Download An Investigation Into Second Language Aptitude

An Investigation Into Second
Language Aptitude for Advanced
Chinese Language Learning
PAULA WINKE
Michigan State University
Second Language Studies Program
Department of Linguistics and Languages
B252 Wells Hall, 619 Red Cedar Road
East Lansing, MI 48824
Email: [email protected]
In this study I examine the construct of aptitude in learning Chinese as a second language (L2) to
an advanced level. I test 2 hypotheses: first, that L2 aptitude comprises 4 components—working
memory, rote memory, grammatical sensitivity, and phonemic coding ability—and second, that
L2 aptitude affects learning both directly and indirectly (mediated by strategy use and
motivation). Native speakers of English (n ¼ 96) studying advanced Chinese took the Modern
Language Aptitude Test and a phonological working memory test and responded to motivation
and strategy use questionnaires. Using end‐of‐course listening, reading, and speaking proficiency
test results as measures of Chinese learning, I constructed a structural equation model to test the
hypotheses. The model fit the observed data. Of the 4 components foreseen to comprise L2
aptitude, rote memory contributed the most and working memory the least. Aptitude, strategy
use, and motivation had about the same impact on learning but varied in how well they predicted
the individual skills of listening, reading, and speaking. The results shed light on L2 aptitude in
the particular context of an advanced L2 Chinese course.
COGNITIVE PROCESSES AND AFFECTIVE
variables shape how individuals acquire a foreign
or second language (L2) and predict how well they
are likely to learn one (Beckner et al., 2009; Ellis,
2004; Robinson, 2002c; Skehan, 1989). This study
investigates the plausibility, via structural equation
modeling (SEM), of a model of language learning
that includes cognitive (rote memory, phonemic
coding ability, grammatical sensitivity, and phonological working memory), cognitively oriented
(strategy use), and affective (motivation) variables
as learning predictors. It also examines how the
factors affect each other within the model.
First, I review research that addresses what I
suggest is a narrow definition of L2 aptitude as a set
of purely cognitive constructs. Second, I argue that
The Modern Language Journal, 97, 1, (2013)
DOI: 10.1111/j.1540-4781.2013.01428.x
0026-7902/13/109–130 $1.50/0
© 2013 The Modern Language Journal
a broader concept of L2 aptitude should be
accepted, one that recognizes the effects of
mediating, cognitively oriented, and/or affective
variables—most vitally, strategy use and motivation. Third, because L2 aptitude may be best
understood in terms of the context of the language
learning situation (Robinson, 2007), I detail the
particular conditions of this study’s L2 learning
context: native‐English‐speaking adults learning
Chinese in an intensive (6 to 8 hours a day in class),
63‐week course intended to bring them to an
advanced level of proficiency in Chinese. Finally, I
describe the use of SEM to investigate L2 learning
aptitude in this context, which has been rarely
used in L2 aptitude research.
THE COGNITIVE CONSTRUCTS OF SECOND
LANGUAGE APTITUDE
Acquiring proficiency in an L2 in an instructed
setting is considered challenging for adults
(Doughty, 2004; Ellis, 2004, 2005). Understanding
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why has engrossed L2 acquisition researchers for
decades. Since the 1950s, researchers have posited
that L2 aptitude is a construct separate from
general intelligence and predicts adult, classroom‐
based, L2 learning success (Carroll, 1962, 1981,
1990; Corno et al., 2002; Dörnyei, 2005b;
Robinson, 2005a; Skehan, 2002). The variables
that were first recognized as part of L2 aptitude
were identified prior to and/or independent of
modern second language acquisition (SLA) theory. Carroll (1958), tasked with creating an L2
aptitude test for the U.S. government, administered 34 cognitive tests to military personnel
taking intensive, 1‐week‐trial, beginner‐level, Mandarin Chinese courses that focused on speaking
skills. By using exploratory factor analysis, Carroll
identified five tests that were both practical to
administer and highly predictive of L2 learning
success. These tests now comprise the Modern
Language Aptitude Test, or MLAT, Form A
(Carroll & Sapon, 1959b).1 Three of the five tests’
underlying constructs identified by Carroll
(1958, 1962) will be explored here: rote memory
for learning (the ability to store verbal information
in memory and recall it later); phonetic coding
ability (the ability to analyze incoming sounds so
that they can be retained); and grammatical
sensitivity (the ability to recognize, in whatever
manner, the function of a word in a sentence).2
(For a more detailed description of these constructs, see Nagata, Aline, & Ellis, 1999, p. 135,
and Skehan, 1998, p. 190.) Carroll followed this
research with several MLAT validation studies
(Carroll, 1962, 1963, 1966) and concluded that
under intensive learning conditions with heterogeneous groups of learners, the MLAT correlates
fairly well with L2 achievement, with most
correlation coefficients between .40 and .65 (cf.
Carpenter, 2009; Skehan, 1998).
Over the decades, the predictive validity of the
MLAT has prevailed. The MLAT is still used by
the U.S. Foreign Service and the Canadian Foreign
Service in selecting candidates for L2 study. The
MLAT is also used in research that attempts to
define the cognitive traits that explain differences
in adult L2 acquisition (e.g., research on critical
period effects, DeKeyser, 2000; Harley & Hart,
1997, 2002; Ross, Yoshinaga, & Sasaki, 2002).
Other aptitude tests, based on the MLAT or shown
to be valid through correlations with the MLAT,
have been developed (e.g., the Cognitive Ability
for Novelty in Acquisition of Language [Foreign]
Test or CANAL–FT, Grigorenko, Sternberg, &
Ehrman, 2000; the Defense Language Aptitude
Battery or DLAB, Peterson & Al–Haik, 1976; the
Pimsleur Language Aptitude Battery or PLAB;
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Pimsleur, 1966).3 But differing tests of L2 aptitude
do not fundamentally alter our understanding of
the underlying cognitive constructs of L2 aptitude
(Skehan, 1998). Moreover, they do not extend the
construct of L2 aptitude beyond what the tests
measure (Dörnyei, 2005b; Neufeld, 1979; Sáfár &
Kormos, 2008).
It may profit the field to take aptitude research
to another level and investigate aptitude for
dynamic complexity. For example, Skehan (2002)
proposed that aptitude abilities are dynamic and
change over time. Robinson (2005a, 2007) suggested that different aptitude abilities are needed
at different stages of learning. Recent theorizing in
SLA has proposed that learning is fundamentally
a slave to the environment, with certain contexts
nourishing some mental processes and other
contexts nourishing other mental processes (Beckner et al., 2009; Dörneyi, 2009a; Larsen–Freeman
& Cameron, 2008). It is not, therefore, an
understatement to claim that aptitude as a
standalone cognitive trait is problematic. One
place to start is to investigate aptitude constructs
individually and within a larger picture of cognitive, cognitively oriented, and affective variables
and situated within a particular language learning
context. We can at least then begin to understand
if aptitude is dynamic and relates to the learning
context, as has been suggested (Neufeld, 1979;
Snow, 1987; Sternberg, 2002; Sternberg &
Grigorenko, 2002).
The MLAT
Many researchers have challenged the view that
L2 aptitude is something measured by the MLAT
(Dörnyei, 2005b; Robinson, 2002b, 2007; Sáfár &
Kormos, 2008; Skehan, 2002). One criticism is that
the constructs underlying the MLAT do not
represent a complete definition of L2 aptitude.
This has been confirmed by research that has
shown that the MLAT does not strongly predict
language learning when instruction is less intense
(Carroll, 1962, 1990) or more communicative in
nature (Robinson, 2007; Sáfár & Kormos, 2008).
Furthermore, in the original validation studies by
Carroll, learners only received lower level instruction. Thus, even though there have been decades
of studies on L2 aptitude, we still do not know what
cognitive variables are important for learning in
moderately intense courses, in communicative,
task‐based language programs, or for achieving
advanced L2 skills.
