Effects of an Explicit Decoding Plus Frequency Building Intervention on Word Reading Fluency for Students With Disabilities in an Urban Elementary Setting

Abstract

Students with disabilities in upper elementary grades who read well below grade level often require one-to-one intensive intervention. The following study examines the effects of a combined explicit decoding plus frequency building intervention on consonant–vowel–consonant (CVC) word reading fluency. Participants included two third-grade students and one fourth-grade student from an urban elementary school receiving special education services. Delivered during the intervention block, the students practiced 5 to 8 min per day over 8 to 9 days per word list. The multiple probe design demonstrated an experimental effect for all three participants, with significant gains revealed on individual word lists, curriculum-based assessment, and curriculum-based measurement.

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https://doi.org/10.1177/07319487221136279
Learning Disability Quarterly
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Original Research
Reports by the National Reading Panel (NRP, 2000) and
Institute of Educational Sciences (IES, Foorman et al.,
2016) present evidence-based methods on how to effec-
tively teach reading, especially in the early years of school-
ing. The reports highlight the importance of incorporating
phonemic awareness, phonics instruction, and reading flu-
ency to tackle the critical topics of decoding and reading
without hesitation. Fluency, in particular, garnered a con-
siderable amount of attention in both reports. The NRP
(2000) pointed to fluency as the “often neglected” facet of
reading instruction, while the most recent review conducted
by IES (Foorman et al., 2016) provided evidence from 18
additional studies (2000–2014).
Fluency refers to the ability to read with accuracy, speed,
and expression (Foorman et al., 2016; Kostewicz et al., 2016).
Reading fluency further serves as the bridge between decod-
ing and comprehension (NRP, 2000). Students who struggle
with decoding and reading fluently in the formative years of
schooling raise concern as a large degree of later academic
success hinges on language development, vocabulary, and
understanding complex text. Unfortunately, standardized
assessment paints a grim picture for students with disabilities
as the National Assessment for Educational Progress (National
Center for Educational Statistics, 2020) reports a majority of
fourth-grade students with disabilities scoring at Basic (18%)
and Below Basic (70%). Urban schools fared even worse,
with 79% scoring Below Basic. Longitudinal research further
reports a clear association between illiteracy, low attendance,
drop-out, disciplinary referrals, and limited postsecondary
opportunities (Newman et al., 2009)—notably in the skilled
technical workforce area of STEM (science, technology, engi-
neering, and math) occupations (Carnevale et al., 2011). To
strengthen economic competitiveness and opportunity, the
United States must close the gap that persists along socioeco-
nomic, race, gender, geography, and disability that hinder
equitable access and success in reading, mathematics, and sci-
ence education (Kena et al., 2016).
1136279LDQXXX10.1177/07319487221136279Learning Disability QuarterlyStocker et al.
research-article2022
1University of North Carolina Wilmington, USA
2The Pennsylvania State University, State College, PA, USA
3The Hill School of Wilmington, NC, USA
Corresponding Author:
James D. Stocker Jr., University of North Carolina Wilmington, 601
College Road, Wilmington, NC 28403, USA.
Email: stockerj@uncw.edu
Effects of an Explicit Decoding Plus
Frequency Building Intervention on
Word Reading Fluency for Students With
Disabilities in an Urban Elementary Setting
James D. Stocker Jr., PhD, BCBA-D1,
Richard M. Kubina Jr., PhD, BCBA-D2,
Emily R. Crumpler, MA1, Martin Kozloff, PhD1,
and Erica Swanton-Derushia, MEd3
Abstract
Students with disabilities in upper elementary grades who read well below grade level often require one-to-one intensive
intervention. The following study examines the effects of a combined explicit decoding plus frequency building intervention
on consonant–vowel–consonant (CVC) word reading fluency. Participants included two third-grade students and
one fourth-grade student from an urban elementary school receiving special education services. Delivered during the
intervention block, the students practiced 5 to 8 min per day over 8 to 9 days per word list. The multiple probe design
demonstrated an experimental effect for all three participants, with significant gains revealed on individual word lists,
curriculum-based assessment, and curriculum-based measurement.
Keywords
explicit decoding, frequency building, multi-tiered systems of support, response to intervention, reading fluency, intensive
intervention

2 Learning Disability Quarterly 00(0)
To ameliorate the underlying issues associated with
decoding and non-fluent readers, evidence suggests teach-
ing phonological awareness and phonics through explicit
and systematic methods of instruction (Archer & Hughes,
2011) delivered in a balanced and sequenced manner
(Foorman et al., 2016; NRP, 2000). Early decoding activi-
ties include skills such as letter naming followed by letter-
sound correspondence. In turn, students begin to learn
words through blending letter sounds by chunking or sound-
ing out individual letters slowly and then repeating the
sounds quickly. Blending typically starts with simple con-
sonant–vowel–consonant (CVC) words, with the teacher
demonstrating and providing feedback as the students work
toward independently demonstrating the skill. Students also
learn to read irregular words (e.g., sight words; high-fre-
quency words) that do not follow regular phonics or spell-
ing rules. Research suggests teachers should provide ample
and consistent practice opportunities to build fluency
throughout the reading continuum (Levin et al., 2006; Piasta
et al., 2010). The rapid processing of familiar letters and
sounds leads to rapid recognition of words (Ehri, 2005),
supporting fluency and understanding when reading con-
nected text (Berninger et al., 2006).
Evidence from curriculum-based measurement (CBM)
research denotes moderate to strong predictive outcomes
between measures of letter sound fluency, word reading flu-
ency (e.g., sight words, decodable words), and reading
comprehension (Clemens et al., 2011, 2014; Fuchs et al.,
2004; Zumeta et al., 2012). Cognitive researchers suggest
that reading words fluently allows students to process
meaning and ideas conveyed in the text and apply the text to
prior knowledge. Conversely, students who struggle to read
divert cognitive resources to decoding versus understanding
the text (Therrien, 2004). NRP (2000) and IES (Foorman
et al., 2016) report that when decoding text serves as the
primary learning concern, intensive intervention should
focus more on phonological awareness and decoding versus
allocating instructional time toward monitoring compre-
hension and summary writing (Gersten et al., 2008).
Intensive Intervention
Struggling readers often receive intensive reading interven-
tions within multi-tiered systems of support framework
(MTSS). Some research suggests interventions operate more
effectively when delivered in small groups of three students
(Vaughn et al., 2003; Wanzek & Vaughn, 2007). Students not
responding to intensive intervention in small groups often
require one-to-one instruction, especially when functioning
significantly below grade level (Denton, 2012; Denton et al.,
2006; Wanzek et al., 2010). One-to-one instruction for
decoding and beginning fluency allows the interventionist to
directly observe the student without distraction and deter-
mine whether the mouth, lips, and tongue meet the
appropriate position and articulate an accurate response
(Carnine et al., 2017). One-to-one instruction further allows
the student to receive tailored feedback from the interven-
tionist. Curriculum-based assessment (CBA) serves as a
direct and sensitive measurement of the material taught ver-
sus an indirect measurement such as a norm-referenced
CBM that may not recognize the progress made by the
student.
Given the reality associated with limited time, person-
nel, and funding in public schools, issues delivering one-to-
one or small group intensive intervention with fidelity often
compound in urban schools where a disproportionate num-
ber of students require the most concentrated levels of aca-
demic support (Braun et al., 2020). Despite these obstacles,
a body of research suggests that well-trained and well-sup-
ported teachers, paraprofessionals, specialists (e.g., art,
music), volunteers, and preservice teachers can work in
teams to effectively implement intervention (Elbaum et al.,
2000; Vadasy et al., 2007). For decoding and supporting the
beginning stages of fluency (i.e., reinforcing accuracy and
then building speed), efficiency and accelerated learning
may translate to delivering higher doses of the most labor-
intensive elements of intervention with fidelity in short 5 to
8 min doses (Carnine et al., 2017). As a result, intervention
teams can achieve an optimal learning rate across a large
number of students. Uncovering the frequency, length, cost,
and impact of intervention requires researchers and educa-
tors to test, evaluate, and retest evidence-based procedures
and program components across different contexts (Bramlett
et al., 2010; Harris et al., 2021; Skinner, 2010).
Explicit Decoding Plus Frequency
Building
One option for a low-cost yet high-yield approach resides in
applying an explicit decoding plus frequency building pack-
aged intervention. The explicit decoding component
involves an evidence-based “model, prompt, check” proce-
dure (Carnine et al., 2017; Coyne & Koriakin, 2017;
Foorman et al., 2016). For example, the teacher or interven-
tionist models how to sound out a CVC word by saying the
sound of each letter and then blending the sounds together
to read the word. During the prompt component, the teacher
provides guided support for decoding the same word by
pointing to each letter while the student decodes the word.
Finally, for the check, the student sounds out and reads the
word without support from the teacher. When correcting a
sound error, the teacher uses a limited model, which
involves modeling and testing only the isolated sound rather
than the entire word. Over time, the student reads words
independently without teacher mediation and engages in
frequency building to increase speed.
Frequency building refers to a method used in precision
teaching that involves the timed repetition of a target skill

