SCIENTIFIC STUDIES OF READING, 10(1), 31–57
Copyright © 2006, Lawrence Erlbaum Associates, Inc.
Reading Efficiency in Native EnglishSpeaking and English-as-a-SecondLanguage Children: The Role of Oral
Proficiency and Underlying
Cognitive-Linguistic Processes
Esther Geva
University of Toronto
Zohreh Yaghoub Zadeh
Canadian Council on Learning
The research examined the extent to which (a) Grade 2 English-as-a-second-language (ESL) and English-as-a-first-language (EL1) children resemble each other on
word and text reading efficiency and (b) whether individual differences in word and
text reading efficiency in the two language groups can be understood in terms of similar underlying component processes. Despite an oral language proficiency advantage in the EL1 group, no EL1 advantage existed on any of the cognitive and reading
measures. Oral language proficiency, phonological awareness, rapid automatized
naming, and accurate word recognition were significant predictors of word and text
efficiency in the ESL group. Only rapid automatized naming and word recognition
were significant in the EL1 group. Overall, with the exception of English-language
oral proficiency skills, EL1 and ESL profiles of three efficiency subgroups (poor decoders, low efficiency, and high efficiency) were highly similar.
The goal of this article is to investigate the development of word and text reading efficiency in young children learning to read in their second language (L2). Because almost
no research has been conducted on the development of fluency and efficiency in L2 reading,webeginbyprovidingabriefoverviewofthefirstlanguage(L1)-basedliterature.
Correspondence should be sent to Esther Geva, The Department of Human Development and Applied Psychology, Ontario Institute for Studies in Education of the University of Toronto, Toronto, Ontario, Canada M5S 1V6. E-mail: [email protected]
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GEVA AND YAGHOUB ZADEH
No consensus exists in the reading literature with regard to the concept of reading fluency (Wolf & Katzir-Cohen, 2001). Terms such as fluency, efficiency, and
automaticity overlap to some degree and are often used as synonyms (National
Reading Panel, 2000). Some use the term automaticity to refer to various aspects of
word identification that can be processed with little effort and attention. Theories
of automaticity (LaBerge & Samuels, 1974) assume that accuracy develops before
speed and that an efficient reader is one who can read words both accurately and
fast. Theories of verbal efficiency (Perfetti, 1985) emphasize the importance of effective lexical retrieval processes and their impact on individual differences in
reading comprehension. Researchers have suggested that comprehension can be
enhanced when lexical access processes are executed efficiently and automatically, so that cognitive-processing resources are not overtaxed (Bowers, Golden,
Kennedy, & Young, 1994; Bowers & Wolf, 1993; Carver, 1997; LaBerge &
Samuels, 1974; Perfetti, 1985; Shany & Biemiller, 1995; Stanovich, 1992). The
National Reading Panel (2000) expanded the concept of reading fluency to include
“the ability to group words appropriately into meaningful grammatical units for interpretation” (pp. 3–6). In this article we use the term word reading efficiency to refer to accurate and fast reading of isolated words, and text reading efficiency to refer to accurate and fast reading of text.
Recent models espouse a more dynamic, developmental, and componential approach to the study of reading fluency (e.g., Berninger, Abbot, Billingsly, & Nagy,
2001; Kame’enui, Simmons, Good, & Harn, 2001; Levy, 2000; Wolf & Katzir-Cohen, 2001). Kuhn and Stahl (2000) discussed how the development of reading fluency relates to the stages of development described by Chall (1996). Wolf and
Katzir-Cohen suggested that in the early stages of reading development fluency
entails the gradual development of accurate and automatic execution of lower level
components involving orthographic, phonological, lexical, morphological, and
syntactic skills. To be efficient readers, children need to increase their repertoire of
large orthographic units that are easily accessible from memory and to carry out
these lower level operations with speed. Once readers have developed efficiency
with these basic aspects of reading and word decoding becomes effortless and fast,
text reading efficiency is reflected in paralinguistic features such as prosody. Efficient text reading allows for the allocation of attentional resources to higher level
reading skills involved in comprehension.
Wolf and Katzir-Cohen maintained that dysfluent reading can be the result of
impairment in any component process (Meyer & Felton, 1999; Wolf, Bowers, &
Biddle, 2000). Overall, less skilled L1 readers are described in the L1 reading literature as recognizing printed words more slowly than skilled readers (Biemiller,
1977/78; Carver, 1997; Denckla & Rudel, 1976; Ehri, 1998; Manis, Seidenberg,
Doi, McBride-Chang, & Patterson, 1996; Perfetti, 1985; Torgesen, 2001; Wagner
et al., 1997). Two underlying processes that have been implicated in efficient word
reading are phonological awareness and naming speed. Some theoreticians main-
READING EFFICIENCY IN ESL CHILDREN
33
tain that naming speed and phonological awareness tap common underlying phonological processes (e.g., Wagner et al., 1997). Others maintain that naming speed
may involve problems with a distinct phonological and visual timing mechanism
necessary for establishing unitized orthographic and phonological codes (e.g.,
Bowers & Wolf, 1993; Breznitz, 2001, 2002; Wolf & Bowers, 1999; see Savage,
2004, for a review). Notwithstanding this unresolved theoretical debate, L1-based
studies on the speed of naming various stimuli indicate that individual differences
in speed of letter, digit, or word naming predict fluent reading in L1 (Kirby et al.,
2003; Stage, Sheppard, Davidson, & Browning, 2001; Wimmer, Maryinger, &
Landerl, 2000; Young & Bowers, 1995).
READING DEVELOPMENT IN L2
Research on L2 reading development in L2 children is not as extensive as the literature on L1 reading development. However, in the last decade the database on L2
reading development has expanded. It has shown that (a) accuracy indexes of word
recognition and spelling skills in young L2 children are often identical to those of
L1 children (e.g., Geva, Yaghoub Zadeh, & Schuster, 2000; Lesaux & Siegel,
2004; Lipka, 2003; Wade-Woolley & Siegel, 1997; Wang & Geva, 2003); (b) cognitive-linguistic components such as phonological awareness and rapid automatized naming (RAN) can be measured reliably and utilized to predict performance
on word reading skills in L1 and L2 children alike (e.g., Chiappe & Siegel, 1999;
Comeau, Cormier, Grandmaison, & Lacroix, 1999; Durgunoglu, Nagy, &
Hancin-Bhatt, 1993; Geva et al., 2000; Gottardo, Yan, Siegel, & Wade-Woolley,
2001; Lesaux & Siegel, 2003; Lindsey, Manis, & Bailey, 2003; Wade-Woolley &
Siegel, 1997); and (c) once individual differences in phonemic awareness and
rapid naming have been taken into account, oral language proficiency skills do not
add substantially to the variance in accurate basic reading skills in L2 children
(Arab-Moghaddam & Sénéchal, 2001; Durgunoglu et al., 1993; Geva & Siegel,
2000; Geva et al., 2000; Gholamain & Geva, 1999; Lindsey et al., 2003).
