who will collect, score, enter the data?
• Who will monitor student growth, when, and how
  often?
• What changes should be made to instruction
  intervention?
• What about monitoring off of grade level?

160
Who is At-Risk and needs progress monitoring?

• Below level on universal screening

Entering 4th Grade Example Entering 4th Grade Example Entering 4th Grade Example Entering 4th Grade Example Entering 4th Grade Example
DORF (110) ISIP TRWM (55) 4Sight (1235) PSSA (1235)
Student A 115 58 1255 1232
Student B 85 48 1216 1126
Student C 72 35 1056 1048
161
Who will collect, score, and enter the data?

• Using MBSP for math, teachers can administer
  probes to whole class.
• DORF probes must be administered one-on-one, and
  creativity pays off (train and use art, music,
  library, etc. specialists).
• Schedule for progress monitoring math and reading
  every-other week.

162
Week 1 Week 1 Week 2 Week 2
Reading Math Reading Math
1st X X
2nd X X
3rd X X
4th X X
5th X X
163
Who will monitor student growth, when, and how
often?

• Best Practices in Data-Analysis Teaming
  (Kovaleski Pedersen, 2008)
• Chambersburg Area School District Elementary
  Response to Intervention Manual (McCrea et. al.,
  2008)
• Derry Township School District Response to
  Intervention Model (http//www.hershey.k12.pa.us/5
  6039310111408/lib/56039310111408/_files/Microsoft_
  Word_-_Response_to_Intervention_Overview_of_Hershe
  y_Elementary_Model.pdf)

164
What changes should be made to instruction
intervention?

• Ensure treatment fidelity!!!!!!!!
• Increase instructional time (active and engaged)
• Decrease group size
• Gather additional, diagnostic, information
• Change the intervention

165
Final Exam

• Student Data 27, 29, 26, 34, 27, 32, 39, 45, 43,
  49, 51, --, --, 56, 51, 52, --, 57.
• Benchmark Data BOY 40, MOY 68.
• What is students RoI?
• How does RoI compare to expected and needed RoIs?
• What steps would your team take next?
• What if Benchmarks were 68 and 90 instead?

166
The RoI Web Site

• http//sites.google.com/site/rateofimprovement/
• Download powerpoints, handouts, Excel graphs,
  charts, articles, etc.
• Caitlin Flinn Bennyhoff
• CaitlinFlinn_at_hotmail.com
• Andy McCrea
• andymccrea70_at_gmail.com
• Matt Ferchalk
• mferchalk_at_norleb.k12.pa.us