An Investigation Into Second Language Aptitude for Advanced

An Investigation Into SecondLanguage Aptitude for AdvancedChinese Language LearningPAULA WINKEMichigan State UniversitySecond Language Studies ProgramDepartment of Linguistics and LanguagesB252 Wells Hall, 619 Red Cedar RoadEast Lansing, MI 48824Email: [email protected]

In this study I examine the construct of aptitude in learningChinese as a second language (L2) toan advanced level. I test 2 hypotheses: !rst, that L2 aptitude comprises 4 components—workingmemory, rote memory, grammatical sensitivity, and phonemic coding ability—and second, thatL2 aptitude affects learning both directly and indirectly (mediated by strategy use andmotivation). Native speakers of English (n ! 96) studying advanced Chinese took the ModernLanguage Aptitude Test and a phonological working memory test and responded to motivationand strategy use questionnaires. Using end!of!course listening, reading, and speaking pro!ciencytest results as measures of Chinese learning, I constructed a structural equationmodel to test thehypotheses. The model !t the observed data. Of the 4 components foreseen to comprise L2aptitude, rote memory contributed the most and working memory the least. Aptitude, strategyuse, andmotivation had about the same impact on learning but varied in how well they predictedthe individual skills of listening, reading, and speaking. The results shed light on L2 aptitude inthe particular context of an advanced L2 Chinese course.

COGNITIVE PROCESSES AND AFFECTIVEvariables shape how individuals acquire a foreignor second language (L2) and predict howwell theyare likely to learn one (Beckner et al., 2009; Ellis,2004; Robinson, 2002c; Skehan, 1989). This studyinvestigates the plausibility, via structural equationmodeling (SEM), of a model of language learningthat includes cognitive (rote memory, phonemiccoding ability, grammatical sensitivity, and phono-logical working memory), cognitively oriented(strategy use), and affective (motivation) variablesas learning predictors. It also examines how thefactors affect each other within the model.

First, I review research that addresses what Isuggest is a narrow de!nition of L2 aptitude as a setof purely cognitive constructs. Second, I argue that

a broader concept of L2 aptitude should beaccepted, one that recognizes the effects ofmediating, cognitively oriented, and/or affectivevariables—most vitally, strategy use and motiva-tion. Third, because L2 aptitude may be bestunderstood in terms of the context of the languagelearning situation (Robinson, 2007), I detail theparticular conditions of this study’s L2 learningcontext: native!English!speaking adults learningChinese in an intensive (6 to 8 hours a day in class),63!week course intended to bring them to anadvanced level of pro!ciency in Chinese. Finally, Idescribe the use of SEM to investigate L2 learningaptitude in this context, which has been rarelyused in L2 aptitude research.

THE COGNITIVE CONSTRUCTS OF SECONDLANGUAGE APTITUDE

Acquiring pro!ciency in an L2 in an instructedsetting is considered challenging for adults(Doughty, 2004; Ellis, 2004, 2005). Understanding

The Modern Language Journal, 97, 1, (2013)DOI: 10.1111/j.1540-4781.2013.01428.x0026-7902/13/109–130 $1.50/0© 2013 The Modern Language Journal

why has engrossed L2 acquisition researchers fordecades. Since the 1950s, researchers have positedthat L2 aptitude is a construct separate fromgeneral intelligence and predicts adult, classroom!based, L2 learning success (Carroll, 1962, 1981,1990; Corno et al., 2002; Dörnyei, 2005b;Robinson, 2005a; Skehan, 2002). The variablesthat were !rst recognized as part of L2 aptitudewere identi!ed prior to and/or independent ofmodern second language acquisition (SLA) theo-ry. Carroll (1958), tasked with creating an L2aptitude test for the U.S. government, adminis-tered 34 cognitive tests to military personneltaking intensive, 1!week!trial, beginner!level, Man-darin Chinese courses that focused on speakingskills. By using exploratory factor analysis, Carrollidenti!ed !ve tests that were both practical toadminister and highly predictive of L2 learningsuccess. These tests now comprise the ModernLanguage Aptitude Test, or MLAT, Form A(Carroll & Sapon, 1959b).1 Three of the !ve tests’underlying constructs identi!ed by Carroll(1958, 1962) will be explored here: rote memoryfor learning (the ability to store verbal informationin memory and recall it later); phonetic codingability (the ability to analyze incoming sounds sothat they can be retained); and grammaticalsensitivity (the ability to recognize, in whatevermanner, the function of a word in a sentence).2

(For a more detailed description of these con-structs, see Nagata, Aline, & Ellis, 1999, p. 135,and Skehan, 1998, p. 190.) Carroll followed thisresearch with several MLAT validation studies(Carroll, 1962, 1963, 1966) and concluded thatunder intensive learning conditions with hetero-geneous groups of learners, the MLAT correlatesfairly well with L2 achievement, with mostcorrelation coef!cients between .40 and .65 (cf.Carpenter, 2009; Skehan, 1998).

