![Page 1: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/1.jpg)
Data Rich, Information Poor? Making Sense of Progress Monitoring Data to Guide Intervention DecisionsLynn Fuchs, Ph.D., Vanderbilt UniversityLee Kern, Ph.D., Lehigh University
![Page 2: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/2.jpg)
2
Help educators understand how to review progress monitoring and other accessible data to guide intervention planning for students with intensive needs in behavior and academics.
Purpose
![Page 3: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/3.jpg)
3
1. Common progress monitoring measures in academics and behavior
2. Key considerations for optimizing data collection
3. Structured questioning for analyzing progress monitoring data patterns:– What patterns do the data reveal?– What might the data reveal about the student’s needs?– What adaptations may be needed to make the intervention
more effective?
We will discuss
![Page 4: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/4.jpg)
4
1. Common Progress Monitoring Measures in Academics and Behavior
![Page 5: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/5.jpg)
5
A standardized method of ongoing assessment that allows you to• Measure student response to instruction/intervention• Evaluate growth and facilitate instructional planning
For more information about progress monitoring:• www.intensiveintervention.org; www.rti4success.org
– Tools– Charts– Training modules
Quick Review:What is progress monitoring?
![Page 6: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/6.jpg)
6
Why Implement Progress Monitoring?
Data allow us to
Estimate the rates of improvement (ROI) over time and set
goals.
Compare the efficacy of different forms of
instruction.
Identify students who are not demonstrating
adequate progress.
Determine when an instructional change is
needed and help to hypothesize potential
sources of need.
![Page 7: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/7.jpg)
7
Brief assessments
Repeated measures that capture student learning or behavior over time
Measures of grade, age, or instructionally appropriate outcomes
Reliable and valid
Progress Monitoring Tools Should Be
![Page 8: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/8.jpg)
8
Reading Letter Sound Fluency Word Identification
Fluency Oral Reading Fluency;
Passage Reading Fluency Maze
MathematicsNumber IdentificationQuantity DiscriminationMissing NumberComputation Curriculum-
Based MeasuresConcepts and
Applications Curriculum-Based Measures
Common Progress Monitoring Measures: Academics
![Page 9: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/9.jpg)
9
Common Progress Monitoring Measures: Behavior
Systematic Direct
Observation
Direct Behavior Rating
![Page 10: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/10.jpg)
10
Event-Based
• Frequency• Duration• Latency
Time-Based
• Whole- interval
• Partial- interval
• Momentary time sampling
10
Common Progress Monitoring Measures: Behavior
Systematic Direct Observation
![Page 11: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/11.jpg)
11
Behavior Date
Disr
uptio
n 9+ 5 5 5 5 5 7 – 8 4 4 4 4 4 5 – 6 3 3 3 3 3 2 – 4 2 2 2 2 2 0 -1 1 1 1 1 1
Target Behavior Reading Writing Math Art
Writes name on worksheet
Follows rules
Prepared to learn
Total Points Earned = 6 or 50%
Common Progress Monitoring Measures: Behavior
Direct Behavior Rating
M Tu W Th F
![Page 12: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/12.jpg)
12
Graphing Progress Monitoring DataDisruptive
Place a mark along the line that best reflects the percentage of total time the
student was disruptive during mathematics today.
Interpretation: The student displayed disruptive behavior during 30 percent of small-group science instruction today.
![Page 13: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/13.jpg)
13
2. Key Considerations for Optimizing Data Collection
![Page 14: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/14.jpg)
14
Aligning measure to content of instruction
Sensitivity to change
Distinguishing from other classroom-based and diagnostic assessments
Frequency of data collection
Common Challenges: Academic Data
![Page 15: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/15.jpg)
15
Defining target behavior
Aligning measure with target behavior
Consistency of administration and frequency of data collection
Common Challenges: Behavior Data
![Page 16: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/16.jpg)
16
3. Structured Questioning for Analyzing Progress
Monitoring Data Patterns
![Page 17: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/17.jpg)
What can a graph tell you?
17
![Page 18: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/18.jpg)
18
Data and assessment
Dosage and fidelity
Content and
intensity
Use Structured Questioning to Arrive at a Hypothesis
![Page 19: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/19.jpg)
19
Data and assessment
Dosage and fidelity
Content and intensity
Structured QuestioningI
• Am I collecting data often enough?• Is the progress monitoring tool sensitive to
change?• Does the measure align to the content of the
intervention?• Am I collecting data at the right level?
![Page 20: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/20.jpg)
20
Data and assessment
Dosage and fidelity
Content and intensity
Structured QuestioningI
• Did the student receive the right dosage of the intervention?
• Did the student receive all components of the intervention, as planned?
• Did other factors prevent the student from receiving the intervention as planned? (Example: absences, behavior issues, scheduling challenges, group size, staff training)
![Page 21: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/21.jpg)
21
Data and assessment
Dosage and fidelity
Content and intensity
Structured QuestioningI
• Is the intervention an appropriate match given the student’s skill deficits or target behavior?
• Is the intensity of the intervention appropriate, given the student’s level of need, or are adaptations or intensifications needed?
• Are academic and behavioral issues interrelated?
![Page 22: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/22.jpg)
22
Trend: Improvement in Scores After Change (Behavior)
The situation:Responding improves
more after an intervention change, with an ascending
trend.
Pre-intervention After intervention change
![Page 23: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/23.jpg)
23
Trend: Improvement in Scores After Change (Academic)
The situation:Scores improve more after an intervention change,
making the trend line steeper than it was.
