Using Data to Support Teachers’ Accommodation Decision Making
Leanne R. Ketterlin-Geller
Southern Methodist University
lkgeller@smu.edu
Lindy Crawford
Texas Christian University
Paper Presented at the Annual Meeting of the American Educational Research Association
New Orleans, Louisiana
2011
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Researchers studying the accuracy of teachers’ accommodation decision making have
found inconsistencies between teachers’ assignment of accommodations and those that students
actually need (Fuchs & Fuchs, 2001; Fuchs, Fuchs, Eaton, Hamlett, Binkley, & Crouch, 2000;
Ketterlin-Geller, Alonzo, Braun-Monegan, & Tindal, 2007). As a result, considerable variability
exists in accommodation assignment procedures, and ultimately, the use of accommodations for
individual students. Such variability may compromise the validity of inferences about students’
knowledge and skills in the targeted domain.
Studies on the accuracy of teachers’ decisions about accommodations assignment
suggests that teachers tend to assign more accommodations than are actually needed by the
student. Fuchs and Fuchs (2001) summarized a series of studies comparing teacher assignments
of accommodations to measures of the degree to which students benefit from particular
accommodations including extended time, large print, read aloud, and calculator use. Students’
performance on accommodated tests was compared to their performance on non-accommodated
tests to determine the differential boost in scores caused by implementing each accommodation.
They found that teachers tend to assign accommodations to a larger number of students than may
actually benefit from them.
In a study with 16 4th- and 31 5th-grade students and their teachers, Fuchs, Fuchs, Eaton,
Hamlett, Binkley, and Crouch (2000) found that students for whom teachers had assigned
accommodations did not experience a greater boost in scores than students for whom teachers
had not assigned accommodations. Additionally, these researchers administered a series of basic
skills tests to determine students’ potential skill deficits. Using the results of these basic skills
measures as criteria for assigning accommodations, they found that teachers awarded 73% of the
students with accommodations as compared to 41% when using student performance data as a
criterion. As a result, the authors identified student performance data on basic skills tests as a
possible mechanism for supplementing and enhancing teacher judgments about accommodation
assignment.
Similarly, in a study conducted with 3rd graders, Ketterlin-Geller, Alonzo, BraunMonegan, and Tindal (2007) found inconsistencies between the accommodations listed on the
students’ IEP and teachers’ recommendations for accommodations in mathematics. In most
cases, teachers over assigned frequently administered accommodations such as reading text
aloud, extended time, and simplifying text. When compared to recommendations based on
student performance data including results from basic skills tests, teachers similarly
recommended more accommodations than student performance might warrant.
Combined, findings from these studies suggest that student performance data may be a
viable tool for improving accommodation assignment procedures. In this paper, we describe an
approach to making accommodation decisions using data that is readily available to teachers and
IEP teams. We describe the use and potential role student performance data for assigning
accommodations as well as perceptions of students’ need based on teacher and parent
observations. We also discuss the value of student preferences when assigning accommodations.
By systematically incorporating both objective and subjective sources of data, IEP teams can
make informed and justifiable decisions about accommodations.
Using Data to Make Accommodation Decisions
On a daily basis, many different types of data are collected about students’ academic
achievement, progress in meeting instructional goals, perceptions and preferences, and behavior.
By gathering data from new and existing sources, IEP team member can form a comprehensive
data inventory for making accommodation assignment decisions. With a systematic approach,
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diverse sources of objective and subjective data can help provide the IEP team with a sound basis
for accommodation decisions.
Objective data sources
Teachers and schools amass large amounts of objective data about students. Office
referrals, attendance rate, achievement test scores, and number of homework assignments
completed are just a few of the objective data sources that are frequently collected. These data
can often be tallied, the mean and standard deviation calculated, and results compared over time.
Because these data are collected using systematic and standardized procedures, the type and
quality of the information is typically consistent regardless of when it is collected, by whom, or
using which forms. These sources provide a stable basis for making accommodation decisions.
Two common types of objective data that can be used for making accommodation
assignment decisions are results from standardized large-scale assessments and results from
curriculum-based measurements (CBM). Large-scale assessments such as statewide
accountability tests can provide information about students’ general strengths and weaknesses in
the domain. For IEP teams, this information is likely to be more useful for determining students’
present level of performance than for identifying which accommodations may be beneficial.
