The document discusses a study examining how different presentations of progress monitoring (PM) data influence educators' decisions during the Response to Intervention (RTI) process. The study used an experimental design to compare educators' likelihood of referring students for special education evaluations and confidence in decisions when PM data was presented in graph versus table formats. A survey was administered to educators in two school districts. Preliminary findings showed educators were less likely to refer responding students for evaluation when data was in graph form compared to tables. Confidence in decisions also varied based on the data presentation format. The document recommends further professional development on using PM data to make informed instructional decisions.

1. THE INFLUENCE OF PROGRESS
MONITORING DATA
PRESENTATIONS ON
EDUCATORS’ DECISION MAKING
DURING THE RTI PROCESS
Dr. Anthony D. Mercado

2. INTRODUCTION
 Individuals with Disabilities Education
Act (IDEA) 2004
Free and Appropriate Public
Education
Least Restrictive Environment
(LRE)
Interventions required before
referral for Special Education
evaluation.
2

3. BACKGROUND TO THE
PROBLEM
Tier III
PM 1x/week
Tier II
PM 2x/month
Tier I
PM 1x/3months
Multi-Tiered Systems of Support (MTSS)
3

4. Retrieved from
https://www.emaze.com/@ALWZTQWZ/Presentation-Name-
Graphs vs.Tables
Graphs vs.Tables
4

5. PROBLEM STATEMENT
 The study will address the
problems associated with
decision-making when using
different forms of PM data (e.g.
graph or table) when making a
referral for a special education
evaluation.
5

6. PURPOSE OF THE
RESEARCH
 The Purpose:
To examine the decision making of
educators when given different
presentations of PM data.
 Why is this important?
How we present PM data will lead to:
Better decision making
Improved student achievement
Enhanced instructional practices
6

7. RESEARCH QUESTIONS
 How do different presentations of PM data
influence educators’ decisions to refer
students for a special education
evaluation?
 How do the different presentations of PM
data influence educators’confidence in
their referral decision?
7

8. LITERATURE
 HISTORICAL CONTEXT
 IDEA 2004 - RTI for SLD Eligibility
 Use of MTSS for decision making
 THEORETICAL FOUNDATION (Sansosti and Noltemeyer,
2008)
 Change Paradigm (3 Step Process)
① Evaluation of need
② Developing a plan
③ Implementation, evaluation, and future
planning
8

9. CONCEPTUAL FRAMEWORK
Educators
Perceptions
of RTI
Process
Importance of
Progress
Monitoring
(Graphs vs.
Tables)
Informed
Instruction
Student
Learning
Improved
Instructional
Practice
Improved
Academic
Achievement
Data Driven
Decision
Making
Implementati
on of RTI
Educators
Perceptions
9

10. PM Graph Example
 Responder  Non-Responder
10

11. RESEARCH DESIGN
Quantitative Study
Experimental Design
 IV – Data presentation (Graph
or Table)
 DV – Referral Decision,
Confidence in Decision
 Internet Survey (Qualtrics)
11

12. SETTING
 Public School District (ORANGE)
 Enrollment – 9,503
 43% Latino; 38% Asian
 11% in Special Ed.
 Public School District (LOS ANGELES)
 Enrollment – 18,960
 80% Latino; 9% White
 13% in Special Ed.
12
Dataquest, California Department of Education, 2015

13. POPULATION
 Gen. Ed. Teachers
 Sp. Ed. Teachers
 School
Psychologists
 Speech
Pathologists
 School
Administrators
 School Counselors
13

14. DATA COLLECTION
 Web-based survey using Qualtrics
 Emails provided by two districts
 Delivered via email to participants
 Voluntary participation
 10-15 minute survey
 Open to participants 4 weeks
14

15. DEMOGRAPHICS
 Gender
 Age
 Race/Ethnicity
 County of
employment
 Current Position
 Years of experience
 Grade level
assigned
 Level of Exposure
RTI
SST
Baselines
Trend lines
PM Tables
PM Graphs
15

16. VIGNETTES
PRESENTATION OF VIGNETTES TO PARTICIPANTS
Description of Student Scenarios
Student Table (a) Graph (b)
1 (Responder) Student A (1a) Student D (1b)
2 (Non-Responder) Student E (2a) Student B (2b)
3 (Responder) Student C (3a) Student F (3b)
16