Stocker et al. 3
followed with immediate feedback expressed by a dimen-
sional quantity such as frequency or rate (Kubina & Yurich,
2012; Pennypacker et al., 2003). Frequency building distin-
guishes itself from simple practice and more elaborate
approaches to automaticity and expertise such as deliberate
practice (Kubina, 2019). Namely, frequency building serves
as a specific method for intensively practicing a selected
skill but in a manner designed to promote efficient learning
and robust outcomes. Frequency (i.e., count) over a set time
provides a more precise representation of student perfor-
mance versus alternative measures such as percent correct
(Cooper et al., 2020; Kubina, 2019). Feedback delivered
immediately after each timing reinforces correct versus
incorrect responding (Hattie & Timperley, 2007).
A recent review of frequency building and precision
teaching with school-aged children (Gist & Bulla, 2022)
revealed 11 studies that align with standards set forth by
What Works Clearinghouse Standards 4.1 (What Works
Clearinghouse, 2020, [WWC]). Two studies focused on
word reading fluency with elementary school students but
did not include students with reading disabilities. Both stud-
ies reported large effect sizes and occurred in either the
United Kingdom or Ireland (Lambe et al., 2015; Mannion &
Griffin, 2018). Lambe et al. (2015) and Mannion and Griffin
(2018) tested Dolch word lists and Irish sight words, respec-
tively. A subsequent review of precision teaching research
from 1990 to 2020 (McTiernan et al., 2022) added six stud-
ies with elementary school students that involved a word
reading fluency component (Brosnan et al., 2018; Downer,
2007; Griffin & Murtagh, 2015; Hayes et al., 2018;
Kessissoglou & Farrell, 1995; Kubina et al., 2009). Five of
the six studies occurred in the United Kingdom or Ireland
and did not either report outcomes or include students with
reading disabilities. Kubina et al. (2009) conducted a study
(n = 162) in an urban area summer school program in cen-
tral Pennsylvania investigating the use of precision teaching
with Reading Mastery. Word lists included a combination
of sight words and regular words (e.g., CV, CVC, CVCC)
that yielded a moderate effect size (d = .6) but did not dis-
aggregate outcomes for students with reading disabilities.
Present Study
Given the importance of examining evidence-based meth-
ods that produce durable effects in a timely and cost-effec-
tive manner for students with reading disabilities in different
settings, a combined explicit decoding plus frequency
building intervention may hold value in an urban MTSS
context. The evidence-based components of the interven-
tion package require little time to implement, and research
indicates both instructional practices separately support
decoding and fluency. The capacity of a team of personnel
to provide a consistent, daily intervention package with
fidelity can plausibly facilitate delivering a higher level of
doses to upper elementary students when intervention has
shown to lose momentum (Wanzek et al., 2010). Therefore,
the researchers posed the following experimental questions
to test the packaged intervention:
1. What effect does explicit decoding plus frequency
building have on the word reading fluency of three
individual lists of CVC words for 8 or 9 days for
students with reading disabilities?
2. What effect does explicit decoding plus frequency
building have on the word reading fluency of a com-
bined list of CVC words, a CBA, for students with
reading disabilities?
3. What effect does explicit decoding plus frequency
building have on a word reading fluency assessment
of a CBM for students with reading disabilities?
4. How will students with reading disabilities in an
urban setting value the implementation of explicit
decoding plus frequency building?
Method
The researchers adhered to the guidelines set forth by What
Works Clearinghouse Standards, Version 4.1 (WWC, 2020).
Study rating determinants applied in this research include:
(a) data availability, (b) independent variable, (c) inter-
assesor agreement, (d) residual treatment effects, and (e)
attempts to demonstrate effect over time and data points per
phase. Non-design components include (a) face validity and
(b) reliability (WWC, 2020).
Context
The study took place in the elementary school building (K–
5) within an urban K–8 public school in the southeastern
United States. Ninety-eight percent of the school popula-
tion met the eligibility criteria for free and reduced break-
fast and lunch. School numbers indicated that 24% of
elementary students received special education services,
and 45% performed below the 20th percentile in reading
and needed intensive support.
Participants
Student participants had to meet three criteria. First, students
had an identified reading disability and scored below the
10th percentile on the oral reading fluency sub-test of the
digital schoolwide CBM. Second, the students had to per-
form within a range of researcher-administered assessments.
Specifically, scores that fell at or below the 10th percentile
and <90% accurate on the word reading fluency sub-test on
easyCBM (Pearson, 2017). Third, the students needed to
exhibit difficulty articulating introductory phonemes and
graphemes within CVC words. Two third-grade students and