However, subscores such as comprehension levels on reading tests and computation skills on
math tests can provide useful information about which areas within the larger domains of reading
and mathematics might be problematic. For example, a student who performs poorly on the math
computation subtest may have difficulty applying basic math facts. This student might benefit
from using a calculator on applications problems. Similarly, a student who performs poorly on a
literal comprehension test may have difficulty retaining story information in working memory.
This student might benefit from segmenting the text into smaller sections and responding to
comprehension questions during the reading process. Alternatively, this student may benefit
from training in using a graphic organizer to keep track of story information.
On a cautionary note: Because subscores are based on a small number of items, they may
not be reliable for making decisions about an individual’s specific needs. Data from other
objective sources such as CBMs can be used to verify the results from large-scale assessments.
CBMs are brief standardized tests that are easy to administer and score, and provide reliable
results for decision making. CBMs have been developed in the areas of writing and spelling,
reading fluency and comprehension, and math computation and applications. Data from each
measure can help identify students’ skills and possible accommodation needs. Figure 1 provides
a listing of possible accommodations that may be considered based on CBM data.
<Insert Figure 1>
Poor performance on a CBM does not necessarily indicate a need for an accommodation.
Results from these tests may also help identify areas in which the student needs additional
instruction and an accommodation alone may not improve the sutdent’s performance. Subjective
data sources can provide important perspectives on, and further inform, data from objective
sources.
Subjective data sources
Subjective data sources include perceptions, preferences, and opinions from teachers,
parents, and students. These data may provide valuable insights needed to interpret objective
student performance data for making accommodations decisions. Subjective data can bee useful
for interpreting the findings from performance tests and exploring the underlying causes of poor
performance. The perceptions, preferences, and opinions of teachers, parents, and students can
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raise ideas not found in objective data, or help link the objective data to the need for specific
accommodations.
Types of subjective data that are useful for making accommodations decisions include
perceptions of students’ skills across the curriculum, preferences for activities students enjoy
engaging in, instructional supports that might be beneficial for the student, and the ease or
difficulty of typical school-related tasks. These data may point to the potential benefit of an
accommodation for a student. Additional information about the frequency of accommodations
use can be asked to determine what accommodations are already implemented in instruction.
Such data, when collected from the various members of the IEP team, can be aggregated to
evaluate similarities and differences between the reports of the IEP team members. Along with
the objective data, this information provides a profile of the student’s strengths and weaknesses
that can be informative for making accommodation decisions.
Figure 2 illustrates a sampling of possible survey questions represented in a summary
report that indicates the level of agreement across sources and provides possible
recommendations for accommodations. Input from the special education teacher, general
education teacher, parent, and student are collected. The summary judgment provides the mode
of the scores to indicate the rating most often received. In instances where there is discrepancy of
more than one point across raters, a check mark is placed in the column labeled “Topic for
Discussion.” These disagreements can be discussed in detail at the IEP team meeting. The
column labeled “Accommodation to Consider” lists possible accommodations that may benefit a
student for whom the activities are difficult or very difficult.
<Insert Figure 2>
Systematic data collection using a survey such as the one presented here elicits responses
from multiple IEP team members on similar questions of student strengths and needs. The data
are subjective, but focused on common questions directly relating to a student’s potential need
for accommodations. Frequently updating and checking perceptions can help avoid confirmation
biases that might arise when teachers, parents, and even students have outdated perceptions of
need. Also, by completing the surveys separately, different members of the IEP team can provide
their input without inadvertent influence from other members of the team. For example, students
may defer to their parents or teachers about what instructional supports would be helpful. The
perceptions gathered on a survey of this kind can then be compared and discussed among IEP
team members to arrive at points of strong agreement on the student’s potential need for
accommodations. The subjective data can then be compared with objective data sources, in a
deliberate process, to inform the accommodation decisions of the IEP team.
