Letter Knowledge, Phonological Processing, and Print Knowledge:

Letter Knowledge, Phonological Processing, and Print Knowledge: Skill Development in Nonreading Preschool Children Victoria J. Molfese, Arlene A. Modglin, Jennifer L. Beswick, Jessica D. Neamon, Shelby A. Berg, C. Jeffrey Berg, and Andrew Molnar

Abstract Development of reading skills was examined in 4-year-old children from low-income homes attending a prekindergarten program. Fall to spring gains in letter identification were examined and compared with skills in phonological processing, rhyme detection, and environmental print, and with performance on a screening tool (Get Ready to Read). It was anticipated that participants might show slow skill development. However, the identification of a large group of children (n = 30) who made little or no gains in letter identification compared to their classmates (n = 27), whose gains averaged 7 letters, was not anticipated. Fall to spring gains in letter identification correlated with phonological processing, rhyme detection, environmental print, and Get Ready to Read! scores. Age and general cognitive skills influenced performance on some tasks. More knowledge of the characteristics of children who show the most variations in skill development may lead to insights on using classroom curriculum to focus on skill development.

C

oncerns about low educational achievement have been expressed through federal and state public policies, by business and community leaders, by educators, and by parents—for good reasons. The National Assessment for Educational Progress (NAEP) statistics for fourthgrade achievement tests have shown no changes in student performance in reading between 1990 and 2003, with 60% or more of students still scoring “below proficient” (Center for Education Statistics, 2003). Furthermore, these data showed particularly low scores for children from racial or ethnic minorities or from low-income families. Efforts to improve academic skills, especially in reading, and to remediate weaknesses have been focusing on the preschool period as an important time for the early identification of children who may show slow development of important cognitive skills. Researchers

are hoping that the developmental status of important cognitive skills can be identified early in the preschool period and that those children who are lagging behind their peers in cognitive development can be targeted for early remediation, so that they can enter kindergarten with the skills they need to learn. One reason for the focus on early identification is the growing evidence that skills in the preschool period affect the development of proficiencies in elementary school. Indeed, both national studies of large populations of children and studies of smaller samples of children have shown that preschool children’s cognitive skills are linked with their achievement in reading in kindergarten and primary grades. For example, Denton and colleagues (Denton & West, 2002; West, Denton, & GerminoHausken, 2000) reported statistics on a general sample of 22,000 children from

JOURNAL OF LEARNING DISABILITIES VOLUME 39, NUMBER 4, JULY/AUGUST 2006, PAGES 296–305

kindergarten through fifth grade. Clear relations were found between specific skills related to reading and later word decoding skills. At kindergarten entry, 66% of the children could name upperand lowercase letters of the alphabet; 29% recognized the beginning sounds of words; 17% recognized ending sounds; and 1% to 2% could read sight words or words in context. Children who were proficient (based on quartile scores) in identifying letters at kindergarten entry showed stronger skills at the end of kindergarten and in first grade on measures of phonological processing and word reading compared to children who were not proficient. Also, more of these proficient children (34%) scored in the top 25% in reading at the end of first grade. Only 5% of children who were not proficient in letter identification scored in the top 25%. Although focusing specifically on letter knowledge may seem simplistic,

VOLUME 39, NUMBER 4, JULY/AUGUST 2006

these skills have been identified as robust markers for skills that subsequently lead to the development of reading (Adams, 1990), with fluency in letter naming specifically linked with later reading skills (Badian, 1995; Walsh, Price, & Gillingham, 1988). Letter knowledge has been found to be a strong predictor of reading skills in English and non–English speaking children (Muter & Diethelm, 2001). The development of letter knowledge skills has been found to relate to the background characteristics of children. West et al. (2000) reported that children whose mothers had less education and children whose families had low levels of income had fewer letter naming skills in the preschool period, which placed them at risk for low achievement in reading. Bowey (1995) reported that 5-year-olds from low-SES homes could name half as many letters as children from higher SES homes (5.72 vs. 11.09). These scores are consistent with Badian’s (1995) report that 5-year-old children from a broad range of SES homes had an average letter naming score of 8.4. However, not all children whose parents had less education or low income had poor letter knowledge skills. For example, Brady, Fowler, Stone, and Winbury (1994) reported average letter naming scores for two groups of 5-year-old children in inner-city kindergarten programs as 9.12 and 9.86 in the fall and 17.60 for both groups in the spring. Although some of the differences in findings across studies may arise from the different ways in which letter knowledge skills are assessed (e.g., identification of upper- and lowercase letters, producing letter names and letter sounds), it is important to understand more about the etiology and implications of letter knowledge skills in populations of children at risk for delays in the development of a variety of cognitive skills. Moreover, researchers have reported a relation between letter knowledge skills and phonological processing skills (i.e., the ability to discriminate phonetic contrasts, to segment and ma-

