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Evidence for the Importance of Academic Word Knowledge for the Academic Achievement of Diverse Middle School Students

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Despite the current theoretical momentum for the importance of academic English and the acknowledgment that academic materials increase in complexity through the grades, little empirical attention has been devoted to the role of academic English in academic achievement. This study examined the amount of variance in academic achievement explained by academic word knowledge for diverse middle school students. A linguistically and socioeconomically diverse sample of grade 7 and 8 students (N�=339) was administered measures of overall breadth of vocabulary knowledge and general (i.e., cross-discipline) academic word knowledge, and the explanation of variance in standardized academic achievement tests across 4 disciplines was explored. For the entire sample, knowledge of general academic words explained a considerable and significant amount of variance in academic achievement across 4 disciplines. Findings lend empirical support to current calls for providing academic language support for early adolescents from non-native English speaking and low socioeconomic backgrounds.
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Evidence for the Importance of Academic Word Knowledge for the Academic Achievement of
Diverse Middle School Students
Author(s): Dianna Townsend, Alexis Filippini, Penelope Collins, and Gina Biancarosa
Reviewed work(s):
Source:
The Elementary School Journal,
Vol. 112, No. 3, Academic Language (March 2012), pp.
497-518
Published by: The University of Chicago Press
Stable URL: http://www.jstor.org/stable/10.1086/663301 .
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EVIDENCE FOR THE IMPORTANCE OF
ACADEMIC WORD KNOWLEDGE FOR
THE ACADEMIC ACHIEVEMENT OF
DIVERSE MIDDLE SCHOOL STUDENTS
Dianna Townsend
  ,

Alexis Filippini
 

Penelope Collins
 
, 
Gina Biancarosa
  

Despite the current theoretical momentum for the impor-
tance of academic English and the acknowledgment that
academic materials increase in complexity through the
grades, little empirical attention has been devoted to the
role of academic English in academic achievement. This
study examined the amount of variance in academic
achievement explained by academic word knowledge for
diverse middle school students. A linguistically and socio-
economically diverse sample of grade 7and 8students (N
339) was administered measures of overall breadth of vo-
cabulary knowledge and general (i.e., cross-discipline) aca-
demic word knowledge, and the explanation of variance in
standardized academic achievement tests across 4disci-
plines was explored. For the entire sample, knowledge of
general academic words explained a considerable and sig-
nificant amount of variance in academic achievement
across 4disciplines. Findings lend empirical support to cur-
rent calls for providing academic language support for early
adolescents from non-native English speaking and low-
socioeconomic backgrounds.
DESPITE the increasing research attention to struggling older readers
(Deshler et al., 2008; Roberts, Torgesen, Boardman, & Scammacca, 2008),
the current theoretical momentum for the importance of academic English
(i.e., Zwiers, 2008), and the acknowledgment that academic materials in-
crease in complexity through the grades (Fang, Schleppegrell, & Cox, 2006), little
     ,  
©2012 by The University of Chicago. All rights reserved. 0013-5984/2012/11203-0005 $10.00
empirical attention has been devoted to the role of academic English in academic
achievement. In other words, while there is a theoretical and practical push to im-
prove students’ academic English proficiency, there is little empirical evidence that
academic English proficiency explains variance in academic success above and be-
yond general language proficiency skills (i.e., breadth of vocabulary knowledge and
reading comprehension). Such empirical evidence, if it exists, would be instrumental
in creating buy-in among educators, educational leaders, policy makers, and funding
agencies to effect the kind of educational change needed to support the advanced
literacy skills of middle school readers.
Our study attempts to address this gap in the literature by empirically examining
the role of academic word knowledge, one component of academic language, in
academic achievement across disciplines. Academic vocabulary is one feature among
many that make up the register of academic English. A register of a language is “the
constellation of lexical and grammatical features that characterizes particular uses of
language” (Schleppegrell, 2001,p.431). While competency with the overall register of
academic English—not just academic vocabulary—is essential for success in school,
in research, a focus on academic vocabulary is advantageous both from a research
and a pedagogical point of view. From a research point of view, there is a method-
ological advantage in that a published measure of academic vocabulary exists and has
been validated with large samples of language-minority students. From a pedagogi-
cal perspective, academic vocabulary can serve as a scaffold for teachers’ understand-
ing of the larger register of academic English. Teachers can better support their
students’ academic language development if they can recognize the challenging lin-
guistic demands of disciplinary texts, and vocabulary can serve as an accessible entry
point into the building of a rich understanding of the many linguistic features of
academic English.
With these potential advantages of looking at academic vocabulary as a ratio-
nale for our study, this research draws on two main areas of the literature on
academic language. First, we review the role of academic vocabulary within the
larger register of academic English. Next, we present an overview of the theory
and research on academic vocabulary, as well as a brief overview of the possible
relationship between academic word knowledge and academic text comprehen-
sion. While parts of the following literature review address academic vocabulary
as an independent construct, we want to emphasize that academic vocabulary, by
definition, is academic because of its role within academic language. In other
words, the power of academic word knowledge lies not in simply knowing defi-
nitions of words but in knowing what those words mean and how they are used
in academic contexts.
Defining Academic English
Competency with the register of academic English is defined by Bailey (2007)as
“knowing and being able to use general and content-specific vocabulary, specialized
or complex grammatical structures, and multifarious language functions and dis-
course structures—all for the purpose of acquiring new knowledge and skills, inter-
acting about a topic, or imparting information to others” (pp. 10 11). In addition, the
linguistic features of academic language are both typical of and functional within
academic settings, whether language use is written or oral (Fang et al., 2006). Using
      