4 Learning Disability Quarterly 00(0)
one fourth-grade student participated in the investigation.
All three students identified as African American and quali-
fied for free lunch.
Setting
Intervention implementation took place in the resource
room during the scheduled intervention block between 9:00
and 10:00 a.m. Special education reading instruction for the
students occurred directly after the intervention block. The
classroom contained eight small non-traditional shaped
desks. The interventionist, described later, sat straight
across from the student during the intervention.
Classroom Curriculum
For phonics instruction, the special education teacher used
explicit methods of instruction that included word and pas-
sage reading activities in small groups. The teacher refer-
enced word lists from Teach Your Child to Read in 100
Easy Lessons (Engelmann et al., 2011). Based on DISTAR
Fast Cycle, the book includes prepared formats for deliver-
ing instruction with letter sounds and blending sounds to
make words. The special education teacher used several dif-
ferent curricular resources as part of explicit instruction for
phonological awareness and sought a systematic solution
for fluency instruction.
Materials
Preintervention materials consisted of a researcher created
20-question interest interview to build rapport and a prefer-
ence assessment to target reinforcers. Intervention packets
consisted of 8.5 × 11 photocopied sheets with text oriented
in landscape format. Each packet had a script, a visual scaf-
fold for modeling and guided practice, practice sheets for fre-
quency building, and a daily score sheet. Additional materials
included a cell phone timer, pencils, and erasers. Daily rein-
forcers included a choice between small snacks and tangi-
bles. The researchers posted a classroom star chart to earn
weekly rewards that later led to a culminating activity.
Independent Variable
The explicit decoding component involved a model, prompt,
and check procedure to support accurate responding. The
frequency building component included three timed trials to
increase accuracy and speed. Between each 60-s timing, the
student received immediate feedback from the intervention-
ist that included a limited model as an error correction pro-
cedure. The student set goals by drawing a star next to the
word they wanted to reach and received instructions before
each timing to try and “beat their last score.” The daily dose
of explicit instruction plus frequency building consisted of
an individual student receiving four opportunities to prac-
tice CVC words. The intervention session lasted between 5
and 8 min per student, per day, with 9 days or doses occur-
ring for List 1, 9 days or doses for List 2, and 8 days or
doses for List 3 that reflected the school calendar and MTSS
intervention block schedule. In the present study, three stu-
dents each received one dose per day multiplied by 26 days
for a total of 78 doses (Fuchs et al., 2017).
Dependent Variables
Words correct per minute (WCPM) and words incorrect per
minute (WIPM) quantified student performance from the
(a) first 60-s timed trial each intervention day for the cor-
responding list of words, (b) six researchers created CBAs
administered from prebaseline to postintervention, and (c)
the second-grade easyCBM word reading fluency assess-
ment used at the school that served as a pre and postassess-
ment of the 6-week MTSS intervention cycle. The CBAs
contained a random mix of CVC words from Lists 1 to 3.
The second-grade easyCBM word reading fluency assess-
ment populates rows of words that increase in difficulty.
Selecting a CBM below grade level offers a more sensitive
measure of student growth as part of a data-based individu-
alization process (Fuchs & Fuchs, 2011; Lemons et al.,
2014).
Interventionists
An undergraduate group of student interventionists (n = 4)
comprised of special education majors and applied behavior
analysis minors delivered one-to-one intervention to the
elementary students. The interventionists received initial
training during lecture instruction. The instructor modeled
the process, and then interventionists practiced with peers.
The interventionists subsequently rehearsed the interven-
tion in the school with second-grade students prior to the
investigation. Academic tutoring served as part of field
experience requirements. Two faculty members supervised
the undergraduate interventionists every day throughout the
investigation. Multiple interventionists delivered interven-
tion to the three student participants to remove confounding
factors in the experimental design (WWC, 2020).
Procedure
The interventionists conducted a student interest interview
and preference assessment with the participants to build
rapport and a daily menu of reinforcers. Results from the
preference assessment indicated all three students’ preferred
reinforcers in the following rank order: attention (e.g.,
behavior-specific praise, high-fives), small snacks (e.g.,
popcorn), and then tangibles (e.g., pencils, stickers). Under
the supervision of two faculty members and the special

Stocker et al. 5
education teacher, the undergraduates assessed students to
identify appropriate intervention materials. The elementary
students grasped individual letter sounds but exhibited dif-
ficulty blending sounds and reading words on preprimer
and primer Dolch word lists. Coupled with the resulting low
scores on schoolwide CBMs for reading and the third-grade
word reading fluency assessment from easyCBM delivered
by the intervention team, the researchers and special educa-
tion teacher scaled back to building phonemic awareness
through CVC words (O’Connor & Padeliadu, 2000).
Before baseline, the elementary students completed two
assessments. The first included the second-grade word read-
ing fluency assessment from easyCBM and the first 60-s
CBA for the three combined lists of words. Starting the next
day, the first day of baseline, the interventionist delivered
three, 60-s probes representing List 1, List 2, and List 3,
respectively (see Table 1). The same procedure occurred the
following 2 days for 3 days of initial baseline (WWC, 2020).
On the first day of intervention, the interventionist
picked up the materials, organized the student desk, and
then waited for and greeted the student at the classroom
door. Both engaged in conversation as the student chose
reinforcers. The first step of the explicit decoding compo-
nent included the teacher modeling each word on the visual
scaffold for List 1. The visual scaffold had the target list
words bolded and in 24-font. For the prompt, the student
would point below the first letter and then move their fin-
ger to “say it slow” and sound out the word. For the test,
the student would then point and “say it fast.” If correct,
the student moved to the next word. If the student had dif-
ficulty decoding, the interventionist modeled the same pro-
cess by pointing and touching the top of the word by
sounding out the word slowly with the student and then
sounding out the word fast.
For frequency building, the interventionist instructed the
student to read the words from left to right, not skip a word
or row, and read as accurate and rapid as possible. After the
first timing ended, the student received feedback on the
words pronounced incorrectly. The interventionist then
delivered behavior-specific praise or a small portion of
snack while recording the score and preparing for the next
timing. The same process occurred through the next two tim-
ings, with the interventionist adding encouragement to beat
“their last score.” On subsequent days, the student selected a
goal and placed a star next to the target word. After the inter-
vention, the researchers and tutors entered scores.
Experimental Design
The researchers applied a multiple probe design across sets
to evaluate the effects of the explicit decoding plus fre-
quency building intervention on CVC word reading flu-
ency. Similar to a multiple-baseline design (i.e., time-lagged
design), a multiple probe design evaluates behavior changes
through (a) simultaneous A and B comparisons and (b) A
and B condition changes that occur at least three or more
points in time (Ledford & Gast, 2018). A multiple probe
design differs in that it decreases the frequency of data col-
lected in the preintervention condition to protect against
threats to internal validity such as fatigue, lack of motiva-
tion, and other issues linked to reactivity of assessment
(Graham et al., 2005; Ledford & Gast, 2018). Lists 1 to 3
used for the intervention aligned to the three A and B condi-
tion changes during the investigation (WWC, 2020).
Data and Visual Analysis—Within Condition
Analysis
Level. Level visually and quantitatively represents the mean
performance for both WCPM and WIPM. A level line dis-
plays the average amount of responding in conditions and
directs graph readers to inspect the degree of stability (Coo-
per et al., 2020; Kubina, 2019). The present study used the
median to calculate the level within the baseline and experi-
mental conditions.
Celeration. The researchers recorded, visually displayed,
and evaluated data on standard celeration chart (SCC;
Pennypacker et al., 2003). Features of the SCC include
changes in behavior (a) recorded in calendar time, (b) dis-
played proportionally, and (c) quantified to yield precise,
quantitative measures. The SCC provides a measure of per-
formance called celeration. Celeration (i.e., the slope dis-
played on a ratio graph) refers to a standard unit of
measurement that quantifies a change in frequency or rate
of performance over time (Kubina, 2019). For instance, a
student who accelerates from 30 WCPM to 60 WCPM in 1
week doubles in performance and produces a celeration
value of × 2.0 per week or a 100% weekly rate of change in
performance. The SCC also quantifies a decrease in fre-
quency of performance. A student who produces 10 WIPM
on a CBA and decelerates to 5 WIPM on the following
week’s assessment yields a celeration value of ÷1.5 per
Table 1. Word Lists 1 to 3.
List number Consonant–vowel–consonant words
List 1 man, mat, sat, cat, pat, pip, tip, sip, dip, pin, nip, tin, sit, pit, tan
List 2 did, dim, din, dad, mad, sad, pad, map, nap, cap, tap, pan, can, ram, kip
List 3 not, pot, cot, pop, cap, sap, tag, nag, gag, sag, pig, dig, gig, dog, kit