17. Student 1 Vignette
17
Grade: 2nd
Tier of Intervention: Tier II
Student A is in the second grade at an Elementary School. Student has had good
attendance from Kindergarten through second grade. Work habits, homework
completion and effort have been reported as satisfactory. Vision and hearing screenings
conducted by school personnel are within normal limits. There are no health concerns
that have been reported by the student’s parent that would negatively impact educational
progress. Student has average levels of family support based on teacher report and
consistent parent conference attendance. Report card comments indicate that Student A
appears to have average abilities, but exhibits low achievement in oral reading fluency.
Student A has been participating in an RTI Tier II intensive oral reading fluency
intervention program since the beginning of the second grade year. The frequency of
intervention delivery is one time a day for 30 minutes. The progress made has been
monitored and documented in the (table or graph) below. It is recorded twice a month in
equal intervals. The last recorded data was at Student A’s Second Grade Middle
Benchmark period.
Review the data below and answer the questions that follow:

18. Student 1 Vignette
18

19. Student 1 Vignette Data
Examples19

20. VIGNETTE QUESTIONS
Two closed ended questions
1. What is the likelihood of you
referring this student for special
education?
Not Likely (1) to Very Likely (6)
2. How confident are you in your
decision?
Not Confident (1) to Very Confident (7).
20

21. DATA ANALYSIS
 Descriptive statistics
 Participant demographics
 T-Test
 Paired Sample
 Multiple Regression
Y (Likelihood to Refer) = b0 + b1(Ethnicity) +
b2 (Experience) + b3 (Position) + b4
(Gender)+b5 (County) +b6 (Exposure to PM
Table/Graph)
21
Yi = b0 +b1X1 +b2 X2 +e

22. Demographics
Research Questions
Major Findings
FINDINGS

23. Participant Data by Job Title, County, and
Gender
23
Job Titles N %
General Education Teacher 113 48.9
Special Education Teacher 45 19.5
Site Administrator 27 11.7
Speech/Language Pathologist 13 5.6
School Psychologist 14 6.1
Counselor 6 2.6
Others 13 5.6
Total 231 100
County N %
OC 58 25.1
LAC 173 74.9
Total 231 100
Gender N %
Male 56 24.2
Female 175 75.8
Total 231 100

24. Participant Data by Years of Work
Experience, Age Ranges, and Ethnicity
24
Years of Work Experience N %
0-5 18 7.8
6-10 32 13.9
11-15 41 17.7
16-20 66 28.6
21-25 30 13.0
26-30 25 10.8
31+ 19 8.2
Total 231 100
Age Ranges N %
20-25 3 1.3
26-30 10 4.3
31-35 27 11.7
36-40 30 13.0
41-45 44 19.0
46-50 38 16.5
51+ 79 34.2
Total 231 100
Ethnicity N %
Asian or Asian-American 33 14.3
Mexican/Hispanic/Latino/Chicano 48 20.8
Black or African-American 7 3.0
American Indian/Alaskan Native 2 0.9
White 129 55.8
Native Hawaiian/Other Pacific
Islander
1 0.4
Multiethnic/Multiracial 10 4.3
Other 1 0.4
Total 231 100

25. Participant Data by Level of
Exposure25
RTI N %
No Exposure 20 8.7
Minimal 48 20.8
Moderate 90 39.0
Frequent 73 31.6
Total 231 100
SST N %
No Exposure 21 9.1
Minimal 37 16.0
Moderate 80 34.6
Frequent 93 40.3
Total 231 100
Baselines N %
No Exposure 49 21.2
Minimal 54 23.4
Moderate 55 23.8
Frequent 73 31.6
Total 231 100

26. Participant Data by Level of Exposure
26
Goal Lines N %
No Exposure 60 26.0
Minimal 61 26.4
Moderate 54 23.4
Frequent 56 24.2
Total 231 100
Trend Lines N %
No Exposure 92 39.8
Minimal 82 35.5
Moderate 39 16.9
Frequent 18 7.8
Total 231 100
PM Tables N %
No Exposure 55 23.8
Minimal 64 27.7
Moderate 69 29.9
Frequent 43 18.6
Total 231 100
PM Graphs N %
No Exposure 54 23.4
Minimal 70 30.3
Moderate 68 29.4
Frequent 39 16.9
Total 231 100

27. RESEARCH QUESTIONS
 How do different presentations of PM data
influence educators’ decisions to refer
students for a special education
evaluation?
 How do the different presentations of PM
data influence educators’confidence in
their referral decision?
27

28. Likelihood of Referring the Student for
Special Education Evaluation.
28
Students Mean SD t p
Student 1 (Responder) 3.26 .001
Table 2.99 1.44
Graph 2.65 1.37
Student 2 (Non-Responder) -0.28 .783
Table 3.81 1.42
Graph 3.84 1.42
Student 3 (Responder) 4.48 .000
Table 3.95 1.45
Graph 3.47 1.57