a Functional Grammar framework (Halliday & Matthiessen, 2004), scholars such as
Fang et al. (2006), Schleppegrell (2004), and Zwiers (2006) have identified linguistic
features of academic English as well as specific academic language demands of the
content areas. In a recent review of the scholarship on academic language, Snow and
Uccelli (2009) identifed five central components of academic language: interper-
sonal stance, information load, organization of information, lexical choices, and
representational congruence (p. 119). The interpersonal stance generally adopted in
academic text is authoritative and detached. In other words, the author or speaker of
an academic text makes grammatical and lexical choices that convey detachment
from the audience and expertise regarding the content. The information load of an
academic text is simultaneously characterized by density and conciseness. While the
information load is a heavy one, by virtue of the fact that academic texts often express
abstract and/or technical concepts, academic writers and speakers are expected to
minimize wordiness and express those concepts as concisely as possible. The organi-
zation of information of academic texts is marked by “tightly constructed” argumen-
tation and a “logical unfolding” of ideas (Snow & Uccelli, 2009,p.119). This organi-
zation of information is realized with the use of specific grammatical features, such as
subordinate clauses. The lexical choices that make up an academic text result in high
lexical diversity and include formal or prestigious expressions, precision of both key
content words and connective devices, and the use of abstract and technical terms
relevant to the topic. As Snow and Uccelli (2009) wrote, “at the lexical level, a diverse,
precise and formal repertoire that includes appropriate cross-discipline and
discipline-specific terms is desirable” (p. 120). Finally, representational congruence
relates to academic language in that academic language is often incongruent. In other
words, academic language often employs words as parts of speech that are not their
typical forms, a process called grammatical metaphor by Halliday (Halliday & Mat-
thiessen, 2004). Nominalization is a kind of grammatical metaphor that is typical of
academic language and leads to increased lexical and informational density. Another
aspect of representational incongruence comes from using abstract ideas rather than
human “actors” as agents. The current research is primarily concerned with the
component of lexical choices, but these components are all integrated in any aca-
demic text. Thus, while we argue for the merit of measuring the variance explained in
academic achievement by academic word knowledge, we also acknowledge that ac-
ademic word knowledge is one subset of knowledge that is integrated with knowl-
edge of other linguistic features.
Related to the scholarship reviewed by Snow and Uccelli (2009) is a body of
literature exploring the nature and frequency of academic vocabulary words. This
body of scholarship draws on a number of frameworks including corpus linguistics
and information-processing models of cognition (Corson, 1997; Coxhead, 2000;
Hancioglu, Neufeld, & Eldridge, 2008; Hiebert & Lubliner, 2008; Hyland & Tse, 2007;
Zeno, Ivens, Millard, & Duvvuri, 1995). Many of the scholars exploring academic
vocabulary are concerned with frequencies of academic words and how words are
used in different disciplinary contexts. However, while the nature and frequency of
general academic words have been established, our understanding of students’
knowledge of academic words and the variance explained by that word knowledge in
achievement lacks an empirical foundation. We address this gap in the current study.
A review of the research on academic word knowledge follows.
     
Academic Word Knowledge
Academic words may be discipline specific or cross-disciplinary in their use.
Discipline-specific academic words, sometimes referred to as Tier 3words (Beck,
McKeown, & Kucan, 2002), are used primarily in one discipline and often have
technical or specialized meanings. Examples of discipline-specific words include anti-
oxidant,rhombus, and metonymy. General academic words, the focus of the current
study, are used in academic settings and academic texts in most content areas (Cox-
head, 2000; Hiebert & Lubliner, 2008). Coxhead’s (2000) Academic Word List
(AWL) comprises the 570 most common general academic words in English, ac-
counting for 10% of words in academic texts across disciplines. It includes words
such as structure,function, and procedures.
Academic words, including general academic words, contribute to the overall
density and abstraction that characterize academic texts. Density comes from the
lexical and grammatical choices made by authors that enable them to present a great
deal of information very concisely. Academic words are often abstractions that en-
able the communication of ideas about social and natural phenomena that are not
easily expressed in the language of everyday communication (Schleppegrell, 2004).
Consequently, the language used to express these phenomena is rife with abstraction,
and general academic words play a role in carrying this abstraction. For example, con-
sider the word role in the previous sentence. The definition of role, beyond that of the
character played by an actor, is “proper or customary function” (http://dictionary
.reference.com/browse/role). Such an abstract definition is likely to be rendered use-
less when presented to students without concrete examples of the concept; indeed,
three additional general academic words—proper, customary, function—are used to
define role. It is only with many exposures in many contexts, a hallmark of successful
vocabulary instruction in general (Blachowicz & Fisher, 2000), that students can
come to understand what “proper or customary function” actually means in differ-
ent contexts.
In addition, because of the high frequency of general academic words, the chal-
lenge that these words pose is very real. Coxhead (2000) found that the 60 most
frequent general academic words on the AWL account for roughly 12 words per page
in academic texts at the college level. While there is no published empirical research
on the exact frequency of general academic words in middle school texts, there is
little debate that words from the AWL, as well as other words that fit the character-
istics of general academic words, do appear with considerable frequency in middle
school texts.
The frequency with which general academic words appear, and their abstract
nature, suggest the importance of these words for comprehension. In general, the
link between vocabulary knowledge and reading comprehension is uncontroversial,
and correlations generally fall in the .70 to .95 range (Biemiller, 1999; Stahl & Nagy,
2006). However, Stahl and Nagy (2006) explained that “knowledge of individual
words is just the tip of the iceberg” (p. 10) in the overall knowledge of a topic that
allows for good reading comprehension. The same could be said for academic words;
specifically, knowledge of academic words is just the tip of the iceberg in overall
academic English proficiency. Since academic words interact with a larger set of
linguistic features in academic texts, simply learning the definitions of more aca-
demic words may not translate to improved reading comprehension of academic
      
texts. However, investigating students’ academic word knowledge and the relation-
ship between that knowledge and academic success is a logical first step in empirically
identifying the importance of academic English proficiency.
Differences and Challenges in Academic Word Acquisition
Individual differences in mastering general academic words may, in part, contribute
to individual differences in student achievement across the content areas. Although
academic words occur in a variety of contexts, these words appear much more fre-
quently in text than in speech (Corson, 1997). Thus, typically achieving and above-
average readers, who tend to read more and comprehend more of what they read
(Stanovich, 1986), have more exposures and thus more opportunities to master gen-
eral academic words. In contrast, struggling readers, who tend to be less prolific
readers, have fewer exposures to general academic words, leaving them ill prepared
to comprehend what they read and infer the meanings of academic words from the
contexts in which they are used.
Even students who do not present with specific reading disabilities may struggle;
limited exposure to academic English outside of school often prevents students, such
as language-minority students and students from low-socioeconomic-status (SES)
backgrounds, from accessing content in academic texts (Zwiers, 2007). Research on
academic English generally recognizes the linguistic capital that accompanies aca-
demic English (Zwiers, 2008), as well as the density and abstraction that characterize
the language of schooling (Fang et al., 2006). However, non-native speakers of Eng-
lish and students from low-SES backgrounds typically receive limited exposure to
academic English in contrast to students coming from homes in which English is the
first language and/or the parents or caregivers are proficient in academic English
discourse (Corson, 1997; Short & Fitzsimmins, 2007). Research in this area demon-
strates that students who do not speak English as a first language require several years
of English language support before they can successfully meet the linguistic demands
of the content areas (Guerrero, 2004; Hakuta, Butler, & Witt, 2000; Short & Fitzsim-
mins, 2007). In addition, Corson (1997) has noted that many students from low-SES
backgrounds are also underprepared for the abstraction and density of academic
English. Indeed, exposure to and opportunities to practice with academic words are
likely essential for accessing academic texts. Therefore, the current research focuses
on those students (i.e., language minority [LM] and low-SES students) who, though
equipped with other types of linguistic resources, may have had few opportunities to
develop English academic language proficiency outside of school.
Challenges in mastering general academic words are further complicated by the
fact that, unlike discipline-specific words, they are rarely targeted for instruction by
content-area teachers (Corson, 1997; Snow, Lawrence, & White, 2009). Consider, for
example, a student with a rich representation of the word benefit; she is unlikely to
struggle when she encounters the word in various content areas. A student who has
only a preliminary notion of the word benefit, equivalent to Carey and Bartlett’s
(1978) process of fast-mapping, is likely to struggle with contexts in which the word
is used. For example, if a ninth-grade social studies teacher says, “Let’s discuss the
benefits of capitalism,” there are two potential vocabulary impediments to compre-
hension: benefit and capitalism. The focus of the discussion will likely be unpacking
the complex construct of capitalism. If the student is a typically achieving reader, she
     