Although L2 oral proficiency may not make a unique contribution over and
above other basic reading components to accuracy in L2 word recognition and
spelling skills, it is reasonable to expect that it should play a more pronounced role
when word and text reading efficiency are targeted. This issue has received very
little attention in the L2 literature and has been largely limited to the reading performance of adult L2 readers (e.g., Nassaji & Geva, 1999; Segalowitz, 1986;
Shimron & Sivan, 1994; Wade-Woolley & Geva, 1999). To date, only a handful of
L2 studies have systematically examined the development of word and text efficiency in L2 children (e.g., Geva & Clifton, 1994; Geva, Wade-Woolley, & Shany,
1997; Quiroga, Lemos-Britton, Mostafapour, Abbott, & Berninger, 2002). The
studies by Geva and her colleagues focused primarily on L1–L2 transfer. Both
34
GEVA AND YAGHOUB ZADEH
studies have shown that primary-level poor decoders were less fluent than good decoders in reading texts in English, in their L1, and in their L2 (French and Hebrew,
respectively). Of particular relevance in the present context is the Geva et al.
(1997) study in which performance on both accuracy and speed indexes of word
reading in L1 (English) and L2 (Hebrew) were very similar. This result is consistent with the observation that oral language proficiency did not make a unique contribution to word recognition in L2 children. In addition, in a result replicating
Jackson and Donaldson (1989), Geva et al. reported that in their L1 (English), children’s text reading time was faster than reading the same words presented out of
context. However, context did not have a facilitating effect in Hebrew, as their Hebrew proficiency was minimal.
Questions concerning the role of language proficiency in enhancing reading efficiency are particularly pertinent in L2 contexts. Carlisle and Beeman (2000) reported that primary-level Spanish–English bilingual children’s vocabulary knowledge predicted reading comprehension in the same language. An intuitively
sensible hypothesis is that word recognition skills of L2 children should be slower
than those of their L1 counterparts because their vocabulary is not as developed
and because they may be slower in accessing linguistic information (e.g., phonological, semantic, morphological) than their L1 counterparts. The same would be
expected to be true for text reading fluency in young L2 learners.
The question then becomes to what extent are L1-based theories of reading efficiency, which attribute slower reading to less efficient word recognition skills and
to reduced access to linguistic information, applicable to L2 learners? Are individual differences in reading efficiency related to individual differences in underlying
processes such as phonological awareness and rapid naming just as they appear to
be in L1 children? Alternatively, are oral proficiency and the ability to benefit from
linguistic context the driving forces in enhancing L2 word reading efficiency and
text efficiency? According to the latter explanation, L2 children would be expected
to have less efficient word recognition skills than their L1 counterparts and to be
less fluent readers because of limitations in the efficiency of retrieving phonological, semantic, and grammatical information.
In summary, although recent research has shed some light on the cognitive, linguistic, and orthographic underpinnings of accurate word recognition skills in L2
children, a dearth of knowledge exists about the development of reading efficiency
in L2 children. This study focuses on the emergence of reading efficiency in
young, primary-level English-as-a-second-language (ESL) children who have
mastered the basic principles of word reading. The study’s first objective is to compare ESL and English-as-a-first-language (EL1) children on word and text reading
efficiency and to examine the effects of context on reading efficiency. A comparison of reading efficiency in EL1 and ESL children would shed light on the assumption that EL1 children should be more efficient readers than ESL children due to
their better command of the language. A second objective is to examine the extent
to which a similar set of underlying processes drive word and text reading effi-
READING EFFICIENCY IN ESL CHILDREN
35
ciency in EL1 and ESL children. To achieve this objective, we explore the relative
contribution of individual differences in oral language proficiency, underlying
cognitive-linguistic processes (e.g., nonverbal ability, rapid serial naming, phonological awareness), and word recognition to text and word reading efficiency in
EL1 and ESL children. The third objective is to examine similarities and differences in profiles of EL1 and ESL groups that differ in their reading efficiency.
METHOD
Participants
ESL and L1 participants were recruited from three subsequent cohorts of children in
12 schools in four school boards in a large, multiethnic metropolis in Canada, where
4 of 10 residents were born outside the country. It is not the objective of this research
to examine the impact of socioeconomic status on reading achievement in ESL children. Moreover, we did not have access to individual family level socioeconomic status data. The 2001 Canadian Census data1 indicate that, on average, a nonofficial language (i.e., neither English nor French) was the language spoken by 58% of the
people living in the communities feeding into the participating schools (the percentages ranged from 43% to 73%). On average, 68% of the families living in these communities immigrated to Canada when they were at least 20 years old (with little variability between the communities); another 23% indicated that they immigrated
between the ages of 5 and 19. In other words, the majority of the residents were
first-generation immigrants. The incidence of poverty2 varied somewhat among the
communities. Whereas in 2 of the communities no incidence of poverty was reported, in each of the other communities a certain proportion (12%–50%) were classified as poor; the average incidence of poverty was 23%. It is also important to note
that the median family income in each of the 12 communities was substantially lower
than the median reported for the metropolis. Another key index of socioeconomic
status is parental education. Quite a bit of variability exists in terms of the highest
level of education achieved, reflecting immigration trends and Canadian immigration policies. On average, 17% of the individuals in the census district had less than 9
years of education, 27% obtained at least some secondary education, 13% had a high
school certificate, 8% had training in a trade, another 19% had college education, and
20% had obtained a bachelor’s degree or a higher university degree.
1It is possible to access the census demographic data for dissemination areas from Statistics Canada. Dissemination areas are small, relatively stable geographic units, composed of one or more blocks,
which respect the boundaries of census subdivisions and census tracts. They are uniform in terms of
population size (400–700), and they are the smallest standard geographic area for which all census data
are disseminated. The demographic information provided is based on the data from the dissemination
areas in which the participating schools are located.
2Poverty is defined as a family income of $30,000 (Canadian) or less for a family of four.
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GEVA AND YAGHOUB ZADEH
The initial sample consisted of 183 ESL children and 70 EL1 children. The data
of 3 (4.3%) EL1 children and 18 (8.8%) ESL children who had a standard score below 80 on a measure of nonverbal ability (Matrix Analogies Test [MAT]; Naglieri,
1989) were not included in the analyses, leaving final samples of 183 Grade 2 ESL
children (46% girls) and 67 (62% girls) EL1 children.
The native language groups represented in the ESL sample are Cantonese
(19%), Punjabi (41%), Tamil (18%), and Portuguese (22%). The ESL children
come from 12 schools, and the EL1 children from 7 of these schools. The mean age
was 88.43 months for the EL1 sample and 87.32 months for the ESL sample.
Consent forms in English and children’s home language were distributed in
each of the participating classrooms. Only children with parental consent participated in the study. Interviews of parents about home literacy and the extent to
which the native language was used at home were not conducted due to language
barriers, budgetary constraints, and reluctance by the school districts to allow access to parents. However, information about the EL1 or ESL language status of
children was determined through information about each child recorded in school
files and parent responses included in the consent forms. This information was
subsequently validated during interviews with classroom teachers. Only children
whose school records and teacher interviews indicated an ESL status were considered as such in the study. In addition, children who had not lived in an English-speaking country for at least 4 months at the onset of Grade 1 were not included. This precaution was taken to ensure that children who were included had
some systematic exposure to the rudiments of language and literacy instruction.
