When hearing children begin to learn to read, most are competent language users. The task of reading can be mapped onto existing phonological, syntactic, semantic and discourse skills. The deaf child brings to the reading task very different sets of language experiences. The frequently reported low literacy levels among students with severe-profound hearing impairment are in part due to the discrepancy between their incomplete spoken language system and the demands of reading a speech based system (Perfetti & Sandak, 2000). Bottom-up models of reading development emphasize sublexical processing, where the reader uses phonological decoding strategies in which printed text is translated into previously acquired acoustic units. This strategy relies on the ability to use letter-sound generalizations to decode words (Gough, 1972). Studies have demonstrated that the use of phonemic knowledge or phonological processing in reading is typical of successful hearing readers (Meyer, Schvaneveldt, & Ruddy, 1974). These bottom-up processes have previously been very difficult for deaf children (Paul, 1998). However, with cochlear implant technology these skills are now potentially accessible to the deaf child.
On the other hand, it has been proposed that a top-down model of reading comprehension is possible for deaf readers where they bypass the auditory-based syntactic skills and phonological decoding strategies and process print on the basis of meaning through semantic cues that depend on vocabulary knowledge and the ability to bring sufficient world knowledge to the task (Ewoldt, 1993). According to this model, comprehension of printed words may be achieved by simply memorizing the visual representation and associating it with a word in the existing vocabulary base. Presumably, signing deaf children (particularly those with Deaf parents) have a larger vocabulary base than children taught orally, but the representation of these words is visual rather than auditory. Some studies have reported superior reading scores in groups of children with deaf as compared with hearing parents and children exposed to manual English compared with those exposed to oral English (see Kampfe & Turecheck, 1987, for a review).
On the other hand, the most efficient and ultimately most successful readers are able to map printed symbols onto already known elements of spoken language in a process known as phonological decoding. Students who are deaf and who use a predominantly phonological (speech) based code during reading tasks are better readers than those who do not use a phonological code (Conrad, 1979;Hanson, 1989;Leybaert, 1993). For deaf readers, development of phonological awareness appears to be facilitated by speech perception and production skills. Traditionally, access to the phonological components of language has been limited in deaf children. Lack of access to the phonemes of a spoken language results in decreased opportunity for deaf individuals to master the alphabetic system in a way normal-hearing persons do. Profoundly deaf children begin reading with little phonological awareness, which makes the early stage of learning to read difficult (Harris & Beech, 1998). Waters and Doehring (1990), Hanson (1986), and Hanson and Fowler (1987), found that access to the phonological form of words was related to deaf individuals’ speech intelligibility. The use of speech based coding strategies appears to be associated with better performance in reading tasks (Schaper & Reitsma, 1993). Deaf students can use visual (lipreading) and sign coding to access the phonological code. However, neither alternative is an effective substitute for a phonological speech based code processed in verbal short-term memory (Hanson, Goodell, & Perfetti, 1991).
Short-term (working) memory serves the function of temporarily maintaining and manipulating information during the performance of complex cognitive tasks. Speech-based information is handled by a phonological loop, which includes a short-term store, capable of holding memory traces for 1 to 2 seconds, together with an articulatory control process that converts visually presented material into a phonological code by a process of naming. Use of the phonological loop is reflected in an individual’s digit span, the number of spoken digits he or she is able to repeat back in the correct order (forward span) or in the reverse of the order presented (backward span). The finding that children with a specific reading disability typically have reduced digit span has led some theorists to postulate an association between memory span and reading (Baddeley & Gathercole, 1992).
Sensitivity to phonological structure of words has been assessed through lexical decision tasks and through tasks that examine the perception of rhyme. Both kinds of tasks evaluate the child’s strategy for approaching unfamiliar words. The lexical decision task requires the child to discriminate between real words and non-words. If readers make more errors in classifying homophonic non-words that sound like real words (e.g., frend) than they do in classifying non-homophonic non-words (e.g., liston), this would be evidence for phonological coding in reading. Beech, Harris, Chasin, and Green (Reference Note 1) used this task with hearing and prelingually deaf subjects who were matched at reading ages of 7 yr. The deaf subjects were hardly affected by homophony in contrast to the hearing controls.