will probably have had multiple exposures to the word benefit, thereby supporting
her understanding of the teacher’s statement. However, if the student is a struggling
reader and has had limited exposure to general academic words like benefit, the
words themselves may hinder her access to the instruction. Thus, students who are
reading below grade level are not able to build word knowledge of general academic
words through reading due to the comprehension challenges they face, and they are
rarely explicitly taught these general academic words. It is often LM students who fall
into this category of “double jeopardy” with respect to building academic literacy
skills (Torgesen et al., 2007).
Finally, mastering general academic words is more complicated than just learning
their definitions. Hancioglu, Neufield, and Eldridge (2008) noted that learning the
definitions of the words on the AWL is hardly a sufficient remedy for students who
struggle with academic discourse; rather, educators should “focus on revisiting and
recycling the most commonly used words in order to unravel the contexts, varied
meanings, register, etc., that would help turn these words into powerful tools of
understanding and expression” (p. 468). In addition, many of the words on the AWL
operate with distinct differences in the disciplines (Hyland & Tse, 2007). For exam-
ple, the word function has a highly technical meaning in mathematics, but it appro-
priates different meanings in civics or science. Finally, while academic word knowl-
edge is essential for comprehension, facility with the entire register of academic
English is necessary for full comprehension of academic texts. Thus, while the pur-
pose of this study was to isolate the variance explained by academic word knowledge
in academic achievement, we hypothesized that the explained variance would be
significant but not so large that it overwhelms the variance that may be explained by
proficiency with the academic register as a whole. This research is intended to show
just one piece of the empirical evidence, or a lack thereof, for the importance of
academic English proficiency.
The Current Study: Rationale and Research Questions
The importance of vocabulary for reading comprehension and academic achieve-
ment in general is uncontroversial (Beck, Perfetti, & McKeown, 1982; Carlisle, 2007;
McKeown, Beck, Omanson, & Perfetti, 1983; Nagy, 2007; Snow & Kim, 2007; Stahl &
Fairbanks, 1986). However, our understanding of this relationship does not extend to
general academic word knowledge. Given that word knowledge is essential to text
comprehension, it follows that knowledge of general academic words may be impor-
tant for academic achievement. Additionally, given the likely importance of multiple
exposures to building understanding of academic words, students who have less
exposure to these words may have more difficulty with them. However, there is little
empirical information on either of these issues. We address this gap by attempting to
identify the academic word knowledge of students from differing language and eco-
nomic backgrounds and to quantify the variance explained by general academic
word knowledge in standardized academic achievement measures. If there is an
empirical basis for the importance of academic word knowledge, such data could
serve to both clarify ambiguities in the academic language literature and help to
support “buy-in” on the part of educators and educational leaders. Additionally,
identifying assessment tools that evaluate students’ academic language proficiency
      
can help teachers target instructional support for students well before they perform
poorly on end-of-year, summative assessments.
We do acknowledge the limitation of focusing solely on academic vocabulary
knowledge outside of the academic register to which it belongs; indeed, the meanings
of individual words are related to the functions they serve in a given text. In other
words, looking at students’ academic vocabulary knowledge outside of their overall
academic English proficiency will not tell the whole story. However, isolating aca-
demic vocabulary has the potential to outweigh this limitation for two main reasons.
First, focusing on word knowledge, as opposed to the full constellation of linguistic
features that makes up the register of academic English, allows for a first step in
empirically identifying an important component of academic English proficiency.
According to the research on academic word knowledge, density and abstraction are
not only a result of the sum of academic English features throughout a given text, but
they can also characterize individual words (Corson, 1997; Coxhead, 2000). Thus,
identifying students’ knowledge of academic words may serve as either a proxy for or
entry point to their overall academic English proficiency. In addition, the link be-
tween vocabulary and reading comprehension in general is well established, and this
relationship likely extends to academic words and texts. Second, given that few
teachers have the background in applied linguistics to support students with all the
features of academic English, a focus on academic words allows for an accessible
“way in” for teachers to begin supporting their students’ academic language devel-
opment. This is not to suggest that teachers should teach academic words in isola-
tion; indeed, all new vocabulary is best learned in authentic contexts (Blachowicz &
Fisher, 2000). However, as teachers learn to support their students in building aca-
demic English proficiency, an understanding of academic words can serve as a scaf-
fold for teachers’ understanding of other linguistic features of academic language
within disciplinary texts.
Because general academic words, by definition, have high frequencies across ac-
ademic disciplines, we considered academic achievement outcomes in several disci-
plines. Also, it may be the case that general academic word knowledge is not a unique
construct from overall breadth of vocabulary knowledge, which, in this study, refers
to one’s entire mental lexicon, or entire body of word knowledge. In other words, it
is possible that the variance accounted for by students’ knowledge of general aca-
demic words may be subsumed by the variance accounted for by their knowledge of
words in general. It is also possible that with sufficient overall breadth of vocabulary
knowledge, limited knowledge of general academic words may not hinder achieve-
ment. While we expected that these two types of word knowledge would signifi-
cantly correlate, this study examines the relationship of general academic word
knowledge to performance on academic measures above and beyond the vari-
ance explained by overall breadth of vocabulary knowledge. Finally, this study
examines this relationship for LM students and students from low-SES back-
grounds, two groups of students who often lack exposure to academic English
outside of school. With respect to this final goal, exploring LM students and
students from low-SES backgrounds, we anticipated difficulty in identifying
whether these two variables are independently related to academic word knowl-
edge because these two populations so often overlap.
The research questions for this study are as follows: (1) Does general academic
word knowledge differ as a function of language and economic background? (2)
     