In English-speaking Canada, school-age children who are recent arrivals from a
non-English-speaking country typically attend school-based ESL classes for up to
2 years. In the school districts where this study was conducted, ESL instruction,
which is provided on a withdrawal basis, typically occurs in daily 30- to 40-min
sessions with groups of three to five children. In these small groups all children
have similar levels of English language proficiency but not necessarily the same
L1. Teachers with ESL specialist training conduct these classes. ESL classes focus
on the development of spoken English and on readiness for literacy skills. Besides
the ESL tutoring, new immigrant children attend regular classrooms, in which all
instruction takes place in English.3
Classroom teachers are expected to provide appropriate adaptations to the curriculum. Some of the ESL children in this study were attending ESL classes at the time
of testing or had attended such classes in the recent past. Except for pull-outs for
small-group ESL tutoring, the ESL children were completely integrated into the regular classroom.
3Classrooms in large urban centers in Canada are ethnically and linguistically diverse. Therefore,
providing systematic language and literacy instruction in the home language is not feasible. In some
communities, however, children may attend heritage language programs. Typically, these classes take
places after school or on weekends.
READING EFFICIENCY IN ESL CHILDREN
37
Measures
Cognitive and Linguistic Measures
Nonverbal intelligence. Children completed the MAT (Naglieri, 1989), a
measure of nonverbal intelligence. In this test, children are presented with an illustration of an incomplete visuo-spatial matrix and asked to complete it by locating
the missing piece among five or six patterned segments. The test has four subtests
(pattern completion, reasoning by analogy, serial reasoning, and spatial visualization), each of which consists of 16 matrixes. Testing within each subtest is discontinued after four consecutive errors. Results are reported in standard scores.
RAN. The RAN task, developed by Denckla and Rudel (1976), was used to
measure speed of rapid serial naming. In this continuous naming task, children are
asked to name five letters as fast as they can. Each letter appears 10 times in random order within 10 sets of five items. Prior to administering the RAN task, the
child is asked to name each of the five letters to ascertain familiarity with the letters. The RAN measure is not administered to those children who cannot name all
five letters without assistance. The children’s time (in seconds) to name all the letters on the board is used as the naming speed measure. Note that the lower the
score is the faster is the naming speed.
Phonological awareness (PA). PA was measured with a task adapted from
the Auditory Analysis Task developed by Rosner and Simon (1971). In this segmentation-deletion task, children must isolate and delete syllables or phonemes
and indicate the resulting word (e.g., “Say meat. Now say it without the /m/.”).
Methodological considerations guided us in adapting this task to the ESL population. In particular, it was necessary to minimize the possible confounding of language proficiency with performance on this phonological awareness task. Our primary concern was that many of the words or the resulting words on the original
Auditory Analysis Task are not likely to be familiar to young ESL children (e.g.,
stale). The items on the adapted task are all high-frequency words, and the resulting words after the child deletes the phoneme or syllable are also high-frequency
words (e.g., “Say leg. Now say it without the /l/.”). Of the 25 items on the task, the
first 4 involve syllable deletion, and the remainder require phoneme deletion in
word initial, word final, or word medial position. Some items require the deletion
of a phoneme in a consonant cluster. Four practice items precede the administration of the test items. Administration is discontinued after five consecutive errors.
The total correct scores are reported. The Cronbach α is .92.
Expressive One-Word Picture Vocabulary Test–Revised. The Expressive One-Word Picture Vocabulary Test–Revised (Gardner, 1990) was used to assess language proficiency. It is a standardized measure of expressive vocabulary in
which children are asked to provide one-word labels to line drawing pictures pre-
38
GEVA AND YAGHOUB ZADEH
sented to them one at a time. The test, which includes 100 nouns and verbs, is discontinued once a criterion of six consecutive errors has been established. The total
number of correct responses was used in the analyses.
Grammatical judgment. Children’s syntactic knowledge was assessed using items adapted from a grammatical judgment measure developed by Johnson
and Newport (1989). In this 40-item measure of receptive syntactic skills, the child
listens to prerecorded taped sentences that are either grammatically correct (e.g.,
“We ate the whole pizza by ourselves.”) or incorrect (e.g., “January is the most cold
month of the year.”). Half of the sentences are syntactically correct, and the other
half are syntactically incorrect. Each sentence is played twice on a tape recorder,
and the child is asked to indicate whether the sentence is said “the right way” or
“the wrong way.” No discontinue rule is used on this test. The total score was based
on the number of correctly judged sentences. The Cronbach α value is .77.
Basic Reading Skills Measures
Word Attack. The Word Attack subtest of the Woodcock Reading Mastery
Test–Revised (Woodcock, 1987) was used to assess children’s ability to utilize
their knowledge of grapheme–phoneme correspondence rules and orthographic
representations to decode or “attack” pseudowords in English. This test consists of
50 pronounceable pseudowords that comply with English orthographic rules (e.g.,
plip, cigbet). Children read the pseudowords one at a time, and testing is discontinued when the child makes six consecutive errors. Results are reported in terms of
total correct scores.
Word Recognition. To assess children’s English word recognition skills the
Word Recognition subtest of the Wide Range Achievement Test–Revised (Jastak
& Wilkinson, 1984) was used. This test consists of 42 unrelated words. It begins
with highly familiar, short words (e.g., cat), and gradually the words become less
frequent and more complex orthographically (e.g., pseudonym). Testing is discontinued when the child makes 10 consecutive errors. Results are reported in terms of
total correct scores.
Reading Efficiency Measures
The Biemiller Test of Reading Processes (Biemiller, 1981) was used to measure
word and text reading efficiency. The test yields measures of accuracy and speed in
reading isolated words and connected text. The isolated word lists come from the
corresponding texts.
Word efficiency. This subtest, which requires children to read isolated
words, provides an indication of children’s ability to identify unrelated words accurately and quickly. Children are presented with two lists, each consisting of 50
READING EFFICIENCY IN ESL CHILDREN
39
randomly ordered, single-morpheme words (taken from the two corresponding
texts; see the following discussion) and asked to read them as fast as possible. The
first list has easier words than the second.
Text efficiency. The Text Reading subtest focuses on text reading efficiency.
Participants are presented with two narrative texts (containing 100 words each).
They are required to read each text aloud as quickly as they can. The subtest includes two narratives, an “easy” text, which, according to Biemiller (1981) uses
primer-level words (e.g., bear, thank, no, they, water, fish, tried, and father), and a
relatively more “difficult” text, which consists of words that are typical of middle
elementary-level reading (e.g., register, asked, interested, saw, things, wood, tourists, and also). The reading materials are easy to minimize confounding of speed
and accuracy (Jackson & Donaldson, 1989).