nipulate phonemes, and to detect rhyming) and between these skills in the preschool period and subsequent reading skills at school age. However, there are disagreements about whether one skill facilitates the development of the other skills or whether the development of one or both skills is needed for the development of subsequent reading skills. For example, Wagner, Torgesen, and Rashotte (1994) reported a causal relation between letter name knowledge in kindergarten and measures of phonological processing abilities in first grade. Muter and Diethelm (2001) reported that kindergarten letter knowledge and phonological processing skills were both predictive of first grade reading, and Badian (1995) reported that letter naming skills in kindergarten were the most consistent predictors of reading in Grades 1 to 6. In a study that specifically targeted nonreading preschool children, Johnston, Anderson, and Holligan (1996) reported that children who could identify few or no letter names had difficulty on phonological processing tasks compared to children who knew an average of 8 letters. Burgess and Lonigan (1998) reported that letter name knowledge and phonological processing skills were reciprocally related in nonreading preschool children, with each skill effective in predicting growth in the other skill. Burgess and Lonigan did not find a relation between letter name knowledge and rhyme detection, but Wood and Terrell (1998) reported that rhyme detection skills measured in nonreading preschool children were significant predictors of reading skills at school age. Other researchers (Johnston et al., 1996; Riley, 1996) have linked letter naming skills to environmental print skills, although there is considerable disagreement about the role of environmental print as a component in the development of wordlevel reading skills (Cronin, Farrell, & Delaney, 1999; Share & Gur, 1999). Although the manner in which specific skills were measured in these studies varied to some extent, which may account for some of the differences in

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findings, it is clear that letter knowledge, phonological processing, and possibly rhyme detection skills and environmental print play a role in the development of reading skills. The purpose of this study is to focus on preschool children from lowincome homes with regard to the development of cognitive skills that facilitate the development of reading. Children from low-income homes are most likely to be “left behind” in educational achievement despite their participation in preschool programs (Lee & Croninger, 1994; McLoyd, 1998; Peisner-Feinberg et al., 2001). Understanding that preschool children from low-income homes can enter preschool and kindergarten with fewer experiences and less developed cognitive skills than children from higher income homes helps to explain these findings. The effects of low income, low parental education, and less stimulating family environments on children’s cognitive development has been well documented (for reviews, see Bradley & Corwyn, 2002; U.S. Department of Health and Human Services, 2001, 2003). However, regardless of background, children whose cognitive scores are significantly lower than those of their classmates are at a disadvantage for fully participating in classroom activities if the cognitive skills needed for participation are not adequately developed. This study focuses on a sample of nonreading children participating in a 1-year prekindergarten program for economically at-risk children. All children participated in a districtwide program with a common curriculum that emphasized an array of social– emotional, motor, and cognitive skills, including language and literacy. The curriculum included elements intended to link with the development of reading skills, including phonological skills (rhyming, beginning word sounds), environmental print (print recognition, recognition of words as a unit of print), book knowledge (exploring books, using picture clues to retell stories), and letter identification (knowl-