Over the decades, the predictive validity of theMLAT has prevailed. The MLAT is still used bytheU.S. Foreign Service and theCanadian ForeignService in selecting candidates for L2 study. TheMLAT is also used in research that attempts tode!ne the cognitive traits that explain differencesin adult L2 acquisition (e.g., research on criticalperiod effects, DeKeyser, 2000; Harley & Hart,1997, 2002; Ross, Yoshinaga, & Sasaki, 2002).Other aptitude tests, based on theMLAT or shownto be valid through correlations with the MLAT,have been developed (e.g., the Cognitive Abilityfor Novelty in Acquisition of Language [Foreign]Test or CANAL–FT, Grigorenko, Sternberg, &Ehrman, 2000; the Defense Language AptitudeBattery or DLAB, Peterson & Al–Haik, 1976; thePimsleur Language Aptitude Battery or PLAB;

Pimsleur, 1966).3 But differing tests of L2 aptitudedo not fundamentally alter our understanding ofthe underlying cognitive constructs of L2 aptitude(Skehan, 1998). Moreover, they do not extend theconstruct of L2 aptitude beyond what the testsmeasure (Dörnyei, 2005b; Neufeld, 1979; Sáfár &Kormos, 2008).

It may pro!t the !eld to take aptitude researchto another level and investigate aptitude fordynamic complexity. For example, Skehan (2002)proposed that aptitude abilities are dynamic andchange over time. Robinson (2005a, 2007) sug-gested that different aptitude abilities are neededat different stages of learning. Recent theorizing inSLA has proposed that learning is fundamentallya slave to the environment, with certain contextsnourishing some mental processes and othercontexts nourishing othermental processes (Beck-ner et al., 2009; Dörneyi, 2009a; Larsen–Freeman& Cameron, 2008). It is not, therefore, anunderstatement to claim that aptitude as astandalone cognitive trait is problematic. Oneplace to start is to investigate aptitude constructsindividually and within a larger picture of cogni-tive, cognitively oriented, and affective variablesand situated within a particular language learningcontext. We can at least then begin to understandif aptitude is dynamic and relates to the learningcontext, as has been suggested (Neufeld, 1979;Snow, 1987; Sternberg, 2002; Sternberg &Grigorenko, 2002).

The MLAT

Many researchers have challenged the view thatL2 aptitude is something measured by the MLAT(Dörnyei, 2005b; Robinson, 2002b, 2007; Sáfár &Kormos, 2008; Skehan, 2002). One criticism is thatthe constructs underlying the MLAT do notrepresent a complete de!nition of L2 aptitude.This has been con!rmed by research that hasshown that the MLAT does not strongly predictlanguage learning when instruction is less intense(Carroll, 1962, 1990) or more communicative innature (Robinson, 2007; Sáfár & Kormos, 2008).Furthermore, in the original validation studies byCarroll, learners only received lower level instruc-tion. Thus, even though there have been decadesof studies on L2 aptitude, we still do not know whatcognitive variables are important for learning inmoderately intense courses, in communicative,task!based language programs, or for achievingadvanced L2 skills.

This does not mean that researchers shouldabandon the MLAT but rather that the

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constellation of factors that contribute to success-ful language acquisition needs to be expandedbeyond the constructs represented by the MLATand more !rmly situated within the context oflearning. Furthermore, aggregating L2 aptitudetest subscores into a single composite score (whichindirectly implies what L2 aptitude is—a singlefactor) needs to be revisited. Skehan (1998, 2002)called for researchers to view aptitude as trulydifferentiated or multifaceted, meaning that onecould have high ability in one aptitude constructand low ability in others, resulting in individuallyunique L2 aptitude pro!les. This is different fromCarroll’s view that there are across!the!board havesand have!nots in L2 aptitude or that L2 aptitude is“a special, inherent talent that not all individualspossess” (CASL, 2009, p. 1). Robinson (2007)proposed the Ability Differentiation Hypothesis,which claims that some L2 learners have clearlydifferentiated cognitive skills and abilities, whileothers do not, and that these different talentschemes correspond with different aptitude com-plexes, which must be matched to instructionalconditions to maximize L2 learning potential.According to Skehan and Robinson, within themodel, the individual components may be corre-lated for some learners, but not for others.