Test administration. The child first reads the easy text and then the corresponding word list. The more difficult text and the corresponding word list are administered next. Both sets contain words that were chosen because they can be decoded with minimal difficulty. As Biemiller (1981) pointed out, the goal of the test
is to ascertain not how many words children can read but rather the speed at which
they read these words. Assessment of reading efficiency, however, can be compromised if children make too many errors. The assessor is thus instructed to aid the
child who stumbles on a word by providing without delay the unknown word as
well as the subsequent two words. The Biemiller test stipulates that if a child
makes more than 25% errors on the first text they should not be administered the
second text. Accordingly, in this study, when children failed to read the first passage with an acceptable degree of accuracy (75% accuracy regardless of speed),
they were considered to be poor decoders and were not administered the second
passage and corresponding word list.4
Speed scoring. To obtain a speed score in the isolated words condition, we
divided the total number of seconds that a participant took to read the easy and hard
lists by the total number of correct words read across the two lists. The same procedure was repeated to calculate text speed scores. The EL1 and ESL groups did not
differ on word accuracy (85% and 88%, respectively) or on text accuracy (89% and
90%, respectively).
4A comparison of efficiency scores in the EL1 and ESL groups with those published by Biemiller
(1981) revealed that the speed scores reported by Biemiller for the same age group were higher than
those obtained in this study. However, Biemiller excluded data of children who made more than three
errors on the tasks because he focused on the relationship between speed and accuracy in children who
read with accuracy. The objective of this study is to compare ESL and EL1 children, and, therefore, the
exclusion criterion was different—only children who made more than 25% errors when they read the
first and easy text were excluded.
40
GEVA AND YAGHOUB ZADEH
Reading efficiency scores. Two dependent measures were derived—a
word efficiency score and a text efficiency score. Efficiency scores were computed
based on a procedure used by Stanovich and West (1989). First, for each participant the number of errors on each task and the time to perform each task were converted into their respective Z scores. The resulting accuracy and speed Z scores for
each task were then combined and averaged to yield a single composite efficiency
score. The two word-based composite efficiency scores (based on the easy and difficult lists) were averaged to yield an overall score that represents the child’s ability to read words efficiently. The same procedure was applied to develop an overall
text efficiency score that represents the child’s ability to read with accuracy and
speed the two narratives. Note that the lower the Z score, the more efficient the
child’s reading.
Procedures
As part of a larger project (which began when the children were in Grade 1), children were administered a series of tests in the first half of their 2nd year in elementary school. At that time children in the EL1 and ESL groups have spent 1½ to 2½
years in schools in which English is the language of instruction (i.e., senior kindergarten and Grade 1). These tasks were part of a larger battery of tests, which was
administered across four testing sessions, each lasting approximately 30 min. Although the batteries were administered in a random order, tests within the batteries
were administered according to a fixed random order. Children were tested on an
individual basis by one of a number of experienced graduate students. For the
MAT, standardized scores were used. However, raw scores were used when other
standardized tests such as the Expressive One-Word Picture Vocabulary Test
(Gardner, 1990) and the Woodcock Reading Mastery Test–Revised (Woodcock,
1987) were used, because the norms were not developed for ESL children.
RESULTS
Of the 67 EL1 children, 12 (18%) were deemed to be poor decoders, as they made
too many decoding errors on the easy story and were, therefore, not able to progress to the difficult story. Of the 183 ESL children, 32 (17%) were not able to progress to the difficult story and were likewise considered poor decoders. Fifty-four
EL1 and 151 ESL children met the minimum requirements for becoming efficient
readers because they were able to complete both the easy and difficult subtasks of
the Biemiller (1981) test. A cross-tab chi-square analysis was done to examine
whether the proportion of poor decoders was higher in the ESL group than in the
EL1 group. This analysis reveals that the proportion of poor decoders in the EL1
and ESL groups was similar, χ2(1, N = 250) = .07, p = .460.
READING EFFICIENCY IN ESL CHILDREN
41
The focus of this article is on a comparison of underlying processes associated
with reading efficiency in ESL and EL1 children. The bulk of the Results section
focuses on a comparison of EL1 and ESL children who were able to read both stories with relative accuracy and speed. At the end of the Results section we revisit
the poor decoders group, when we compare the profiles of the relatively efficient
readers with the profiles of the poor decoders. Bonferoni correction was applied to
adjust the alpha level for multiple analyses. An alpha of .01 was selected as the
minimum acceptable level.
Comparison of EL1 and ESL Groups on Cognitive,
Language, and Reading Measures
Table 1 provides summary statistics by language group associated with all the cognitive, linguistic, reading, and oral proficiency measures. A multivariate analysis
TABLE 1
Differences Between EL1 and ESL Groups on Age, MAT, Syntactic
Knowledge, Vocabulary, PA, and RAN: Descriptive Statistics and
Multivariate Analysis of Variance Summary Table
EL1a
Measures
Age (months)
MAT
GramJudg
ExpVoc
PA
RAN
WordRec
WordAtt
WordAcc
TextAcc
WordSp
TextSp
ESLb
M
SD
M
SD
88.43
101.50
29.63
61.81
12.04
34.66
11.00
15.83
84.74
88.56
1.50
1.17
3.61
7.25
4.76
11.53
4.74
7.71
4.50
8.66
11.82
15.45
.71
.55
87.32
101.03
26.05
46.42
13.63
29.98
12.00
17.95
87.67
89.66
1.13
.99
3.58
9.46
5.34
14.80
6.15
6.44
4.27
10.59
11.97
17.99
.61
.55
Language Group, Fc
3.79
0.11
18.92***
47.92***
2.99
18.85***
2.11
1.73
2.40
0.64
14.02**
4.83
Note. EL1 = English as a first language; ESL = English as a second language; MAT = Matrix
Analogies Test; PA = Phonological Awareness; RAN = Rapid Automatized Naming (in seconds);
GramJudg = Grammatical Judgment Test; ExpVoc = Expressive One-Word Picture Vocabulary Test;
WordRec = Total words read correctly on Wide Range Achievement Test–Revised word recognition;
WordAtt = Total correct on Pseudoword reading; WordAcc = % words read correctly on 2 Biemiller
word lists (out of 100); TextAcc = % correct words over 2 Biemiller texts (out of 200); WordSp = speed
per word (in seconds) over 2 Biemiller word lists (lower is faster); TextSp = speed per word (in seconds)
to read 2 Biemiller texts (lower is faster).
an = 54. bn = 151. cAnalysis is based on low- and high-efficiency groups only.
**p < .001. ***p < .0001.
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GEVA AND YAGHOUB ZADEH
of variance (MANOVA) revealed that the language groups did not differ from each
other on the MAT, and the MANOVA did not reveal significant differences between the two language groups on age. However, as can be seen in Table 1, on the
two oral language proficiency indexes (Expressive One-Word Picture Vocabulary
Test and the Grammatical Judgment task) the mean in the EL1 group was significantly higher than in the ESL group. In this regard, it is useful to note as well that a
comparison of group means with grade equivalent norms revealed that the Expressive One-Word Vocabulary mean in the EL1 group was within the average range
(with a mean age equivalent of 7 years 10 months), whereas the age equivalent
score in the ESL group was about 2 years below that of the EL1 group (with a mean
age equivalent of 5 years 9 months).
On the naming speed measure (i.e., RAN), children in the ESL group were significantly faster than their EL1 counterparts. On the phonological awareness (i.e.,
PA) task, the word recognition task and the word attack task no differences existed
between the ESL and EL1 children.