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edge of upper- and lowercase letters). The present study measured changes in the letter knowledge skills of preschool children at program entry in the fall and after 5 months in the program in order to determine how the development of letter knowledge skills changed over the course of the preschool year and how changes in these skills were related to phonological processing, rhyming, and print knowledge skills that have been found to be important markers for the subsequent development of reading skills. Furthermore, we compared children’s gains in letter identification with their performance on an early reading screening tool, Get Ready to Read! (GRTR; Whitehurst & Lonigan, 2001). GRTR is a brief assessment instrument designed for use with 4-yearold children. It includes 20 items and taps phonological awareness (letter sounds, rhyming, segmenting words), print knowledge (understanding of

books, printed letters and words), and text knowledge. Molfese, Molfese, Modglin, Walker, and Neamon (2004) reported that children’s performance on GRTR was primarily influenced by their vocabulary, followed by their phonological processing skills and, to a lesser extent, by their environmental print knowledge. By using a combination of assessment strategies, we hoped to determine the interrelations between the development of letter identification and specific measures of phonological processing, rhyming, and environmental print and a global assessment of these skills using the GRTR screening tool.

Method Participants Participants were 57 four-year-old, typically developing children who spoke

English as their first language and who were enrolled in preschool programs for economically disadvantaged children in public schools. Districtwide enrollment in these preschool programs is based on family income eligibility (total annual family income less than $22,945 for a family of four). The mean age of the participants was 48.57 months (range = 42–55 months; 26 girls, 31 boys). The racial composition of participants was 77% European American, 14% African American, 5% Hispanic, and 4% other. Descriptive statistics of the participants are shown in Table 1.

Measures General Cognitive Measures. Measures of verbal, nonverbal, and overall cognitive abilities from the Differential Ability Scales (DAS; Elliott, 1990) were obtained to characterize the sample. The preschool level of the scale can be used with children ages 2 years

TABLE 1 Means, Standard Deviations, and Range of Scores for Study Variables for Full Group and Subgroups Full Groupa Variable

M

Group 1b

SD

M

Group 2c

SD

M

SD

td

Fall Skills Age

48.58

3.97

50.37

3.38

46.97

3.83

3.57**

DAS GCA Nonverbal Verbal

87.16 89.93 87.05

15.19 17.93 13.34

95.00 98.26 92.70

14.02 16.99 12.01

80.10 82.43 81.97

12.69 15.47 12.57

4.21** 3.68** 3.29**

PPVT-III

85.00

15.63

91.70

15.57

78.97

13.23

3.34**

1.98

3.63

3.88

4.56

0.27

0.64

4.21**

WRAT Letter Identification

Spring Skills WRAT Letter Identification Gains

5.72 3.81

5.62 4.27

10.93 7.03

3.63 4.18

1.03 0.90

1.13 1.06

4.21*** 4.21***

Phonological Processing

8.77

2.98

8.53

2.88

8.23

2.98

1.45

Rhyme Detection

2.98

2.23

3.63

2.54

2.40

1.75

2.14*

Environmental Print

8.84

3.52

10.04

3.90

7.78

2.81

2.54**

GRTR

9.72

4.14

9.37

2.92

7.13

2.77

6.52***

Note. DAS = Differential Ability Scales (Elliott, 1990); GCA = General Cognitive Ability score; PPVT-III = Peabody Picture Vocabulary Test, 3rd ed. (Dunn & Dunn, 1997); WRAT = Wide Range Achievement Test (Wilkinson, 1993); GRTR = Get Ready to Read (Whitehurst & Lonigan, 2001). aN = 57. bLarge gains; n = 27. cSmall gains; n = 30. dGroup 1 vs. Group 2; df = 55. *p < .05. **p < .01. *** p < .001.