29. Confidence in Referring the Student
for a Special Education Evaluation
29
Students Mean SD t p
Student 1 -3.71 .000
Table 5.04 1.49
Graph 5.36 1.36
Student 2 1.53 .127
Table 5.16 1.29
Graph 5.03 1.43
Student 3 -0.64 .522
Table 5.26 1.27
Graph 5.30 1.31

30. 30
MAJOR FINDINGS
1. Participants were less likely to refer
responding students when
presented with a graph form of PM
data.
1. No differences were found between
participants’ decisions for non-
responders.

31. MAJOR FINDINGS
3. Confidence of educators varied
across conditions
4. Exposure to PM tables or graphs
and gender slightly predicted
decision making.
31

32. 32
So What?!!

33. DISCUSSION/RECOMMENDAT
IONS
Evaluation of
need
Developing a
plan
Implementation,
Evaluation, and
Future Planning
Sansasti and Notlemeyer (2008)

34. DISCUSSION
34
Evaluation of
need
Developing a
plan
Implementation,
Evaluation, and
Future Planning
 Presentations of Data Influence Decisions
(RQ1)
 Implications for Students
 Exposure to PM Graphs or Tables
Gender Differences
 Table Vs. Graph: Confidence in Referral (RQ2)

35. RECOMMENDATIONS
 Implementing the use of PM graphs
 Establishing a data driven culture
 Professional Development of PM
Exposure to Trend lines
Comprehension of Data Visualization
35
Evaluation of
need
Developing a
plan
Implementation,
Evaluation, and
Future Planning

36. RECOMMENDATIONS
 Professional Learning Communities for
Sustainability
 Individual Learning
 Shared Learning
 Embedded Learning through Policy
36
Evaluation of
need
Developing a
plan
Implementation,
Evaluation, and
Future Planning

37. 37
So What?!!

38. REFERENCES
38
 DataQuest (CA Dept of Education). (n.d.). Retrieved June 19, 2015, from
http;//data1.cdu.ca.gov/dataquest/
 DIBELS Next Benchmark Goals and Composite Scores. (2010, December 1).
Retrieved April 22, 2015, from https://dibels.org/dibelsnext.html
 Hagan-Burke, S., & Jefferson, G. L. (2002). Using data to promote academic benefit for
included students with mild disabilities. Preventing School Failure: Alternative Education
for Children and Youth, 46(3), 112-118. doi:10.1080/10459880209603355
 Panero, N.S., & Talbert, J.E., (2013). Strategic Inquiry: Starting small for big results in
education. Cambridge, MA: Harvard Education Press.
 Paniello, R., Neely, J., Rich, J., Slattery, E., & Voelker, C. (2011). Practical guide to
choosing an appropriate data display. Otolaryngology–Head and Neck Surgery, 145(6),
886-894. doi:10.1177/019459981142360
 Kavale, K., & Spaulding, L. (2008). Is response to intervention good policy for specific
learning disability?. Learning Disabilities Research & Practice, 23(4), 169-179.
 Martinez, R., & Young, A. (2011). Response to intervention: how is it practiced and
perceived?. International Journal of Special Education, 26(1), 44-52

39. REFERENCES
 Powers, K. , Hagans, K. , & Busse, R. (2008). School psychologists as instructional
consultants in a response-to-intervention model. California School Psychologist, 13,
41-53.
 Sansosti, F. J., & Noltemeyer, A. (2008). Viewing response-to-intervention through an
educational change paradigm: what can we learn?. The California School
Psychologist, 13(1), 55-66.
 Stecker, P. M., Fuchs, L. S., & Fuchs, D. (2005). Using curriculum-based
measurement to improve student achievement: Review of research. Psychology in
the Schools, 42(8), 795-819
 Sugai, G., & Horner, R. (2009). Responsiveness-to-intervention and school-wide
positive behavior supports: Integration of multi-tiered system
approaches. Exceptionality: A Special Education Journal 17(4), 223-237. doi:
10.1080/09362830903235375
 Wilcox, K. A., Murakami-Ramalho, E., & Urick, A. (2013). Just-in-time pedagogy:
Teachers' perspectives on the response to intervention framework. Journal of
Research in Reading, 36(1), 75-95.
 Zirkel, P. A., & Thomas, L. B. (2010). State laws for RTI: An updated
snapshot. Teaching Exceptional Children, 42(3), 56-63.
39

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THANK YOU!