As can be seen in Table 1, despite the differences in oral language proficiency,
ESL and EL1 groups did not differ on accuracy indexes of basic reading skills (i.e.,
word recognition and word attack). Based on the test norms, the group means observed for both the EL1 and ESL groups were within the normal range for their
grade in school (grade equivalent = 2.1–2.2). In the same vein, a comparison of the
group means on the word attack task revealed that the ESL group was slightly
higher (grade equivalent = 2.2) than the EL1 group (grade equivalent = 1.9).
To examine context effects on accuracy and speed in the EL1 and ESL groups,
two-way analyses of variance (ANOVAs) were conducted with context (words vs.
text) as a repeated measure and group (EL1 vs. ESL) as a nonrepeated, independent variable. Results pertaining to reading accuracy indicated that a main effect existed for context, F(1, 248) = 64.32, p < .0001, suggesting that in both language
groups reading in context was somewhat more accurate than reading isolated
words. The language group effect was not significant, indicating that the two
groups read at the same level of accuracy. However, a significant Language Group
× Context interaction exists, F(1, 248) = 6.59, p < .01, indicating that context was
more facilitating in the EL1 group. Results of the two-way ANOVA pertaining to
speed show that a significant main effect exists for context, F(1, 248) = 162.26, p <
.0001, indicating that in both language groups texts were read faster than isolated
words. A main effect was also found for language group, F(1, 248) = 8.28, p <
.005, indicating that ESL children were faster, and a significant Context × Language Group interaction, F(1, 248) = 26.02, p < .0001, indicating again that context was more facilitating in the EL1 group than in the ESL group.
To summarize, the EL1 group outperformed the ESL group on the language
proficiency measures, but the ESL group outperformed the EL1 group on letter-naming speed (i.e., RAN) and word-naming speed. The groups did not differ on
PA, word recognition, and pseudoword reading. Finally, the context effect was
more pronounced in the EL1 group than in the ESL group.
READING EFFICIENCY IN ESL CHILDREN
43
Correlates of Reading Efficiency
To examine patterns of individual differences in reading efficiency in the EL1 and
ESL groups, we first examined intercorrelations among the reading efficiency
scores and the potential predictor variables. Table 2 provides a summary of these
analyses within each language group. In general, it is noteworthy that in the ESL
group most variables correlated significantly with each other, whereas in the EL1
group this correlation was not consistent. The results of the correlational analyses
coupled with theoretical considerations guided us in the selection of predictor variables in subsequent hierarchical regression analyses and in determining the order
in which variables were entered into the regression. In particular, potential predictor variables that are of a more general nature, such as nonverbal ability and language proficiency indexes, were entered first. The variables of interest (i.e., basic
processing indices such as phonemic awareness and naming speed) were entered
in subsequent steps. Word recognition was entered last to examine the extent to
which accurate word recognition makes a unique contribution to reading efficiency over and above the contribution of the general and basic processing variables listed above. Note the positive and significant correlation between Expressive One-Word Picture Vocabulary and Grammatical Judgment (r = .48).
Predictors of Reading Efficiency
As can be seen in Table 2, very high correlations exist between the two (untimed)
basic word reading measures (word attack and word recognition) and the word and
text efficiency measures. To avoid multicolinearity, word attack was not included
in the regression analyses, but word recognition was entered last for theoretical
reasons. In particular, the word recognition task includes a large array of words
varying in frequency and regularity, and it correlates with spelling and orthographic skills. In these hierarchical regression analyses, conducted separately for
the EL1 and ESL samples, text efficiency and word efficiency were the dependent
measures. In the first set of regression analyses, all potential predictors were included. In subsequent analyses nonsignificant predictors were omitted if a given
variable was not significant for both language groups.
Predictors of Reading Efficiency in the ESL Group
The first set of hierarchical regression analyses examined the role of MAT, oral proficiency (as measured by vocabulary knowledge and grammatical judgment), phonological-processing skills (as measured by PA and RAN), and word recognition skills
in explaining individual differences in word and text reading efficiency in the ESL
group. In general, the percentage of variance explained by each of the predictor variables was similar for both the word and text reading efficiency measures. As can be
seen in Table 3, MAT did not play a significant role in explaining variance in word
44
1.000
.030
–.112
.065
.199
–.124
–.073
–.073
.153
.246
1
–.225*
1.000
.280
.298
.184
–.084
–.238
–.228
.246
.293
2
–.121
.368**
1.000
.367*
.268
–.216
–.307
–.250
.346
.381*
3
.096
.466***
.479***
1.000
.248
.108
–.084
–.014
.254
.127
4
.001
.356***
.401***
.293**
1.000
–.149
–.327*
–.279
.562***
.662***
5
.033
–.169
–.147
–.243*
–.267**
1.000
.590***
–.610***
–.556***
–.581***
6
–.064
–.193
–.273**
–.378***
–.508***
.604***
1.000
.966***
–.798***
–.680***
7
–.090
–.142
–.239*
–.337***
–.493***
.624***
.967***
1.000
–.790***
–.661***
8
.029
.339***
.380***
.416***
.693***
–.543***
–.838***
–.813***
1.000
.826***
9
.017
.369***
.345***
.368***
.731***
–.447***
–.739***
.719***
.850***
1.000
10
Note. EL1 n = 54, and ESL n = 151. EL1 correlations are above the diagonal and ESL are below the diagonal. MAT = Matrix Analogies Test; PA = phonological awareness; RAN = Rapid Automatized Naming; WordEff = word efficiency; TextEff = text efficiency; EL1 = English as a first language; ESL = English
as a second language; GramJudg = Grammatical Judgment Test; ExpVoc = Expressive One-Word Picture Vocabulary Test; WordRec = total words read correctly
on Wide Range Achievement Test–Revised; WordAtt = total correct on Pseudoword Reading.
*p < .01. **p < .001. ***p < .0001.
EL1
1. Age
2. MAT1
3. GramJudg
4. ExpVoc
5. PA
6. RAN
7. TextEff
8. WordEff
9. WordRec
10. WordAtt
ESL
TABLE 2
Intercorrelations Among Age, MAT, Language Measures, PA, RAN, WordEff, and TextEff in the EL1 and ESL Groups
45
READING EFFICIENCY IN ESL CHILDREN
TABLE 3
Hierarchical Regression Analyses Predicting Reading Efficiency in ESL,
With MAT, Syntactic Knowledge, Vocabulary, PA, and RAN
as Independent Variables
Word Efficiency
Text Efficiency
Variable
R2
∆R2
∆F
p
β
R2
∆R2
∆F
p
β
MAT
GramJudg
ExpVoc
PA
RAN
WordRec
.02
.06
.13
.31
.54
.73
.02
.04
.07
.18
.23
.19
3.03
6.21
10.72
36.21
69.05
96.33
ns
.014
.001
.0001
.0001
.0001
.12
.09
–.08
–.01
.24
–.72
.03
.09
.16
.32
.53
.75
.03
.05
.07
.16
.20
.22
5.10
8.54
12.43
33.90
60.34
120.76
ns
.005
.001
.0001
.0001
.0001
.07
.07
–.09
.03
.19
–.77
Note. ESL = English as a second language; MAT = Matrix Analogies Test; PA = phonological
awareness; RAN = Rapid Automatized Naming; GramJudg = Grammatical Judgment Test; ExpVoc =
Expressive One-Word Picture Vocabulary Test; WordRec = total words read correctly on Wide Range
Achievement Test–Revised.