6 Learning Disability Quarterly 00(0)
week or 33% decay rate for WIPM. As a result, the celera-
tion lines depicting the magnitude of slope on the SCC pro-
vide an unambiguous display for visual analysis of student
performance in WCPM and WIPM.
Data Analysis—Between Conditions Analysis
Level Multiplier. The level multiplier quantifies the differ-
ence in levels of WCPM versus WCPM and WIPM versus
WIPM between baseline and intervention conditions
(Kubina, 2019). The calculation involves dividing the larger
value by the smaller value. The quotient then incorporates a
multiply or divide sign noting the multiplicative or divi-
sional rise or drop of the two compared levels. For example,
a student produces a level of 15 WCPM during baseline and
a level of 30 WCPM during an experimental condition. The
level multiplier in performance between the student’s base-
line and experimental condition performance equals a ×2.0
(100%) rise in average words read (i.e., 30 ÷ 15 = 2.0).
Apply the multiply (×) sign because the corrects doubled in
level from baseline to intervention.
Celeration Multiplier. The celeration multiplier indicates the
speed change in performance from baseline to intervention
(Pennypacker et al., 2003). The celeration multiplier quanti-
fies the magnitude of change in speed from the baseline to
the intervention. For instance, if Student A produced a ÷1.4
celeration in performance in the baseline condition and
×2.0 in the intervention condition, the calculation (1.4·2.0
= 2.8) yields a ×2.8 celeration multiplier. Stated differ-
ently, the intervention celeration occurred 2.8 times faster
than in baseline. The speed change quantifies the degree to
which an intervention grew or decayed. Higher celeration
multipliers speak to an intervention’s overall impact on the
previous condition.
Interobserver Agreement and Procedural
Integrity
Special education faculty members provided procedural
integrity (PI) and interobserver agreement (IOA) mea-
sures for 30% of baseline, 30% for List 1, 30% for List 2,
and 37.5% for List 3. Prior to performing IOA and PI, the
researchers engaged in training sessions. Training occurred
outside of school hours and then during practice sessions
with second-grade students before the intervention with
the research participants. The researchers measured words
read correctly and incorrectly and then compared scores.
The researchers used a total agreement approach for IOA
for both correct and incorrect words (Cooper et al., 2020).
To calculate total agreement per measurement, the research
team divided the larger number of observed words read
either correctly or incorrectly by the smaller number of
observed words read either correctly or incorrectly. The
IOA calculated to 91.4%. To calculate PI, the researchers
applied a checklist that aligned with the specific steps of
the intervention procedure. Average PI came to 98.3%.
Results
Figures 1 through 3 display WCPM and WIPM read on
SCC segments (SCCS). The paper SCCs traditionally used
in schools appear on 8 ½ × 11 paper and cover 140 school
days, too big of visual display for many journals. Therefore,
using SCCS allow readers to visually analyze the data in a
configuration typical to the experimental design presently
used. Figures 1 to 3 show individual student weekly perfor-
mance on Lists 1 to 3, while Figure 4 displays monthly per-
formance on the six CBAs. The solid black dots reflect
WCPM, and X’s represent WIPM. Table 2 serves as a mas-
ter reference that contains level, celeration, level multiplier,
and celeration multiplier metrics for within and between
conditions to cross-reference when evaluating the results.
The median serves as the level of WCPM and WIPM pro-
duced by the students over the course of the study. Table 2
contains the first and last CBA and CBM results for indi-
vidual students and the group.
Within Conditions Analysis
Baseline Condition. The interventionists collected concur-
rent baseline data for List 1 (3 days), List 2 (6 days), and
List 3 (9 days). As illustrated in Table 2 and Figures 1 to 3,
individual students maintained a consistent mean level of
performance for both WCPM and WIPM across Lists 1 to 3
in the baseline condition. All three students showed insig-
nificant growth in WCPM, and in two instances, Jakeb (List
1) and Carter (List 3) showed no decay in WCPM (<÷
1.00). For WIPM, anecdotal records taken by the interven-
tionists indicate Carter and Jakeb made persistent errors
with the new phonemes associated with the word list, and
Jahir demonstrated high accuracy, typically making one to
two random errors. Visual analysis of the SCCS supports
the interventionists’ remarks.
Intervention Condition. The students received 9-day “doses” of
intervention for List 1, 9 days for List 2, and 8 days for List 3.
As in the baseline condition, each student had similar median
levels across Lists 1 to 3 except for Carter, who exhibited a
significantly higher level of WCPM for List 2 (42) when com-
pared with List 1 (35) and List 3 (34). Carter and Jakeb
showed significant weekly growth in WCPM (range: ×1.30
[30% weekly growth rate] to ×2.29 [129% weekly growth
rate]) on two of three word lists, and Jahir did not meet the
minimum threshold for significant weekly growth for all three
lists (range: ×1.12 [12% weekly growth rate] and ×1.19
[19% weekly growth rate]). Yet, all three students showed a
significant decay in WIPM (range: ÷1.35 [26% weekly decay

Stocker et al. 7
Figure 1. Carter.

8 Learning Disability Quarterly 00(0)
Figure 2. Jahir.

Stocker et al. 9
Figure 3. Jakeb.

10 Learning Disability Quarterly 00(0)
Figure 4. Curriculum-Based Assessments.

Stocker et al. 11
rate] to ÷4.13 [76% weekly decay rate]) after receiving the
intervention and upon visual inspection of the SCC, exhibited
sustained accuracy in WIPM by the fifth or sixth day.
Between Conditions Analysis
Level Multiplier. All three participants showed a significant
rise in WCPM (range: ×1.36 [36% rise] to ×2.57 [157%
rise]). Of particular interest, Jahir showed a similar rise in
WCPM across all three lists of words (range: ×1.52 [52%
rise] to ×1.54 [54% rise]) but did not show a rise or drop in
WIPM (×1) as he performed with a higher degree of accu-
racy on individual word lists in both the baseline and inter-
ventions conditions. Jakeb entered the intervention
condition with the lowest levels of WCPM and highest lev-
els of WIPM. He experienced some of the most significant
changes in WCPM (range: ×1.83 [83% rise] to ×2.57
[157% rise]) and WIPM (÷2.00 [50% drop] to ÷5.00 [80%
drop]) after receiving intervention. Both Carter and Jakeb
showed a significant drop in WIPM (range: ÷2.00 [50%
drop] to ÷5.50 [82% drop]).
Celeration Multiplier. The celeration multipliers for
WCPM revealed consistent accelerating speed changes
for Carter (i.e., ×1.14, ×1.19, ×1.24) and Jahir (i.e.,
×1.02, ×1.05, ×1.14). In each condition, Carter and
Jahir had speed changes that became faster. Although the
same stepwise accelerating pattern does not appear in
WIPM, both students demonstrated remarkable decelera-
tive changes. For example, Carter’s ÷4.13 celeration
multiplier means the intervention saw WIPM disappear
4.13 times faster than baseline. Both Carter and Jahir had
celeration multipliers that quantified the visual patterns
during the intervention; WIPM quickly dissipated and
ended at zero for all three word lists for both students.
However, Jakeb had robust celeration multipliers for
WCPM with Lists 1 and 3 but did not have a speed
change for List 2 (i.e., ×1.01). The WIPM also saw two
positive speed changes with Lists 2 and 3, but List 1
accelerated modestly by ×1.1.
Maintenance
All three students received two maintenance probes. The
students received the first maintenance probe 1 week after
the last day of receiving intervention for Lists 1 and 2. The
students did not receive the first maintenance probe for List
3 due to the start of winter break. The experimenters deliv-
ered the second maintenance probe five weeks later in
January. Results from Figures 1 to 3 and Table 2 demon-
strate all three students preserved gains when compared
with levels reported from intervention.
Table 2. Within and Between Conditions Analyses and Maintenance.
Within conditions Between conditions
Maintenance probes Student Metrics Baseline Intervention Multipliers
Carter WCPM WIPM WCPM WIPM WCPM WIPM WCPM WIPM
List 1 Level 25 6 35 3 Level x1.40 ÷2.00 First 34 2
Celeration ×1.15 ×1.00 ×1.31 ÷4.13 Celeration ×1.14 ÷4.13 Second 36 1
List 2 Level 24 6 42 1 Level ×1.79 ÷5.50 First 48 1
Celeration ÷1.03 ×1.09 ×1.15 ÷1.82 Celeration ×1.19 ÷1.98 Second 39 0
List 3 Level 25 4 33.5 2 Level ×1.34 ÷2.00 First — —
Celeration ×1.05 ÷1.09 ×1.30 ÷3.84 Celeration ×1.24 ÷3.52 Second 33 2
Jahir
List 1 Level 28 1 43 1 Level ×1.52 ×1.00 First 51 0
Celeration ×1.17 ×3.67 ×1.19 ÷1.35 Celeration ×1.02 ÷4.94 Second 41 0
List 2 Level 29 1 44 1 Level ×1.52 ×1.00 First 52 1
Celeration ×1.07 ×1.14 ×1.12 ÷1.41 Celeration ×1.05 ÷1.61 Second 43 0
List 3 Level 28 1 43 1 Level ×1.54 ×1.00 First — —
Celeration ×1.05 ×1.01 ×1.19 ÷3.36 Celeration ×1.14 ÷3.40 Second 47 1
Jakeb
List 1 Level 7 4 18 2 Level ×2.57 ÷2.00 First 26 2
Celeration ÷7.17 ÷2.19 ×2.29 ÷2.0 Celeration ×16.4 ×1.10 Second 23 0
List 2 Level 10 5 22 1 Level ×2.20 ÷5.00 First 27 1
Celeration ×1.18 ×1.02 ×1.18 ÷1.64 Celeration ×1.01 ÷1.67 Second 25 2
List 3 Level 12 5 22 1 Level ×1.79 ÷5.00 First — —
Celeration ×1.18 ×1.06 ×1.51 ÷2.39 Celeration ×1.51 ÷2.39 Second 26 1
Note. WCPM = words correct per minute; WIPM = words incorrect per minute.