Another method of assessing phonological coding has been the child’s sensitivity to rhyme. Blanton, Nunnally, and Odom (1967) presented hearing-impaired children with a target word followed by two alternatives. The children were to indicate which of the alternatives rhymed with the target. One of the alternatives was an orthographically dissimilar rhyme and the other was an orthographically similar non-rhyme. Only a few of the deaf children were able to score significantly better than chance in detecting those pairs that rhymed. Hanson and Fowler (1987) presented pairs of words and asked deaf college students to indicate whether or not the words rhymed. When they were orthographically similar, subjects overwhelmingly answered that the pairs rhymed, even when they did not. When the pairs were orthographically dissimilar, subjects generally answered that the pairs did not rhyme, even when they did. Thus deaf subjects were found to frequently use an orthographic strategy rather than a phonological coding strategy to determine rhyme.
Studies examining the effects of cochlear implantation on reading indicate that the improved auditory skills may be associated with better reading outcomes. Boothroyd and Boothroyd (2002) studied eight children with implants longitudinally over a 4-yr period. Average reading performance lagged behind grade placement by an amount that increased with increasing language demand of the task. They concluded that auditory limitations, combined with language deficits already present at the time of implantation, present a continuing educational challenge. However, the average age of implantation for this group was 5.8 yr. It was hypothesized that earlier implantation might result in more normal reading development. Unthank, Rajput, and Goswami (Reference Note 2) studied the phonological awareness skills of children after cochlear implantation. They found a significant relation between phonological awareness skills and word reading and vocabulary development in both aided and implanted children. Furthermore, children who received a cochlear implant before 3½ yr of age exhibited higher vocabulary and word reading scores than children fitted after 5 yr of age.
Above and beyond the positive effects of the cochlear implant, it is anticipated that auditory/speech training may increase the deaf child’s access to phonological information and thereby facilitate word identification and word comprehension. Johnston and Thompson (1989) found that for a group of hearing 8 yr olds, the teaching approach students had been exposed to influenced their use of phonological information in a lexical decision task. Spencer, Tomblin, and Gantz (1997) reported reading scores for 40 children, primarily from simultaneous-communication public classrooms, who received multichannel implants between 2 and 13 yr of age. Nearly one-half of the children were reading at or within 18 mo of their grade level, an improvement over earlier reports of deaf children with hearing aids. On the other hand, only 18% scored within 8 mo of grade level and 32% scored more than 30 mo below grade level. Moog and Geers (1999) reported reading scores for 22 children who had been implanted between 2 and 9 yr of age and had been enrolled in a private oral school. Reading quotient scores of 80 or higher were obtained by 86% of this sample, suggesting that educational factors may play a role in the accelerated reading progress in children with implants.
The present study sought to document the word reading and comprehension levels attained in children who were implanted by 5 yr of age. It was hypothesized that children who achieved greater auditory speech perception would exhibit greater use of phonological coding in both lexical decision and rhyming tasks and would, in turn, use phonological strategies to pronounce unfamiliar non-words. Furthermore, the variables best predictive of word reading and comprehension levels, after variance due to child and family characteristics was removed, would be associated with processing variables including memory span and use of phonological coding skills and educational variables including auditory-oral emphasis.
Characteristics of the 181 children with hearing loss who participated in this study are described in detail elsewhere (Geers & Brenner, 2003). Participants were 8 or 9 yr old at the time the reading tests were administered. All were deafened under 3 yr of age and were implanted by 5½ yr of age (most under age 5). Although most of the children were reportedly deaf from birth, almost one-fourth of them had some known etiology of deafness after birth. For the 140 children who were deaf from birth, the mean age first aided was 1 yr 3 mo.