and text reading efficiency in the ESL group. Of the two oral language proficiency
measures, grammatical judgment was marginally significant (p = .014), explaining
4% of the variance in word reading efficiency, and it was significant, explaining 5%
of the variance in the case of text reading efficiency. Vocabulary, the second measure
of oral language proficiency, was significant, explaining an additional 7% of the
variance in both word efficiency and text reading efficiency. PA explained an additional 18% of the variance in word reading efficiency and 16% of the variance in text
reading efficiency. RAN added a further 23% to the explained variance in word reading efficiency and 20% to text reading efficiency. It is noteworthy that RAN and PA
explained together 41% of the variance in word reading efficiency and 37% of the
variance in text reading efficiency, even though they were entered after MAT and the
oral proficiency indexes. Finally, word recognition, entered last, explained an additional 19% of the variance in word reading efficiency and 22% in text reading efficiency. Altogether, 73% of the variance in word efficiency, and 75% of the variance
in text efficiency was explained by the predictor variables.
Predictors of Reading Efficiency in the EL1 Group
As was the case in the ESL group, the hierarchical regression analyses examine the
role of MAT oral proficiency, PA, RAN, and word recognition in explaining word
and text reading efficiency in the EL1 group. Due to power constraints in the EL1
sample, in the first step the predictor measures were entered in three blocks: MAT
in the first block, the two oral proficiency measures in the second block, and the
two phonological-processing measures (RAN and PA) and word recognition in the
third block.
46
GEVA AND YAGHOUB ZADEH
TABLE 4
Hierarchical Regression Analyses Predicting Reading Efficiency in EL1,
With MAT, Vocabulary, Syntactic Knowledge, Vocabulary, PA, and RAN
as Independent Variables
Word Efficiency
Variable
Block 1
MAT
Block 2
GramJudg
ExpVoc
Block 3
PA
RAN
WordRec
PA
RAN
WordRec
Text Efficiency
R2
∆R2
∆F
p
R2
∆R2
∆F
p
.06
.06
2.82
ns
.06
.06
2.89
ns
.13
.08
1.99
ns
.14
.09
2.27
ns
.71
.08
.42
.69
.57
.08
.34
.28
27.37
4.04
26.44
40.08
.0001
ns
.0001
.0001
.69
.11
.41
.68
.54
.11
.31
.27
24.12
5.66
24.10
37.08
.0001
ns
.0001
.0001
Note. EL1 = English as a first language; MAT = Matrix Analogies Test; PA = phonological awareness; RAN = Rapid Automatized Naming; GramJudg = Grammatical Judgment; ExpVoc = Expressive
One-Word Picture Vocabulary Test; WordRec = total words read correctly on Wide Range Achievement Test–Revised.
Results of the first set of analyses pertaining to word and text reading efficiency
(see top panel of Table 4) show that only the third block—consisting of PA, RAN,
and word recognition—was significant. Because the first two blocks were not significant for either word or text efficiency, we eliminated these variables from subsequent analyses. The lower panel of Table 4 provides a summary of these results. In
the second set of analyses PA, RAN, and word recognition were entered one at a time.
These analyses revealed that PA was not significant for word or text reading efficiency, and RAN was highly significant, explaining an additional 34% of the variance for word efficiency and 31% for text efficiency. Word recognition, entered last,
explained an additional 28% of the variance in word efficiency and 27% in text efficiency. Altogether, 62% of the variance in word efficiency and 58% of the variance in
text efficiency were explained by RAN and accurate word recognition.5
5It should be noted that multiple regression analyses, using speed of word and text reading as a dependent variables (rather than efficiency scores) and the identical set of predictor variables reported in
the text, yielded similar results to those reported in this article, with only slight fluctuations in the percentage of variance explained by each additional variable.
READING EFFICIENCY IN ESL CHILDREN
47
By definition, it is not possible to measure reading efficiency in children whose
reading skills are not sufficiently developed. At the same time, it is useful to examine profiles of EL1 and ESL children to better understand the underlying mechanisms that distinguish subgroups of children that differ in reading efficiency. We
turn to this topic in the next section.
Subgroup Reading Efficiency Profiles
The efficient EL1 and ESL readers (i.e., those who completed both texts on the
Biemiller, 1981, task) were divided further into two subgroups, high efficiency and
low efficiency, based on the median of the reading efficiency distribution. To exclude borderline cases around the cutoff point, data of children whose text reading
efficiency Z scores were between –.05 and .05 were dropped. To this end, the data
of 6 (9%) EL1 participants were excluded, as were the data of 5 (3%) ESL participants. The high-efficiency group consisted of children whose Z scores were at least
.05 above the median on the text reading efficiency index. The low-efficiency
group consisted of children whose Z scores were at least –.05 below the median on
the text reading efficiency index. The EL1 low-efficiency group consisted of 20
(30%) children, and the EL1 high-efficiency group consisted of 29 (43%) children.
The ESL low-efficiency group consisted of 42 (24%) children, and the ESL
high-efficiency group consisted of 104 (57%) children. The third subgroup integrated into this analysis consisted of the poor decoders who were not able to com-
FIGURE 1 ESL and EL1 profiles of poor decoders, low-efficiency, and high-efficiency readers (Z scores).
48
GEVA AND YAGHOUB ZADEH
plete the two stories. The poor decoder subgroup consisted of 12 (18%) children in
the EL1 group and 32 (17%) children in the ESL group.
Figure 1 provides a visual depiction of the EL1 and ESL high-efficiency,
low-efficiency, and poor decoder profiles. To examine the reading efficiency
group and language group effects on the cognitive, linguistic, and reading measures, a MANOVA with Scheffé post hoc analyses was carried out. The results
of these analyses are summarized in Table 5. Consistent with the previous analyses, a significant language–group effect exists on the two oral language indexes
as well as on RAN but not on MAT, PA, or any of the reading measures. A significant reading efficiency subgroup effect occurs on all the measures except for
the Expressive One-Word Vocabulary Test, which was marginally significant (p
= .015). Post hoc analyses indicated that the poor decoder subgroup and the
low-efficiency subgroup were significantly lower than the high-efficiency group
on MAT and on the language proficiency measures. The efficiency groups also
differed from each other on RAN, PA, word recognition, and word attack. The
interaction between language group and reading efficiency group was not significant for any of the variables.