This does not mean that researchers should
abandon the MLAT but rather that the
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constellation of factors that contribute to successful language acquisition needs to be expanded
beyond the constructs represented by the MLAT
and more firmly situated within the context of
learning. Furthermore, aggregating L2 aptitude
test subscores into a single composite score (which
indirectly implies what L2 aptitude is—a single
factor) needs to be revisited. Skehan (1998, 2002)
called for researchers to view aptitude as truly
differentiated or multifaceted, meaning that one
could have high ability in one aptitude construct
and low ability in others, resulting in individually
unique L2 aptitude profiles. This is different from
Carroll’s view that there are across‐the‐board haves
and have‐nots in L2 aptitude or that L2 aptitude is
“a special, inherent talent that not all individuals
possess” (CASL, 2009, p. 1). Robinson (2007)
proposed the Ability Differentiation Hypothesis,
which claims that some L2 learners have clearly
differentiated cognitive skills and abilities, while
others do not, and that these different talent
schemes correspond with different aptitude complexes, which must be matched to instructional
conditions to maximize L2 learning potential.
According to Skehan and Robinson, within the
model, the individual components may be correlated for some learners, but not for others.
Working Memory
In the 1990s, building on research in cognitive
psychology that identified working memory as
a cognitive trait separate from long‐ and short‐
term memory systems4 and important for learning
(Baddeley, 1986, 1992, 2007; Cowan, 1995, 2005;
Daneman & Carpenter, 1980; Hitch & Baddeley,
1976), applied linguists investigated the relationships between working memory and L2 acquisition
and ascertained that working memory is an
essential component for L2 learning (Gathercole
& Baddeley, 1993; Harrington & Sawyer, 1990,
1992; Miyake & Friedman, 1998; Miyake, Friedman, & Osaka, 1998; Service, 1992). Concomitantly, researchers suggested that working memory is
most likely an additional cognitive construct of L2
aptitude (Daneman & Merikle, 1996; Harrington
& Sawyer, 1992; McLaughlin, 1995; Miyake &
Friedman, 1998; Robinson, 1995, 2002b) and may
be the “the key to elaborating the concept of
language aptitude itself” (Sawyer & Ranta, 2001,
p. 340).
Generally defined, working memory underlies the
ability to process linguistic input and store
information from that input for later retrieval.
Specifically, working memory has been defined as
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the “set of processes that hold a limited amount of
information in a readily accessible state for use
in an active task” (Cowan, 2005, p. 39). In other
words, working memory is a cover term for
multiple processes, including short‐term memory,
the real‐time manipulation of linguistic material
through effortful processing (Cowan, 2005) and
storing information in long‐term memory. These
are dissociable abilities, each with limited capacities, but that must work together for learning
(Baddeley, 2007; Miyake & Friedman, 1998).
Empirical investigation into working memory’s
role in relation to previously identified L2 aptitude
constructs is of current interest in the field for
both practical and theoretical reasons. Several
government‐sponsored research projects support
such research with the goal to increase the
government’s ability to identify military and
foreign service candidates for language training
(see CASL, 2009; Kenyon & MacGregor, 2004).
Theoretically, researchers are interested because
they need to understand L2 aptitude as a construct
and how it fits within other theories about learning
(Corno et al., 2002). Thus far, research suggests
that working memory is an essential part of L2
aptitude (Robinson, 2002b; Sáfár & Kormos,
2008), but evidence is limited. Some research
suggests that it may only significantly differentiate
learning at lower levels of instruction (Hummel,
2009). Other research that focuses on the short‐
term memory component of working memory
suggests that limited short‐term memory capacity
may impede learning for those with limited
capacities while failing to differentiate other
learners (Carpenter, 2009). Such findings question the entire concept of L2 aptitude: That is, is
there really a single latent trait that can reliably
predict advanced‐level language skills or L2 skills
acquired through nonintense or more naturalistic
conditions? Researchers may have pinned their
hopes on working memory as a promising
additional component of L2 aptitude that reliably
predicts advanced proficiency, but initial research
is inconclusive. More studies are needed.
SECOND LANGUAGE APTITUDE AND
NONCOGNITIVE AND AFFECTIVE
VARIABLES
It is simplistic to think that language learning
depends solely on cognitive traits. Researchers
have long attributed language learning success to a
number of noncognitive factors, such as high
motivation for learning the particular language
at hand (Dörnyei, 1990; Gardner, 1990, 2001;
Gardner & Lambert, 1959; Gardner, Tremblay, &
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Masgoret, 1997; Masgoret & Gardner, 2003;
Oxford, 1996; Pimsleur, 1966; Schmidt, 1991;
Skehan, 1989; Tremblay & Gardner, 1995) and
the use of various language learning strategies
(Cohen, 1998; Dörnyei, 2005b; Ehrman &
Oxford, 1990; Hsiao & Oxford, 2002; O’Malley
& Chamot, 1989; Oxford, 1990a, 1990b, 1994,
2001, 2011; Schmitt, 1997; Skehan, 1989). Debated
in the L2 aptitude literature is whether these
noncognitive variables should be considered as
part of a broader construct of L2 aptitude. On one
hand, some propose that they should not: Aptitude is purely a static, cognitive trait resistant to
change (Carroll, 1990; Parry & Child, 1990;
Pimsleur, 1966; Stansfield & Reed, 2004). On
the other hand, some propose that L2 aptitude,
broadly defined, includes the noncognitive variables of motivation (Dörnyei, 1990; Gardner, 1990;
Pimsleur, 1966) and strategy use (Ehrman, Leaver,
& Oxford, 2003; Ehrman & Oxford, 1990, 1995;
Grigorenko et al., 2000; Oxford, 1990b, 2011;
Vandergrift, 2003). Such a broader view of L2
aptitude holds that the underlying cognitive
constructs interact with one’s motivation for
learning the particular language at hand and the
way in which the language is taught and learned,
which may, in part, explain why a person is better
able to learn an individual L2 over another L2
(Sternberg, 2002).5
Motivation
Motivation affects L2 acquisition in multiple
ways. It is a necessary precondition for L2
acquisition (Csizér & Dörnyei, 2005; Dörnyei,
Csizér, & Németh, 2006; Edmondson, 2004;
Noels et al., 2000), and it must be healthily
sustained over time for acquisition to continue
(Dörnyei, 2005b, 2009b; Hiromori, 2009). For over
three decades, motivation has been shown to
be a predictor of L2 learning success (Csizér
& Dörnyei, 2005; Gardner, 1985; Gardner &
MacIntyre, 1991; Noels, Clément, & Pelletier,
1999; Schmidt & Watanabe, 2001; Tremblay &
Gardner, 1995). These studies and others have
conceptualized motivation differently, but most
view it from a social–psychological perspective.
In other words, a highly motivated L2 learner will
(a) want to integrate with speakers of the language
(integrativeness is the desire to become a
passable member of the community of speakers
of the L2) (Gardner, 1985, 2001; Gardner et al.,
1997), (b) have a very good attitude about learning
the L2 (Dörnyei, 1990, 2003; Yashima, Zenuk–
Nishide, & Shimizu, 2004), and (c) be eager to
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communicate in the target language (Dörnyei &
Kormos, 2000; MacIntyre et al., 2001; Yashima,
2002; Yashima et al., 2004). A highly motivated
language learner is further characterized by strong
and clear incentives for learning the language,
such as for future employment, a pay raise, travel
opportunities, or to communicate with family
members (these are classified as instrumental
and/or intrinsic reasons; for definitions see
Gardner, 1985; Gardner, 2001; Gardner &
MacIntyre, 1991; Gardner et al., 1997; Tremblay
& Gardner, 1995). The individual will also
have higher confidence in his or her abilities in
using the L2 (Clément, Dörneyi, & Noels, 1994;
Dörnyei, 2003, 2005a; Ushida, 2005)—and correspondingly lower anxiety in using the language
(MacIntyre et al., 2002; Matsuda & Gobel, 2004).
The overall consensus is that high motivation may
make up for deficiencies in cognitive abilities
(Dörnyei, 1990; Pimsleur, 1966; Schmidt, 1991;
Sternberg, 2002), which suggests that the effects of
cognitive abilities on L2 learning are mediated by
motivation.