![Page 24: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/24.jpg)
24
This is good news!
The student is steadily improving and is on target to reach the end of year goal.
Continue to monitor progress to ensure ongoing improvement.
Consider creating a more ambitious goal if the student continues to outperform the goal.
What could this pattern be telling you?
![Page 25: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/25.jpg)
25
Trend: Flat Line (Behavior)
Pre-intervention The situation:The data from the intervention phase is similar to pre-intervention or
baseline, creating a flat or stable
trend.
After intervention change
![Page 26: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/26.jpg)
26
Trend: Flat Line (Academic)
The situation: Data in the intervention phase is similar to the baseline
phase, creating a flat trend line.
![Page 27: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/27.jpg)
27
The student is not responding to the intervention.
The progress monitoring tool is not appropriate.
The student has not received the intervention with fidelity.
The intervention is not an appropriate match for the student’s needs.
What could this pattern be telling you?
![Page 28: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/28.jpg)
28
Data and assessment
Dosage and fidelity
Content and intensity
• Select progress monitoring measure that aligns with intervention.
• Ensure progress monitoring tool is sensitive to change.
• Ensure the behavior measurement reflects the behavior you need to change.
• Address barriers to adequate dosage and fidelity.
• Target specific student need or function of behavior and determine more appropriate match.
• Add motivational or behavioral component.
• Add academic supports.
• Modify schedules of reinforcement.
![Page 29: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/29.jpg)
29
1 2 3 3 5 6 7 8 9 101112131415160123456789
10
Trend: Highly Variable (Behavior)
Dis
rupt
ive
DB
R R
atin
g
Number of School Days
The situation:The data from the intervention phase is similar to pre-intervention or
baseline, creating a variable trend.
After intervention change
Pre-intervention
![Page 30: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/30.jpg)
30
Trend: Highly Variable (Academic)
The situation: Scores are highly variable, with significant changes
from day to day.
![Page 31: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/31.jpg)
31
The progress monitoring tool is not reliable.
Administration of the assessment is inconsistent.
Engagement and motivation vary greatly by day.
Other situational or external factors affect performance or behavior.
What could this pattern be telling you?
![Page 32: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/32.jpg)
32
Data and assessment
Dosage and fidelity
Content and intensity
• Verify that progress monitoring tool has evidence of reliability.
• Ensure consistency of administration.
• Ensure consistency of intervention delivery and dosage.
• Create plan to help student manage situational factors.
• Add motivational or behavioral component.
![Page 33: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/33.jpg)
33
1 2 3 4 5 6 7 8 9 10 11 120123456789
10
Trend: Slow Rate of Improvement (Behavior)
Eng
agem
ent D
BR
Rat
ing
Number of School Days
The situation:The data in the
intervention phase is increasing but slowly, creating a
gradual ascending trend.
After intervention change
Pre-intervention
![Page 34: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/34.jpg)
34
Trend: Slow Rate of Improvement (Academic)
The situation: The student’s scores are improving, but not as steeply as the goal line.
![Page 35: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/35.jpg)
35
The student is making some progress, but at a slow rate.
Continuation will not result in student reaching goal.
The goal is inappropriate for the measure being used or student characteristics.
The student requires an intervention change to increase intensity.
What could this pattern be telling you?
![Page 36: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/36.jpg)
36
Data and assessment
Dosage and fidelity
Content and intensity
• Set feasible goal by researching rate of improvement.
• Increase intensity by increasing frequency or duration of intervention or decreasing group size.
Increase intensity by• Providing more
frequent opportunities for feedback
• Adding explicit instruction in skill deficit area
• Adding practice opportunities
![Page 37: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/37.jpg)
37
• Begin with a valid, reliable, and appropriate progress monitoring measure.
• Graph your data to see patterns.
• Ask questions about data patterns to arrive at hypothesis about student responsiveness.
• Use your hypothesis to inform changes to intervention or assessment (if the data indicate that a change is needed).
In Summary
![Page 38: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/38.jpg)
38
Center on Response to Intervention: www.rti4success.org
National Center on Intensive Intervention, DBI Training Series: http://www.intensiveintervention.org/content/dbi-training-series
National Center on Student Progress Monitoring: http://www.studentprogress.org/
Additional Resources
![Page 39: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/39.jpg)
Questions and Discussion
39
![Page 40: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/40.jpg)
National Center on Intensive Intervention (NCII)E-Mail: [email protected]
1050 Thomas Jefferson Street, NWWashington, DC 20007- 3835Website: www.intensiveintervention.org
While permission to redistribute this webinar is not necessary, the citation should be: National Center on Intensive Intervention. (2014). Data Rich, Information Poor? Making Sense of Progress Monitoring Data to Guide Intervention Decisions. Washington, DC: U.S. Department of Education, Office of Special Education Programs, National Center on Intensive Intervention.
40
![Page 41: Lynn Fuchs, Ph.D., Vanderbilt University Lee Kern, Ph.D., Lehigh University](https://reader036.vdocuments.us/reader036/viewer/2022062302/568165c5550346895dd8d1b2/html5/thumbnails/41.jpg)
This webinar was produced under the U.S. Department of Education, Office of Special Education Programs, Award No. H326Q110005. Celia Rosenquist serves as the project officer. The views expressed herein do not necessarily represent the positions or policies of the U.S. Department of Education. No official endorsement by the U.S. Department of Education of any product, commodity, service or enterprise mentioned in this presentation is intended or should be inferred.
National Center on Intensive Intervention