Educational placement variables were quantified at the time of implant and each of 4 yr thereafter. These variables included type of school (public/private), type of classroom (mainstream/special education), amount of therapy, experience of therapist, parent participation in therapy, and classroom communication mode. Assessment of these variables is described in detail by Geers and Brenner (2003). Most of the children with cochlear implants (83%) were enrolled in mainstream classes with hearing children for at least part of each school day at the time of data collection. Most of the children had just completed the 2nd grade, with the rest having completed 1st or 3rd grade or in ungraded classrooms. Determining grade placement for reading was not possible for all children due to their individualized reading programs. Children were about equally divided between oral (N = 98) and TC (N = 83) modes at the time of testing. Ratings of classroom communication mode that were provided by the child’s parents included three levels of TC programs (1-sign emphasis; 2-equal speech and sign emphasis; and 3-speech emphasis) and three levels of Oral programs (4-cued speech; 5-auditory/oral; and 6-auditory/verbal). Average communication mode ratings for the preimplant and for each of the 4 yr after implantation were used to summarize the amount of emphasis on speech and auditory skill development in the child’s educational history. A total of 89 children obtained average mode ratings between 1.0 and 3.9, indicating predominant placement in total communication classrooms. The remaining 92 children obtained average mode scores between 4.0 and 6.0, indicating predominant placement in oral classrooms.
• Three subtests were selected from diagnostic reading assessment batteries standardized on hearing children. Scores were expressed as number correct (raw score) and as grade equivalents and standard scores based on the normative sample that ranged from kindergarten to college.
Woodcock Reading Mastery Tests (WRMT)-Revised (Woodcock, 1987) : Word Attack. This 45-item subtest of the individually administered WRMT battery assesses the student’s ability to pronounce nonsense words (e.g., raff, chad, yeng, cigbet, bafmotben) using phonic and structural analysis skills. It is constructed so that students are tested only on those items within their operating range. All students begin this subtest at the first item and proceed to a ceiling level of six consecutive incorrect responses. It was anticipated that results on this test would be influenced by the children’s speech production ability, because correct articulation of each printed phoneme was required.
Peabody Individual Achievement Test (PIAT)-Revised (Dunn & Markwardt, 1989). Both reading subtests of this individually administered academic achievement test were given to every child. No basal was established, and both subtests began with the first item and proceeded to a ceiling of five consecutive incorrect responses.
- Reading Recognition. This 100-item subtest begins with letter matching and naming. The remaining items are single words that the student reads aloud. The words were selected from a basal reading series using both sight word and phonic approaches. The response could be either a signed (not fingerspelled) or a spoken version that was recognizable as the target. Thus, performance on this subtest less directly influenced by speech production ability.
- Reading Comprehension. This subtest includes 82 multiple-choice items. For each item, the student is presented with a page that contains one sentence to be read silently. The next page contains four illustrations, and the student selects the picture that best illustrates the sentence. No speech is required for this task.
• The use of phonological strategies was measured using a lexical decision task and with a rhyming task, neither of which required any spoken responses. Because the ability of a deaf person to use phonological information may not be related to the intelligibility of their speech (Hanson, 1989), it was important to include tasks that did not require spoken responses.
Lexical Decision Task. This task was modeled after the procedure used by Harris and Beech (1995) in their study involving deaf and hearing children reading at the level of an average 7 yr old. Their task included 62 irregular words, 31 homophonic pseudowords (i.e., non-words), and 31 non-homophonic pseudowords. The child was given a stack of cards, each containing a word or a non-word and asked to sort the cards accordingly. All of the real words were irregularly spelled (e.g., answer) and matched for word frequency. Half of the non-words were homophonic to their corresponding real word (e.g., word versus werd), whereas the rest of the non-words are non-homophonic to real words (e.g., some versus somo). The non-words were matched in letter length to the real words and were designed to be as visually similar to the real word as possible. This close visual similarity to real words was designed to make the use of a logographic strategy to discriminate between the words and non-words as difficult as possible. According to Harris and Beech (1995), one would expect subjects who are using phonological processing to misclassify more non-words as real words when they are homophonic to (i.e., sound like) real words. If a homophonic pseudoword (e.g., frend) is read correctly, it will generate a phonological code that sounds like a real word. This will produce interference with the requirement to respond that this printed pseudoword is not a real word. If children read without phonology, no such effect would occur. The score used to represent this variable was the percentage of non-word errors that occurred for homophonic pseudowords.