Strategy Use
Similarly, strategy use has been shown to affect
L2 acquisition, but it is unclear how. Strategies are
the “steps or actions taken by the learners to
improve the development of their language skills”
(Oxford & Cohen, 1992, p. 1) or “the L2 learner’s
toolkit for active, conscious, purposeful, and
attentive learning” (Hsiao & Oxford, 2002,
p. 372). More recently, strategies have been viewed
as cognitively oriented—as summarized by Macaro
(2006), “strategies are the raw material of conscious cognitive processing, and their effectiveness
or noneffectiveness derives from the way they
are used and combined in tasks and processes”
(p. 325). Studies that have measured strategy use
through questionnaires have shown that learners
who use many different kinds of strategies, and
use them often, have more success in instructed
L2 acquisition settings (Cohen, 1998; Ehrman &
Oxford, 1990; Green & Oxford, 1995; Griffiths,
2003; O’Malley & Chamot, 1990; Oxford, 1990a,
1994). (See Oxford and Burry–Stock, 1995, for a
full review.) Other research has found that less
successful learners use various, random strategies,
while more successful learners are more systematic
about their strategy usage and use specific ones for
specific tasks (Ehrman & Oxford, 1995). More
recent research has suggested that strategy use
changes over time depending on L2 proficiency
and the learning context (Macaro, 2006), with
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advanced L2 learners using fewer strategies
overall than intermediate learners (Hong–Nam
& Leavell, 2006) because at the advanced levels
of proficiency, learners’ processes for L2 acquisition are more automatized, resulting in a smaller
range of strategies needed for acquisition (see
Oxford, 2011, for a comprehensive review of the
research).
Most interestingly, research has shown that L2
learning strategies and motivation are related
(Schmidt & Watanabe, 2001). More strategy use
equals more or better learning, and more motivated learners use more learning strategies more
often (Gardner et al., 1997; MacIntyre & Noels,
1996). Thus, a full model of L2 learning that
considers the impact of L2 aptitude on learning
should also investigate the combined mediating
role of motivation and strategy use. As far as I
know, no study thus far has done so.
THE SECOND LANGUAGE LEARNING
CONTEXT
One of the problems with L2 aptitude research
is that it often assumes that L2 aptitude exists in a
vacuum and is independent from the L2 learning
context. This may not be true, especially if we
assume motivation and strategy use mediate
aptitude’s effects on language learning. One
might be more motivated to learn a certain L2
over another (Sternberg, 2002); likewise, one
might be more motivated to learn an L2 in a
certain classroom situation over another. This
corresponds with what is known as the resultative
or spiraling effect of motivation: Learners who
do well in a certain learning situation become
even more motivated to learn, while those who
do poorly get discouraged and try less hard
(Hermann, 1980). Classroom‐based variables
such as materials, instructional techniques, teachers, and peers might affect motivation and strategy
use, which may positively impact learning, minimizing or distorting the effects of L2 aptitude. As
stated by Corno et al. (2002), “to understand the
effects of person characteristics on performance,
one must specify the performance situation”
(p. 216). Thus, researchers must investigate L2
aptitude in terms of the context of the L2 learning
situation (Robinson, 2007).
This current study is unique in that all learners
stemmed from the same learning context. The
learners began with no prior Chinese language
instruction. All received the same instruction
throughout a 63‐week course, which was intended
to develop in each language learner advanced‐
level proficiency (also known as General Professional
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Proficiency—a 3 on the Interagency Language
Roundtable scale; see http://www.govtilr.org for
a description) in the skills of listening, reading,
and speaking so that the learners could go on to be
interpreters, analysts, and interrogators for the
military branches for which they worked. All
students received instruction from the same
teachers, used the same materials, and followed
the same curriculum.
Equal instructional conditions across participants is important because prior research has
found that L2 aptitude is sensitive to exposure
conditions, such as the amount of implicit versus
explicit instruction (Robinson, 1997, 2002c,
2005b). Prior studies on aptitude may have had
significant sampling errors in relation to instructional variance. For example, Hummel (2009)
explored the relationships among the cognitive
constructs of L2 aptitude (phonetic coding ability,
grammatical sensitivity, rote memory, and phonological working memory) and L2 proficiency. She
found that the cognitive constructs of L2 aptitude
combined predicted 29% of the variance in L2
(English) proficiency. The 77 participants in her
study were recruited from a first‐year Teaching
English as a Second Language degree program. L2
proficiency was measured directly after entrance
(within their first month of study) in the program;
the tests of L2 aptitude, including working
memory, were administered at some point after
the proficiency test (Hummel did not indicate
when). Although Hummel described the participants as being a homogeneous group—all were
native French speakers with at least 7 years of
English study—the quality and methods of their
prior English language instruction were not
controlled. One could imagine a situation in
which those who attended better language programs received better instruction on strategy use
(which has been shown to improve L2 learning—
see Plonsky, 2011) and were more motivated,
which might have contributed to the relationships
observed. Furthermore, if the participants attended differentially performing schools, with
school access determined through a college
entrance exam (another cognitive test or an
intelligence test), any observed correlations between L2 aptitude and proficiency could be
interpreted as noncausal: Other factors (i.e.,
general intelligence, instructional differences)
may underlie both high aptitude and high
performance. This is not a fatal research design
flaw, but it allows one to question the reliability of
such a study. It would be better to make certain
that all learners received the same instruction
(and the same amount of instruction), which
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would help ensure that only the variables present
in the hypothesized model of L2 learning were
responsible for the study’s outcomes.
USING STRUCTURAL EQUATION
MODELING TO INVESTIGATE SECOND
LANGUAGE APTITUDE
The relationships among L2 aptitude constructs
have been explored through correlation analyses
(e.g., Wesche, Edwards, & Wells, 1982), factor
analyses (e.g., Carroll, 1958, 1962, 1963, 1966,
1993), and simple and multiple regression (e.g.,
Carpenter, 2009; Hummel, 2009). However, these
are essentially descriptive techniques, making
complete hypothesis testing difficult (Byrne,
2009). For example, with confirmatory factor
analysis (CFA), even though the researcher
postulates how the underlying latent variables
relate and then tests this structure statistically, CFA
focuses on whether and the extent to which the
observed variables are linked to the underlying
latent traits. The strengths of the regression paths
(the factor loadings) from the latent variables to
the observed variables (the items) are the focus of
CFA. CFA does not consider direct effects among
the factors, and thus CFA misses essential elements
a full latent variable model (such as SEM) has.
SEM combines a CFA measurement model (SEM
runs a CFA as part of its analysis) and a structural
model. In other words, a full SEM model allows
researchers to estimate both the links between
the latent variables and their observed measures
(the measurement portion of the model) and the
direct effects among the variables (the structural
portion of the model).
With SEM researchers can investigate the
plausibility of a full latent variable model, which
is defined as a single proposed set of relationships
among one or more independent variables and
one or more dependent variables (Byrne, 2009).
The relationships within the proposed model can
be discussed in terms of their causality, but the
cause–effect relationships must be backed up by
relevant theory—that is, the proposed set of
relationships in a model of L2 aptitude and L2
learning must stem from hypotheses and theories
on L2 aptitude and L2 learning. As explained by
Lewis and Vladeanu (2006):
While there are similarities between structural equation modeling and multiple regression (e.g., they are
based on analysis of intercorrelations) there are
fundamental differences. Multiple regression provides simple definitive results about which independent variables produce significant effects on which
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dependent variables. In structural equation modeling,
the analysis requires a theoretical model that can be
tested against the available data. It can be determined
whether this model is consistent with the data and also
how good a fit it is. (p. 981)
A model that is found to be consistent with the data
through SEM has nothing to say about whether a
better model might be possible with the existing
set of data or with other data. Thus, the results
stemming from SEM are grounded within the
context of the data set (in this case, within the data
provided by adults learning Chinese in a communicative, task‐based, 63‐week course that focuses
on the skills of listening, reading, and speaking).
But, with SEM, competing models can be compared, a model can be tested over time, or a model
can be tested across different data sets. This is
exactly what L2 aptitude research needs, especially
because theory now suggests that cognitive abilities
may be represented differently by different
individuals, may change over time, or may depend
on the particular dynamics of the first language
(L1)–L2 learning context. SEM also allows structural relations to be plotted in a picture, making it
easy to visualize the theory being studied
(Byrne, 2009). While other studies on L2 aptitude
have employed SEM (i.e., Sasaki, 1993, 1996),6 this
is the first L2 aptitude study using SEM that
ensures participants received the same amount
and type of instruction.