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6 months through 5 years 11 months and includes assessments of verbal abilities (Verbal Comprehension and Naming Vocabulary) and nonverbal abilities (Picture Similarities, Pattern Construction, and Copying). These measures plus Block Building and Early Number Concepts are used to obtain General Conceptual Ability (GCA) scores. GCA scores have a mean equal to 100 and a standard deviation set at 15. Preschool-level test–retest reliabilities over a period averaging 30 days range from .90 to .94. The criterion-related validity of the preschool level of the DAS GCA was reported against the Stanford-Binet Intelligence Scale–Fourth Edition (SB-IV; Thorndike, Hagen, & Sattler, 1986) composite, with which it correlated .77, and against the Wechsler Preschool and Primary Scale of Intelligence–Revised (WPPSI-R; Wechsler, 1989), with which it correlated .89 (Elliott, 1990). Phonological Awareness Measures. Phonological Processing. The NEPSY (Korkman, Kirk, & Kemp, 1998) is a developmental neuropsychological assessment for children ages 3 through 12 years with five functional domains. Phonological Processing is a subtest in the Language domain. For the age range from 3 to 6 years, the Phonological Processing subtest contains 14 items and requires the child to identify one of three pictures after the examiner says a segmented word. Additional items for children ages 5 years and older ask the child to say a word and then repeat the word, deleting one phoneme. The subtest scores have a mean of 10 and a standard deviation of 3. The test–retest reliabilities for the Phonological Processing subtest range from .79 to .88 for 3- to 5-year-olds across an average period of 38 days. The criterion-related validity has been reported for the Language domain at the ages comparable to those used in the present study (36–59 months) against the WPPSI-R (Wechsler, 1989). The correlation between Language domain subtests and WPPSI-R Verbal IQ is .60 (Korkman et al., 1998).

Rhyme Detection. Rhyme Detection is one of eight subtests of the Phonological Abilities Test (PAT; Muter, Hulme, & Snowling, 1997). The PAT is designed to be a rapid assessment of phonological awareness skills and is suitable for children from 4 to 7 years of age. Internal consistency reliabilities for the subtests ranged from .67 to .97. Test–retest reliability scores obtained over a 3-week interval ranged from .58 to .86. To establish criterion-related validity, PAT scores were correlated with Single Word Reading scores from the British Abilities Scales (BAS; Elliott, Smith, & McCulloch, 1996). Correlations ranged from .37 to .66, which reflects the differences between the word reading skills tapped by the BAS and the letter knowledge skills tapped by the PAT. The Rhyme Detection subtest has three demonstration items and 10 test words. Children are shown a page with four pictures. A target picture appears above three test pictures. The child is asked to point to the picture whose name rhymes with the target word (e.g., “What rhymes with cat: Fish, bell, or hat?”). Environmental Print Measure. Environmental print knowledge was assessed using colored pictures of logos and signs for products (CocaCola®, Pepsi®), stores (McDonalds®, Burger King®, Taco Bell®, Kroger®, Blockbuster®, Target®), and services (restroom, school crossing) appearing in print in the environment around the children’s schools or neighborhoods. Ten test items plus two demonstration items (McDonalds, Cheerios®) were used. Children were asked to name each item and received credit for providing specific labels (e.g., McDonalds; 2 points) or for general labels (e.g., burgers, fries, Happy Meal; 1 point). Although environmental print is a popular measure in preschool education curricula, and measures of environmental print knowledge are used in research, the specific pictures used and the products, services, signs, or stores selected vary widely. This variability is due to the need to match the environ-

mental print items to the print examples actually found in the children’s environments. Early Reading Measures. Get Ready to Read! is a screening tool in which children point to one of four pictures in response to a question or command. The 20-item assessment includes 4 items related to print knowledge (understanding of books, printed letters, and words), 6 items on emergent writing (text knowledge), and 10 items on phonological awareness (letter sounds, rhyming, segmenting words). The entire screening tool and technical report are available online The standardization sample of 4-year-olds yielded a mean total scale score of 9 with a standard deviation of 4. Split-half reliability was reported by Whitehurst and Lonigan (2001) to be .80 for their sample of 342 four-yearolds. The criterion-related validity of GRTR was reported against the Developing Skills Checklist (DSC; CTB/ McGraw-Hill, 1990). The correlation between GRTR and DSC for children from low-income families (Head Start) was .70, and the correlation for children from middle-income homes was .79 for the standardization sample. The Wide Range Achievement Test (WRAT; Wilkinson, 1993) Reading subscale includes subtests of letter identification and word decoding, in which the child first identifies and then names 15 uppercase letters presented in random order. The child then is asked to name words on a list of 42 words presented in isolation. Alternate test forms of the WRAT Reading subscale were administered in the fall (tan version) and in the spring (blue version). Both the letter identification and the word decoding sections were administered. Two children in the fall could read one word, three children in the spring could read one word, and one child could read three words.

