Systematic Instruction of Phonics Skills Using an iPad for Students With Developmental Disabilities Who Are AAC Users

Abstract

A phonics-based reading curriculum in which students used an iPad to respond was created for students with developmental disabilities not able to verbally participate in traditional phonics instruction due to their use of augmentative and assistive communication. Time delay and a system of least prompts used in conjunction with text-to-speech software enabled students to participate in phonics instruction that included segmenting, decoding, sight words, and comprehension after reading a decodable short passage. Students were randomly assigned to a treatment group who received the phonics instruction or a control group who received sight word instruction on the iPad. A repeated-measures ANOVA found that students who received the iPad-based phonics curriculum outperformed the control students. Hierarchical linear model (HLM) analysis supports a two-level model with a time by group membership interaction effect, the inclusion of student-level variables was not statistically significant.

Keywords

phonics time delay systematic instruction autism intellectual disability

Learning to read is a cornerstone skill in a literate society. Lack of reading skills and exposure to print, including the knowledge shared in print, can lead to long-term consequences including less-developed skills in other academic areas (Lonigan, 2006). The National Reading Panel (NRP; National Institute of Child Health and Human Development, 2000) recommendations for teaching reading identified five essential components, including (a) phonemic awareness, (b) phonics, (c) fluency, (d) vocabulary, and (e) text comprehension. The NRP research review, however, did not include research conducted with students with disabilities. This left a void in understanding what and how to teach reading skills to students with developmental disabilities, especially individuals with intellectual disability and autism spectrum disorder.

In contrast to the NRP recommendations, reading research with students with both intellectual disability and autism spectrum disorder has overly emphasized sight words with few demonstrations of phonics instruction. In a comprehensive review, Joseph and Seery (2004) found only seven studies targeting code-based strategies for students with intellectual disability. Browder, Wakeman, Spooner, Ahlgrim-Delzell, and Algozzine (2006) identified 128 studies for students with moderate/severe intellectual disability or autism spectrum disorder, but only 17 included phonics instruction. Instead, most studies targeted sight word learning using systematic prompting strategies like time delay. Spector (2011) found similar strategies used to teach sight words to students with autism spectrum disorder. In a review focused specifically on reading for students with autism spectrum disorder, Whalon, Al Otaiba, and Delano (2009) found 11 studies with only six that targeted phonics. Given that students with developmental disabilities often struggle with memory capacity, students who are taught to read using a sight word memorization approach will be limited in the amount of text they can read and comprehend (Connor, Alberto, Compton, & O’Connor, 2014). In addition, students with moderate-to-severe disabilities have not traditionally had access to literacy-rich environments, due at least in part to the assumption that they could not learn or benefit from literacy instruction (Kliewer, 2008).

Studies published since these reviews have provided additional promise that students with developmental disabilities can learn code-focused skills as well as sight words. Browder and colleagues (Browder, Ahlgrim-Delzell, Courtade, Gibbs, & Flowers, 2008; Browder, Ahlgrim-Delzell, Flowers, & Baker, 2012) developed and evaluated a comprehensive reading curriculum using systematic instruction and response prompting for elementary students with moderate-to-severe intellectual disability. They found that students engaged in the curriculum were able to make significant gains in phonological awareness as compared with students in the comparison group. Other researchers (Allor, Mathes, Roberts, Cheatham, et al., 2010; Allor, Mathes, Roberts, Jones, et al., 2010; Chai, Vail, & Ayres, 2015; Flores, Shippen, Alberto, & Crowe, 2004; Lemons, Mrachko, Kostewicz, & Paterra, 2012) have found significant increases in both phonological awareness and phonics skills for elementary students with mild-to-moderate intellectual disability who received systematic instruction in a comprehensive phonics-based program. Similarly, several researchers have shown promise for phonics instruction for students with autism spectrum disorder (Bailey, Angell, & Stoner, 2011; Grindle, Hughes, Saville, Huxley, & Hastings, 2013; Leytham, Pierce, Baker, Miller, & Tandy, 2015; Travers et al., 2011). A common feature of the research for students with intellectual disability and that for students with autism spectrum disorder is the use of explicit instructional strategies like systematic prompting (e.g., Browder et al., 2012) or direct instruction (e.g., Flores et al., 2004). A difference is that more studies for students with autism spectrum disorder have used computer-assisted instruction (Whalon et al., 2009).

There may be differences in how reading is acquired with subgroups of students with developmental disabilities (e.g., Lemons et al., 2012). One subgroup especially underrepresented in reading research is students who rely on alternative augmentative communication (AAC) and do not have the speech that most reading programs assume in teaching phonics. Almost 40% or 138,000 of the 347,000 children with developmental disabilities below the age of 15 have severe difficulty with speech (U.S. Census Bureau, 2010). The need exists to identify interventions that make it possible for students with developmental disabilities who rely on AAC to participate in phonics instruction.