Rhyming Task. For this task, the student was presented with a set of cards containing pairs of words that resembled one another either graphemically (look alike) or phonologically (sound alike). Word pairs were equally distributed among two experimental categories in which orthography and phonology were in conflict: look alike, do not sound alike (e.g., men, man); do not look alike, sound alike (e.g., word, bird); and two control categories: look alike and sound alike (e.g., year, dear); do not look alike, do not sound alike (e.g., big, school). The child was asked to indicate whether the words “sound alike” (rhyme) or “do not sound alike” (do not rhyme). Bradley and Bryant (1983)) found that sensitivity to rhyming and alliteration predicted reading performance 3 yr later, presumably because it reflects phonemic knowledge and used of phonological processing. It was hypothesized that students who used phonological processing strategies would make fewer errors in judging rhyming pairs, particularly on experimental items where orthography and phonology were in conflict. The score used to represent this variable is the percentage of rhyming errors made on these experimental items.
• The Digit Span subtest of the Wechsler Intelligence Scale for Children–III (Wechsler, 1991) was administered in the child’s preferred communication mode. Standard procedures from the test manual were used to determine the longest series of digits the child could repeat (forward span) and the longest series of digits the child could repeat in reverse order (backward span), using speech and/or sign. Digit span is considered a measure of working memory capacity, which has been found to be related to a variety of postimplant outcome measures (see Pisoni & Cleary, 2003).
Grade equivalent scores on the standardized reading ability measures are summarized in Table 1. The group of 8 to 9 yr olds averaged mid to high 2nd grade reading levels. Ten of the children were non-readers according to this measure (below 1st grade), 54 read at 1st grade level, 63 at 2nd grade, 25 at 3rd grade, and 29 at 4th grade or higher. A standard score was obtained for the total reading score on the PIAT. Standard scores were derived from test normative tables based on expected grade placement according to chronological age rather than actual grade placement, which could not be determined for the entire sample. Fifty-two percent of the children scored within the average range for their age on the PIAT (standard score of 85 or higher). Alternately stated, 48% achieved below-average reading scores for their age. However, because deaf children are frequently mainstreamed in a class that is a grade lower than expected for their age, many of these “below average” readers may have been reading at an appropriate level for their instructional placement. Eighty percent scored within two standard deviations of their hearing age-mates (standard score of 70 or higher).
Intercorrelations among reading measures are presented in Table 2 along with their respective loadings from the principal components analysis (see Strube, 2003). All three reading measures loaded highly on a single component. Therefore, in the following analyses, reading outcome is represented by a principal component score, which accounted for 87% of the variance in the reading measures
Means and standard deviations on the processing and working memory measures are summarized in Table 3. Comparison of average error rates on the lexical decision task indicate a significant tendency for the group to produce more errors on homophonous non-words (10.17) than on non-homophonous non-words (7.38) (t = 10.5;p < 0.0001). On average, children sorted 63% of the non-words as words when they sounded like real words if decoded phonologically.
Average error rates on the rhyme task indicated that children exhibited relatively low error rates in judging rhyme, even when the words were orthographically similar but did not rhyme (e.g., man, men). An ANOVA comparing error rates in the four categories indicated a significant effect of word-pair type [F (3,720) = 72.5;p < 0.0001]. This suggests that children were using both phonological and visual cues when making rhyming judgments. On average, children made 72% of their rhyming decision errors when these cues were in conflict. These errors occurred most frequently when rhyming pairs (+Sound Alike) were orthographically dissimilar (−Look Alike) as in the pair: word, bird.