TABLE 5
The Effect of Language Group, Efficiency Group, and the Language Group
× Efficiency Group Interaction on MAT, Syntactic Knowledge, Vocabulary,
PA, RAN, Word Recognition, Word Attack, Word and Text Accuracy, and
Word and Text Speed: Multivariate Analysis of Variance Summary Table
Language Group
Effect
MAT
GramJudg
ExpVoc
RAN
PA
WordRec
WordAtt
Efficiency Group Effect (a,b,c)a
Language ×
Efficiency
Effect
F
F
Post Hoc
F
1.03
22.64***
84.17***
26.41***
0.05
0.26
0.76
8.03***
8.28***
5.67*
68.88***
31.80***
165.07***
83.24***
a < c***; b < c**
a < c**; b < c**
a < c***; b < c**
a < b < c***
a, b < c***; a < b**
a < b < c***
a, b < c***; a < b**
0.02
0.34
3.18
1.79
4.01
0.82
2.45
Note. All measures converted to Z scores. MAT = Matrix Analogies Test; PA = phonological
awareness; RAN = Rapid Automatized Naming (in seconds); GramJudg = Grammatical Judgment;
ExpVoc = Expressive One-Word Picture Vocabulary Test; WordRec = total correct on Wide Range
Achievement Test–Revised; WordAtt = total correct on Pseudoword Reading.
aa = poor decoders, b = low-efficiency readers, c = high-efficiency readers.
*p < .01. **p < .001. ***p < .0001.
READING EFFICIENCY IN ESL CHILDREN
49
DISCUSSION
ESL and EL1 Differences on Cognitive-Linguistic and
Reading Measures
Results of this study are in line with previous research that has shown that even
though EL1 and ESL children differ on their oral proficiency, they can perform at the
same level on accuracy indexes of basic reading skills such as word recognition and
word attack (e.g., Geva et al., 2000; Gottardo et al., 2001; Lesaux & Siegel, 2003).
This study takes the research in this area one step further by examining reading efficiency of ESL children at the primary level. Results of this study suggest that ESL
children can read words and simple texts with the same efficiency as EL1 children.
These findings challenge the intuitive belief that ESL children would read isolated
words and text less efficiently because of lower command of the L2.
Surprisingly, the ESL children read isolated words significantly faster than the
children in the EL1 group, and they were also faster than the EL1 group on a sequential letter-naming task. This finding is consistent with the findings of Lesaux
and Siegel (2003), who found an ESL advantage on naming speed. The ESL advantage on word reading speed and letter-naming speed can be approached from
three different angles.
First, these differences could be attributed to socioeconomic status factors.
However, children in the EL1 and ESL groups were sampled from schools located
in the same working-class neighborhoods, thus reducing the strength of this argument. Second, given that EL1 and ESL children were selected from the same
schools, the argument that the results might reflect systematic differences in instructional approaches can be ruled out. Third, group differences in speed could be
attributed to group differences in ability in one of two ways. One argument is that
the groups might differ in general cognitive ability. However, results indicate that
the EL1 and ESL groups did not differ on nonverbal aspects of cognitive ability.
Relatedly, some might argue that the differences reflect the positive impact of bilingualism on underlying basic cognitive processes that are essential for reading
development (Bialystok, 2001; Bialystok & Herman, 1999). To date, this argument
has been examined with regard to metalinguistic skills, and no sound theoretical or
empirical evidence exists to suggest that bilingualism might have a facilitating effect on basic naming speed processes.
In this context it is important to be mindful of Cummins’s (2000) threshold hypothesis that attempts to reconcile research evidence on positive and negative effects of bilingualism. This hypothesis suggests that language and cognitive development might be enhanced in bilinguals, provided that relatively high levels of
proficiency have been attained in both languages. Clearly, this hypothesis is not applicable to young ESL learners. None of these explanations are satisfactory, and
50
GEVA AND YAGHOUB ZADEH
further research is necessary to explore the factors that might contribute to the ESL
advantages, reported in this article and elsewhere (e.g., Lesaux & Siegel, 2003).
It is noteworthy that ESL children are similar to their EL1 counterparts in their
ability to read simple narratives with accuracy and speed. In other words, primary-level ESL children can develop accuracy and speed in L2 reading and, indeed, achieve efficiency similar to that of their EL1 counterparts, provided that
they are exposed to systematic instruction in language and literacy in English
(Gersten & Baker, 2003; Gersten & Geva, 2003; Lesaux & Siegel, 2003; Stuart,
1999) and that they have well-developed word recognition skills. As Chall (1996)
remarked, in Stage 2 most children learn to use their decoding knowledge and utilize the “redundancies of the language and the stories they read” (p. 19), which can
be the case for ESL children as well, provided that the language of the narratives is
not too demanding. At the same time, it is important to remember that the route traversed by EL1 and ESL children is not identical: The EL1 children come to school
already equipped with the language they need to enable them to read simple texts
(Chall, 1996), whereas the ESL children develop their language skills in parallel to
the development of their reading skills. It is also important to bear in mind that,
notwithstanding home language, reading efficiency is a construct whose very nature changes with development and that it is not an all-or-none phenomenon.
Previous literature on context effects (e.g., Jackson & Donaldson, 1989) has
shown that context enhances word reading efficiency. Likewise, in this research
monolingual and ESL children are found to read words faster and more accurately
when the words are presented in context than when the same words are presented
in isolation. However, the context facilitation was more pronounced in the EL1
group than in the ESL group, presumably due to the fact that the EL1 children have
better command of the English language than the ESL children. A better command
of the language helps children to anticipate words presented in context and enhances accurate and fast recognition of printed words. In a study of primary-level
children, Geva et al. (1997) showed that when children read in their L1 context facilitation is used and words are read more accurately and faster. However, this facilitation was not detected when the same children read in their L2 (Hebrew), a language in which they had minimal proficiency. The discrepancy between the results
of the Geva et al. study regarding the context effect for L2 readers may be due to
differences in morphosyntactic complexity (Hebrew is denser) and the fact that the
level of English oral language proficiency of the ESL participants was more advanced in this study than was the case in the Geva et al. study. Finally, future research needs to examine the extent to which the results can be replicated in a design in which word-reading latency is measured for words presented one at a time
on a computer screen. Jackson and Donaldson found that precocious younger readers were disadvantaged on the Biemiller (1981) scrambled list but not when
word-reading latency was measured with words presented individually on a computer screen. It is possible that because the EL1 children might be more sensitive to
READING EFFICIENCY IN ESL CHILDREN
51
grammatical patterns and grammatical expectancies than the ESL children, the
reading of a scrambled, serially presented list is more debilitating to them than to
ESL children.
Factors Contributing to Reading Efficiency in EL1
and ESL Children
The second objective of this study is to examine the relative contribution of oral
language proficiency, underlying cognitive-linguistic processes, and word recognition to reading efficiency. This objective embraces two related questions: What
are the processing factors contributing to word and text reading efficiency, and to
what extent does the same pattern exist in the ESL and EL1 groups?
An intuitively appealing argument might be that to read words efficiently children need to have well-developed vocabulary and well-developed decoding skills
but that syntactic and discourse level knowledge is necessary to read texts with efficiency. However, the results did not reveal a different path for word and text reading efficiency in the EL1 or ESL groups. In the ESL group vocabulary and syntactic knowledge explained jointly 11% of the variance in the case of word efficiency
and 12% in the case of text efficiency. These results suggest that the ESL participants reached some kind of a threshold in their English language proficiency that
enabled them to read with ease texts that do not challenge their current linguistic
knowledge. That is, it appears that when the language used in the reading materials
is below or perhaps just at the level of oral proficiency (Chall, 1996), L2 oral proficiency contributes only marginally to word or text reading efficiency of ESL children, and it plays no role in the case of EL1 children. The scenario might be different when the reading materials are more demanding in terms of the range of
vocabulary and syntactic structures. This issue needs to be explored in future research. More research is needed to examine the contribution of oral language proficiency when linguistically more demanding texts are presented. Furthermore, in
this research accuracy aspects of oral language proficiency were tapped. The extent to which oral language fluency might contribute more substantially to reading
efficiency is another question that should be targeted.