12 Learning Disability Quarterly 00(0)
Curriculum-Based Assessment
Figure 4 and Table 3 show the students’ six CBAs indicating
a significant monthly change for the combined Lists 1 to 3.
For each student, an SCCS with data on the left and the cel-
eration and average correct words per minute (ACWR) and
average incorrect words per minute (AIWR) appear on the
right. Carter exhibited a ×1.55 celeration (55% monthly
growth rate) in WCPM and ÷5.08 deceleration (80%
monthly decay rate) in WIPM. His raw scores showed an
increase from 19 WCPM to 33 WCPM and a decrease from
7 WIPM (73% accuracy) to 0 WIPM (100% accuracy) on
the last CBA. Jahir had a ×1.50 celeration (50% monthly
growth) in WCPM and ÷2.89 deceleration (66% monthly
decay) in WIPM. Raw scores showed an increase from 22
WCPM to 46 WCPM for a 24 WCPM increase in respond-
ing from pretest to posttest and accuracy from 84.6% to
100%. Jakeb generated ×2.72 celeration (172% monthly
growth) for WCPM, ÷3.3 celeration (70% monthly decay)
for WIPM. His raw scores increased from 8 WCPM to 27
WCPM for a total of 19 WCPM and decreased from 5 WIPM
to 1 WIPM with a gain in accuracy from 61.5% to 96.4%.
Curriculum-Based Measurement
Table 3 includes the scores and percentiles from the second-
grade easyCBM word reading fluency pre- and postassess-
ments. Over the 6 weeks of intervention, the three students
gained a mean of 7.3 percentile points (range: 6–9 points).
Correct responses increased by a mean of 7 WCPM (range:
5–8 WCPM).
Discussion
Many students with disabilities struggle with basic reading
in the upper elementary grades. Although meta-analyses
and literature reviews reveal larger effects for reading inter-
ventions delivered in K–1 than in Grades 2 to 5 (Wanzek &
Vaughn, 2007), some research suggests that students in
Grades 3 to 5 respond positively to one-to-one interventions
(Wanzek et al., 2010). Yet, delivering one-to-one interven-
tions prove a challenge when large numbers of students
require intensive decoding instruction in schools with lim-
ited resources—a pervasive concern in urban contexts.
Using an evidence-based framework, the present study
employed an explicit and systematic process supported by
decades of intervention (e.g., Jimerson et al., 2007)
and reading research (e.g., Foorman et al., 2016; NRP,
2000). The novelty of the present investigation involved
combining explicit decoding and frequency building as a
method to accelerate learning through short 5-to-8-min
doses of intervention that could have future capacity to
reach larger numbers of students in urban elementary
schools.
The first question examined whether the intervention
delivered 5- to 8-min per day over 8 to 9 days per list of
words (2 weeks of scheduled MTSS) could effectively
increase CVC word reading fluency. Evidence from the
three independent applications of a multiple probe design
indicates an experimental effect occurred only after the
intervention appeared for each word list (Kazdin, 2011).
To ensure internal validity, the study implemented three
concurrent baseline start points before applying the inter-
vention (WWC, 2020). The intervention prompted an
immediate change in WCPM and WIPM in the proximal
and adjacent conditions suggesting a strong and uniform
experimental effect (Ledford & Gast, 2018; WWC,
2020). Overall visual analysis demonstrates a replicable
increase in rate of responding, upward trends in WCPM,
and flat trends or steep declines in WIPM for each inde-
pendent implementation of the multiple probe design.
The use of SCCS further augments the meaningfulness
of the experimental effect. The between-condition statistics
indicate robust changes expressed with quantitative convic-
tion. The level multipliers for WCPM for all students show
average overall words rising by 34% to 157%. Even more
compelling, the drop in level for Carter and Jakeb show
WICM dropping sharply with values ranging from 50% to
82% less words read incorrectly for the condition average.
Jahir’s levels for WICM did not show as dramatic drops due
to his variability in responding. The data not only augment
visual analysis but also offer exact magnitudes of change.
The level multiplier, for example, has more sensitivity than
Table 3. Curriculum-Based Assessment and Curriculum-Based Measurement.
Pretest (CBA 1) Posttest (CBA 6) CBM 1 CBM 2
Student WCPM WIPM
Accuracy
(%) WCPM WIPM
Accuracy
(%) WCPM Pctl WCPM Pctl
Carter 19 7 73.0 33 0 100 10 7th 16 14th
Jahir 22 4 84.6 46 0 100 8 5th 16 14th
Jakeb 8 5 61.5 27 1 96.4 5 2nd 12 9th
M16.3 5.3 73.0 35.3 .33 98.8 7.7 4.7 14.7 12.3
Note. CBA = curriculum-based assessment; CBM = curriculum-based measurement; WCPM = words correct per minute; WIPM = words incorrect
per minute; Pctl = percentile.