Connor et al. (2014) reviewed the results of reading studies funded by the Institute of Education Sciences (IES) and noted the need for more research focused on students with intellectual disability and limited speech. In addition to concluding that students with low-incidence disabilities (e.g., moderate intellectual disability, autism spectrum disorder, and deaf/hard of hearing) benefit from explicit, systematic instruction in phonemic awareness and phonics instruction, Conner et al. recommended that future research include developing comprehensive curricula utilizing systematic instruction for teaching phonics that accommodates students with communication needs who require AAC.

Several researchers have explored ways to teach phonics-related skills to students with communication support needs. Swinehart-Jones and Heller (2009) used a three-step decoding strategy to teach students with cerebral palsy and dysarthric speech to decode a word; however, demonstration of the skill was indirectly measured through student identification of each word’s corresponding picture. Similarly, Copeland and Keefe (2007) provided students with an object or symbol to indicate recognition of letter sounds. Coleman-Martin, Heller, Cihak, and Irvine (2005) used PowerPoint software to present words and phonemes to students with severe speech impairments (one had autism spectrum disorder). In their approach, the teacher asked the student to sound out words “in their head.” In contrast, Bailey et al. (2011) had students with autism spectrum disorder who used AAC demonstrate decoding by pointing to pictures for first sounds or for segmented words produced by the interventionist. Chai et al. (2015) used an iPad and constant time delay to teach phonemic awareness by asking students to select pictures that began with the same sound as the target phoneme.

Researchers have used a variety of response options when implementing code-based strategies. Using the Early Literacy Skills Builder (ELSB) curriculum (Browder, Gibbs, Ahlgrim-Delzell, Courtade, & Lee, 2007), Browder et al. (2012) provided students with several receptive language response options to indicate their understanding of letters and words such as indicating initial sounds from an array of letters, finding the picture for a segmented word, and indicating pictures with first sounds of words. Coyne, Pisha, Dalton, Zeph, and Smith (2012) used e-books and letter-word recognition games as a part of a comprehensive reading curriculum for students with intellectual disability including several word attack skills. Although each of these interventions demonstrated some use of letter-sound correspondence, none of them provided a way for students to directly manipulate phonemes (e.g., producing letter sounds, blending or segmenting phonemes), a crucial component of phonics and phonemic awareness instruction (National Institute of Child Health and Human Development, 2000).

Ahlgrim-Delzell, Browder, and Wood (2014) investigated the use of an AAC device that had the technology to allow students with limited speech to segment and blend speech sounds. The researchers paired the device with systematic and explicit instruction to evaluate the acquisition of phonics skills for students with intellectual disability who used AAC. In a single-case multiple-probe design across participants, instruction was provided on three phonics skills (letter-sound correspondence, decoding, and blending) and participants were asked to use the AAC device to demonstrate these skills. All three participants improved their phonics skills using the device. In contrast, the AAC device was restrictive due to its fixed surface size and use of overlays that had to be changed manually as the students’ phonics skills evolved. Further investigation is needed for the use of computer-based Assistive Technology (AT) that can incorporate more flexible, digital text and formatting. Portable electronic devices, such as tablet computers, are especially promising for increasing motivation and accessibility to instructional content for students with disabilities (Kagohara et al., 2013; Mechling, 2011).

Students with developmental disabilities can learn to read phonetically. Previous research has not examined outcomes for students with developmental disabilities who also use AAC. The purpose of the current study was to build on the recommendations of Connor et al. (2014) and the research of Ahlgrim-Delzell et al. (2014) to develop and evaluate phonics instruction that accommodates students who use AAC. Specifically, our research questions were as follows:

Research Question 1: What are the effects of a phonics curriculum with systematic instruction and an iPad™ on the identification of phonemes and decoding skills by students with developmental disabilities who use AAC?

Research Question 2: What student or teacher characteristics mediate the changes in growth between students?

Method

Sample

Teachers were recruited by Exceptional Children Department administrators of two school districts in the southeastern region of the United States. Teachers who agreed to participate were asked to identify students who met the eligibility criteria and obtain signed parental consent forms. Student eligibility criteria included (a) use of any AAC system needed to supplement verbal responding during instruction such as a picture system or a technology device, (b) completion or current use of a foundational literacy program or demonstration of competence in identifying at least five letters or sight words, (c) physical capacity to use the iPad2™ by touching response options, (d) inability to decode text, and (e) diagnosed with either intellectual disability or developmental delay by their school system. Researchers observed each student to reconfirm eligibility criteria. An informal assessment of letter identification (e.g., “Which of these letters is the letter s?” with four options) and reading of common, simple consonant-vowel-consonant (CVC) words such as mat (e.g., “Read this word and point to the picture of the word” with four picture options) was used to confirm eligibility for criteria d in addition to teacher report of previous student literacy instruction. Teachers also confirmed eligibility criteria e by referencing school records.