Working Memory

In the 1990s, building on research in cognitivepsychology that identi!ed working memory asa cognitive trait separate from long! and short!termmemory systems4 and important for learning(Baddeley, 1986, 1992, 2007; Cowan, 1995, 2005;Daneman & Carpenter, 1980; Hitch & Baddeley,1976), applied linguists investigated the relation-ships between workingmemory and L2 acquisitionand ascertained that working memory is anessential component for L2 learning (Gathercole& Baddeley, 1993; Harrington & Sawyer, 1990,1992; Miyake & Friedman, 1998; Miyake, Fried-man, & Osaka, 1998; Service, 1992). Concomitant-ly, researchers suggested that working memory ismost likely an additional cognitive construct of L2aptitude (Daneman & Merikle, 1996; Harrington& Sawyer, 1992; McLaughlin, 1995; Miyake &Friedman, 1998; Robinson, 1995, 2002b) and maybe the “the key to elaborating the concept oflanguage aptitude itself” (Sawyer & Ranta, 2001,p. 340).

Generally de!ned, working memory underlies theability to process linguistic input and storeinformation from that input for later retrieval.Speci!cally, working memory has been de!ned as

the “set of processes that hold a limited amount ofinformation in a readily accessible state for usein an active task” (Cowan, 2005, p. 39). In otherwords, working memory is a cover term formultiple processes, including short!term memory,the real!time manipulation of linguistic materialthrough effortful processing (Cowan, 2005) andstoring information in long!term memory. Theseare dissociable abilities, each with limited capaci-ties, but that must work together for learning(Baddeley, 2007; Miyake & Friedman, 1998).

Empirical investigation into working memory’srole in relation to previously identi!ed L2 aptitudeconstructs is of current interest in the !eld forboth practical and theoretical reasons. Severalgovernment!sponsored research projects supportsuch research with the goal to increase thegovernment’s ability to identify military andforeign service candidates for language training(see CASL, 2009; Kenyon & MacGregor, 2004).Theoretically, researchers are interested becausethey need to understand L2 aptitude as a constructand how it !ts within other theories about learning(Corno et al., 2002). Thus far, research suggeststhat working memory is an essential part of L2aptitude (Robinson, 2002b; Sáfár & Kormos,2008), but evidence is limited. Some researchsuggests that it may only signi!cantly differentiatelearning at lower levels of instruction (Hummel,2009). Other research that focuses on the short!term memory component of working memorysuggests that limited short!term memory capacitymay impede learning for those with limitedcapacities while failing to differentiate otherlearners (Carpenter, 2009). Such !ndings ques-tion the entire concept of L2 aptitude: That is, isthere really a single latent trait that can reliablypredict advanced!level language skills or L2 skillsacquired through nonintense or more naturalisticconditions? Researchers may have pinned theirhopes on working memory as a promisingadditional component of L2 aptitude that reliablypredicts advanced pro!ciency, but initial researchis inconclusive. More studies are needed.

SECOND LANGUAGE APTITUDE ANDNONCOGNITIVE AND AFFECTIVEVARIABLES

It is simplistic to think that language learningdepends solely on cognitive traits. Researchershave long attributed language learning success to anumber of noncognitive factors, such as highmotivation for learning the particular languageat 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) andthe 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). Debatedin the L2 aptitude literature is whether thesenoncognitive variables should be considered aspart of a broader construct of L2 aptitude. On onehand, some propose that they should not: Apti-tude is purely a static, cognitive trait resistant tochange (Carroll, 1990; Parry & Child, 1990;Pimsleur, 1966; Stans!eld & Reed, 2004). Onthe other hand, some propose that L2 aptitude,broadly de!ned, includes the noncognitive varia-bles 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 L2aptitude holds that the underlying cognitiveconstructs interact with one’s motivation forlearning the particular language at hand and theway in which the language is taught and learned,which may, in part, explain why a person is betterable to learn an individual L2 over another L2(Sternberg, 2002).5