As expected, forward digit span was significantly longer (5 digits) than backward span (3 digits) (t = 14.6;p < 0.0001). The average digit span scaled score (based on norms from Wechsler, 1991) of 6.1 could be compared with hearing age-mates for whom a scaled score of 7 represents one standard deviation below average. Thus, the average child with an implant was somewhat delayed on this measure of working memory capacity.
Correlation coefficients were calculated to compare performance on the reading outcome measures with scores on the processing/memory measures. Results appear in Table 4. All correlations were significant at p < 0.0001. Thus it appears that the better readers are those who demonstrate a preference for phonological coding strategies and those with longer working memory spans.
Table 5 summarizes correlation coefficients between the speech intelligibility measure reported by Tobey, Geers, Brenner, Atluna, and Gabbert (2003) and the reading outcome and processing measures described here. As anticipated, the reading of non-words was most highly related to speech production (r = 0.74). However, speech intelligibility was significantly related to all reading process and outcome measures, indicating that production may be part of the reading decoding process.
Independent Predictors of Reading Outcome
Child and Family Characteristics
• Seven child and family characteristics were entered into a multiple linear regression analysis to predict reading outcome: the child’s age at test, at implant and at onset of deafness, the Performance IQ on the WISC-III (Wechsler, 1991), the number of family members, the family socio-economic status, represented by a standardized sum of the ratings for parents’ income and education, and the child’s gender. Measurement of each of these variables is described in detail in Geers and Brenner (2003). Results are presented in Table 6. Together child and family variables accounted for 25% of variance in reading outcome. Older children (9 yr olds) scored higher than younger children (8 yr olds). Children with later onset of deafness, higher performance intelligence quotients, higher socio-economic status and female gender exhibited an advantage in their development of reading skills. Age at implant and family size had no impact on reading outcome.
Next, a series of multiple linear regression analyses were used to assess the amount of variance in the reading principal components score independently predicted by cochlear implant characteristics, educational placement, phonological processing and speech/language levels after variance due to the child and family characteristics had been removed. The results of these analyses are presented in Table 7 and are listed in the order of their relative importance for reading outcome.
• Six educational variables were entered into the analysis. Each of these variables was quantified according to procedures described in Geers and Brenner (2003) and averaged over 4 postimplant yr: number of hours of individual therapy, therapist experience, parent participation in therapy, public or private educational setting, mainstream or special education classroom, and type of communication mode used in the child’s classroom. Only one factor contributed significant additional variance once the child and family characteristics were removed. Children in mainstream classes had better developed reading abilities. This factor accounted for 6% of added variance for a total predicted variance of 31%.
• Four implant characteristics were entered into the analysis: Duration of use of the SPECTRA processor with the improved SPEAK coding strategy, number of active electrodes in the child’s map, average dynamic range across the electrode array, and growth of loudness. Measurement of these factors is described in detail in Geers, Brenner, and Davidson (2003). Two factors contributed significant independent variance to reading outcome. Children who had used SPECTRA longer and children with a larger dynamic range were better readers. Together, these variables accounted for 12% of added variance beyond child and family characteristics for a total predicted variance of 37%.
• The contribution of the four processing variables was examined after removing variance due to child and family characteristics: Percent homophonic errors in the lexical decision task, percent rhyming errors on experimental items, digit span forward and digit span reversed. Digit span contributed significant additional variance, with both forward and backward span making independent contributions. Additional independent variance was attributed to the proportion of errors made on experimental items on the rhyme task (where orthography and phonology were contrasted). Together these factors accounted for 26% of added variance in reading score, for a total predicted variance of 51%. Thus, after child and family characteristics have been accounted for, these measures of phonological processing and memory account for more of the variance in reading outcome postimplant than did characteristics of the implant itself (12% of added variance) or characteristics of the educational program (6% of added variance).