Students

A prospective power analysis was conducted prior to the study to estimate the number of participants needed based on an estimated effect size (Cohen’s d = 2.65) from a previous single-case study (Ahlgrim-Delzell et al., 2014), power of .8, with eight repeated measurements using Optimal Design Plus software (Spybrook et al., 2013). The prospective power analysis estimated the need for 40 participants. Thirty-one students in Grades K through 8 met the eligibility criteria and participated in the study. Although it was possible that the study would be underpowered, it was decided to continue with the research because the systematic process would still be useful in developing the curriculum and future research.

Table 1 displays the demographic characteristics of the students by treatment/control group. In addition to a diagnosis of either intellectual disability or developmental delay, 13 students had a diagnosis of autism spectrum disorder. An IQ could not be obtained for three students because they were unable to participate in ability testing. The eligibility criteria included a diagnosis of intellectual disability or developmental delay and not IQ to be more inclusive of the kind of students who were ready to learn to read, but did not have the ability to participate in traditional reading instruction because of the use of AAC to communicate, this resulted in a wide range of IQs (40–88).

Table 1.

Characteristics of Student Participants at Pre-Test.

Characteristic	Treatment		Control		P
Characteristic	N	%	N	%	P
Gender					.25
Male	14	82.4	9	64.3
Female	3	17.6	5	35.7
Ethnicity					.44
African American	6	35.3	5	35.7
Caucasian	8	47.1	4	28.6
Asian	1	5.9	1	7.1
Latino	2	11.8	4	28.6
Grade					.76
K	2	11.8	1	7.1
1	3	17.6	3	21.4
2	2	11.8	1	7.1
3	1	5.9	4	28.6
4	1	5.9	0	.0
5	2	11.8	1	7.1
6	2	11.8	0	.0
7	2	11.8	2	14.3
8	2	11.8	2	14.3
Disability
Autism	8	47.1	5	35.7	.52
Developmental delay	5	29.4	5	35.7	.72
Intellectual disability	12	70.6	9	64.3	.71
Highest level of communication					.42
Verbalize with words	0	0	1	7.1
Manual signs/AAC	8	47.1	9	64.3
Picture symbols	6	35.3	3	21.4
Gestures	3	17.6	1	7.1
ELSB experience	14	87.5	11	84.6	.82
IQ					.43
IQ 40−55	10	58.8	9	64.3
IQ 56−70	3	17.6	4	28.6
IQ 71−85	1	5.9	0	0
IQ 86−100	0	0	1	7.1
Unavailable	3	17.6	0	.0
M (SD)	50.1 (9.5)		53.4 (12.5)
Range of scores	40−71		40−88
Weekly hours in general education					.40
M (SD)	2.4 (6.1)		4.7 (8.8)
Range of Scores	4−24		4−28

Note. AAC = alternative augmentative communication; ELSB = Early Literacy Skills Builder.

Teachers

Twenty-two teachers from 16 schools participated in the study. Table 2 displays teacher characteristics by group (treatment, control or both as described below). All of the teachers indicated they had some type of literacy or reading training prior to the study provided by either the school system or completion of a university reading course.

Table 2.

Characteristics of Teacher Participants at Pre-Test.

Characteristic	Treatment		Control		Both		p
Characteristic	M	SD	M	SD	M	SD	p
No. of years teaching	13.3	10.9	14.6	9.4	12.0	7.7	.86
No. of years teaching at current school	7.17	9.17	8.4	11.3	6.0	4.9	.85
No. of years teaching special education students	13.3	10.9	11.7	10.5	12.0	7.7	.95
No. of years teaching students with Intellectual Disability or Autism Spectrum Disorder	12.7	11.1	10.6	11.0	11.1	7.69	.92
Characteristic	F	%	F	%	F	%	p
Type of certification
Regular, standard	4	66.7	7	100	7	77.8	.28
Probationary, temporary	2	33.3	0	—	2	22.2
Highest level of education
Bachelor’s	4	66.7	4	57.1	5	55.6	.91
Master’s	2	33.3	3	42.9	4	44.4

Setting

The study was implemented from October 2012 to June 2013. Eight of the 16 schools were located in a large, urban district, and eight schools were located in a rural district. Teacher-delivered instruction primarily occurred daily at a table or desk in the self-contained classrooms in which the students were assigned. Individual lessons ranged from 15 to 20 min.

Teachers implemented the interventions after receiving 1 day of training for both the treatment and control conditions. Training included a theoretical presentation on the NRP (National Institute of Child Health and Human Development, 2000) components of learning to read and principles of systematic instruction including time delay and system of least prompts. Demonstrations of the interventions tailored for both treatment and control groups and practice with the teaching procedures with fidelity were provided. Each teacher was observed by a researcher and received a fidelity score of 80% or higher before training ended. Following training, teachers received ongoing feedback and support for implementation of the intervention and management of behaviors incompatible with learning through weekly visits, email communications, and video examples posted online.