HYPOTHESIZED MODEL
This study’s hypothesized model is presented in
Figure 1. What I hypothesize is a model of L2
learning that includes both cognitive and affective
traits. L2 aptitude is defined as comprising the
cognitive traits of rote memory, phonetic coding
ability, grammatical sensitivity, and working memory. L2 aptitude affects L2 learning both directly
and indirectly—the indirect effects coming from
mediation by the affective traits of strategy use and
motivation.
METHOD
Participants
Ninety‐six native English‐speaking, adult learners of Chinese volunteered to be participants in
this study. All were students in a 63‐week, intensive
Chinese program at the Defense Language Institute (DLI) in Monterey, California. All participants were U.S. military personnel. Sixty‐six were
male, 30 were female. Ages ranged from 19 to 36,
with an average age of 25. As explained above, all
received the same task‐based, L2 instruction in
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FIGURE 1
The Hypothesized Model
Independent Variables
Cognitive
Rote Memory
Dependent Variable
Cognitively Oriented / Affective
Strategy Use
Phonetic Coding
Ability
Aptitude
L2 Learning
Grammatical
Sensitivity
Phonological
Working Memory
Motivation
Chinese and received 6 to 8 hours of classroom‐
based instruction 5 days a week during the 63‐week
course.
Materials
Modern Language Aptitude Test (MLAT). The
learners’ phonetic coding ability, grammatical
sensitivity, and rote memory were assessed by
sections one through five of the paper‐and‐pencil‐
based MLAT (Carroll & Sapon, 1959a). Based on
Carroll (1962, 1990) and Skehan’s (1998, 2002)
view of the underlying constructs of L2 aptitude
assessed by the MLAT, rote memory was measured
by sections one (number learning) and five
(paired associates), phonetic coding ability by
sections two (phonetic script) and three (spelling
clues), and grammatical sensitivity by section four
(words in sentences). The MLAT was obtained in
2004 through the Language Learning and Testing
Foundation (http://lltf.net). For information
concerning the five sections and their specific
tasks, see Dörnyei (2005b) and the Language
Learning and Testing Foundation’s (2012) Web
site.
Phonological Working Memory Span Test. The
participants’ working memory capacity was assessed through an online, verbal working memory
span test adapted from Mackey et al. (2002),7
which was based on other span tests used in prior
research (Daneman & Carpenter, 1980; Turner &
Engle, 1989; Waters & Caplan, 1996). The test
comprised 48 unrelated sentences, half being
grammatically correct and half semantically
plausible, which resulted in four sentence types
(grammatical and plausible, grammatical and
implausible, ungrammatical and plausible, ungrammatical and implausible). The 48 sentences
were grouped into 12 sets: four sets each of three,
four, and five sentences, groupings similar to those
used by Daneman and Carpenter (1980) and
Turner and Engle (1989). The test was self‐paced;
however, sentences within a set were controlled to
play over the computer with 3‐second intervals.
While the participants listened to a set of sentences
(the sentences were only presented aurally with no
visual support), they clicked a mouse to indicate
whether the sentence was grammatical and
whether it was semantically plausible. After selecting “enter” to submit the answers for a set, the
participants were prompted to type (on a new
screen) the last word of each sentence. All
sentence‐final words were common (not abstract,
because abstract words may be more difficult to
recall; Turner & Engle, 1989), noncompound,
concrete nouns of one to three syllables in length.
No sentence‐final word of a given set was
semantically associated with another word in
that set, and no words within a set rhymed.
Strategy Inventory for Language Learning (SILL).
Strategy use was measured through the Strategy
Inventory for Language Learning, or SILL
(Oxford, 1990a), which was administered online
for this study. The SILL comprises 80 five‐
point, Likert‐scale questions pertaining to (a)
social, (b) metacognitive, (c) memory, (d) compensation, (e) cognitive, and (f) affective strategies (Hsiao & Oxford, 2002; Oxford, 1990a). It has
been used in numerous studies on language
learning strategies (e.g., Carson & Longhini,
2002; Engelbar & Theuerkauf, 1999; Green &
116
Oxford, 1995; Griffiths, 2003; Hong–Nam &
Leavella, 2006; Hwu, 2007; Nakatani, 2006; Oxford
& Burry–Stock, 1995; Wharton, 2000) and measures the number and type of strategies applied by
the learner at a specific point in the learning
process (Tseng & Schmitt, 2008).
Motivation Questionnaire. The motivation questionnaire consisted of 38 five‐point, Likert‐scale
questions adapted from Kormos and Dörnyei
(2004). The questions, which tap into learners’
(a) integrativeness, (b) incentive values, (c)
attitudes toward learning the L2, (d) linguistic
self‐confidence, (e) language use anxiety, (f) task
attitudes, and (g) willingness to communicate
(Dörnyei, 2002) have been used in multiple
research studies on motivation for learning an
L2 (Dörnyei, 2002; Dörnyei & Kormos, 2000;
Kormos & Dörnyei, 2004; Weger–Guntharp, 2008).
The questions appear in Appendix A.
L2 Learning. L2 learning was measured
through the Defense Language Proficiency Tests
(DLPTs) in listening, reading, and speaking.
Robust descriptions of and sample items from
these secure, government tests can be found at the
Defense Language Institute Foreign Language
Center (n.d.) Web site. These test scores can be
considered gain scores because all study participants began the 63‐week Chinese course with no
prior instruction in Chinese.
Procedure
Data collection took place at the DLI in
Monterey, California, during the learners’ 42nd
week of instruction. All participants volunteered to
participate in the research, which was conducted
outside of normal class time. In computer labs on
campus, participants took the following measures
with 15‐ to 20‐minute breaks in between: the MLAT,
the working memory span test, the motivation
questionnaire, and the SILL. Data collection,
with breaks, lasted approximately 2 1/2 hours.
After 63 weeks of instruction, the participants
took the DLPTs in speaking, reading, and listening.
The DLPT scores were forwarded to the author for
use in the structural equation model.
Scoring. The MLAT was scored conventionally,
with each item on the test scored as right or wrong,
and a right answer worth 1 point. Rote memory
was a composite of sections one and five. Phonetic
coding ability was a composite of sections two and
three. Grammatical sensitivity was measured by
section four.
The phonological working memory test was
scored according to partial‐credit load (PCL) scor-
The Modern Language Journal 97 (2013)
ing procedures outlined in Conway et al. (2005). For
any given set of sentences, if processing scores fell
below 80%, the data from that set were discarded. If
processing scores were above 80%, then all
sentence‐final words recalled correctly were awarded 1 point. Therefore, items with a higher memory
load contributed more to the overall phonological
working memory score. As summarized by Conway
et al., “for load‐weighted scoring procedures, PCL
represents the sum of correctly recalled elements
from all items, regardless of whether the items are
perfectly recalled or not (also without respect to
serial order within items)” (p. 775).
Items on the SILL were combined into a
composite SILL score to indicate what normally is
considered a latent variable. The same was done
with the items on the motivation questionnaire. I did
this to make the whole model easier for the computer program Analysis of Moment Structures
(AMOS) 18 (structural equation modeling software)
to identify. For each learner (and for each measure),
items pertaining to the same category were averaged, and then all categories were averaged together
so that each category would have equal weight in the
learner’s final score. (The categories for each
measure are described above in the materials
section.) In both questionnaires, values of negative
items were reversed before the aggregation.
Analysis. I applied SEM to evaluate the conjectured causal relations among the various
variables investigated in the study. First, a full
model with both measurement and structural
components was designed in accordance with the
theories in L2 aptitude and L2 learning reviewed
earlier (see Figure 2). The measurement portion
of the model hypothesizes cause indicators, rather
than the more common effect indicators (Bollen,
1989, p. 65). Maximum likelihood estimated
procedures were used to analyze the variance/
covariance matrix of the observed variables using
AMOS 18. To assess the overall model fit, I used
chi‐square and a pair of fit indices advised in the
SEM literature (Byrne, 2009; Kline, 2011; McDonald & Ho, 2002): the comparative fit index (CFI)
and the root‐mean‐square error of approximation
(RMSEA). A nonsignificant chi‐square and a CFI
above .95 suggest model acceptance, and an
RMSEA value below .05 indicates a good fit of
the model to the data (Hu & Bentler, 1999).