Does general academic word knowledge explain variance in academic achievement
above and beyond the variance explained by overall breadth of vocabulary knowl-
edge?
Method
Participants
Participants (N339) were grade 7(n193) and 8(n146) students from three
Title 1middle schools in a western metropolitan school district. Students were des-
ignated as English-only (EO) students if they came from monolingual, English-
speaking homes, or language-minority (LM) students if they regularly spoke a lan-
guage other than English or a combination of English and another language at home.
This classification is aligned with August and Shanahan’s (2006) explanation of
language-minority students. We used qualification for free and reduced-price lunch
as a proxy for SES. While there are limitations to using this proxy, particularly because it
lacks information on parental education levels, we deemed it sufficient for the explor-
atory nature of this work. Students who qualified were considered to be of low SES; those
who did not were considered to be standard SES. In the sample, there was considerable
overlap between students who were both LM and low SES,
2(334)27.47,p.001.
Specifically, 60% of the EO students were standard SES, while only 30% of the LM
students were standard SES. This overlap of SES and LM status is mirrored in related
literature (Institute of Educational Sciences [IES], 2007; see Table 1for the demographic
information for the complete sample). However, only students for whom complete data
were available were considered in the various analyses we conducted. There were no
significant differences between those students who were omitted from and those who
were included in analyses.
Measures
To determine the variance explained in academic outcomes by general academic
word knowledge above and beyond the variance explained by overall breadth of
vocabulary knowledge, measures that sampled both types of words were used. Both
measures are multiple-choice tasks of similar length.
Overall breadth of vocabulary knowledge. The Vocabulary Subtest of the Gates-
MacGinitie Reading Test (MacGinitie, MacGinitie, Maria, & Dreyer, 2000) was used
as a measure of overall breadth of word knowledge. In this multiple-choice test,
students are presented with each target word in a context that suggests the part of
Table 1. Demographic Information for Sample
(N339)
Frequency Percent of Total Sample
English only (EO) 212 62.5
Language minority (LM) 125 36.9
Standard SES 165 48.7
Low SES 172 50.7
Grade 7 193 56.9
Grade 8 146 43.1
      
speech of the target word but does not provide information about its meaning.
Students then choose the word that most closely matches the target word. Raw and
percentile scores were obtained for each student. The technical report reports a
reliability coefficient using the Kuder-Richardson Formula 20 for grade 7spring
scores as .90 and for grade 8spring scores as .91.
General academic word knowledge. The Academic Word Level of the Vocabu-
lary Levels Test (VLT; Schmitt, Schmitt, & Clapham, 2001) was used to assess knowl-
edge of general academic words (as opposed to discipline-specific academic words).
The VLT was initially designed as a diagnostic tool for English language learners and
has been used and validated with large samples of LM students. The VLT is made up
of five subtests, four of which represent knowledge of groups of words that occur in
decreasing frequency in English and one that measures students’ academic word
knowledge. For this study, only the Academic Word Level, which measures students’
knowledge of general academic words as opposed to discipline-specific academic
words, was used. In this test, students match target words with synonyms or brief
definitions, and each item includes distracter words. (See Fig. 1for examples of items,
and see Schmitt et al. [2001] for a description of the design of the measure.) The
Academic Word Level consists of 60 words; 30 words have definitions that can be
matched to them and the other 30 words are distracters. (See Fig. 2for a list of all
words appearing on the Academic Word Level of the VLT.) In the design of the
academic level of the VLT, the 60 words were drawn from all sublists of the Academic
Word List (a total of 570 words; Coxhead, 2000). While the Academic Word List was
created using college-level texts, we used the VLT because it is the only published
measure of general academic word knowledge. Furthermore, many of the words on
the list are arguably relevant for middle school students as well as college students.
For example, words appearing in the first item are area,contract,definition,evidence,
method, and role. Although it has not been normed with middle school students, the
VLT has been rigorously examined and revised for validity and reliability purposes;
the reliability (Cronbach’s alpha) for this measure is reported as .96 (Schmitt et al.,
2001).
Rationale for the two vocabulary measures. These two vocabulary measures
allow for a comparison of different kinds of vocabulary knowledge. The Gates-
Figure 1. Examples of items from the Academic Word Level of the Vocabulary Levels Test.
     
MacGinitie vocabulary test is a test of reading vocabulary, and it captures students’
overall breadth of vocabulary knowledge across many varieties of texts (e.g., infor-
mational, narrative, academic, etc.). The items from this measure were based on the
Living Word Vocabulary, which is based on word frequencies of all words appearing
in a multigenre corpus. This is in direct contrast to the corpus used to identify the
general academic words that are tested in the Academic Word Level of the VLT. The
VLT, like the Gates-MacGinitie test, is a measure of reading vocabulary; indeed,
academic words occur much more frequently in written language than in oral lan-
guage. However, the VLT measures knowledge of general academic words, or only
those words that occur in academic texts across disciplines. The VLT was crafted
using Coxhead’s (2000) Academic Word List, and this list was generated using a
corpus of academic texts from four disciplines. The general academic words tested
on the VLT, because they come from the Academic Word List, account for far fewer
words in nonacademic texts than in academic texts (Coxhead, 2000). Therefore, the
VLT measures word knowledge that is predominantly gained with exposure to or
instruction in academic language, while the Gates-MacGinitie test measures word
knowledge that can be gained by exposure to a variety of texts. In other words,
students who are well read across genres would be expected to perform well on the
Gates-MacGinitie test, but only those students who are well read specifically in aca-
Figure 2. All words appearing in the items from the Academic Word Level of the Vocabulary Levels
Test.
      