Research Design and Random Assignment

This study utilized a randomized control trial (RCT) design where students were randomly assigned to treatment and control conditions using simple random assignment blocked by teacher when multiple students in a classroom were determined eligible to participate. There were nine teachers with both treatment and control students, six teachers with only treatment students, and seven teachers with only control students. Teachers were directed to provide only the instructional condition assigned to individual students. The study began with a pre-test followed by implementation of the intervention for 8 months. Eight monthly probes on the dependent variable were collected for each student. The final monthly probe served as the post-test. Independent samples t tests and chi-square analyses were conducted at pre-test to identify differences between the randomly assigned treatment and control groups. There were no statistically significant differences on phoneme identification (t = .903, df = 29, p = .37), blending sounds to identify words (t = .828, df = 29, p = .41), decoding for picture word-matching (t = 1.51, df = 29, p = .14), or any of the demographic characteristics as noted in Table 1. There were no statistical differences between the groups of teachers as noted in Table 2.

Treatment Fidelity

Treatment fidelity was assessed each week by a graduate research assistant who observed and video recorded teachers’ implementation of treatment and control group procedures during instruction and completed a procedural fidelity checklist. Fidelity for teachers in both conditions was calculated by dividing the total number of required steps by the total number of steps performed correctly, multiplied by 100. Inter-rater reliability occurred for 30% of sessions by a second member of the research team who independently scored the fidelity checklist using video recording. Agreement was calculated by dividing the number of possible agreements by the number of agreements reached, multiplied by 100. Mean fidelity for the treatment intervention was 94.8% with means for individual teachers ranging from 88.4% to 98.0%. Inter-rater reliability was 97.4%. Mean fidelity for the control intervention was 96.8% with means for individual teachers ranging from 75.0% to 100%. Inter-rater reliability was 100%. Observations of treatment fidelity also offered the opportunity to discern possible treatment diffusion. No instances of treatment diffusion were noted.

Intervention for the Treatment Group

The Early Reading Skills Builder (ERSB) was the name given to the intervention created for the treatment group. The ERSB curriculum blended iPad-based technological speech supports using GoTalk Now (GTN; The Attainment Company, n.d.) and systematic instruction using time delay and shaping/fading of model prompts. The skills to be taught and phoneme sequence were derived from existing curricula (i.e., Reading Mastery, Corrective Reading, Early Interventions in Reading). The beginning levels of the ERSB were designed to overlap the phonemes and sight words taught in the ELSB (Browder et al., 2007) and to serve as the next step reading curriculum for students who had received this or similar early literacy instruction. Three literacy experts (one general education literacy expert and two experts with expertise in developmental delay/intellectual disability) reviewed the curriculum materials, sequence of skills, and phonemes.

The ERSB was divided into eight levels with five lessons per level taught in a 1:1 teacher–student ratio. The skills taught and the descriptions are provided in Table 3. The curriculum presented three new phonemes and a review of three previous phonemes at each level. Target words used in the skill instruction included not only words that were also in the connected text presented as a story but also novel words that were not included in the stories. The stories were comprised of words using phonemes and sight words introduced to that point in the skills training (e.g., Level 6, Lesson 3: I have a pet. The pet is Jed. Jed can be bad.). The comprehension questions were literal that could only be answered through correct reading of the text. To move to the next level in the curriculum, students needed 80% correct answers for two consecutive sessions across all skills.

Table 3.

Description of ERSB Skills and Instructional Procedures.

Skill	Description	Instructional procedures
Identify phonemes in isolation	Given a letter sound, select the letter that represents the sound	Time delay with model prompt and physical guidance if incorrect or no response.
Identify phonemes in words	Given a voiced word, select the letter that represents the first sound in the word.	Time delay with model prompt and physical guidance if incorrect or no response.
Segment sounds in words	Given a voiced word, select the letters that represent the sounds in the word.	Time delay with model prompt and physical guidance if incorrect or no response.
Blend sounds to identify words	Given a stretched word (e.g., ssaamm), select the word.	Time delay with model prompt and physical guidance if incorrect or no response.
Decode words to identify a picture	Given a decodable word, read the word and select the picture that represents the word.	Time delay with model prompt and physical guidance if incorrect or no response.
Identify sight words	Given a voiced non-decodable^a word, select the word.	Time delay with model prompt and physical guidance if incorrect or no response.
Read connected text	Read a sentence using sight words and decodable^a words.	Least intrusive prompting with fading of voiced reading of the passage.
Answer literal comprehension questions about the text	Given a literal question from the sentence(s) read, select the correct answer.	Least intrusive prompting redirecting student to read the sentence that contains the correct answer and physical guidance.