Motivation

Motivation affects L2 acquisition in multipleways. It is a necessary precondition for L2acquisition (Csizér & Dörnyei, 2005; Dörnyei,Csizér, & Németh, 2006; Edmondson, 2004;Noels et al., 2000), and it must be healthilysustained over time for acquisition to continue(Dörnyei, 2005b, 2009b;Hiromori, 2009). For overthree decades, motivation has been shown tobe 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 haveconceptualized motivation differently, but mostview 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 apassable member of the community of speakersof the L2) (Gardner, 1985, 2001; Gardner et al.,1997), (b) have a very good attitude about learningthe L2 (Dörnyei, 1990, 2003; Yashima, Zenuk–Nishide, & Shimizu, 2004), and (c) be eager to

communicate in the target language (Dörnyei &Kormos, 2000; MacIntyre et al., 2001; Yashima,2002; Yashima et al., 2004). A highly motivatedlanguage learner is further characterized by strongand clear incentives for learning the language,such as for future employment, a pay raise, travelopportunities, or to communicate with familymembers (these are classi!ed as instrumentaland/or intrinsic reasons; for de!nitions seeGardner, 1985; Gardner, 2001; Gardner &MacIntyre, 1991; Gardner et al., 1997; Tremblay& Gardner, 1995). The individual will alsohave higher con!dence in his or her abilities inusing the L2 (Clément, Dörneyi, & Noels, 1994;Dörnyei, 2003, 2005a; Ushida, 2005)—and corre-spondingly lower anxiety in using the language(MacIntyre et al., 2002; Matsuda & Gobel, 2004).The overall consensus is that high motivation maymake up for de!ciencies in cognitive abilities(Dörnyei, 1990; Pimsleur, 1966; Schmidt, 1991;Sternberg, 2002), which suggests that the effects ofcognitive abilities on L2 learning are mediated bymotivation.

Strategy Use

Similarly, strategy use has been shown to affectL2 acquisition, but it is unclear how. Strategies arethe “steps or actions taken by the learners toimprove the development of their language skills”(Oxford & Cohen, 1992, p. 1) or “the L2 learner’stoolkit for active, conscious, purposeful, andattentive learning” (Hsiao & Oxford, 2002,p. 372).More recently, strategies have been viewedas cognitively oriented—as summarized by Macaro(2006), “strategies are the raw material of con-scious cognitive processing, and their effectivenessor noneffectiveness derives from the way theyare used and combined in tasks and processes”(p. 325). Studies that have measured strategy usethrough questionnaires have shown that learnerswho use many different kinds of strategies, anduse them often, have more success in instructedL2 acquisition settings (Cohen, 1998; Ehrman &Oxford, 1990; Green & Oxford, 1995; Grif!ths,2003; O’Malley & Chamot, 1990; Oxford, 1990a,1994). (See Oxford and Burry–Stock, 1995, for afull review.) Other research has found that lesssuccessful learners use various, random strategies,whilemore successful learners aremore systematicabout their strategy usage and use speci!c ones forspeci!c tasks (Ehrman & Oxford, 1995). Morerecent research has suggested that strategy usechanges over time depending on L2 pro!ciencyand the learning context (Macaro, 2006), with


advanced L2 learners using fewer strategiesoverall than intermediate learners (Hong–Nam& Leavell, 2006) because at the advanced levelsof pro!ciency, learners’ processes for L2 acquisi-tion are more automatized, resulting in a smallerrange of strategies needed for acquisition (seeOxford, 2011, for a comprehensive review of theresearch).

Most interestingly, research has shown that L2learning strategies and motivation are related(Schmidt & Watanabe, 2001). More strategy useequals more or better learning, and more motivat-ed learners use more learning strategies moreoften (Gardner et al., 1997; MacIntyre & Noels,1996). Thus, a full model of L2 learning thatconsiders the impact of L2 aptitude on learningshould also investigate the combined mediatingrole of motivation and strategy use. As far as Iknow, no study thus far has done so.