Results of this study demonstrate that over and above the contribution of oral
language proficiency, RAN, PA, and well-developed word recognition skills contribute substantially to word and text reading efficiency of ESL children. RAN and
PA are the same processing constructs that have been shown elsewhere (e.g., Geva
et al., 2000; Gholamain & Geva, 1999; Lesaux & Siegel, 2003) to account for individual differences in accurate word recognition skills of EL1 and ESL children. In
this research we examine the role of these processes as well as word recognition in
reading efficiency. The results show that the roles played by PA, rapid naming, and
accurate word recognition skills in accounting for individual differences in word
and text efficiency are not identical for EL1 and ESL children. Each of these vari-
52
GEVA AND YAGHOUB ZADEH
ables contributes substantially to reading efficiency in ESL children. In the case of
EL1 children, rapid naming and word recognition play a substantial role in word
and text efficiency, whereas PA does not.
One way of thinking about these results is that, in fact, ESL children are more
challenged by the reading tasks precisely because they read in a language in which
they are not fluent. Therefore, they need to draw on all the cognitive and linguistic
resources that they have. This explanation is supported by the observation that in
the ESL group (but not in the EL1 group) most of the cognitive, linguistic, and
reading measures correlated significantly with each other. That is, those ESL children who are more efficient readers are more likely also to have better language
skills, faster naming speed, better developed PA skills, and more advanced word
recognition skills. It is interesting to consider possible theoretical underpinnings of
the differences in the amount of variance explained by rapid naming and PA in
each language group. We propose that perhaps these EL1–ESL differences reflect
that in ESL learners the effect of underlying cognitive-linguistic processes is more
generic or more extensive. It is likely that with increased language proficiency and
additional development of more advanced reading skills these underlying processes may gradually become more specialized and domain specific, the way they
are in the EL1 group. This hypothesis is worth exploring in future research.
In general, young ESL school children who have acquired a certain level of oral
language proficiency in English and who developed their oral and literacy skills in
an immersion context can be rather similar to their EL1 counterparts in terms of the
ease with which they can execute basic word recognition processes and read efficiently simple narratives. Clearly, aspects of language proficiency such as lexical
and grammatical skills are essential for reading efficiency; the ESL children who
were efficient readers also had higher oral proficiency in English than those ESL
children who were less efficient. At the same time, simplistic notions of L2 reading
performance that emphasize primarily oral language proficiency need to be refined. This refinement can be achieved by considering the cognitive-linguistic processes that underlie reading processes and that appear to be sources of individual
differences in efficient reading of EL1 and ESL children. Less efficient reading in
ESL and EL1 children alike can be attributed to less efficient word recognition
skills and to reduced or less effective access to linguistic information.
Comparing the Profiles of ESL and EL1
Efficiency Subgroups
L1-based research suggests that not all children who reach Grade 2 are ready to develop their reading efficiency and that some continue to be “dysfluent” (Wolf &
Katzir-Cohen, 2001). Results of this study extend this statement to ESL children in
the primary level. The examination of profiles of EL1 and ESL subgroups that differ in their reading efficiency underscores the range of language and reading skills
READING EFFICIENCY IN ESL CHILDREN
53
that exist among ESL (and EL1) learners and provides useful insights into the type
of reading skills that EL1 and ESL children develop and the cognitive and linguistic resources they draw on to read with efficiency.
Several observations can be made on the basis of an examination of the efficiency group profiles depicted in Figure 1. Most important, the subgroups defined
on the basis of reading efficiency are consistently different from each other on cognitive, oral language, and reading measures. Next, clearly, regardless of efficiency
group, EL1 children have better developed oral language skills in English than
ESL children. However, the language group effect concerning rapid naming is in
the reverse direction, with ESL children within each efficiency group outperforming their EL1 counterparts. (Possible explanations for this finding have been previously discussed with regard to ESL–EL1 differences on rapid automatized letter
naming.) Third, it is of interest that ESL children who were classified as high-efficient readers had significantly higher scores on PA than the high-efficient EL1
children, whereas among low-efficient and poor decoders, EL1 children outperformed their ESL counterparts. Because the high-efficiency EL1 and ESL groups
did not differ on nonverbal ability, an explanation for this unexpected result based
on general, nonverbal cognitive ability can be ruled out. However, possibly this
ESL advantage in the high-efficient group may be related to the enhancing effects
of bilingualism. It is possible that the high-efficient ESL children benefit from
their bilingual status in a way that distinguishes them from ESL children who are
less efficient readers. They may be better able to extract subtle phonological distinctions and have more precise phonological representations that may be related
also to their relatively better-developed vocabulary (Metsala & Walley, 1998).
Finally, it is informative to note that the high-efficiency ESL children had better
developed oral language proficiency in English than the ESL children who were
poor decoders or low-efficiency readers.
There are clinical and research implications to these observations. When young
ESL children have problems in developing reading efficiency even when they read
simple materials, attribution to lack of adequate oral language proficiency is not
warranted automatically—especially when ESL children develop their oral and literacy skills in a systematic reading instruction program. The culprit in this case
may be inaccurate and dysfluent word recognition skills and deficits in the underlying cognitive-linguistic processes necessary for the development of accurate and
fluent word recognition skills. In addition to oral language development, such children may benefit from intervention approaches that focus on the development of
efficient word recognition skills (Gersten & Baker, 2003; Gersten & Geva, 2003;
Quiroga et al., 2002).
Future research should continue to explore the development of reading efficiency
with L2 children of different ages, with different levels of oral language proficiency,
and with academic reading materials that vary in linguistic and orthographic demands. Special attention is also needed to examine the relevance of these findings to
54
GEVA AND YAGHOUB ZADEH
children who, in addition to being L2 learners, may have a learning disability or a language impairment. This research was conducted in urban and suburban areas that are
ethnically and linguistically mixed, but some immigrant communities are more homogeneous in socioeconomic status and home language. To examine the
generalizability of these results, large-scale, multilevel studies need to be conducted
that consider the effects of within-child factors and contextual factors, including
level of ethnic diversity at the community and school level for language and literacy
outcomes. Finally, given the large number of children who learn to read in an L2
without the option of first mastering their L1 reading skills, questions pertaining to
L2 reading efficiency and to the impact of different educational practices on reading
efficiency and on reading comprehension of academic and narrative texts deserve
sustained and systematic attention in the near future.
ACKNOWLEDGMENTS
This research was supported by grant 410–96–0851 from the Social Sciences Research Council of Canada and a grant from the Ontario Ministry of Education to
Esther Geva. Zohreh Yaghoub Zadeh is an analyst at the Canadian Council on
Learning.
We thank the staff and children at the Peel Board of Education, the Toronto District School Board, the Toronto Separate School Board, and the York Region Separate School Board of Education for their patience and cooperation.
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Manuscript received April 23, 2004
Accepted April 19, 2005