Note. ERSB = early reading skills builder.

Decodable words were those that could be decoded using the taught phonemes.

To indicate their responses, students pressed buttons on the iPad. Phonemes, words, and pictures were programmed into pages on the GTN iPad app. Auditory cueing was activated within the app; this feature allowed students to press buttons on the iPad to voice phonemes or words for review before final selection of their answer. Two unique technological features of the GTN app was that it also included a blending feature, in which students could select a series of individual phonemes to hear the phonemes voiced as a blended word using text-to-speech software and a quiz feature that would randomize the response options on the page each time the page was accessed. This iPad-based curriculum was designed so that all responses could be made with the device, but verbal approximations of the words were also encouraged, such as “Good using your voice!”

Overview of Intervention for the Control Group

The control group received a structured business-as-usual intervention. To control for possible confounds of iPad use, systematic instruction, and opportunities to respond, teachers also used the iPad with the GTN app for students in the control group to teach the literacy and/or reading skills that they were already implementing with the same number of response opportunities as the ERSB. Teachers were given a list of the target number of responses for each lesson. In this way, these students were considered a control group that did not receive phonics instruction as opposed to a comparison group that received a different, but equivalent, type of phonics instruction. As noted in the review of literature, there is no alternative phonics curriculum for students who require communication support and sight words have been the most frequently taught alternative. Teachers were trained to use the GTN app and the same systematic instructional procedures (i.e., time delay and system of least prompts) as the treatment intervention and develop materials using the app to teach sight words or to read-aloud stories. For instance, teachers programmed sight words into the app and taught the words using constant time delay procedures. They also used the iPads to supplement read-aloud stories using images from the Internet, the GTN image library, or a photo gallery, and had students make responses such as identifying vocabulary words or answering questions on the iPad.

Dependent Variables

The dependent variables were created by the researchers from the ERSB in a 106-item curriculum-based measure (CBM) that included three ERSB skills: (a) phoneme identification (25 items, actual range of scores was 0–25, α = .90), (b) blending sounds to identify words (41 items, actual range of scores was 0–41, α = .87), and (c) decoding for picture-word-matching (40 items, actual range of scores was 2–39, α = .83). Subtest-total score correlations ranged from .831 (phoneme identification) to .931 (blending sounds to identify words). A description of each measure is provided in Table 4. Decoding also provided a measure of comprehension where the student had to find a picture that illustrated the meaning of a decodable word.

Table 4.

Description of the CBM Dependent Variable.

Skill	Description	Screen shot of response options
Phoneme identification	Select the letter to match a voiced phoneme. “What letter says /s/?”
Blending sounds to identify words	Select a written word to match a voiced word that was stretched. “/r/ /aaag/. Which word am I saying?”
Decoding for picture-word-matching	Read a printed word and select a picture to match the word. “Read the word and find the picture.”

Note. CBM = curriculum-based measure.

The items tested in the CBM were similar in format to the ERSB curriculum using the GTN app on the iPad, but included a mix of trained and untrained items. In this way, the dependent measure was a proximal measure of the intervention. A proximal CBM measure was needed to evaluate the development of the ERSB curriculum, which was a primary purpose of the study. The link between the CBM and the curriculum provides support for content validity of the CBM. An extensive search for an accessible distal measure of blending and decoding skills for this population of students was not successful.

Analytical Techniques and Statistical Analyses

To answer the first research question regarding the effect of the ERSB, a repeated-measures ANOVA was used to examine the interaction effects of the ERSB instruction and group membership. Cohen’s d, using a pooled standard deviation, was calculated to measure the magnitude of the difference between the treatment and control groups at post-test for each of the individual subtests and the total score.

To answer the second research question to investigate possible mediating variables, a three-level Hierarchical Linear Model (HLM) was used. HLM analysis accounts for the effects of nested, or hierarchical, data. In this study, nesting occurred within the individual students, as repeated measures of student data were collected 8 times across the school year and each student data were nested within respective teachers. HLM provides an opportunity to examine the influence of each level of the hierarchy on the outcome measure. Three-level models were examined with measurement occasions at Level 1 (across eight monthly measurements), student group membership (treatment vs. control) at Level 2, and student/teacher characteristics at Level 3 with total score as the outcome measure.