THE SECOND LANGUAGE LEARNINGCONTEXT

One of the problems with L2 aptitude researchis that it often assumes that L2 aptitude exists in avacuum and is independent from the L2 learningcontext. This may not be true, especially if weassume motivation and strategy use mediateaptitude’s effects on language learning. Onemight be more motivated to learn a certain L2over another (Sternberg, 2002); likewise, onemight be more motivated to learn an L2 in acertain classroom situation over another. Thiscorresponds with what is known as the resultativeor spiraling effect of motivation: Learners whodo well in a certain learning situation becomeeven more motivated to learn, while those whodo poorly get discouraged and try less hard(Hermann, 1980). Classroom!based variablessuch as materials, instructional techniques, teach-ers, and peers might affect motivation and strategyuse, which may positively impact learning, mini-mizing or distorting the effects of L2 aptitude. Asstated by Corno et al. (2002), “to understand theeffects of person characteristics on performance,one must specify the performance situation”(p. 216). Thus, researchers must investigate L2aptitude in terms of the context of the L2 learningsituation (Robinson, 2007).

This current study is unique in that all learnersstemmed from the same learning context. Thelearners began with no prior Chinese languageinstruction. All received the same instructionthroughout a 63!week course, which was intendedto develop in each language learner advanced!level pro!ciency (also known as General Professional

Pro!ciency—a 3 on the Interagency LanguageRoundtable scale; see http://www.govtilr.org fora description) in the skills of listening, reading,and speaking so that the learners could go on to beinterpreters, analysts, and interrogators for themilitary branches for which they worked. Allstudents received instruction from the sameteachers, used the same materials, and followedthe same curriculum.

Equal instructional conditions across partici-pants is important because prior research hasfound that L2 aptitude is sensitive to exposureconditions, such as the amount of implicit versusexplicit instruction (Robinson, 1997, 2002c,2005b). Prior studies on aptitude may have hadsigni!cant sampling errors in relation to instruc-tional variance. For example, Hummel (2009)explored the relationships among the cognitiveconstructs of L2 aptitude (phonetic coding ability,grammatical sensitivity, rote memory, and phono-logical working memory) and L2 pro!ciency. Shefound that the cognitive constructs of L2 aptitudecombined predicted 29% of the variance in L2(English) pro!ciency. The 77 participants in herstudy were recruited from a !rst!year TeachingEnglish as a Second Language degree program. L2pro!ciency was measured directly after entrance(within their !rst month of study) in the program;the tests of L2 aptitude, including workingmemory, were administered at some point afterthe pro!ciency test (Hummel did not indicatewhen). Although Hummel described the partic-ipants as being a homogeneous group—all werenative French speakers with at least 7 years ofEnglish study—the quality and methods of theirprior English language instruction were notcontrolled. One could imagine a situation inwhich those who attended better language pro-grams 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 relationshipsobserved. Furthermore, if the participants at-tended differentially performing schools, withschool access determined through a collegeentrance exam (another cognitive test or anintelligence test), any observed correlations be-tween L2 aptitude and pro!ciency could beinterpreted as noncausal: Other factors (i.e.,general intelligence, instructional differences)may underlie both high aptitude and highperformance. This is not a fatal research design"aw, but it allows one to question the reliability ofsuch a study. It would be better to make certainthat all learners received the same instruction(and the same amount of instruction), which

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would help ensure that only the variables presentin the hypothesized model of L2 learning wereresponsible for the study’s outcomes.

USING STRUCTURAL EQUATIONMODELING TO INVESTIGATE SECONDLANGUAGE APTITUDE

The relationships among L2 aptitude constructshave been explored through correlation analyses(e.g., Wesche, Edwards, & Wells, 1982), factoranalyses (e.g., Carroll, 1958, 1962, 1963, 1966,1993), and simple and multiple regression (e.g.,Carpenter, 2009; Hummel, 2009). However, theseare essentially descriptive techniques, makingcomplete hypothesis testing dif!cult (Byrne,2009). For example, with con!rmatory factoranalysis (CFA), even though the researcherpostulates how the underlying latent variablesrelate and then tests this structure statistically, CFAfocuses on whether and the extent to which theobserved variables are linked to the underlyinglatent traits. The strengths of the regression paths(the factor loadings) from the latent variables tothe observed variables (the items) are the focus ofCFA. CFA does not consider direct effects amongthe factors, and thus CFAmisses essential elementsa full latent variable model (such as SEM) has.SEM combines a CFA measurement model (SEMruns a CFA as part of its analysis) and a structuralmodel. In other words, a full SEM model allowsresearchers to estimate both the links betweenthe latent variables and their observed measures(the measurement portion of the model) and thedirect effects among the variables (the structuralportion of the model).