RESULTS
Preliminary Data Analysis
The means, standard deviations, and reliabilities
(Cronbach’s alpha) for the different measures are
Paula Winke
117
FIGURE 2
Structural Equation Model with Parameter Estimates
.34
e2
Rote Memory
.12
Phonetic
Coding Ability
.45**
.00
.23
Grammatical
Sensitivity
1
Strategy Use
-.11
.97
-.06
-.30
e1
.14
-.25*
Listening
.02
.59**
.06
Aptitude
-.86
.16
-.27
.03
Phonological
Working Memory
e4
Reading
-.03
.08
Motivation
.2
e5
.34**
.16
Speaking
e6
-.06
e3
Note. All values adjacent to arrows are standardized estimates. Single-headed arrows are direct
effects. Double-headed arrows are correlations. e = error variance.
*Standardized coefficient p < .05.
**Standardized coefficient p < .01.
in Table 1. Reliabilities for the different measures
were mostly high or very high (Bachman &
Palmer, 2010), ranging from .72 to .91. The
sample correlation matrix is presented in Appendix B; the matrix allows readers to form an
independent judgment of the relationships
among the observed variables and will aid in
the interpretation of the direct effects among
the variables in the structural portion of the
model. Rote memory was significantly correlated
with both grammatical sensitivity (r ¼ .45,
p ¼ .00) and phonological working memory
(r ¼ .23, p ¼ .04). Strategy use significantly correlated with motivation (r ¼ .40, p ¼ .00). Reading
was significantly related to both listening (r ¼ .59
p ¼ .00) and speaking (r ¼ .32, p ¼ .00).
Model Results
The full structural equation model, with the
resulting standardized coefficients, appears in
Figure 2. It includes 1 latent variable (L2 aptitude,
TABLE 1
Measures and Descriptive Statistics
Measure
Rote Memory
Phonetic Coding Ability
Grammatical Sensitivity
Phonological Working Memory
Strategy Use
Motivation
Reading
Listening
Speaking
Items
Pts.
M
SD
Rel.
88
80
24
48
80
38
60
60
1*
88
80
24
48
400
190
60
60
20
59
47
15
39
240
143
49
45
18
5.52
7.34
5.19
7.10
15.20
17.56
4.44
3.03
2.16
0.91
0.79
0.83
0.88
0.72
0.91
na
na
na
Note. Items ¼ number of items on measure; Pts. ¼ number of points possible; M ¼ mean; SD ¼ standard deviation;
Rel. ¼ reliability coefficient Cronbach’s alpha; na ¼ not available; these tests are maintained by the Defense Language
Institute and reliability for the sample was not available.
*
The speaking test is an oral proficiency interview (OPI) and is scored holistically. Within this sample, all learners
obtained a 10 (2þ on the Interagency Language Roundtable [ILR] scale) or a 20 (a 3 on the ILR scale).
118
represented with an oval) and 9 observed variables
(the measures in Table 1, represented with
rectangles). To examine the predictors of L2
learning, the proposed model was fitted to the data
with listening, reading, and speaking test scores as
dependent variables. The model was accepted
and fit the data very well, x2(12, N ¼ 96) ¼ 7.6,
p ¼ .815, CFI ¼ 1.00, RMSEA ¼ .00, RMSEA confidence interval ¼ .00, .07.
In Figure 2, the model’s direct effects are shown
as single‐headed arrows and correlations are
shown as double‐headed ones. Also shown are
standardized coefficients. Within this model, only
one direct effect was significant at the .05 level:
the effect of strategy use on reading (r ¼ #.25,
p ¼ .03). Low strategy use significantly predicted
higher L2 reading ability. Strategy use did not
significantly predict listening or speaking ability.
Learners’ L2 aptitude and motivation failed to
predict significantly greater success in listening,
reading, or speaking ability.
The model estimated the total variance explained in strategy use, motivation, listening,
reading, and speaking (the squared multiple
correlations provided in the AMOS output).
Aptitude explained 9% of strategy use’s variance
and 7% of motivation’s variance. (In other words,
the error variance of strategy use is approximately
91% of the variance of strategy use itself, and
the error variance of motivation is approximately
93%.) The predictors (strategy use, aptitude, and
motivation) explained 2% of listening’s variance,
3% of speaking’s, and 6% of reading’s.
DISCUSSION
This study used structural equation modeling to
investigate the accuracy of a current understanding of L2 aptitude within the context of Chinese
language acquisition. The model, diagrammed
in Figure 2, included four cognitive aspects of L2
aptitude (rote memory, phonetic coding ability,
grammatical sensitivity, and working memory)
and two noncognitive variables (motivation and
strategy use). Within this model, L2 aptitude was
predicted to affect L2 learning directly. It was also
hypothesized that L2 aptitude would indirectly
affect L2 learning, that its effects would be
mediated by motivation and strategy use. The
model was found to be plausible (the data
displayed a very good fit to the model), which
allows me to discuss the individual effects the
factors have on one another within the model. I
first discuss the significant effect of strategy use on
reading. After explaining why I examine other
effects even though they were not significant, I
The Modern Language Journal 97 (2013)
discuss the (nonsignificant) effects of motivation
on L2 learning. I then discuss two additional
nonsignificant but interesting results concerning
the variables that contribute to aptitude: the small
effect that working memory had on aptitude and
the negative effect that grammatical sensitivity had
on aptitude. To conclude, I discuss the model’s
overall represented construct of L2 aptitude and
its effect on learning.
The Effects of Strategy Use on L2 Learning
The model showed only one statistically significant effect: Strategy use inversely affected success
in reading (r ¼ #.25, p ¼ .03). In other words,
when a learner’s SILL average rose by 1 standard
deviation, his or her reading score fell by .25
standard deviations. (The absolute value of the
standardized regression coefficient r represents
how much the independent variable affects the
dependent variable or latent trait: A positive sign
represents a direct relationship, and a negative
sign represents an inverse relationship.)
Although this inverse relationship might initially
seem surprising, it is consistent with past research.
Ehrman and Oxford (1995) reported that less
successful learners randomly use various strategies, while more successful learners systematically
use specific strategies for specific tasks. This
replicates aspects of a curvilinear pattern commonly
found by L2 strategy researchers using the SILL:
Advanced learners use fewer strategies than intermediate learners, advanced learners use a subset
of their former strategies, and advanced learners
use their select strategies in new, creative ways that
relate to the complexities of advanced language
learning (Green & Oxford, 1995; Hong–Nam &
Leavell, 2006; Leaver, 2005; Oxford, 2011).8
Research focused on reading skills has also shown
the importance of using an effective subset of
strategies (Kember & Gow, 1994). Reliance on a
few select strategies demonstrates automaticity in
learning, which is necessary in upper level classes
(Alyousef, 2005). So in a study examining reading
in an upper level class, it is actually not surprising
that the students who used fewer strategies were
more successful at learning to read.
The inverse relationship between strategy use
and success in reading makes particular sense in
the context of Chinese language acquisition. The
data showed that the students who use fewer
strategies tend to be those with higher aptitude
(aptitude inversely affects strategy use at r ¼ #.30,
p ¼ .58). Aptitude is largely comprised of rote
memory (r ¼ .97, p ¼ .59). And rote memory is
critical to learning to read Chinese (Everson &
Paula Winke
Ke, 1997; Hayden, 2005; Shen, 2004; Xiao, 2002).
So the students who used fewer strategies may have
been more successful at reading in part because
they were the ones with better memories. They
may not have needed to venture into other
strategies because their rote‐memory‐based strategies worked particularly well.
Interestingly, strategy use had a much smaller
effect on listening (r ¼ #.11, p ¼ .35) and almost
no effect on speaking (r ¼ .02, p ¼ .85). These
results lend empirical support to existing theory
about strategy use. Researchers have speculated
that strategies matter less for listening and speaking than for reading (Chamot, 2005; Farrell &
Mallard, 2006) because in listening and speaking
the importance of social and interactive skills
overwhelms the effect of strategy use (Nakatani &
Goh, 2007); reading, of course, does not require
social interaction. Most empirical L2 strategy
research has investigated the effect of strategies
only on the skill of reading (Plonsky, 2011). Thus,
this study provides evidence that strategy use may
affect the various skills differently.