Procedure In the fall of the school year, parents of children participating in preschool

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programs at three schools were sent requests for participation and informed consent letters. Those families returning the consent letters (92%) were sent packets containing questionnaires seeking information about family background. The DAS and WRAT letter naming and word reading measures were administered to the children in the fall (October–November). Measures of phonological processing, environmental print, and GRTR were administered the following spring (April–May), when the WRAT letter naming and word reading measures were readministered. Each child was tested individually by trained researchers at the child’s school in a room near the child’s classroom. Testing continued for each child across several days as needed to complete the assessments due to unavoidable constraints on assessment time during the children’s school day.

Results The mean General Conceptual Ability (GCA) score of the participants was obtained to get a general measure of cognitive skills. The mean score on the GCA as measured by the DAS (Elliott, 1990) was 87.16 (range = 57–118, SD = 15.19). Of the 10 children with GCA scores that were more than 2 SD below the mean (scores range = 57–70), only

3 also had low scores (range = 59–65) on the Peabody Picture Vocabulary Test–Third Edition (PPVT-III; Dunn & Dunn, 1997), a frequently used assessment of verbal (receptive) abilities. These descriptive statistics reflect the breadth of cognitive abilities that characterize children from low-income homes who may enter preschool with fewer experiences or less developed cognitive skills than children from higher income homes. No children were eliminated based on their GCA or PPVT scores. The means and standard deviations for the independent and dependent measures are shown in Table 1. Because some of the instruments yielded scores that can be translated into standard scores, whereas scores on other instruments could not, z scores were used. As can be seen from the descriptive statistics, there is a wide range in the letter identification scores of the children in the fall and in the spring. In the fall, the number of letters out of 15 that children could identify ranged from 0 to 13, and, in the spring, the range was from 0 to 15. The average gain in letter knowledge scores from fall to spring was 3.8 letters. Table 2 shows that the gain scores on letter identification were significantly correlated with the children’s scores on phonological processing, rhyme detection, environmental print, and GRTR (r range = .22–.48, p < .01),

TABLE 2 Correlations Between Fall–Spring Gains in WRAT Letter Identification Scores and Cognitive Scores WRAT score gains Full Groupa

Group 1b

Group 2c


.26**

.35**

.26

Rhyme Detection

.29**

.20

.12

Environmental Print

.22**

.11

.12

GRTR

.48***

.07

.11

Cognitive measure

Note. WRAT = Wide Range Achievement Test (Wilkinson, 1993); GRTR = Get Ready to Read! (Whitehurst & Lonigan, 2001). aN = 57. bLarge gains; n = 27. cSmall gains; n = 30. **p < .01. ***p < .001.

with the strongest correlation between letter knowledge gain scores and GRTR scores. Children who made larger gains in identifying letters had higher scores in these cognitive skills. In comparison with the gain scores, correlations of letter identification scores in the fall and in the spring with the dependent variables are shown in Table 3. Correlations with environmental print and GRTR were significant (r range = .43–.73, p < .01) for both fall and spring scores; correlations with phonological processing and rhyme detection were significant only for spring scores (rs for both = .27, p < .05). There were large differences among children in letter identification skills. Whereas some children made gains in the number of letters they could name, other children did not. To better understand the nature of these skill differences, two subgroups of children were created: Group 1 children, who made large gains in WRAT letter identification scores (M = 7.04, SD = 4.18) between fall and spring, and Group 2 children, who made small gains (M = 0.90, SD = 1.06). Descriptive statistics for the two subgroups are given in Table 1. Differences in scores between these two subgroups were examined using t tests. As shown in Table 1, there were significant differences between subgroups on age at which the assessments were administered, DAS GCA scores, and each of the dependent variables, with the exception of phonological processing scores. Group 2 children were younger and had lower GCA scores and lower scores on the dependent measures. Analyses of covariance (ANCOVAs) were used to explore whether the group differences on the dependent variables would persist if differences in age or in GCA scores were controlled. When the ANCOVA analyses controlled for the effects of age, group differences remained on changes in letter identification scores, F(1, 56) = 7.40, p < .001; environmental print, F(1, 56) = 2.29, p < .02; and GRTR scores, F(1, 56) = 2.62, p < .01, but not on rhyme detection, F(1, 56) = 1.14, p > .05. When