Results

Descriptive Statistics and ANOVA Results

Prior to running the analyses, data were screened and assumptions were evaluated for use of the parametric statistics. The assumptions of normal distribution and equal group variances were tenable. For the treatment group, there were two cases of missing data across the eight probes. For the control group, there was one case of missing data across the eight probes. Descriptive statistics for the pre-test and post-test for each subtest and total score by group are displayed in Table 5. Cohen’s d at post-test was .51 for blending sounds to identify words (moderate effect), .88 (large effect) for decoding for picture-word matching, 1.12 (large effect) for phoneme identification, and .89 (large effect) for the total score. Data were analyzed using a repeated-measures ANOVA comparing pre-test/post-test scores and treatment/control groups. There were statistically significant interaction effects for three of the four comparisons including phoneme identification, F(1, 7) = 3.23, p < .01, $η_{p}^{2}$ = .12; decoding for picture-word matching, F(1, 7) = 3.81, p < .01, $η_{p}^{2}$ = .12; and total score, F(1, 7) = 7.09, p < .01, $η_{p}^{2}$ = .21; whereby students in the treatment group outperformed the students in the control condition. Blending sounds to identify words was not statistically significant, F(1, 7) = .88, p = .53, $η_{p}^{2}$ = .03. Both groups improved in the ability to blend sounds to identify words. These effects are illustrated by the graphs in Figure 1. The slope of the treatment group is steeper for these three measures, suggesting greater growth.

Table 5.

Descriptive Statistics for Pre- and Post-Tests for Treatment and Control Groups.

Dependent variables	Treatment (n = 17)				Control (n = 14)
	Pre-test		Post-test		Pre-test		Post-test
	M	SD	M	SD	M	SD	M	SD
Phoneme identification	11.53	7.25	18.24	5.72	9.36	5.87	11.29	6.81
Blend sounds to identify words	13.94	7.79	19.00	8.28	11.79	6.44	14.79	8.06
Decoding for picture-word matching	8.82	3.73	17.76	7.42	11.71	6.77	11.64	6.33
Total score	34.29	14.50	54.12	18.91	32.86	17.48	37.00	19.60

Figure 1.

Graphs of the interaction effects for the ERSB instruction.

HLM Results

Three HLMs were tested including a null model (Model 1), a time + group interaction model (Model 2), and a time + group interaction and other student background model (Model 3). The effect sizes (proportion of variance explained by the models) and the deviance values of these models were used as the comparison between these models. Table 6 provides the parameter estimates of the fixed and random effects of the three models. As group membership was assigned, a fixed effects model was used. Time was centered at zero at the pre-test prior to implementation of the ERSB. A sequential increase in numbers of time represented each of the monthly assessments during which the ERSB was implemented. An intraclass correlation coefficient of the unconditional model suggests that 81.30% of the variance in scores lies in the growth in time, 18.60% of the variance in scores can be explained by student-level characteristics, and the remaining 0.10% of the variance is attributable to teacher characteristics. Although the variance component at the teacher level is very small, a teacher level was used to account for the nesting of treatment and control group students within teachers. Teacher experience (measured by number of years) in teaching children with special needs was added at the teacher level. Compared with the Unconditional Model (Null Model), Model 2 reduced the Level 1 monthly growth variance by 45.20%, student-level variance by 9.59% and the teacher-level variance by 85.31%, whereas Model 3 reduced the Level 1 monthly growth variance by 49.46%, student-level variance by 18.20%, and the teacher-level variance by 32.80%.

Table 6.

Parameters Estimates of the Fixed and Random Effects of HLMs.

Fixed effects	Model 1		Model 2		Model 3
Fixed effects	Estimate	SE	Estimate	SE	Estimate	SE
Intercept	41.90*	2.37	34.33*	3.87	37.49*	6.08
Time			0.51	0.35	0.04	0.67
Group			1.99	6.54	2.65	7.42
Autism					3.06	6.77
Developmental delay					−0.83	6.70
Intellectual disability					−4.79	5.99
Communicate with AAC					−4.84	3.64
Communicate with picture					−1.62	4.24
Teacher experience			0.25	0.22	0.34	0.18
Time × Group Membership			2.36*	0.49	2.28*	0.49
Time × Teacher Experience			<0.01	0.02	<0.01	0.02
Time × Primary Autism					1.85*	0.86
Time × Developmental Delay					0.16	0.88
Time × Intellectual Disability					0.51	0.75
Time × Communicate With AAC					0.47	0.51
Time × Communicate With Picture Symbols					−0.98	0.56
Random effects
Level 1	271.48		245.44		222.06
Level 2	62.10		34.03		31.39
Level 3	0.36		0.05		0.24
Deviance	1,824.61		1,691.81		1,671.34

Note. HLM = hierarchical linear model; AAC = alternative augmentative communication.