Nonsignificant Effects
Most of the effects in the model were not
statistically significant. Although nonsignificant
results are usually ignored, there are three reasons
they are worth considering in this study. First,
some statisticians contend that nonsignificant
effects can be important (Valentine & Cooper,
2003). They point out that statistical significance
tells us little about the “practical significance or
relative impact of the effect size, and should not be
used as a standalone measure of how much the
intervention ‘matters’” (Valentine & Cooper, p. 1,
emphasis in original). Second, significance testing
has been criticized as an arbitrary means of
interpreting continuous data (Oswald & Plonsky,
2010; Plonsky, 2011; Plonsky & Gass, 2011;
Schmidt, 1996). The data in this model are
continuous rather than dichotomous.
Finally, it makes sense to consider some of the
nonsignificant effects in this study because the
correlations among some of the independent
variables (known as multicollinearity) may have
artificially reduced the significance of the effects.
In this model, for example, rote memory was
correlated with grammatical sensitivity (r ¼ .45,
p ¼ .00) and phonological working memory
(r ¼ .23, p ¼ .04). These correlated independent
variables may be explaining the same parts of
the variation in aptitude (Thayer, 1991), making
the significance of their independent effects on
119
aptitude misleadingly small. The explanatory
power—the true significance—of that part of the
variation in aptitude is, in effect, divided up among
the correlated variables. It is also interesting
because grammatical sensitivity has been shown
to predict instructed language learning and also
the implicit memorization of rule‐based language,
but not incidental language learning (see
Robinson, 2007, p. 263). Thus, perhaps the results
indicate that these advanced Chinese language
learners learned a considerable amount incidentally, learning that did not depend on grammatical
sensitivity. Similarly, strategy use was correlated
with motivation (r ¼ .40, p ¼ .00), reducing the
significance of their independent effects, as well.
For these reasons, significance testing should not
be overemphasized in the interpretation of the
model presented in Figure 2.
The Effects of Motivation on L2 Learning
As with strategy use, the data on motivation are
largely consistent with prior theory and empirical
research. The data showed that aptitude negatively
affected motivation (r ¼ #.27, p ¼ .58), which
squares with Dörnyei’s (2005b) view that the less
aptitude a learner has, the more motivation the
learner needs. Motivation was positively correlated
with strategy use (r ¼ .40, p ¼ .00), as found by
Vandergrift (2005). The data showed that motivation positively affects reading (r ¼ .16, p ¼ .17),
which is consistent with the theory that motivation
is important to learning an L2 (Dörnyei, 2001a,
2005b; Ellis, 1994). (An effect size of .16 could be
viewed as large within the context of advanced
language learning.) While motivation ebbs and
flows (Dörnyei, 2005b, 2009b), these data suggest
that motivation has some enduring power even at
the advanced level. One final point: Motivation
had less of an effect on listening and speaking than
on reading, just as strategy use did. As discussed
above, the reason for the different effect of motivation on the different skills may be that gains in
listening and speaking are more strongly influenced by socially construed interlocutor effects,
diminishing the relative impact of motivation.
The Components of L2 Aptitude
One of the most important features of the
model in Figure 2 is how it represents the construct
of L2 aptitude. L2 aptitude is composed of four
independent variables: rote memory, phonetic
coding, grammatical sensitivity, and working
memory. The regression coefficient (“r”) of each
120
The Modern Language Journal 97 (2013)
reveals how much the score for aptitude is
expected to increase when the score for the
independent variable increases by 1 standard
deviation, holding all other independent variables
constant (Field, 2009; Thayer, 1991). In this
model, aptitude for advanced‐level Chinese consists of high rote memory, phonetic coding, and
working memory abilities, and low grammatical
sensitivity. Rote memory and grammatical sensitivity contribute the most to differences in aptitude.
Increases in rote memory scores lead to major
increases in aptitude scores (r ¼ .97, p ¼ .59), and
increases in grammatical sensitivity lead to major
drops in aptitude scores (r ¼ #.86, p ¼ .58).
Increases in phonetic coding ability scores lead
to much smaller increases in aptitude scores
(r ¼ .14, p ¼ .86), and increases in phonological
working memory make almost no difference to
aptitude scores (r ¼ .03, p ¼ .92).
system of grammar. One could view those with
high scores on grammatical sensitivity as those for
whom the L1 system is firmly entrenched. That is,
their perceptions of grammar may be tuned by the
L1 to the extent that “their learned attention
blocks them from perceiving differences in the L2”
(Beckner et al., 2009, p. 10), which may, in
this case, inhibit them, to some extent, from
advanced Chinese language acquisition, or at least
delay it. This finding is congruent with other
research that has found that learners entrenched
in their L1 patterns may have negative L1–L2
crosslinguistic influence, which might manifest
itself during L2 acquisition by an overgeneralization of rules, avoidance of certain forms or
structures, overproduction, and hypercorrection
(Beckner et al., 2009; MacWhinney, 1997;
Odlin, 1989). Aptitude test score interpretations
as currently applied have not done well in
predicting Chinese learning (Carroll, 1962,
1990, 1993). This study could be indicating why:
According to this model, not all of the aptitude
components are positively oriented for success in
Chinese development.
Such information may have implications for
pedagogy. Chinese instruction for native English
speakers could focus more on non‐L1‐like, Chinese conceptual patterns and ways of thinking
that are different from English, in particular,
explicit instruction on L2 speech and writing
patterns in Chinese that have been shown to
be extremely difficult for nonnative speakers of
English to acquire because they do not conform to
any rules or sequential patterns present in English
(Li, 2009, 2010). It has been proposed that
profiling aptitudes could help match learners to
instructional options and pedagogical tasks that
would improve comprehension and production
(Robinson, 2002a, 2002c). This study does so on a
group level. The profile of aptitude for advanced‐
level Chinese reveals the great importance of rote
memory and the necessity for openness to novel
forms and ways of thinking about language and
grammar.
Grammatical Sensitivity and L2 Aptitude. A
principal puzzle to solve is why, in this model,
grammatical sensitivity inversely contributes to the
construct of aptitude. When grammatical sensitivity (the ability to recognize the function of an
English word in a sentence) goes down by 1
standard deviation, aptitude goes up by .86
standard deviations. It could be that students
who understand English grammar well have
difficulty adapting to the very different Chinese
The Overall Effects of L2 Aptitude on
L2 Chinese Learning
Working Memory and L2 Aptitude. It may seem
difficult to reconcile the results regarding working
memory and L2 learning when other studies have
found working memory to be a significant
predictor of L2 learning success (e.g., Harrington
& Sawyer, 1992; Mackey et al., 2010; Mackey et al.,
2002; Palladino & Cornoldi, 2004; Payne & Ross,
2005; Segalowitz & Lightbown, 1999; Tokowicz,
Michael, & Kroll, 2004). However, as explained by
Thayer (1991), “a variable might be the most
important single predictor of a dependent variable
when used alone but an unimportant predictor
when used in combination with other predictors
due to the amount of shared predicted variance”
(p. 3). In this dataset, working memory correlates
with rote memory (r ¼ .23, p ¼ .04), which might
account for working memory’s diminished role in
defining the construct of L2 aptitude for Chinese.
In any case, these results substantiate those from
Hummel (2009), who found that working memory
is not good at differentiating learning at upper
levels of instruction. Unique in this study’s dataset
is a potential reason why: There are other L2
aptitude factors that account for some variance in
advanced L2 Chinese learning—the primary one
being rote memory.