301


TABLE 3 Correlations Between Fall and Spring Letter Identification Scores and Cognitive Scores WRAT Letter Identification Scores Full Groupa Cognitive measure

Fall

Group 1b

Group 2c

Spring

Fall

Spring

Fall

Spring


.11

.27*

.01

.38*

.24

.14

Rhyme Detection

.07

.27*

.13

.06

.30

.05

Environmental Print

.43**

.43**

.39*

.38*

.15

.21

GRTR

.55**

.73**

.47*

.52**

.04

.13

Note. WRAT = Wide Range Achievement Test (Wilkinson, 1993); GRTR = Get Ready to Read (Whitehurst & Lonigan, 2001). aN = 57. bLarge gains; n = 27. cSmall gains; n = 30. *p < .05. **p < .01.

the ANCOVA analyses controlled for the effects of GCA, group differences remained on changes in letter identification scores, F(1, 56) = 8.51, p < .001, and GRTR scores, F(1, 56) = 2.21, p < .02, but not on rhyme detection, F(1, 56) = 1.86, p > .05, or environmental print, F(1, 56) = 1.11, p > .05. Correlations between each subgroup’s gains in WRAT letter identification skills and their scores on phonological processing, rhyme detection, environmental print, and the GRTR are shown in Table 2. Only the correlation with phonological processing scores for Group 1 was significant (r = .35, p < .01). Correlations between fall and spring WRAT letter identification scores and the dependent variables for each subgroup are shown in Table 3. The only significant correlations were for Group 1 between fall and spring letter identification scores and environmental print and GRTR (r range = .38–.52, p < .05), and between spring WRAT letter identification scores and phonological processing (r = .38, p < .05).

Discussion This study examined changes in nonreading preschool children’s letter knowledge skills at two time points (fall and spring) during their participation in a prekindergarten program for children from low-income homes. The

findings are interesting in a number of respects. The prekindergarten language and literacy curriculum of the school district included activities designed to develop letter identification skills, and the district assessment plan called for teachers to assess children’s letter identification skills at three time points during the school year (September, January, and May). Nevertheless, 12 of the 57 four-year-old children in the study could name no letters in the fall or the spring, an additional 8 children could name only 1 letter in the spring, and 8 more children could name 2 or 3 letters in the spring. These 30 children stand in marked contrast to their classmates, who knew on average 11 of 15 letters in the spring, having entered prekindergarten knowing 0 to 13 letters. As a group, the children in our study correctly identified an average of 38% of the letters in the spring (M = 5.72 of 15 letters), which is slightly higher than the 29% correct (5.72 of 20 letters) previously reported for 5-yearold children from low-SES families (Bowey, 1995). Although the average scores for the two groups of children in this study can be compared to those reported in the literature, it is the variation in changes in letter knowledge between fall and spring that were identified within the sample that is of greatest interest. Within the sample, we identified two groups of children who made

markedly different gains in the acquisition of letter identification skills. Significant differences in age and general cognitive scores were found between these two groups of children. These age and general cognitive score differences did influence the performance scores on the cognitive skills measured, as has been also reported by other researchers (e.g., Bowey, 1994; see Wagner, Torgesen, & Rashotte, 1994, for effects of verbal abilities). The difference in average ages of the children in each group was 3.4 months, close to 1 SD (4.0), and the difference in general cognitive scores (14.9) was effectively 1 SD (15 points). However, group differences in letter identification scores remained even after the differences in age and general cognitive scores were controlled, which is not surprising, as the groups were created based on that variable. Furthermore, group differences on environmental print and GRTR also remained when the differences due to age were controlled, and group differences on GRTR remained when the differences due to general cognitive scores were controlled. It is not uncommon for researchers to automatically covary the effects of age and cognitive scores (e.g., vocabulary, verbal IQ) in studies of letter identification, phonological processing, and various other prereading measures (e.g., Badian, 1995; Bowey, 1995; Burgess & Lonigan, 1998; Johnston