demic texts would be expected to perform well on the VLT. This is not to suggest that
performance on the two measures would not highly correlate; they certainly tap
related constructs. However, one goal of the current study was to determine whether
these two related constructs explained unique variance in academic achievement
across disciplines.
Measures of academic achievement. The Iowa Test of Basic Skills (ITBS; Hoo-
ver, Dunbar, & Frisbie, 2001) was administered to all grade 7students in the state as
part of accountability testing. Participants’ developmental scaled scores on the Read-
ing Comprehension, Math, Social Science, and Science sections of the ITBS were
obtained from the school district for use in this study. Scores were used from all four
disciplines because we were interested in examining the variance explained by gen-
eral academic words, which by definition occur across disciplines (Corson, 1997;
Coxhead, 2000; Hiebert & Lubliner, 2008). The Reading Comprehension test is a
subtest of the Reading Total, and it includes comprehension questions on readings
from a variety of genres. The ITBS Math test includes math concepts and estimation,
math problem solving and data interpretation, and math computation subtests. The
ITBS Social Studies test includes questions on history, geography, government and
society, and economics. The ITBS Science test includes questions on scientific in-
quiry, life science, earth and space science, and physical science. The technical report
reports reliability coefficients with the Kuder-Richardson Formula 20 for grade 7
spring scores as Reading Comprehension .92, Math .95, Social Studies .88,
Science .90. Developmental standard scores are reported with a median grade 7
score of 239. Grade 8students were not administered the ITBS, so all analyses involv-
ing the ITBS included grade 7students only.
State Criterion Referenced Tests (CRTs; Nevada Department of Education,
2008) in reading and mathematics were also used as outcome measures of aca-
demic achievement. The CRTs are standardized, standards-based assessments
administered to all students in the state in grades 38. Because the ITBS was only
administered to seventh graders, use of the CRTs allowed for a parallel set of
analyses that included both seventh and eighth graders. The reading composite
score included subtests on interpreting literature and informational texts, word
analysis, and developing critical stances toward texts. The math composite score
included subtests on algebra, geometry, measurement, probability, conceptual
understanding, and problem solving. Scores are reported using scaled scores on
a100 500 scale.
The use of these standardized measures is aligned with recommendations in the
adolescent literacy research on struggling readers to use outcome measures that
more closely mirror school-based tasks, such as group-administered achievement
tests, as opposed to individually administered comprehension measures often uti-
lized by researchers (Scammacca et al., 2007). The use of such measures allows for an
examination of performance on outcomes that look like what students do in school
tasks. Furthermore, given the established links between vocabulary knowledge and
comprehension, it was expected that both of the aforementioned vocabulary mea-
sures would correlate with the outcome measures. However, the question at hand is
whether or not academic vocabulary knowledge explains variance in achievement
above and beyond the variance explained by overall breadth of vocabulary knowl-
edge.
     
Procedures
Students were administered the ITBS and the CRTs by their classroom teachers as
part of a spring district-wide testing schedule. The vocabulary measures were admin-
istered to participating students by the first author in a single 55-minute testing
session prior to the standardized test administration. Classrooms were randomly
assigned to one of two orders of test administration to control for order effects.
Results
The first research question for this study asked whether general academic word knowl-
edge differs as a function of language or SES background. Table 2presents descriptive
statistics for the two language groups and the two SES groups. All differences between
language groups and between SES groups are significant at the .001 level. In other words,
EO students significantly outperformed LM students on all measures, and standard-SES
students significantly outperformed low-SES students on all measures. The significant
overlap between LM students and low-SES students and between EO students and
standard-SES students makes it difficult to disentangle the importance of these charac-
teristics for performance. However, the goal of this question was to examine factors that
typically impact students’ skill in accessing academic language and to see how, together,
they influence this relationship between word knowledge and achievement.
To determine whether there was an interaction between language and socioeconomic
background, we examined students’ performance on each of the measures as a function
of their language and SES backgrounds. First, we calculated a 2(SES) 2(language
background) multivariate analysis of variance (MANOVA) on seventh graders’ develop-
mental scaled scores from the four subtests of the ITBS (Reading Comprehension, Math-
ematics, Social Studies, and Science). Reported here are the results from the omnibus
multivariate test of effects on the four ITBS outcomes, as opposed to the between-
subjects effects. Therefore, the degrees of freedom reflect the number of outcomes mul-
tiplied by the number of levels minus one. The inconsistency in the error degrees of
freedom is a result of missing data. As noted, students who were missing scores on one or
more of the outcome measures were not included in the analyses pertaining to those
measures, and analyses of variance revealed no significant differences between students
who were omitted from analyses and those who were included. The results of the omni-
bus test revealed evidence for an achievement gap based on SES, Wilks’s lambda .919,
F(4,147)5.66,p.001, partial
2.13, and based on language background, Wilks’s
lambda .867,F(4,147)3.24,p.005, partial
2.08. However, the interaction
between the two factors, Wilks’s lambda .985,F(4,147).55,p.701, was not signif-
icant.
Next, we calculated the same analysis with the two subtests of the CRTs (Reading and
Mathematics), which involved both grade 7and 8students. Scores on the CRTs are scaled
scores and are the same for both. The pattern for the CRTs was similar to that with the
ITBS. The results from the omnibus multivariate test of effects on the two CRT outcomes
revealed evidence for an achievement gap based on SES, Wilks’s lambda .965, SES, F(2,
314)12.04,p.001, partial
2.07, and based on language background, Wilks’s
lambda .929,F(2,314)5.67,p.01, partial
2.04. Again, the interaction between
the two factors, Wilks’s lambda .989,F(2,314)1.83,p.16, was not significant. The
      
lack of significant interactions on the CRTs and the ITBS suggested that the effect of SES
did not vary by language group, nor did the effect of language group vary by SES.
Following the examination of the academic outcomes, we examined each of the
experimental vocabulary measures. We calculated a series of 2(SES) 2(language
background) univariate analyses of variance (ANOVAs), using a Bonferroni adjust-
ment for multiple comparisons to control for experiment-wise error. As with the
outcome measures, there were main effects for the Gates vocabulary test (overall
breadth of vocabulary knowledge) for SES, F(1,331)31.49,p.001, partial
2
.09, and for language background, F(1,331)14.84,p.001, partial
2.04. The
Table 2. Descriptives and Significant Differences between Language and SES Groups
Measure and Demographic Group nMSD
CRT reading:
EO 198 324.06 67.40
LM 121 288.10 62.97
Standard SES 163 332.32 64.61
Low SES 157 287.61 63.65
CRT math:
EO 198 337.28 87.93
LM 121 301.93 89.82
Standard SES 163 351.25 88.89
Low SES 157 294.02 83.71
ITBS reading comprehension:
EO 123 238.85 34.15
LM 68 216.71 29.58
Standard SES 95 240.03 27.13
Low SES 96 216.34 25.90
ITBS math:
EO 123 235.34 23.22
LM 68 222.37 24.79
Standard SES 95 240.18 22.70
Low SES 96 221.36 22.71
ITBS social studies:
EO 107 239.36 34.24
LM 47 210.96 22.38
Standard SES 82 245.87 32.93
Low SES 72 213.40 25.29
ITBS science:
EO 123 243.09 35.20
LM 68 223.15 28.00
Standard SES 95 248.37 33.53
Low SES 96 223.74 30.17
Gates (overall breadth of vocabulary):
EO 212 45.16 28.00
LM 125 28.92 23.15
Standard SES 165 49.65 27.21
Low SES 172 28.69 23.62
Vocabulary levels test (general
academic vocabulary):
EO 199 22.70 6.29
LM 122 19.68 6.98
Standard SES 156 23.77 5.52
Low SES 165 19.39 7.12
Note.—SES socioeconomic status, EO English only, LM language minority, CRT criterion referenced test, ITBS
Iowa Test of Basic Skills.
     