To finalize, as can be seen in Figure 2, no single
component explains the observed variance in
advanced L2 Chinese development. Within this
model, aptitude, strategy use, and motivation have
relatively the same impact on learning, as has been
suggested by previous research (Dörnyei, 2001b;
Schmidt & Watanabe, 2001; Tseng & Schmitt,
Paula Winke
2008; Vandergrift, 2005). While strategy use and
motivation mediate the effects of aptitude and
have an influence on reading ability, they affect
listening and speaking less. Aptitude, on the
other hand, appears to directly affect speaking
the most and does not directly affect reading or
listening much at all. These seemingly discordant
findings make intuitive sense when considering
that language learning is a complex system
affected by the interaction between the language
learner and the language learning environment
(Dörnyei, 2009a; Larsen–Freeman & Cameron,
2008). At the advanced stages of L2 learning, that
interaction has had more time to exert its
influence and creates unaccounted‐for noise in
the model. Within a dynamic systems view of L2
learning, high aptitude, high motivation, and
good strategy use may be significantly advantageous conditions for attaining advanced proficiency, but when instruction is task‐based and
grounded in social interaction, minute distinctions in advanced proficiency may depend more
on unmeasureable and unsystematic factors external to the model.
The predictor variables (aptitude, strategy use,
and motivation) only explain 2%, 3%, and 6% of
the variance in listening, speaking, and reading,
respectively. These results may be disappointing
for those looking for ways to predict which adults
will successfully learn a foreign language to an
advanced level of proficiency, but they are not
surprising. Carroll (1962) found that after native‐
English‐speaking learners of Chinese progressed
beyond the beginning level, significant associations between aptitude (as measured by the
MLAT) and learning became nonsignificant.
Changes in Chinese L2 pedagogy since 1962
have made the question of what predicts advanced‐level, Chinese‐language‐learning success
worth asking again. Because working memory
has been shown to be related to L2 learning
success, it was sensible to include working memory
as an additional component of L2 aptitude in such
an investigation. Likewise, I included measures
of strategy use and motivation because recent
theorizing suggests the effects of aptitude are
mediated by them. Based on the results of this
study, it does not appear that aptitude, updated as
a construct that includes working memory and two
mediator variables (strategy use and motivation),
is any better at explaining differences in advanced
L2 Chinese attainment.
Why does L2 aptitude predict advanced‐level L2
Chinese performance so poorly? What is it that
distinguishes performance at the advanced level? I
speculate that Larsen–Freeman and Cameron
121
(2008) were right—the language learning environment is responsible for much of the learning
that takes place. Different aptitude abilities
are best suited to different stages of learning
(Robinson, 2005a, 2007), and aptitude is not so
important at the later stages of learning. Perhaps
it is not that aptitude abilities are dynamic (as
asserted by Skehan, 2002); rather, it might be that
aptitude interacts with a changing learning
environment (Neufeld, 1979; Snow, 1987; Sternberg, 2002; Sternberg & Grigorenko, 2002), thus
bringing about flux (and a gradual reduction) in
the importance of aptitude for learning. In sum,
at the advanced level, it is not the cognitive or
affective variables—the factors that lie within
the learner (Ellis, 2004)—that matter; rather, it
is how the individual reacts to the learning
context. The learner’s actions in the social
environment, the amount of time spent outside
of class learning, and other personal reactions
and choices of what to focus on ultimately affect
learning.
LIMITATIONS AND FUTURE DIRECTIONS
An important limitation to this study is the time
at which the motivation and strategy surveys were
administered. Researchers have described how
motivation changes over time for any given learner
and have described how a flux in motivation may
be related to temporal components as small as a
task in the language learning classroom or as large
as the flow of a foreign language course (Dörnyei,
2003, 2005b; Dörnyei & Ottó, 1998). Likewise,
strategies that are useful at the beginning of
language learning may not be those that are useful
at the upper levels of language learning (Griffiths,
2003; Hong–Nam & Leavell, 2006); thus, strategy
use, too, must naturally flux in response to the
learning conditions. The motivation and strategy
surveys were administered during the learners’
42nd week of intense instruction, when most
learners were most likely at an intermediate level
of L2 Chinese. It could be that at this stage of such
a lengthy process of highly controlled, classroom‐
based learning, motivation is fairly well established
and generalized to a certain extent (Gardner
et al., 1997; Tremblay & Gardner, 1995). Strategy
use may reflect the learners’ individual proficiency
levels at the time. The survey questions may have
been replied to differently if administered to the
same learners at a different point in their learning
trajectory; thus, the results concerning motivation
and strategies must be interpreted with the
context in mind.
122
Another limitation is how I operationalized
motivation and strategy use in this study. I created
composite scores for these latent traits to keep
the cases‐per‐parameter ratio low. According to
Kline (2011), the number of participants to the
number of free parameters should be 20 to 1 (yet
10 to 1 is perhaps more realistic for applied
linguistics studies). By collapsing scores from
the surveys, I asserted that the traits are one‐
dimensional. In reality, the motivation survey and
the SILL do not each measure a homogeneous
construct. Each trait comprises several subareas:
motivation (as measured by the survey in this
study; see Kormos & Dörnyei, 2004) has seven;
strategy use (as operationalized by the SILL, see
Oxford, 1990a) has five. In future research, not to
violate the assumption of composite score homogeneity, researchers could focus on certain areas
of each latent trait. Or, to understand more
effectively the relationship between, for example,
strategy use and the different advanced skill areas
(in this case, advanced listening, speaking, and
reading), it would be useful to explore which
questions best predict advanced‐level success in
the various skills. Currently, the items on the
motivation survey and the SILL are not balanced
by language learning skill areas (i.e., the SILL has
very few items that address strategies for speaking)
and are not normally segregated into skill areas.
Nor do probabilistic statistics exist that explain
which motivational aspects/strategies are most
likely used by learners at certain levels of proficiency. (Such research would be interesting and
useful.) Furthermore, the items are not streamlined as to which are most relevant for learning
Chinese, a language proven difficult for English‐
language speakers because of its character‐based
writing system and use of tones. Thus, there is
much groundwork to be done in terms of
motivation and SILL instrument adaptation and
refinement.9 Such research needs to be conducted
to make data from the surveys more applicable to
applied linguistics research using SEM.
In this study, the factor loading of working
memory to aptitude is very low, which is inconsistent with the hypothesized model. This needs to
be revisited. I assessed working memory through
a single listening span test. It would be useful to
administer different kinds of working memory
tests because working memory tests, like the
various subtests of L2 aptitude, may tap into
different aspects of working memory. A more
robust analysis of participants’ working memory
skills could include reading span tests, visual–
spatial tests, and perhaps even a nonsense word
or digit span test, as recommended by Conway
The Modern Language Journal 97 (2013)
et al. (2005) and Waters and Caplan (1996). A
more variegated measure of working memory
may tell researchers more about this construct in
relation to aptitude and the other variables in the
model, but adding such parameters requires more
participants.
Although there is substantial support for the
final hypothesized model in this study in terms of
goodness‐of‐fit indices, cross‐validation SEM studies are needed to investigate whether the model in
this study holds with other samples and whether
those model estimates are stable across samples.
Ideally, such studies would include a larger sample
size: In this study, with 15 degrees of freedom and
96 participants, the power to reject the model is
less than .2 (see Table 2 in MacCallum, Browne,
& Sugawara, 1996). To achieve the statistically
desired power of .8 (an 80% probability that the
test would not make a Type II error, that is, fail to
reject a null hypothesis when it is actually not
true), 500 learners would be needed (MacCallum
et al., 1996). Obtaining such participant numbers
is notably difficult, especially when investigating
advanced‐level learners of a less‐commonly‐taught
language such as Chinese. (For example, to obtain
data from 500 participants for this study, I would
have to collect data from Chinese language
learners at the DLI for 5 years in a row; my
1 year of data collection yielded viable data from
96 participants at the cost of US$11,000 in
National Science Foundation funding.) For this
reason, testing the model or similar models out on
diverse datasets may be more feasible. This needs
to be done because we need to understand better
how much variability is due to the learning
context, the level of acquisition, and the specific
L2 being learned. Future studies are also needed
to explore the validity of using other kinds of
working memory tests and other (perhaps more
finely tuned) measures of motivation, strategy use,
and L2 learning. Only then will we come closer to
understanding the construct of L2 aptitude and
how it relates to the complex system of L2 learning.