Stocker et al. 13
just adding or subtracting levels due to the relative changes
provided by multiplication and division processes (Kubina,
2019).
Several researchers have called for the addition of statis-
tics to complement visual analysis (e.g., Shadish, 2014).
The present study offers individualized behavior change
statistics. The celeration multiplier demonstrates how two
students, Carter and Jahir (see Table 2), had accelerating
speed changes for WCPM. The precision evident by quanti-
fied changes allow independent reviewers a different per-
spective from visual analysis alone. All students had
considerable WICM speed changes during the application
of the intervention confirming the impact of combining
explicit instruction with frequency building. The values in
both the within and between condition change meet the
need for evidence-base communities such as LD research-
ers to speak the statistical language within single-case
design (Shadish, 2014).
The second research question addressed the extent to
which the intervention affected word reading fluency on
CBAs (i.e., combined word lists). The CBAs served as a set
of researcher-created assessments designed as a more sensi-
tive measure of overall student progress with CVC words.
Visual analysis of the monthly SCCS indicates steep declines
in WIPM, illustrating the impact of the intervention on accu-
racy over 2 months. Two students reached 100% accuracy,
while the third student was 96% accurate on the final assess-
ment. For WCPM, Carter and Jahir made significant prog-
ress displaying a steady upward trend; Jakeb made robust
progress displaying a steep upward trend. Visual analysis of
the SCCS suggests all three students made the most progress
over CBAs 1 to 4 and displayed a more stable trend through
CBAs 5 to 8. High accuracy and increased speed to a stable
level may suggest some preliminary evidence that the upper
elementary students received enough remediation over 2
weeks or 8 to 9 days of intervention per word list to start the
next list of words.
The third research question focused on results from the
word reading fluency assessment from easyCBM. Using a
data-based individualization approach, the application of
the second-grade CBM provided the research team with
more sensitive data indicative of student progress (Fuchs &
Fuchs, 2011). The results from the CBM suggested modest
but promising growth as subsequent word lists for interven-
tion contained more difficult words (e.g., CVCC, CVCV,
CCVC). The special education teacher provided anecdotal
evidence indicating the students exhibited more confidence
and risk-taking characteristics related to what she referred
to as “word-attack” when sounding out words in a text in
small group reading activities.
For social validity purposes, the fourth research question
involved undergraduate interventionists interviewing the
students for feedback on the intervention process. Sample
questions included “What did you like most about the inter-
vention?” and “What didn’t you like about the interven-
tion?” The students enjoyed the two-fold challenge of goal
setting and the timings. Students enjoyed working toward
new goals and receiving feedback to improve. The diffi-
culty of words and length of word lists did not stymie moti-
vation; however, students did want to move to the next
word list by the end of the 8 to 9 days of intervention that
represented 2 MTSS calendar weeks. The careful leveling
of materials supported the notion that a student could (a)
start and finish the intervention activity, (b) work at a brisk
pace, (c) exhibit momentum through challenge, and (d)
demonstrate immediate and meaningful growth within short
periods of time.
Because the students had exposure to CVC words in the
past, the researchers used larger lists of 15 words versus
small sets of words as recommended for beginning readers
in a 5 to 8 min instructional session (Carnine et al., 2017).
Therefore, Lists 1 to 3 likely maintained an instructional
balance of appropriate challenge for remediation and ensur-
ing immediate success versus the student experiencing
repeated frustration that often leads to escaping or avoiding
an academic task (Daly et al., 2007).
Motivation plays a critical role in learning (Cooper et al.,
2020). Before initial assessment and leveling materials, the
interventionists worked on building rapport with the stu-
dents. First, student interest interviews in prebaseline activ-
ities provided a foundation for ongoing conversation.
Second, using a preference assessment pinpointed the types
of reinforcers (e.g., small tangible or snack, free time, atten-
tion) to motivate students to meet expectations. The inter-
ventionist delivered behavior-specific praise, high fives,
and encouragement throughout the process which assisted
in maintaining a lively, brisk pace of instruction and instruc-
tional control. In situ goal setting (e.g., “beat your last
score”) served as motivation to increase word reading speed
in the next timed trial (Morgan et al., 2012).
The explicit decoding component of the intervention
provided students a daily opportunity to participate in the
model, prompt, check procedure to sound out and blend
individual words with accuracy before repeated timed prac-
tice (i.e., frequency building). Data indicate that after the
first 2 to 3 days per word list, the students relied less on the
model and prompt components and started to vocalize
words independently. As a result of improved accuracy and
routine, the pace of instruction increased while the length of
the daily session decreased. Frequency building elevated
opportunities to respond and provided the student with three
separate occasions to receive feedback for incorrect
responses. The limited model procedure had the student
sound out and blend the incorrect word to accuracy. Hence,
immediate feedback reinforced correct responding versus
incorrect responding (Hattie & Timperley, 2007).

14 Learning Disability Quarterly 00(0)
Limitations and Implications for Future Research
A multiple probe design across sets has limitations, including
practice effects that can cause treatment diffusion (Ledford &
Gast, 2018). To meet the guidelines of concurrent baseline
measures set forth by WWC (2020), the elementary students
received 3 days of baseline for List 1, 6 days of baseline for
List 2, and 9 days of baseline for List 3 before receiving the
intervention. Although the students did not make significant
gains in baseline performance, the application of the inter-
vention did lead to small incremental improvements on sub-
sequent word lists in the baseline condition. Furthermore, the
additional data points perhaps caused fatigue and otherwise
underestimated the effect of the intervention. Future research-
ers may consider collecting fewer baseline data points or
using a non-concurrent multiple baseline design.
Other limitations include the small sample size and nar-
row timeframe employed for the study. Future research
should scale up to larger sample sizes and continue to inves-
tigate dosage effects within different levels of tiered sup-
port. The present study occurred during one intervention
cycle of MTSS that involved 2 weeks of prebaseline and
baseline followed by 6 weeks of intervention. In a typical
8-week cycle, students may have completed four lists of
words. And although the outcome shows potential in an
urban school context, the results do not reflect the likely
impact of the intervention on student achievement in the
mid to long term. One year to multi-year studies that target
achievement, generalization, and social validity can provide
valuable evidence as to the feasibility of explicit decoding
plus frequency building across different contexts. Future
research should also focus on manipulating dosage, the
number of words per list, and the inclusion of sight words.
Practical Implications
Urban schools often have a high proportion of novice teach-
ers with limited knowledge on how to deliver intensive
decoding and fluency-based interventions. The problem
persists when only a small group of personnel in a school
bear the responsibility of delivering an unsustainable level
of intensive intervention, and teacher turnover remains a
consistent issue. One solution for effective use of explicit
decoding plus frequency building rests in an “all-hands-on-
deck” approach where teams of trained teachers, parapro-
fessionals, and volunteers can reach a large number of
students during scheduled intervention blocks of time. After
considering the need to level and organize materials, facili-
tate transitions, collect and record data, and write anecdotal
notes, the authors propose that a trained interventionist can
deliver five to six doses of intensive intervention per 60
min. Thus, a team of 10 interventionists could plausibly
reach 50 to 60 elementary students per day and 250 to 300
doses delivered over 4 or 5 days of intervention.
Although the explicit decoding plus frequency building
intervention shows promise in providing schools a high
yield, cost-effective solution and may seem basic in
approach, the steps involved require significant investment
in training, follow-up, ongoing support, and maintenance of
well-trained interventionists (Denton, 2012). The authors
suggest teams adhere to standard protocols and choose a
small but manageable set of evidence-based interventions.
Lead interventionist(s) can apply a train the trainer model to
support fidelity of implementation, data collection systems,
and learning outcomes in significantly shorter periods of
time.
Conclusion
It is important to note that preservice teachers delivered the
intervention with efficiency and fidelity to address a press-
ing academic issue related to equity and reading instruction
in urban public schools. Preservice teachers had the oppor-
tunity to build relationships, apply evidence-based strate-
gies, and realize that significant improvement stemmed
from their hard work. The experience provided a model for
the preservice teachers to refer to as practitioners and a
blueprint for the school to implement an all-hands-on-deck
approach within an MTSS framework. In conclusion, future
replication that involves the manipulation of different vari-
ables (e.g., word lists, timings, teams of interventionists)
may further accelerate student outcomes and provide addi-
tional evidence that the intervention and process used in an
urban setting can work across different contexts.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research,
authorship, and/or publication of this article.
ORCID iD
James D. Stocker Jr. https://orcid.org/0000-0002-4330-2354
References
Archer, A., & Hughes, C. (2011). Explicit instruction: Efficient
and effective teaching. The Guilford Press.
Berninger, V. W., Abbott, R. D., Vermeulen, K., & Fulton, C. M.
(2006). Paths to reading comprehension in at-risk second-
grade readers. Journal of Learning Disabilities, 39, 334–351.
https://doi.org/10.1177/00222194060390040701
Bramlett, R., Cates, G. L., Savina, E., & Lauinger, B. (2010).
Assessing effectiveness and efficiency of academic interven-
tions in school psychology journals: 1995-2005. Psychology in
the Schools, 47, 114–125. https://doi.org/10.1002/pits.20457