With SEM researchers can investigate theplausibility of a full latent variable model, whichis de!ned as a single proposed set of relationshipsamong one or more independent variables andone or more dependent variables (Byrne, 2009).The relationships within the proposed model canbe discussed in terms of their causality, but thecause–effect relationships must be backed up byrelevant theory—that is, the proposed set ofrelationships in a model of L2 aptitude and L2learning must stem from hypotheses and theorieson L2 aptitude and L2 learning. As explained byLewis and Vladeanu (2006):

While there are similarities between structural equa-tion modeling and multiple regression (e.g., they arebased on analysis of intercorrelations) there arefundamental differences. Multiple regression pro-vides simple de!nitive results about which indepen-dent variables produce signi!cant effects on which

dependent variables. In structural equationmodeling,the analysis requires a theoretical model that can betested against the available data. It can be determinedwhether this model is consistent with the data and alsohow good a !t it is. (p. 981)

Amodel that is found to be consistent with the datathrough SEM has nothing to say about whether abetter model might be possible with the existingset of data or with other data. Thus, the resultsstemming from SEM are grounded within thecontext of the data set (in this case, within the dataprovided by adults learning Chinese in a commu-nicative, task!based, 63!week course that focuseson the skills of listening, reading, and speaking).But, with SEM, competing models can be com-pared, a model can be tested over time, or a modelcan be tested across different data sets. This isexactly what L2 aptitude research needs, especiallybecause theory now suggests that cognitive abilitiesmay be represented differently by differentindividuals, may change over time, or may dependon the particular dynamics of the !rst language(L1)–L2 learning context. SEM also allows struc-tural relations to be plotted in a picture, making iteasy to visualize the theory being studied(Byrne, 2009). While other studies on L2 aptitudehave employed SEM (i.e., Sasaki, 1993, 1996),6 thisis the !rst L2 aptitude study using SEM thatensures participants received the same amountand type of instruction.

HYPOTHESIZED MODEL

This study’s hypothesized model is presented inFigure 1. What I hypothesize is a model of L2learning that includes both cognitive and affectivetraits. L2 aptitude is de!ned as comprising thecognitive traits of rote memory, phonetic codingability, grammatical sensitivity, and working mem-ory. L2 aptitude affects L2 learning both directlyand indirectly—the indirect effects coming frommediation by the affective traits of strategy use andmotivation.

METHOD

Participants

Ninety!six native English!speaking, adult learn-ers of Chinese volunteered to be participants inthis study. All were students in a 63!week, intensiveChinese program at the Defense Language Insti-tute (DLI) in Monterey, California. All partic-ipants were U.S. military personnel. Sixty!six weremale, 30 were female. Ages ranged from 19 to 36,with an average age of 25. As explained above, allreceived the same task!based, L2 instruction in


Chinese and received 6 to 8 hours of classroom!based instruction 5 days a week during the 63!weekcourse.

Materials

Modern Language Aptitude Test (MLAT). Thelearners’ phonetic coding ability, grammaticalsensitivity, and rote memory were assessed bysections one through !ve of the paper!and!pencil!based MLAT (Carroll & Sapon, 1959a). Based onCarroll (1962, 1990) and Skehan’s (1998, 2002)view of the underlying constructs of L2 aptitudeassessed by the MLAT, rote memory was measuredby sections one (number learning) and !ve(paired associates), phonetic coding ability bysections two (phonetic script) and three (spellingclues), and grammatical sensitivity by section four(words in sentences). The MLAT was obtained in2004 through the Language Learning and TestingFoundation (http://lltf.net). For informationconcerning the !ve sections and their speci!ctasks, see Dörnyei (2005b) and the LanguageLearning and Testing Foundation’s (2012) Website.

Phonological Working Memory Span Test. Theparticipants’ working memory capacity was as-sessed through an online, verbal working memoryspan test adapted from Mackey et al. (2002),7

which was based on other span tests used in priorresearch (Daneman & Carpenter, 1980; Turner &Engle, 1989; Waters & Caplan, 1996). The testcomprised 48 unrelated sentences, half beinggrammatically correct and half semanticallyplausible, which resulted in four sentence types(grammatical and plausible, grammatical and

implausible, ungrammatical and plausible, un-grammatical and implausible). The 48 sentenceswere grouped into 12 sets: four sets each of three,four, and !ve sentences, groupings similar to thoseused by Daneman and Carpenter (1980) andTurner and Engle (1989). The test was self!paced;however, sentences within a set were controlled toplay over the computer with 3!second intervals.While the participants listened to a set of sentences(the sentences were only presented aurally with novisual support), they clicked a mouse to indicatewhether the sentence was grammatical andwhether it was semantically plausible. After select-ing “enter” to submit the answers for a set, theparticipants were prompted to type (on a newscreen) the last word of each sentence. Allsentence!!nal words were common (not abstract,because abstract words may be more dif!cult torecall; Turner & Engle, 1989), noncompound,concrete nouns of one to three syllables in length.No sentence!!nal word of a given set wassemantically associated with another word inthat set, and no words within a set rhymed.