interaction between SES and language background, F(1,331)1.26, was not significant.
The same pattern held for the VLT (general academic vocabulary) with both SES, F(1,
316)22.66,p.001, partial
2.07, and language background, F(1,316)6.43,p
.05, partial
2.02. Again, the interaction between SES and language background, F(1,
316).17, was not significant.
Results from the MANOVA and ANOVAs guided our next set of analyses, which
were a series of linear regression models on each of the academic outcomes. Given
that the MANOVA results showed significant differences between language groups
and SES groups, both of these variables were controlled by forcing them into the
models first. Next, performance on the VLT was entered to determine the variance
explained by general academic word knowledge in achievement across content areas.
We ran six regression models on the six major outcomes: CRT reading and math and
ITBS reading comprehension, math, social studies, and science. As noted, the anal-
yses with the ITBS included only seventh graders, while the analyses with the CRTs
included the entire sample. See Table 3for the results of these analyses.
Language background and SES combined explained between 11% and 27%ofthe
variance for students’ scores across outcome measures. However, after accounting
for this demographic variance, these analyses showed that general academic word
knowledge explained between 19% and 34% variance in performance on achieve-
ment measures across content areas and across different standardized tests.
Table 3. Variance Explained by General Academic Word Knowledge in Academic Achievement
Variable BSEB
R2Ffor R2
CRT reading:
SES 14.077 6.194 .103 .116 39.78 ***
Language background 14.128 6.165 .101 .031 11.14 **
Academic vocabulary 6.309 .452 .620 .335 195.10 ***
CRT math:
SES 28.465 9.406 .158 .108 36.55 ***
Language background 5.733 9.362 .031 .003 2.96
Academic vocabulary 6.544 .686 .482 .205 91.02 ***
ITBS reading comprehension:
SES 10.838 3.774 .159 .153 34.09 ***
Language background 7.172 3.860 .101 .035 8.10 **
Academic vocabulary 2.936 .252 .623 .341 135.49 ***
ITBS math:
SES 9.863 3.055 .202 .148 32.80 ***
Language background 2.828 3.124 .055 .017 3.74
Academic vocabulary 1.747 .204 .518 .235 73.237 ***
ITBS social studies:
SES 16.247 4.563 .241 .233 46.10 ***
Language background 10.869 4.798 .149 .047 9.77**
Academic vocabulary 2.167 .295 .479 .191 53.93 ***
ITBS science:
SES 11.172 4.255 .164 .131 28.49 ***
Language background 6.668 4.351 .094 .027 6.13*
Academic vocabulary 2.452 .284 .522 .239 74.37 ***
Note.—SES socioeconomic status, CRT criterion referenced test, ITBS Iowa Test of Basic Skills.
*p.05.
**p.01.
***p.001.
      
Our final analyses were also a series of linear regression models on the academic
outcomes. As with the first set of models, we controlled for SES and language back-
ground. However, to account for the possibility that the variance explained by our
measure of academic word knowledge could be accounted for by our measure of
overall breadth of vocabulary knowledge, we forced in the Gates-MacGinitie vocab-
ulary test after the demographic variables. Finally, performance on the VLT was
entered to determine if general academic word knowledge explained variance in
academic achievement above and beyond the variance explained by overall breadth
of vocabulary. Table 4presents the results of these analyses.
Language background and SES combined explained between 11% and 22%ofthe
variance for students’ scores across outcome measures. Overall breadth of vocabu-
lary knowledge explained between 26% and 43% of the variance across outcome
measures. Given repeated findings that students’ vocabulary knowledge is highly
correlated with their comprehension and academic achievement, this was not a sur-
prise. Most relevant to the current study, however, was the pattern on both the CRTs
and the ITBS, across all disciplines, that academic vocabulary knowledge explains
Table 4. Variance Explained by General Academic Word Knowledge in Academic Achievement
Controlling for Overall Breadth of Vocabulary Knowledge
Variable BSEB
R2Ffor R2
CRT reading:
SES 6.335 5.799 .049 .116 39.78 ***
Language background 8.202 5.733 .059 .031 11.14 **
Vocabulary breadth 2.932 .396 .409 .349 208.37 ***
Academic vocabulary 3.689 .546 .362 .067 45.69***
CRT math:
SES 19.584 9.145 .109 .108 36.55 ***
Language background 1.065 9.042 .006 .009 2.96
Vocabulary breadth 3.363 .624 .354 .230 106.06 ***
Academic vocabulary 3.539 .861 .262 .035 16.90***
ITBS reading comprehension:
SES 6.704 3.474 .098 .153 34.09 ***
Language background 4.402 3.519 .062 .035 8.10 **
Vocabulary breadth 1.544 .237 .440 .375 160.01 ***
Academic vocabulary 1.581 .309 .336 .054 26.21 ***
ITBS math:
SES 7.486 2.966 .153 .148 32.80 ***
Language background 1.235 3.004 .024 .017 3.74
Vocabulary breadth .888 .203 .353 .252 80.73 ***
Academic vocabulary .968 .264 .287 .039 13.473 ***
ITBS social studies:
SES 9.639 4.278 .143 .233 46.10 ***
Language background 7.935 4.368 .109 .047 9.767 **
Vocabulary breadth 1.609 .274 .475 .272 90.96 ***
Academic vocabulary .865 .347 .191 .018 6.20 *
ITBS science:
SES 6.647 3.942 .098 .131 28.49 ***
Language background 3.637 3.993 .051 .027 6.13 *
Vocabulary breadth 1.690 .269 .483 .324 117.21 ***
Academic vocabulary .970 .350 .207 .020 7.65 **
Note.—SES socioeconomic status, CRT criterion-referenced test, ITBS Iowa Test of Basic Skills.
*p.05.
**p.01.
***p.001.
     