Stocker et al. 15
Braun, G., Kumm, S., Brown, C., Walte, S., Hughes, M. T., &
Maggin, D. M. (2020). Living in Tier 2: Educators’ percep-
tions of MTSS in urban schools. International Journal of
Inclusive Education, 24, 1114–11129. https://doi.org/10.108
0/13603116.2018.1511758
Brosnan, J., Moeyaert, M., Brooks Newsome, K., Healy, O.,
Heyvaert, M., Onghena, P., & Van den Noortgate, W. (2018).
Multilevel analysis of multiple-baseline data evaluating preci-
sion teaching as an intervention for improving fluency in foun-
dational reading skills for at risk readers. Exceptionality, 26,
137–161. https://doi.org/10.1080/09362835.2016.1238378
Carnevale, A. P., Smith, N., & Melton, M. (2011). STEM: Science
technology engineering mathematics. Georgetown University
Center on Education and the Workforce.
Carnine, D., Silbert, J., Kame’enui, E., Slocum, T., & Travers,
P. (2017). Direct instruction reading (6th ed.). Pearson
Education.
Clemens, N. H., Shapiro, E. S., & Thoemmes, F. J. (2011).
Improving the efficacy of first grade reading screening: An
investigation of Word Identification Fluency with other early
literacy indicators. School Psychology Quarterly, 26, 231–
244. https://doi.org/10.1037/a0025173
Clemens, N. H., Shapiro, E. S., Wu, J., Taylor, A. B., & Caskie,
G. L. (2014). Monitoring early first-grade reading progress: A
comparison of two measures. Journal of Learning Disabilities,
47, 254–270. https://doi.org/10.1177/002219412454455
Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied
behavior analysis (3rd ed.). Pearson Education.
Coyne, M. D., & Koriakin, T. A. (2017). What do beginning spe-
cial educators need to know about intensive reading interven-
tions? Teaching Exceptional Children, 49, 239–248. https://
doi.org/10.1177/0040059916688648
Daly, E. J. III, Martens, B. K., Barnett, D., Witt, J. C., & Olson, S.
C. (2007). Varying intervention delivery in response to inter-
vention: Confronting and resolving challenges with measure-
ment, instruction, and intensity. School Psychology Review, 36,
562–581. https://doi.org/10.1080/02796015.2007.12087918
Denton, C. A. (2012). Response to intervention for reading dif-
ficulties in the primary grades: Some answers and lingering
questions. Journal of Learning Disabilities, 45, 232–243.
https://doi.org/10.1177/0022219412442155
Denton, C. A., Fletcher, J. M., Anthony, J. L., & Francis, D. J.
(2006). An evaluation of intensive intervention for students
with persistent reading difficulties. Journal of Learning
Disabilities, 39, 447–466. https://doi.org/10.1177/00222194
060390050601
Downer, A. C. (2007). The national literacy strategy sight recogni-
tion programme implemented by teaching assistants: A preci-
sion teaching approach. Educational Psychology in Practice,
23, 129–143. https://doi.org/10.1080/02667360701320820
Ehri, L. C. (2005). Development of sight word reading: Phases and
findings. In M. J. Snowling & C. Hulme (Eds.), The science of
reading: A handbook (pp. 135–154). Malden, MA: Blackwell
Publishing. https://doi.org/10.1002/9780470757642.ch8
Elbaum, B., Vaughn, S., Hughes, M. T., & Moody, S. W. (2000).
How effective are one-to-one tutoring programs in read-
ing for elementary students at risk for reading failure?
Journal of Educational Psychology, 92, 605–619. https://doi.
org/10.1037/0022-0663.92.4.605
Engelmann, S., Haddox, P., & Bruner, E. (2011). Teach your child
to read in 100 easy lessons. Touchstone.
Foorman, B., Beyler, N., Borradaile, K., Coyne, M., Denton, C.
A., Dimino, J., Furgeson, J., Hayes, L., Henke, J., Justice, L.,
Keating, B., Lewis, W., Sattar, S., Streke, A., Wagner, R.,
& Wissel, S. (2016). Foundational skills to support reading
for understanding in kindergarten through 3rd grade (NCEE
2016-4008). Washington, DC: National Center for Education
Evaluation and Regional Assistance (NCEE), Institute of
Education Sciences, U.S. Department of Education. http://
whatworks.ed.gov
Fuchs, L. S., & Fuchs, D. (2011). Using CBM for progress
monitoring in reading. National Center on Student Progress
Monitoring.
Fuchs, L. S., Fuchs, D., & Compton, D. L. (2004). Monitoring
early reading development in first grade: Word identification
fluency versus nonsense word fluency. Exceptional Children,
71, 7–21. https://doi.org/10.1177/001440290407100101
Fuchs, L. S., Fuchs, D., & Malone, A. S. (2017). The taxonomy of inter-
vention intensity. Teaching Exceptional Children, 50, 35–43.
https://doi-org.liblink.uncw.edu/10.1177/0040059917703962
Gersten, R., Compton, D., Connor, C. M., Dimino, J., Santoro, L.,
Linan-Thompson, S., & Tilly, W. D. (2008). Assisting students
struggling with reading: Response to Intervention and multi-
tier intervention for reading in the primary grades. A prac-
tice guide (NCEE 2009-4045). U.S. Department of Education.
http://ies.ed.gov/ncee/wwc/Publications/practiceguides/
Graham, S., Harris, K. R., & Zito, J. (2005). Promoting internal
and external validity: A synergism of laboratory experiments
and classroom-based research. In G. Phye, D. H. Robinson,
& J. Levin (Eds.), Experimental methods for educational
intervention (pp. 235–265). Elsevier. https://doi.org/10.1016/
B978-012554257-9/50012-7
Griffin, C. P., & Murtagh, L. (2015). Increasing the sight vocabu-
lary and reading fluency of children requiring reading sup-
port: The use of a Precision Teaching approach. Educational
Psychology in Practice, 31(2), 186–209. https://doi.org/10.10
80/02667363.2015.1022818
Gist, C., & Bulla, A. J. (2022) A systematic review of frequency
building and precision teaching with school-aged children.
Journal of Behavioral Education, 31, 43–68. https://doi.
org/10.1007/s10864-020-09404-3
Harris, M. T., Noell, G. H., McIver, E. B., & Miller, S. J. (2021).
The effect of instructional set size on learning efficiency.
Educational Studies, 47, 123–136. https://doi.org/10.1080/0
3055698.2019.1668257
Hattie, J., & Timperley, H. (2007). The power of feedback.
Review of Educational Research, 77, 81–112. https://doi.
org/10.3102/003465430298487
Hayes, B., Heather, A., Jones, D., & Clarke, C. (2018). Overcoming
barriers to using precision teaching with a web-based programme.
Educational Psychology in Practice, 34, 166–174. https://doi-
org.liblink.uncw.edu/10.1080/02667363.2018.1433129.
Jimerson, S. R., Burns, M. K., & VanDerHeyden, A. M. (Eds.).
(2007). Handbook of response to intervention: The science
and practice of assessment and intervention. Springer. https://
doi.org/10.1007/978-0-387-49053-3
Kazdin, A. E. (2011). Single-case research designs: Methods for
clinical and applied settings (2nd ed.). Oxford University Press.