CONCLUSION
Several varied factors must successfully converge
for an adult learner to obtain advanced proficiency in a foreign language. The learner needs
excellent instruction, frequent opportunities for
different kinds of output, and a heavy dose of
motivation. Access to cultural insights that explain
the pragmatics of the language has to come the
learner’s way; effective language learning strategies need to be found; and, as any learner knows,
real, tangible rewards for learning efforts must
Paula Winke
materialize. It is natural that linguists search for a
concrete reason why some succeed and others do
not. But many individuals achieve advanced‐level
proficiency in a foreign language. And for any
one individual working toward advanced proficiency, if his or her approach to learning changes
regardless of his or her underlying cognitive
strengths, advanced‐level proficiency may come
a few months earlier or later. Trying to pinpoint
who will succeed fastest within a range of 3 to
6 months may be futile. Indeed, as evidenced by
the data in this study, aptitude operationalized as a
construct so extremely sensitive to such minute
discrepancies in L2 learning may be unrealistic.
The results of this study provide useful information that helps us conceptualize the factors
involved in the acquisition of advanced‐level
Chinese by adult native speakers of English. The
results provide evidence that L2 aptitude, defined
as rote memory, phonetic coding ability, grammatical sensitivity, and phonological working
memory, is only a moderately useful construct in
this context. Moreover, the effects of L2 aptitude
on advanced‐level learning are mediated by the
affective variables of motivation and strategy use.
In the 42nd week of a 63‐week, intensive Chinese
program, L2 aptitude predicts end‐of‐course
performance no better than strategy use and
motivation do. Variance in reading, listening, and
speaking is only minimally explained by the
predictors in this study’s model. Future research
might focus on the observed, interactive, reciprocal aspects of motivation and strategy use; the
differential effects aptitude, motivation, and
strategy use have on the different skills of L2
development; or the relationships between rote
memory and the other L2 aptitude factors
observed in these data.
ACKNOWLEDGMENTS
I would like to thank the contributors to this research.
The National Science Foundation funded this project
(Award #0418175). Jeff Connor–Linton, Alison Mackey,
and Charles Stansfield formed my dissertation committee at Georgetown University and guided this research.
Gordon Jackson and John Lett at the Defense Language
Institute provided logistical support and helped me
solicit the Chinese‐language‐learner volunteers. Laura
Klem (University of Michigan) and Alexander von Eye
(Michigan State) provided technical feedback on the
modeling. Comments from Helen Carpenter, Akiko Fujii
Kurata, Heather Weger, and four anonymous MLJ
reviewers assisted me in revising the paper, which I
believe made it stronger. Any mistakes, however, are
solely mine.
123
NOTES
1
Carroll envisioned a parallel “Form B” of the MLAT;
however, Form B was never created. Nonetheless, the
MLAT is still sold as “Form A.”
2
An additional factor, which Carroll identified as
inductive language learning ability (the ability to decode
linguistic material and conceptualize how other linguistic material would be encoded in the same language),
was identified through tests that were acknowledged
by Carroll to be administratively difficult; therefore,
this construct, which Carroll (1962) stated was important
for language learning, is not represented on the
MLAT.
3
The DLAB is currently being revised. Go to CASL’s
Web page (http://www.casl.umd.edu/dlab2) for
more information. For current information on the
PLAB and how it differs from the MLAT, go to
the Language Learning and Testing Foundation’s
Web pages on the PLAB (http://lltf.net/aptitude‐
tests/language‐aptitude‐tests/pimsleur‐language‐
aptitude‐battery) and on L2 aptitude testing in
general (http://lltf.net/aptitude‐tests/what‐is‐language‐
aptitude).
4
Traditional theories of working memory suggest that
short‐term memory works in combination with other
factors within working memory (Baddeley, 2007; Daneman & Carpenter, 1980) and/or that information stored
in long‐term memory can be retrieved during working
memory executive processes (Cowan, 2005). For more
information, see Dehn (2008).
5
A few researchers posit that L2 aptitude is malleable.
See information on and debates concerning Feuerstein’s
Instrumental Enrichment, one purpose of which is
to improve aptitude (Bailey & Pransky, 2010; Savell,
Twohig, & Rachford, 1986).
6
Sasaki collected data from 160 Japanese students
learning English in college in Japan. She used SEM to
investigate the relationships among English proficiency,
general intelligence, and L2 aptitude. But as in
Hummel’s (2009) study, the quality and methods of
the college students’ prior English language instruction
were not controlled.
7
The measure was revised by increasing the number
of sentences from 36 to 48 (Winke, Stafford, & Adams,
2003). The general procedures for this working memory
span test are also reported in Mackey et al. (2010).
The measure can be downloaded from the Instruments
for Research into Second Languages (IRIS) database at
http://www.iris‐database.org
8
I am extremely grateful to an anonymous MLJ
reviewer who recommended this part of the discussion.
9
An anonymous MLJ reviewer noted that one way
to measure the strategies of advanced language
learners would be to interview them individually or
in focus groups. The reviewer noted that advanced
learners’ needs may be more idiosyncratic than
what are measured by standardized surveys such as
the SILL. Such information may help a researcher devise
a skill‐ and language‐specific Advanced Learners’ SILL.
124
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APPENDIX A
Motivation Questionnaire
This questionnaire is designed to gather information about how you, as a student, feel about
learning Chinese. Please read each statement.
Mark the response that tells how much you agree
or disagree with each statement as follows:
1 ¼ Strongly disagree, 2 ¼ Disagree, 3 ¼
Somewhat agree, 4 ¼ Agree, 5 ¼ Strongly agree
Part A.
1. Sometimes I feel that language learning is a
burden for me.
2. I would like to get to know as many native
speakers of the language I am learning as
possible.
3. I am sure that I’ll be able to learn the
language I am studying.
4. I think I am good at learning languages.
5. When I have to speak in my language class, I
often lose confidence.
6. I like to work hard.
7. Unfortunately, I am not too good at
learning the language I am studying.
8. I would rather spend time on subjects other
than the language I am learning.
9. I am pleased with my current level of
language ability in my language class.
10. I would like to spend a lot of energy learning
this language in the future.
11. I am not too interested in my language class.
12. Learning this language often causes me a
feeling of success.
13. In my parents’ view, the language class I am
taking is not a very important course.
14. I would be pleased to be able to master an
intermediate level of this language.
15. I really like the language I am learning.
16. I generally feel uneasy when I have to speak
the language I am learning.
17. I generally feel uneasy when I have to read
the language I am learning.
18. I generally feel uneasy when I have to write
in the language I am learning.
19. We learn things in the language class that
will be useful in the future.
20. Learning this language is one of the most
important activities for me.
21. I rarely do more work for my language
course than what is absolutely necessary.
22. I would like to get to an advanced level in
this language.
130
The Modern Language Journal 97 (2013)
23. I don’t mind it if I have to speak in this
language with somebody.
24. I am satisfied with the work I do in my
language class.
25. I easily give up the hard‐to‐reach goals.
26. I like my language class.
27. I would like to get to know many people who
come from countries where this language is
spoken.
Part B.
Learning this language is important to me…
1. … because I would like to get to know the
culture and art of its speakers.
2. … because I may need it later for work or
further education.
3. … in order to become more educated.
4. … because I would like to spend some time
in a country where this language is spoken.
5. … so that I can read books, magazines and
newspapers published in this language.
6. … because one cannot achieve any kind of
success without it.
7. … in order to get to know the life of people
who speak this language better.
8. … because I would like to make friends with
speakers of this language.
9. … in order to understand films, videos and
TV programs in this language.
10. … because it might be useful during my
travels.
11. … in order to understand the lyrics of songs
in this language.
APPENDIX B
Pearson Product Moment Correlations between All Observed (Indicator) Variables
Measure
1.
2.
3.
4.
5.
6.
7.
8.
9.
2
3
4
Rote Memory
Phonetic Coding Ability
.12
Grammatical Sensitivity
.45 ** .01
Phonological Working Memory .23 * .12
Strategy Use
#.20
#.12
Motivation
#.20
#.07
Listening
.20
.09
Reading
.12
.04
Speaking
.08
#.04
#.09
.10
.09
.15
.13
#.12
#.13
#.05
.01
#.04
.03
Note. *p < .05, **p < .01.
1
5
6
.40 **
#.11
.02
#.19
.07
#.06
#.10
7
8
.59 **
.21
.32 **