Conclusions
In this article, we described the importance of using a variety of information to make
accommodations decisions. We defined objective and subjective data sources and provided
examples of the types of information that can be collected. We presented a list of possible
accommodations to consider when evaluating objective performance to determine which
accommodations may be beneficial. Additionally, we illustrated a method for collecting and
displaying subjective data that directly relates to the potential need for accommodations
(systematically reports perceptions from different stakeholders). No current system exists that
can reliably determine a definite need for an accommodation. IEP team members must currently
work with what tools and data are available, and what additional data can be collected with their
limited time and resources. Having, and not having, appropriate accommodations may be the key
to academic and social success for some students, and accommodations decisions should be
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made through a systematic process, using the most relevant data available. Until a more
comprehensive and reliable system exists for making accommodations decisions, IEP team
members can work to gather reliable, objective data, and focused perceptions of the student’s
unique strengths and needs, in order to make the best possible accommodations decisions. By
providing this information, we hope that IEP teams have a mechanism for integrating objective
and subjective data and can use this information to make appropriate accommodation assignment
decisions to support student learning.
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Figure 1. Using Objective Data to Guide Accommodation Decisions
Tested Domains
Possible Reasons for Poor
Accommodations to Consider
Performance
Writing and spelling
 Poor letter-sound
 Allow student to respond to questions
correspondence
in alternate formats such as typing,
pointing, with the use of a scribe, or
 Poor text organizational
other assistive devices
skills
 Graphic organizer
 Poor fine motor skills
 Provide extended time to allow for
proof reading and editing
 Increase space for constructed
responses or increase size of bubbles
for multiple-choice items
 Allow students to mark answers in test
book
 Allow student to use a spelling
dictionary or spell check
Reading fluency and  Limited fluency with
 Simplify the language of all materials
comprehension
decoding
 Read printed materials aloud
 Poor pre-decoding
 Administer tests in a separate location
skills such as phonemic
to allow student to read material aloud
segmenting and
 Graphic organizer
blending
 Allow student to highlight key words
 Poor text organizational
or phrases
skills

Math computation
 Limited fluency with
 Provide a calculator or arithmetic
and application
math facts
tables
 Poor spatial recognition
 Provide protractors, models, or
skills
manipulatives
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Figure 2. Using Subjective Data to Guide Accommodation Decisions
Accommodations Survey
Zachary Robyns
Grade: 3
Teacher: Ms. Perry
Student ID: 7351
Summary of Proficiency in Academic Domains
Alex’s skills were rated on a 4-point scale. A score of 1 meant “not proficient” and 4 meant “highly proficient.”
Proficiency
Teacher 1
Teacher 2
Student
Parent
Summary
Topic for
Judgment
Discussion
Reading skills
2
2
3
2
Emerging
Proficiency
Writing skills
3
2
3
1
Proficient
*
Math word problem solving skills
2
1
2
1
Not/emerging
proficiency
Summary of Potential Benefit of Instructional Supports
The potential benefit of the following instructional supports for Alex was rated on a 4-point scale. A score of 1 meant
“not helpful” and 4 meant “really helpful.”
Teacher 1
Teacher 2
Student
Parent
Summary
Topic for
Judgment
Discussion
Use a calculator to solve math
4
3
1
3
*
Helpful
problems
Use models or objects
3
3
2
3
Helpful
(manipulatives) to solve math
problems
Summary of Difficulty Completing School Related Tasks
The difficulty Alex faces when completing school-related tasks was evaluated on a 4-point scale. A score of 1 point
meant the task was “not difficult” and a score of 4 meant the task was “very difficult.”
Teacher 1 Teacher 2 Student Parent Summary
Topic for Accommodation
Judgment Discussion to consider
Complete a test within the
2
2
1
2
Somewhat
Extra time
allowed amount of time
difficult
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Read and understand
written directions
Work independently for
45-60 minutes
Understand the meaning of
words in word problems
3
4
2
4
Difficult
2
3
2
2
4
3
3
4
Somewhat
difficult
Difficult/
Very
difficult
Take a test in a room with
other people or distractions
2
1
1
2
*
Not/some
what
difficult
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Read aloud
Multiple short
testing sessions
Simplify
language in
problems and
directions
Allow student to
work alone in a
separate testing
location
Author Note
We would like to thank Magaly Gonzales, Kyle Hoelscher, and Gerald Tindal from the
University of Oregon for support on the initial discussions of these ideas.
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