Using the reduction in the deviance statistics, the group + time interaction model (Model 2) appears to be the best fit. Although Model 3 has more student-level variables and the student’s diagnosis of autism is a statistically significant and positive predictor of the monthly rate increase, the change of the deviance scores from Model 2 to Model 3 is not statistically significant, which means that Model 2 is a good-fit and parsimonious model. This mixed model is presented as follows:

\begin{matrix} T o t a l S c o r e = β_{000} + β_{001} \times Y R S T E A C H_{j} + β_{010} \times G R O U P_{i j} + β_{100} \times T I M E_{m i j} + β_{101} \times T I M E_{m i j} \times Y R S T E A C H_{j} \\ + β_{110} \times T I M E_{m i j} \times G R O U P_{i j} + e_{0 i j} + r_{00 j} + ε_{m i j} . \end{matrix}

The intercept of the model (34.33) was the initial status (pre-test) of control group students. The monthly increase rate of control group students was not statistically significantly different from zero. However, the treatment group students’ monthly increase was 2.36 more than that of control group students, and this difference was statistically significant.

Discussion

This study attempted to address Connor et al.’s (2014) recommendation for a comprehensive literacy program that provides for more frequent and intensive instruction in teaching phonics skills to students with low-incidence disabilities who use AAC. Specifically, this research extended the literature on phonics instruction for students with intellectual disability/developmental delay who use AAC by demonstrating that systematic and explicit instruction and iPad technology that provided accessible receptive and expressive response modes that promoted students ability to manipulate phonemes to learn to decode words to read connected text and answer comprehension questions. This work extends the work of Ahlgrim-Delzell et al. (2014).

Although IQ was not part of the eligibility criteria, 10 of the 12 students who received the ERSB curriculum had moderate-to-severe intellectual disability. Several studies have now demonstrated that students with moderate intellectual disability can acquire phonics skills through explicit instructional strategies (Allor, Mathes, Roberts, Jones, et al., 2010; Browder et al., 2012; Flores et al., 2004). Kagohara et al.’s (2013) comprehensive review of the use of an iPad to teach students with developmental disability identified 15 studies, but none of these focused on phonics instruction. The current study adds to the evidence for the effectiveness of the iPad for teaching phonics skills through the use of a new app that made it possible to manipulate and voice phonemes.

Research Findings

The first variable for which there was a significant difference between the treatment and comparison groups was phoneme identification. When the teacher stated a phoneme, the student pressed the corresponding printed letter on the iPad. The iPad then provided immediate feedback by repeating the phoneme as the letter was pressed. This pivotal skill of letter-sound correspondence has been one of the stumbling blocks for students who use AAC. In typical phonics instruction, the student practices voicing the phonemes as well as matching them to printed letters. This technology made voicing phonemes possible. Students receiving the typical sight word comparison instruction, in which the iPad voiced the whole word, did not show the same improvement. Systematic instruction of phoneme identification was essential.

The second variable for which there was a significant difference was decoding words and then finding the corresponding picture. This skill was critical because it demonstrated both decoding an entire CVC word and comprehension of that word. Comprehension has often been overlooked in research for students with developmental disabilities (Browder et al., 2006; Chiang & Lin, 2007). For phonics instruction to be meaningful, comprehension needs to be demonstrated from the onset.

Blending sounds to identify a word did not produce a significant difference. In this skill, the teacher voiced three phonemes and the student identified the printed word. This required the student to remember this series of phonemes and then find the correct word. The response options included highly similar distractors (e.g., cat and cot). Some modification in this presentation may have been needed. Students may have needed to select each letter as it was sounded and then use the technology to voice the full word.

Limitations and Recommendations for Future Research

Although the ANOVA for two of the three measures from the CBM had a significant interaction effect indicating that the treatment group outperformed the control group, the lack of significant difference for the third measure, blending sounds to identify words, is a limitation of this study. Although it may imply the need for an alternative way to teach students to match a series of phonemes to words, an alternative explanation is that the finding may have been due to insufficient power as there were only 31 students total when 40 students were estimated to be needed. This alternative explanation is supported by the finding of a moderate effect size despite lack of statistical significance. The power analysis was conducted using the total scores by combining scores on the individual three subtests. For small sample sizes, future research may need to consider conducting power analyses on individual subtests rather than one total score.

There are potential threats to internal and external validity in this study. Potential threats to internal validity include history and instrumentation. Differential group instructional interruptions such as illnesses or school/classroom scheduling issues (e.g., Special Olympics) could have affected the ability of teachers to conduct the daily instruction as designed. Even though the weekly observations/consultations assisted in remediating any potential long-term issues, it is possible that the accumulated short-term history events across the academic year may have differentially affected one group over the other. A second possible internal threat involves the sole use of a researcher-developed dependent variable. Although there is evidence of content validity and internal consistency, more rigorous evidence of structural, concurrent or predictive validity were not conducted. Potential threats to external validity include participants and situation given that this study was conducted in only two school systems in self-contained classrooms. Students in general education classrooms with full access to the reading curriculum might respond differently to this intervention. For example, they might have more opportunities to generalize their responses. Generalization of these findings to other participants or situations also may be limited, particularly due to the small sample size.