Strategy Inventory for Language Learning (SILL).Strategy use was measured through the StrategyInventory for Language Learning, or SILL(Oxford, 1990a), which was administered onlinefor this study. The SILL comprises 80 !ve!point, Likert!scale questions pertaining to (a)social, (b) metacognitive, (c) memory, (d) com-pensation, (e) cognitive, and (f) affective strate-gies (Hsiao &Oxford, 2002; Oxford, 1990a). It hasbeen used in numerous studies on languagelearning strategies (e.g., Carson & Longhini,2002; Engelbar & Theuerkauf, 1999; Green &

FIGURE 1The Hypothesized Model

Rote Memory

Phonetic Coding Ability

Grammatical Sensitivity

Phonological Working Memory Motivation

Strategy Use

L2 Learning

Aptitude

Cognitive Cognitively Oriented / Affective

Independent Variables Dependent Variable

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Oxford, 1995; Grif!ths, 2003; Hong–Nam &Leavella, 2006; Hwu, 2007; Nakatani, 2006; Oxford& Burry–Stock, 1995; Wharton, 2000) and meas-ures the number and type of strategies applied bythe learner at a speci!c point in the learningprocess (Tseng & Schmitt, 2008).

Motivation Questionnaire. The motivation ques-tionnaire consisted of 38 !ve!point, Likert!scalequestions adapted from Kormos and Dörnyei(2004). The questions, which tap into learners’(a) integrativeness, (b) incentive values, (c)attitudes toward learning the L2, (d) linguisticself!con!dence, (e) language use anxiety, (f) taskattitudes, and (g) willingness to communicate(Dörnyei, 2002) have been used in multipleresearch studies on motivation for learning anL2 (Dörnyei, 2002; Dörnyei & Kormos, 2000;Kormos&Dörnyei, 2004;Weger–Guntharp, 2008).The questions appear in Appendix A.

L2 Learning. L2 learning was measuredthrough the Defense Language Pro!ciency Tests(DLPTs) in listening, reading, and speaking.Robust descriptions of and sample items fromthese secure, government tests can be found at theDefense Language Institute Foreign LanguageCenter (n.d.) Web site. These test scores can beconsidered gain scores because all study partic-ipants began the 63!week Chinese course with noprior instruction in Chinese.

Procedure

Data collection took place at the DLI inMonterey, California, during the learners’ 42ndweek of instruction. All participants volunteered toparticipate in the research, which was conductedoutside of normal class time. In computer labs oncampus, participants took the following measureswith 15! to 20!minute breaks in between: theMLAT,the working memory span test, the motivationquestionnaire, and the SILL. Data collection,with breaks, lasted approximately 2 1/2 hours.After 63 weeks of instruction, the participantstook the DLPTs in speaking, reading, and listening.The DLPT scores were forwarded to the author foruse in the structural equation model.

Scoring. TheMLAT was scored conventionally,with each item on the test scored as right or wrong,and a right answer worth 1 point. Rote memorywas a composite of sections one and !ve. Phoneticcoding ability was a composite of sections two andthree. Grammatical sensitivity was measured bysection four.

The phonological working memory test wasscored according to partial!credit load (PCL) scor-

ingprocedures outlined inConway et al. (2005). Forany given set of sentences, if processing scores fellbelow 80%, the data from that set were discarded. Ifprocessing scores were above 80%, then allsentence!!nal words recalled correctly were award-ed 1 point. Therefore, items with a higher memoryload contributed more to the overall phonologicalworking memory score. As summarized by Conwayet al., “for load!weighted scoring procedures, PCLrepresents the sum of correctly recalled elementsfrom all items, regardless of whether the items areperfectly recalled or not (also without respect toserial order within items)” (p. 775).