unique, significant variance in achievement after controlling for the variance ex-
plained by language background, SES, and general vocabulary knowledge. Although
the explained variance was small, ranging from 2%to7% across disciplines, the
additional variance was significant and consistent across the standardized outcome
measures and disciplines.
Discussion
The purpose of this study was to answer the questions, does general academic word
knowledge differ as a function of language and economic background? and, does
general academic word knowledge explain variance in academic achievement above
and beyond the variance explained by overall breadth of vocabulary knowledge?
Because this study, like most others on academic English, was driven by a concern for
access to instruction among non-native English speakers and low-SES students, our
first set of analyses focused on differences between students from different language
and socioeconomic backgrounds. On all study measures, standard-SES students out-
performed low-SES students, and English-only (EO) students outperformed
language-minority (LM) students. These results were not surprising, as the patterns
are identical to those found in national data (Institute of Educational Sciences [IES],
2007). Additionally, the interactions between SES and language background were
not significant for any of the measures, indicating that the effect of SES did not vary
by language background. Because of the significant overlap between LM and low-
SES students, we had less statistical power for determining whether effects of lan-
guage background varied by socioeconomic background. This is a common limita-
tion in research on LM students, but it does not detract from the implication that
students from low-SES backgrounds would benefit from academic language support
regardless of their first language.
In addition, these findings highlight the vocabulary gap between low-SES/LM
students and standard-SES/LM students. An important and novel finding from this
study is that the vocabulary gap is not limited to overall breadth of vocabulary knowl-
edge. Rather, there is a distinct gap in general academic vocabulary knowledge as
well, indicating the need for additional support for middle school students who
struggle with accessing instruction because they do not have the academic language
resources necessary to keep up with the pace of mainstream classrooms. The work of
scholars such as Corson (1997), Coxhead (2000), Hiebert and Lubliner (2008), and
Schleppegrell (2004) has highlighted that general academic vocabulary knowledge
support may be particularly important for LM students and economically disadvan-
taged students. The findings from our study provide empirical support for this as-
sertion and should bring additional attention to the need for teachers to scaffold
students’ academic language development in middle school classrooms. Indeed, ev-
idence from three recent intervention studies suggests that evidence-based strategies
for vocabulary development can help middle school LM students build general aca-
demic word knowledge (Lesaux, Kieffer, Faller, & Kelley, 2010; Snow et al., 2009;
Townsend & Collins, 2009). Our study provides the empirical link between students’
general academic word knowledge and their overall academic achievement and
serves as a rationale to continue this line of intervention research.
Our next set of findings support the importance of general academic word knowledge
for academic success. As noted, the theoretical momentum driving research in academic
      
English is rarely supported by empirical data on students’ academic language proficiency.
This study was an attempt to address that empirical gap, and the first set of regression
models suggested that general academic word knowledge explains considerable, unique,
and significant variance in academic achievement across standardized measures and
across disciplines. These results are promising and suggest the importance of general
academic word knowledge for academic success. However, these results were not partic-
ularly conservative; our second set of regression models allowed for a very conservative
examination of general academic word knowledge. In this set of models, we accounted
for the possibility that the variance explained by general academic word knowledge could
be accounted for by overall breadth of vocabulary knowledge. Thus, the second set of
models examined the role of general academic word knowledge while controlling for
overall breadth of vocabulary knowledge. Had we not run this second set of models, this
work would be open to legitimate criticism that academic vocabulary is simply a measure
of low-frequency words that could be captured using established vocabulary measures.
In the second set of models, overall breadth of vocabulary knowledge explained consid-
erable variance across both the ITBS and the CRTs. These findings are consistent with
Stahl’s (2005) assertion that “vocabulary knowledge is knowledge; the knowledge of a
word not only implies a definition, but also implies how that word fits into the world” (p.
95). However, after controlling for overall breadth of word knowledge, general academic
word knowledge still explained additional significant variance in all four disciplines
across both sets of standardized outcome measures. While this additional significant
variance was small, the fact that it existed after controlling for overall breadth of vocab-
ulary knowledge is quite promising. This finding suggests that general academic word
knowledge, which we had anticipated could be subsumed by overall breadth of vocabu-
lary knowledge, does explain unique variance in academic achievement. Additional re-
search with additional measures is needed to confirm this finding, but these results pro-
vide a promising foundation for demonstrating the empirical evidence for the
importance of academic English proficiency. In sum, this finding lends credibility to the
construct of general academic word knowledge and its importance for academic success
(Coxhead, 2000; Hiebert & Lubliner, 2008) and suggests that knowledge of general aca-
demic words is related to students’ access to content and their academic performance. As
addressed earlier, however, academic words garner their meaning from the contexts in
which they are used. Thus, just as building knowledge of academic words may support
comprehension of academic texts, effective instruction in the content areas may yield
richer understanding of academic words.
Implications for Practice
First, and perhaps most importantly, our findings can serve to heighten content
area teachers’ sensitivity to general academic words. As noted, teachers’ understand-
ing of general academic words and their forms and meanings in academic texts can
be an ideal entry point into understanding the larger register of academic English.
When teachers attend to these words in the context of their instruction of content-
area texts, they can help students build knowledge of the words’ meanings as well as
how they are used in authentic contexts. Thus, while we do not argue for teaching
academic words out of the contexts in which they are used, they can be an ideal
starting point for developing students’ academic English proficiency. A primary issue
with general academic words is that, while often opaque to students, they are usually
     