Other areas for future research include more in-depth understanding of prompting strategies that may be more effective in teaching phonics to students with intellectual disability or developmental delay. Some of the teachers began to hold up a finger while voicing each sound as a visual signal for each phoneme. With this signal, the students perhaps realized when the sound was shifting from sound to sound. Future research could examine variations in repetition of prompts. Some phonemes or phoneme combinations may require more repetition than others for discrimination learning. The CBM used distractor items with similar sounds/letters that students sometimes selected by mistake (e.g., tan, ton, pan). One consideration for future research might be to measure the number of correct phoneme locations (e.g., “ton” selected for “tan” would be a score of 2/3) and analyze errors (e.g., selecting “o” for “a”) to show subtle progress toward skill mastery and areas for remediation. A process of systematically varying phonemes in distractor words, as described by Heller, Fredrick, Tumlin, and Brineman (2002), can be used to identify specific problematic phonemes or errors based on the location of the phonemes (e.g., middle or ending sounds).

Future research also needs to build comprehension into early phonics instruction for students with developmental disabilities. The measure of decoding in this study was created so students read the word and selected a picture representation of the word to demonstrate the ability to sound out words and comprehend the meaning of the words. This is consistent with measures used in prior research for early stages of reading (Browder et al., 2006), including studies on phonics with students who are nonverbal to infer decoding (Browder et al., 2012; Copeland & Keefe, 2007; Swinehart-Jones & Heller, 2009). Even though advances in technology now make it possible for students to demonstrate decoding through electronically voicing the response, the picture matching response is an essential measure of comprehension. As students’ progress in their decoding skills, connected text (e.g., short phrases and sentences) should be provided and linked to meaning (e.g., matching to pictures, supplying missing word).

Finally, research is needed on how technology can be used to promote text comprehension. A comprehensive reading program for students with intellectual disability or developmental delay might include a combination of decoding and comprehending decodable text paired with higher levels of comprehension built in through listening to age-appropriate literature read-alouds (Browder et al., 2009).

Implications for Practice

A critical implication is that this population of students can achieve more than only sight word recognition. When given access to a technology-based phonics curriculum, including decodable text, students with intellectual disability, developmental delay, or autism spectrum disorder who use AAC may learn to read and understand connected text. Connor et al. (2014) noted that students with intellectual disability need beginning reading instruction for longer periods of time. Research by Allor, Mathes, Roberts, Cheatham, et al. (2010) suggested that students with moderate intellectual disability might need 3 years of instruction for 1 year of typical growth in reading. Our results were consistent with this finding in that students using AAC gained acquisition of multiple phonemes, but still would need at least 2 more years to master all phonemes. Many of the students in the current study had spent 2 to 3 years mastering skills in an early literacy curriculum. The need for building early literacy skills combined with the time phonics instruction requires means that students with intellectual disability, developmental delay or autism spectrum disorder may need the opportunity for intensive beginning reading instruction well into the middle school years.

Students with intellectual disability, developmental delay, or autism spectrum disorder who use AAC may benefit from daily phonics instruction that is delivered using explicit and systematic instruction. An iPad, which can allow students to produce and manipulate individual phonemes to blend and segment, might enable students to learn phonics skills, including identifying sounds in words and reading words to find pictures. This program, which included repetitions of phonics skills, sight word reading, and comprehension, may allow students to gain the skills necessary to both read and understand connected text.

Given how long phonics instruction may take, it is essential that it be paired with comprehension in both research and practice. This comprehension can focus on the specific decoded words (e.g., word to picture matching) and early understanding of simple connected text. As the need for decodable text will limit the literature accessible through independent reading, students will also need to acquire alternative ways to access age-appropriate literature, for example, by using technology and read-alouds. These alternative modes should also be paired with comprehension measures to promote broad access to literature.

Conclusion

Perhaps the most important implication of this research is that students who use AAC be given the opportunity for phonics instruction through the use of technology. Much more research is needed on how to teach students who use AAC to read, especially as new technology emerges. For example, technology may make it possible for students to receive immediate feedback as they decode words through software that blocks errors. Although progress may be slower for students with developmental disabilities in learning to decode text, being able to do so increases the chance of becoming literate. Literacy is a cornerstone for all academic learning in school and enhances overall quality of life as an adult. With independence in literacy, individuals with developmental disabilities have more opportunities to communicate through social media, access Internet resources, perform jobs, and enjoy leisure resources. The investment of time to teach individuals with intellectual and developmental disabilities to read may be some of the most important hours spent in school.

Footnotes

Authors’ Note

The opinions expressed do not necessarily reflect the position or policy of the Department of Education, and no official endorsement should be inferred.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Support for this research was provided in part by Contract ED-IES-11-C-0027 of the U.S. Department of Education, Institute of Education Sciences, awarded to The Attainment Company.

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