• Finally, the principal components scores associated with speech perception (see Geers et al., 2003), speech production (see Tobey et al., 2003), and total language (see Geers, Nicholas, & Sedey, 2003) were added to the model. Together, these speech and language variables accounted for 45% of added variance in reading outcome. A total of 72% of variance in reading outcome was accounted for by a combination of child and family and speech/language variables. Speech perception scores did not contribute independently to reading outcome. Rather, it was speech production and language skills that predicted reading, with language variables contributing the lion’s share of the variance.
Comparison with Previous Studies
Comparison of mean grade equivalent scores of these cochlear implant users with previous data reported for samples of deaf children suggests that early implantation is associated with improved prognosis for the development of literacy. The graph in Figure 1 compares the growth of reading scores with age by drawing a line of best fit to means reported from various published studies of deaf children (adapted from Moog & Geers, 1985, p. 273). The top line represents normal growth between 2nd and 8th grade reading levels. The bottom line represents Stanford Achievement Test scores based on a nationwide Annual Survey of Hearing-Impaired Children and Youth (DiFrancesca, 1972). The next highest function is plotted from mean scores obtained from a cross-section of hearing-impaired children, 11 to 13 yr old, in 73 programs in the US and Canada summarized by Wrightstone, Aranow, and Muskowitz in 1963. The next highest function represents mean scores of 11-, 12-, and 13-yr-old children at CID reported by Lane and Baker in 1974. The next lines represent results for the EPIC study conducted at Central Institute for the Deaf (Moog & Geers, 1985). The EPIC control group children were enrolled in private oral schools and were tested longitudinally at 7, 9, and 10 yr of age. The EPIC experimental group consisted of a matched group of children tested at the same ages. They were enrolled in an experimental curriculum at CID and received a highly intensified and individualized instructional program with a special emphasis on reading and language. Children in this experimental program achieved significantly faster reading progress than previously reported for groups of profoundly deaf children.
The line labeled CI represents average reading scores for the 8-yr-old and the 9-yr-old children with cochlear implants. The average score for 8-yr-old children with implants was almost one grade level delayed compared with hearing age mates. Their average score at the beginning of 2nd grade is within the range observed in other groups of children with profound deafness. This delay probably reflects a later/slower start in reading skill acquisition associated with their language delay. However, the change in scores between 8 and 9 yr olds suggests that reading progress after cochlear implantation may be at a faster rate than has previously characterized samples of deaf children. The slope of the growth function for the cochlear implant group is 1.0, the same as that observed in hearing children. However, the initial reading delay of about one grade level is still apparent for the 9 yr olds, suggesting that the implanted children are not catching up with hearing age mates in reading.
The grade equivalent scores reported for these groups of children were compared based on real or extrapolated values at age 13 that are listed on the right-hand ordinate. Reading levels at age 13 ranged from 2nd and 3rd grade for the demographic survey data and for the Wrightstone study to 8th grade for hearing children. If a linear trend is maintained, children in the cochlear implant group would be expected to achieve a 7th grade reading level by age 13. Although this level is still delayed in relation to hearing age-mates, comparison with other deaf groups suggests that the advent of the cochlear implant may have done as much or more for the literacy levels of deaf children as was accomplished by the intensive EPIC experimental reading instruction program.
The most noteworthy finding documented here is that over half of these children who received a cochlear implant early in life exhibited reading scores within the average range for hearing children their age. Although this is an impressive result for a group of profoundly deaf children, considerable variability was evident, with many children exhibiting barely developed reading skills. This analysis attempted to identify some of the sources of variance in reading skill development.