transparent and commonplace to educated adults (Corson, 1997). Thus, teachers do
not always attend to them and students miss important opportunities to focus on
and learn them. Attention to general academic words, and the other linguistic fea-
tures of academic English that may be part of content area teachers’ “expert blind
spots” (Nathan & Petrosino, 2003), could allow teachers to better support struggling
readers with the academic language demands of secondary classrooms. Effective
vocabulary instruction and support of incidental word learning are essential for all
students and all kinds of words (Carlisle, 2007). However, given the increasing ab-
straction, density, and morphological complexity of words throughout the grade
levels, effective instruction with academic words is particularly important. While
educators may acknowledge this, they may wonder where to start instruction that
builds academic word knowledge. To begin with, research-based approaches to vo-
cabulary instruction that have proved successful with EO students (Blachowicz &
Fisher, 2000) have been proven successful with diverse students as well (Lesaux et al.,
2010; Snow et al., 2009; Townsend & Collins, 2009), so teachers would do well to keep
these approaches in mind as they organize their vocabulary instruction. Such prin-
ciples of vocabulary instruction include providing multiple exposures and opportu-
nities to practice with word meanings in authentic and engaging contexts, teaching
high-utility academic words (which, by definition, characterizes general academic
words), and explicitly teaching independent word-learning strategies such as use of
context clues, morphological analysis, and cognate analysis. All instructional strate-
gies used in classrooms should align with these principals of vocabulary instruction.
With respect to instructional strategies, there are a number of resources with
specific suggestions for classroom practice. Townsend (2009) provided a list of vo-
cabulary activities and games to be used with general academic words identified in
standards-based texts. As part of instruction around any text, teachers can scan the
text for general academic words and then select any of the activities Townsend de-
scribed in order to give students opportunities to practice and personalize word
meanings. The Word Generation Project (i.e., Snow et al., 2009) has an informative
and engaging website (http://wordgeneration.org/), laying out the elements of the
program and professional development opportunities for teachers. In particular, the
website includes classroom examples of students learning academic words through
an oral discussion framework called “accountable talk”; again, the focus is providing
students with meaningful exposures to and opportunities to practice with academic
language. Kieffer and Lesaux (2007) provided clear guidelines for supporting stu-
dents’ morphological awareness and use of morphological knowledge as a strategy
for independently learning words. With helpful tables, this article is an excellent
resource for teachers who are exploring how to tap into the power of morphological
awareness as a teaching and learning strategy. Finally, a number of recent publica-
tions for elementary teachers (Bailey & Heritage, 2008; Brock, Lapp, Salas, & Town-
send, 2009) and secondary teachers (Fang & Schleppegrell, 2008; Zwiers, 2008) have
explored classroom practice that supports students’ proficiency with the full range of
linguistic features that make up the register of academic English.
Limitations and Future Research
Because this research serves only as an initial empirical exploration into the im-
portance of academic word knowledge for middle school students’ academic
      
achievement, several limitations suggest the need to continue this line of inquiry. The
first set of limitations relates to the measures and procedures. The time allotted for
students to take the word knowledge measures may have been insufficient for low-
performing readers. Conversely, there may have been ceiling effects for students
reading above grade level on the measure of general academic words, resulting in
conservative findings on the role of academic word knowledge for high-performing
readers. The current study shares this limitation with much of the extant research on
vocabulary and academic language; the field is hampered by a paucity of standard-
ized vocabulary measures that go beyond breadth of knowledge and examine either
depth of word knowledge or students’ use of words (Vaughn et al., 2009). For exam-
ple, while the Vocabulary Levels Test has been updated several times and rigorously
examined for validity purposes (Schmitt et al., 2001), it has not been normed with
populations of middle school students and it does not measure knowledge of general
academic words beyond receptive knowledge.
Another limitation relates to the sample. The current sample did not allow for the
statistical power necessary for parsing out the unique effects of SES and language
background in the relationships examined. However, this parsing out of effects may
not be as important as recognizing that students who struggle with academic lan-
guage have likely experienced Matthew effects (i.e., linguistically, the rich get richer
and the poor get poorer; Stanovich, 1986) in academic language prior to and
throughout their schooling experience. Pragmatically, the important understanding
is that many low-SES and LM students have likely had less exposure to academic
language than their peers and therefore need explicit instruction and support to
develop the skills necessary to access academic texts. In addition to explicit instruc-
tion, students need opportunities to read informational texts at their respective read-
ing levels so that comprehension processes can allow for them to build word knowl-
edge incidentally.
Ideally, future work in this area would include more than one measure for each of
the two vocabulary constructs— overall breadth of vocabulary knowledge and gen-
eral academic word knowledge. However, for this exploratory work, findings based
on the two measures used in this study provide a credible first step in identifying
empirical support for the importance of general academic words. A final assessment
issue concerns word frequency. Because academic words are generally lower-
frequency words in English, it is possible that instruments purporting to measure
academic words may be measuring knowledge of low-frequency words as opposed to
words from the register of academic English. Word knowledge measures for future
research should include words that have similar frequencies to academic words but
are not academic words.
Although the current study focused on academic word knowledge, this is just one
linguistic feature in a “constellation” of linguistic features that make up the register
of academic English (Schleppegrell, 2001). Because general academic words function
most authentically in the context of the register of academic English, there is a need
to explore students’ proficiency with multiple linguistic features of academic English,
such as the use of dense noun phrases, passive voice, and subordinate clauses (Snow
& Uccelli, 2009). Future research should also include other subcomponents of lan-
guage comprehension, including morphological awareness, syntactic awareness, and
working memory. For example, morphological awareness plays a significant role in
both word knowledge and reading comprehension (Carlisle, 2007; Nagy, Berninger,
     
& Abbott, 2006). In addition, it may prove to be particularly important for academic
English proficiency given the complex morphology of many academic words (Cor-
son, 1997). If students are able to recognize and break down complex morphological
patterns, they may have sufficient metalinguistic awareness to support their reading
comprehension (Nagy, 2007). Furthermore, morphological and metalinguistic
awareness are related to Halliday’s notion of grammatical metaphor (Halliday &
Matthiessen, 2004). Because grammatical metaphor occurs when words are utilized
as parts of speech other than their typical parts of speech (e.g., when words that
typically function as verbs are used as nouns), knowledge of derivational morphemes
and syntax can support students as they grapple with the density and abstraction that
typically accompany grammatical metaphor. Future research should include mea-
sures of morphology to determine its role in academic English proficiency and aca-
demic achievement for middle school students. Lesaux et al. (2010) provided a model
for exploring these relationships.
In closing, we suggest two additional areas of research. What students do with
academic word knowledge is just as important as what they know about it. Efforts
should be made to understand students’ productive use of academic language, as well
as their depth of knowledge of individual words and other linguistic features of
academic English. Bailey, Huang, Farnsworth, and Butler’s (2007) work on the de-
velopment of academic English measures is one potential model to follow that may
meet this need. Finally, research in coaching and professional development, follow-
ing insights gained from Achugar, Schleppegrell, and Oteiza’s (2007) and Zwiers’s
(2007) work on teachers’ scaffolding for academic English development, should be
conducted to determine what teachers know about academic words and how best to
support students’ development of academic word knowledge. To effect the change
espoused in the equity-driven perspective, it is critical that we continue the line of
inquiry from what students know about academic language to how they use that
knowledge, and to how to effectively teach those skills and knowledge.
Note
Correspondence concerning this article should be addressed to Dianna Townsend, College of
Education, MS 299, University of Nevada, Reno, NV 89511. E-mail: dtownsend@unr.edu
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      
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