16 Learning Disability Quarterly 00(0)
Kena, G., Hussar W., McFarland J., de Brey C., Musu-Gillette,
L., Wang, X., Zhang, J., Rathbun, A., WilkinsonFlicker, S.,
Diliberti M., Barmer, A., Bullock Mann, F., & Dunlop Velez,
E. (2016). The Condition of Education 2016 (NCES 2016-144).
U.S. Department of Education, National Center for Education
Statistics. Washington, DC.
Kessissoglou, S., & Farrell, P. (1995). Whatever happened to preci-
sion teaching? British Journal of Special Education, 22, 60– 63.
https://doi-org.liblink.uncw.edu/10.1111/j.1467-8578.1995.
tb01324.x
Kostewicz, D. E., Kubina, R. M., Selfridge, K. A., & Gallagher,
D. L. (2016). A review of fixed fluency criteria in repeated
reading studies. Reading Improvement, 53, 23–41.
Kubina, R. M. (2019). The precision teaching implementation
manual. Greatness Achieved.
Kubina, R. M., Commons, M., & Heckard, B. (2009). Using preci-
sion teaching with direct instruction in a summer school pro-
gram. Journal of Direct Instruction, 9, 1–12.
Kubina, R. M., & Yurich, K. K. L. (2012). The precision teaching
book. Greatness Achieved.
Lambe, D., Murphy, C., & Kelly, M. E. (2015). The impact of
a precision teaching intervention on the reading fluency of
typically developing children. Behavioral Interventions, 30,
364–377. https://doi-org.liblink.uncw.edu/10.1002/bin.1418
Ledford, J. R., & Gast, D. L. (2018). Single case research meth-
odology: Applications in special education and behavioral
sciences. Routledge.
Lemons, C. J., Kearns, D. M., & Davidson, K. A. (2014). Data-
based individualization in reading: Intensifying interven-
tions for students with significant reading disabilities.
Teaching Exceptional Children, 46, 20–29. https://doi.
org/10.1177/0040059914522978
Levin, I., Shatil-Carmon, S., & Asif-Rave, O. (2006). Learning
of letter names and sounds and their contribution to word
recognition. Journal of Experimental Child Psychology, 93,
139–165. https://doi.org/10.1016/j.jecp.2005.08.002
McTiernan, A. M., McCoy, A., Mendonca, J., Lydon, H., &
Diffley, S. (2022). The implementation of Precision Teaching
for the improvement of academic skills: A systematic review
of the literature over thirty years. Behavioral Interventions, 37,
505–528. https://doi-org.liblink.uncw.edu/10.1002/bin.1852
Mannion, L., & Griffin, C. (2018). Precision teaching through
Irish: Effects on isolated sight word reading fluency and
contextualised reading fluency. Irish Educational Studies,
37, 391–410. https://doi.org/10.1080/03323315.2017.1421
090
Morgan, P. L., Sideridis, G., & Hua, Y. (2012). Initial and over-
time effects of fluency interventions for students with or at
risk for disabilities. The Journal of Special Education, 46,
94–116. https://doi.org/10.1177/0022466910398016
National Center for Education Statistics. (2020). National
Assessment of Educational Progress: An overview of NAEP.
National Center for Education Statistics, Institute of Education
Sciences, U.S. Department of Education.
National Reading Panel. (2000). Teaching children to read: An
evidence-based assessment of the scientific research litera-
ture on reading and its implications for reading instruction
(No. 00-4769). National Institute of Child Health and Human
Development.
Newman, L., Wagner, M., Cameto, R., & Knokey, A. M. (2009).
The post-high school outcomes of youth with disabilities
up to 4 years after high school: A report from the National
Longitudinal Transition Study-2 (NLTS2) (NCSER 2009-
3017). National Center for Special Education Research.
O’Connor, R. E., & Padeliadu, S. (2000). Blending ver-
sus whole word approaches in first grade remedial read-
ing: Short-term and delayed effects on reading and spelling
words. Reading and Writing, 13, 159–182. https://doi.
org/10.1023/A:1008134818771
Pearson, N. C. S. (2017). AIMSwebplus technical manual.
Pennypacker, H. S., Gutierrez, A., & Lindsley, O. (2003).
Handbook of the standard celeration chart. Cambridge
Center for Behavioral Studies.
Piasta, S. B., Purpura, D. J., & Wagner, R. K. (2010). Fostering
alphabet knowledge development: A comparison of two
instructional approaches. Reading and Writing, 23, 607–626.
https://doi.org/10.1007/s11145-009-9174-x
Shadish, W. R. (2014). Analysis and meta-analysis of single-case
designs: An introduction. Journal of School Psychology, 52,
109–122. https://doi.org/10.1016/j.jsp.2013.11.009
Skinner, C. H. (2010). Applied comparative effectiveness
researchers must measure learning rates: A commentary on
efficiency articles. Psychology in the Schools, 47, 166–172.
https://doi.org/10.1002/pits.20461
Therrien, W. J. (2004). Fluency and comprehension gains as a
result of repeated reading: A meta-analysis. Remedial and
Special Education, 25, 252–261. https://doi.org/10.1177/074
19325040250040801
Vadasy, P. F., Sanders, E. A., & Tudor, S. (2007). Effectiveness
of paraeducator-supplemented individual instruction: Beyond
basic decoding skills. Journal of Learning Disabilities, 40,
508–525. https://doi.org/10.1177/00222194070400060301
Vaughn, S., Linan-Thompson, S., Kouzekanani, K., Bryant, D.
P., Dickson, S., & Blozis, S. A. (2003). Reading instruction
grouping for students with reading difficulties. Remedial and
Special Education, 24, 301–315. https://doi.org/10.1177/074
19325030240050501
Wanzek, J., & Vaughn, S. (2007). Research-based implica-
tions from extensive early reading interventions. School
Psychology Review, 36, 541–561. https://doi.org/10.1080/02
796015.2007.12087917
Wanzek, J., Wexler, J., Vaughn, S., & Ciullo, S. (2010). Reading
interventions for struggling readers in the upper elementary
grades: A synthesis of 20 years of research. Reading and Writing,
23, 889–912. https://doi.org/10.1007/s11145-009-9179-5
What Works Clearinghouse. (2020). What Works Clearinghouse
standards handbook, version 4.1. U.S. Department of
Education, Institute of Education Sciences, National Center
for Education Evaluation and Regional Assistance. https://ies.
ed.gov/ncee/wwc/handbooks
Zumeta, R. O., Compton, D. L., & Fuchs, L. S. (2012). Using
word identification fluency to monitor first-grade reading
development. Exceptional Children, 78, 201–220. https://doi.
org/10.1177/001440291207800204