Characteristics of High Achievers
• As has been observed in hearing children, certain demographic characteristics were associated with postimplant reading outcome. Female children with higher nonverbal intelligence and more highly educated parents and slightly later onset of deafness had an advantage in reading. Once variance due to these factors was removed, some characteristics of the implant and the associated map were associated with reading outcome. High reading achievers tended to have had longer use of an updated implant speech processing strategy that provides greater auditory access to the phonemes of English. In addition, better readers had implant maps with a larger dynamic range between soft and loud sounds, so that the full range of loudness present in the speech signal was more readily available.
Reading Decoding Strategy
• Strategies used by the children to learn new words was reflected in their pattern of scores on reading tasks. Good performance on the Word Attack subtest reflects skill in using phonics to decode print. Good performance on the Reading Recognition subtest can be achieved using either a phonics approach or a whole-word (i.e., sight word) approach. The relatively high correlation of non-word with real word recognition scores (r = 0.85) suggests that the skilled readers were using phonic rather than whole-word strategies when reading. This association may be due, in part, to the fact that speech articulation competence was a required component of both of these reading tasks. The Word Attack test required the child to produce non-words with correct articulation. The Reading Recognition test required the child to pronounce real words with sufficient accuracy to be recognized by the examiner (although accurate signed productions were also accepted). However, associations are also evident between reading skill and performance on tasks that do not require speech production. The significant correlations (r = 0.72 and 0.83) of both Word Attack and Reading Recognition performance with scores on the Reading Comprehension task, which did not require any speech production, provides more evidence for an underlying phonological coding strategy in better readers. Furthermore, the two tasks designed to measure phonological processing (rhyme and homophony) without the use of speech, also correlated significantly with all reading outcome measures. The significant association of reading skill with performance on the speech production measures described by Tobey et al. (2003) may reflect an underlying phonological skill that is common to both outcomes.
Language and Reading
• Speech perception skills achieved with the implant were not independently predictive of reading levels. Instead, the variable most strongly associated with reading outcome was overall language competence, a variable that included measures of comprehension, production, verbal reasoning and use of the narrative form (Geers et al., 2003). These results suggest that, without English language competence, the auditory speech perception skills achieved with cochlear implantation may not promote reading development. The relation between reading and language reflects the importance of first creating a language base to which decoding skills can be applied when the child is learning to read. In addition, reading competence undoubtedly also contributes to further language development by increasing the deaf child’s exposure to linguistic content.
Education and Reading
• Surprisingly, the kind of educational program these children received postimplant did not make a substantial contribution to reading outcome after demographic characteristics were accounted for. However, there was a significant tendency for better readers to be placed in mainstream classes. The results of the regression analyses conducted in other articles in this supplement (Geers et al., 2003;Tobey et al., 2003) indicate that there was a significant impact of classroom communication mode on speech and language skill development. Children whose educational program focused on spoken language exhibited an advantage in their ability to produce intelligible speech and more complete and mature English language structures. However, they were not necessarily better readers. It may be that the skills exhibited at the 1st to 3rd grade reading levels reflect a beginning reading curriculum that is common to all types of programs, regardless of the communication mode used or the amount of emphasis placed on speech and audition. Examination of the correlates of more mature reading performance in these children once they are fully proficient readers might better reflect the contributions of educational methods and communication modes to reading outcome.
These results indicate that children who experience severe to profound deafness early in their development have a better prognosis for normal literacy development than ever before. Reading levels documented in these implanted children should raise expectations above those typical for profoundly deaf children with hearing aids. As implant technology continues to undergo improvement in its capacity to deliver speech and as children are being implanted at younger ages, before language delays are established, the prognosis for more normal acquisition of literacy may improve even more.
This study was supported by Grant No. DC03100 from the National Institute on Deafness and other Communication Disorders (NIDCD) of the National Institutes of Health to Central Institute for the Deaf (CID). The design and implementation of the processing tasks was initiated by Linda Suri, Ph.D. The reading tests were administered by Central Institute for the Deaf teachers Monica Rapp and Debbie Carter, and Claire Soete from the Moog Center for Deaf Education.
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