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et al., 1996; Muter & Diethelm, 2001). Thus, although differences in age and cognitive scores may have been present in other studies, such differences often are not discussed beyond noting whether a general cognitive explanation of findings is supported or not. Yet in seeking to identify the characteristics of children that might index slow skill development, more information is needed about the characteristics that influence children’s performance. The results of the present study show that children who are younger than their classmates and children who have less developed cognitive skills are at risk for making little or no gain in letter knowledge skills, for less developed knowledge of environmental print, and for less developed reading skills of the type targeted by GRTR. Children with these characteristics may need special instructional attention by preschool teachers to receive more explicit instruction in these skill areas. It may be that children such as those in Group 2 will not benefit from simple exposure to learning opportunities in the preschool classroom. Some researchers (Naslund & Schneider, 1996; Senechal & LeFevre, 2002; Share & Gur, 1999; Whitehurst, 2001) have argued that the development of some skills, such as letter identification and phonological awareness, is facilitated by explicit instruction, whereas the development of other skills, such as book knowledge and oral language skills, may be facilitated by more informal, spontaneous adult–child conversations and activities commonly engaged in with children, such as book reading. Indeed, there are some data supporting this argument (e.g., Senechal & LeFevre, 2002). Explicit instruction on skills known to facilitate the development of reading may enable children who show slow skill development to more fully participate in classroom activities designed to promote the development of language and literacy skills. It is interesting that there were group differences in scores on the rhyme detection task but not on the


phonological processing task involving sound blending. Given the relation between letter identification and phonological processing skills reported in the literature, it was expected that children who could identify very few etters would also perform differently on the phonological processing and rhyme detection tasks than children who could identify many letters. However, several issues are involved. First, different phonological skills have been included under the general label of phonological processing. Some researchers (e.g., Snowling, Chiat & Hulme, 1991; Wagner et al., 1994) have suggested that the different types of phonological skills, such as segmenting, blending, rhyming, alliteration, and elision, should be studied separately, rather than treating phonological processing as a single construct. There is evidence that phonological tasks vary in difficulty and show different rates of development (Burgess & Lonigan, 1998; Naslund & Schneider, 1996; Wagner et al., 1994). Thus, the choice of which specific phonological skill is included in assessments has important implications for studies of early reading skills. Studies treating such skills separately would enable a better understanding of the relation between specific types of phonological skills and of the reciprocal relations between these and other skills. The second issue pertains to comparing results across studies, which is complicated by the use of different skills to represent phonological processing abilities. For example, Bowey (1995) reported significant group differences between high-SES and lowSES preschool children in their skills on two phonological tasks (sound identification and rhyme oddity). Brady et al. (1994) studied phonological skills in low-SES kindergarten children and used three phonological tasks (rhyme generation, phoneme segmentation, and phoneme deletion). Significant group differences were found only on phoneme deletion. None of the phonological tasks used in these studies were

the same as those used in the present study, making it difficult to determine areas of similarity and differences. The final issue is that significant correlations between phonological processing and letter knowledge scores have consistently been reported despite the use of different phonological tasks and different letter knowledge tasks across studies reported in the literature. Of the five studies examined that specifically reported correlations between phonological and letter knowledge, all showed significant correlations accounting for 5% to more than 80% of the variance (Badian, 1995; Burgess & Lonigan, 1998; Bowey, 1995; Johnston et al., 1996; Share & Gur, 1999). In the present study, gains in letter identification skills were significantly related to phonological skills as measured by sound blending and rhyming scores. It appears that the links between skills in letter knowledge and phonological processing are sufficiently robust to be detected despite measurement differences across studies. As was expected, letter knowledge skills are related to the other cognitive skills assessed in this study. When the children were grouped based on their fall-to-spring gain scores in letter knowledge, group differences were found in rhyme detection, environmental print knowledge, and GRTR scores. Furthermore, gains in letter identification were correlated with scores obtained in the spring on phonological processing, rhyme detection, environmental print, and GRTR. The relation between letter knowledge skills and other cognitive skills seems to argue in favor of the codevelopment of several related skills, but due to study limitations, assessments of children’s performance on phonological processing, rhyme detection, environmental print, and Get Ready to Read! were not possible in the fall as well as in the spring. Had scores for these assessments been available at two time points, it would have been possible to study gain scores in these cognitive

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areas as well as in the development of letter identification. What we do know is that children who make little gains in letter knowledge are lagging behind their classmates in performance on other important cognitive skills. Based on their performance on these cognitive skills in the spring of the prekindergarten year, it is anticipated that these children will lag behind their peers at kindergarten entry in key skill areas on which many kindergarten curricula are based. There is some guidance in the literature on useful strategies for providing instruction to children who are lagging behind their classmates in cognitive skill development. There are numerous reports that instruction of an isolated skill may not be productive in facilitating reading development. For example, studies have reported that teaching letter identification skills explicitly does not result in improvements in early reading skills (e.g., Gibson & Levin, 1975; Tunmer, 1991), especially if the focus is exclusively on letter names rather than also including letter sounds (Ehri, 1983). Although teaching phonological processing skills appears to enable reading skills to develop (e.g., Lundberg, Frost, & Petersen, 1988), others have cautioned that training approaches that have a sole focus on phonological skills are resisted by some children (Naslund & Schneider, 1996). It also appears from the results of this study, as well as from the literature, that knowledge of at least some letters is related to performance on phonological tasks (Johnston et al., 1996; Muter, 1994; Naslund & Schneider, 1996; Stahl & Murray, 1994; Wagner et al., 1994). Thus, training that involves both phonological and letter knowledge has been reported to be successful (Wagner, 1996). Brady et al. (1994) have reported some success with a phonological training program implemented by kindergarten teachers in a classroom with nonreading children. Gains in phonological skills were found for the children participating in the training

sessions, and scores on the Word Identification and Word Attack subtests of the Woodcock Reading Mastery Test– Revised (WRMT-R; Woodcock, 1987) at first grade were higher for the children in the trained group. Ball and Blachman (1991) reported success in training phonemic segmentation to kindergarten children who also received letter name and sound experiences. The trained children outperformed children receiving only language activities, including letter name and sound experiences, and outperformed a control group in phoneme segmentation skills, in performance on Word Identification, and in reading a list of words. The training activities with the children were delivered by specially trained elementary teachers. Incorporating training activities into the curriculum of preschool programs that focus on skill development of both letter knowledge and phonological processing holds promise for making the changes in children’s cognitive skills. Moreover, the possibility for such training to be delivered by classroom teachers offers hope that out-of-classroom interventions are not the only approach that can yield successful outcomes. In conclusion, the results of this study illustrate the diversity of children’s skill development in the classroom, even when the same curriculum is employed. Although it was anticipated that children from low-income families might show slower skill development compared to scores reported by other researchers who studied children from higher income families, it was not anticipated that there would be such a sizeable group of children who made no gains or only very small gains in letter knowledge compared to their classmates. Knowing more about the variations in initial skills within the preschool classroom and about the characteristics of children who show these skill variations is important. This knowledge may lead to insights on how variations in skill development can be addressed in the classroom curriculum so that children

have the opportunities to develop skills at the end of the school year that will facilitate their participation in kindergarten activities. ABOUT THE AUTHORS

Victoria J. Molfese, PhD, is the Ashland/ Nystrand Chair and professor at the University of Louisville and director of the Center for Research in Early Childhood. Her research interests include: factors affecting intelligence and achievement test performance in preschool and school-aged children; prediction of developmental delay; and the identification and prediction of early reading skills in preschool children. Arlene A. Modglin, MA, is a researcher at Southern Illinois University at Carbondale. Her research interests include electrophysiology and behavioral measures of cognitive skills in preschool and school-age children. Jennifer L. Beswick, BA, is working on an MA in elementary school counseling. Jessica D. Neamon, MS, is working in private industry. Shelby A. Berg, BS, is interested in cognitive development in early childhood. C. Jeffrey Berg has a Doctor of Audiology degree and is currently in clinical practice. Andrew Molnar, BA, is interested in the neuropsychology of learning disabilities, emergent literacy, and preschool children’s academic development. Address: Victoria J. Molfese, Center for Research In Early Childhood, Room 236, College of Education and Human Development, University of Louisville, Louisville, KY 40014; e-mail: [email protected]

AUTHORS’ NOTE

This work was supported in part by a grant (R215R000023) from the U.S. Department of Education to the first author and by the University of Louisville.

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