Items on the SILL were combined into acomposite SILL score to indicate what normally isconsidered a latent variable. The same was donewith the items on themotivation questionnaire. I didthis to make the whole model easier for the com-puter program Analysis of Moment Structures(AMOS)18 (structural equationmodeling software)to identify. For each learner (and for eachmeasure),items pertaining to the same category were aver-aged, and then all categories were averaged togetherso that each category would have equal weight in thelearner’s !nal score. (The categories for eachmeasure are described above in the materialssection.) In both questionnaires, values of negativeitems were reversed before the aggregation.

Analysis. I applied SEM to evaluate the con-jectured causal relations among the variousvariables investigated in the study. First, a fullmodel with both measurement and structuralcomponents was designed in accordance with thetheories in L2 aptitude and L2 learning reviewedearlier (see Figure 2). The measurement portionof the model hypothesizes cause indicators, ratherthan the more common effect indicators (Bollen,1989, p. 65). Maximum likelihood estimatedprocedures were used to analyze the variance/covariance matrix of the observed variables usingAMOS 18. To assess the overall model !t, I usedchi!square and a pair of !t indices advised in theSEM literature (Byrne, 2009; Kline, 2011; McDo-nald & Ho, 2002): the comparative !t index (CFI)and the root!mean!square error of approximation(RMSEA). A nonsigni!cant chi!square and a CFIabove .95 suggest model acceptance, and anRMSEA value below .05 indicates a good !t ofthe model to the data (Hu & Bentler, 1999).

RESULTS

Preliminary Data Analysis

Themeans, standard deviations, and reliabilities(Cronbach’s alpha) for the different measures are


in Table 1. Reliabilities for the different measureswere mostly high or very high (Bachman &Palmer, 2010), ranging from .72 to .91. Thesample correlation matrix is presented in Appen-dix B; the matrix allows readers to form anindependent judgment of the relationshipsamong the observed variables and will aid inthe interpretation of the direct effects amongthe variables in the structural portion of themodel. Rote memory was signi!cantly correlatedwith both grammatical sensitivity (r ! .45,

p ! .00) and phonological working memory(r ! .23, p ! .04). Strategy use signi!cantly corre-lated with motivation (r ! .40, p ! .00). Readingwas signi!cantly related to both listening (r ! .59p ! .00) and speaking (r ! .32, p ! .00).

Model Results

The full structural equation model, with theresulting standardized coef!cients, appears inFigure 2. It includes 1 latent variable (L2 aptitude,

FIGURE 2Structural Equation Model with Parameter Estimates

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.

.00

.34

.2

.34**

.59**

-.03.16

.06

.16

-.25*

.08

.02

-.06

-.11

-.27

-.30

-.86

.14

.03

.97

.23

-.06

1

.45**

.12

Rote Memory

Phonetic Coding Ability

Grammatical Sensitivity

Phonological Working Memory Motivation

Strategy Use

Speaking

Reading

Listening

Aptitude

e4e1

e3

e5

e6

e2

TABLE 1Measures and Descriptive Statistics

Measure Items Pts. M SD Rel.

Rote Memory 88 88 59 5.52 0.91Phonetic Coding Ability 80 80 47 7.34 0.79Grammatical Sensitivity 24 24 15 5.19 0.83Phonological Working Memory 48 48 39 7.10 0.88Strategy Use 80 400 240 15.20 0.72Motivation 38 190 143 17.56 0.91Reading 60 60 49 4.44 naListening 60 60 45 3.03 naSpeaking 1* 20 18 2.16 na

Note. Items ! number of items on measure; Pts. ! number of points possible; M ! mean; SD ! standard deviation;Rel. ! reliability coef!cient Cronbach’s alpha; na ! not available; these tests are maintained by the Defense LanguageInstitute and reliability for the sample was not available.*The speaking test is an oral pro!ciency interview (OPI) and is scored holistically. Within this sample, all learnersobtained a 10 (2" on the Interagency Language Roundtable [ILR] scale) or a 20 (a 3 on the ILR scale).

Paula Winke 117

represented with an oval) and 9 observed variables(the measures in Table 1, represented withrectangles). To examine the predictors of L2learning, the proposedmodel was!tted to the datawith listening, reading, and speaking test scores asdependent variables. The model was acceptedand !t the data very well, x2(12, N ! 96) ! 7.6,p ! .815, CFI ! 1.00, RMSEA ! .00, RMSEA con-!dence interval ! .00, .07.

In Figure 2, the model’s direct effects are shownas single!headed arrows and correlations areshown as double!headed ones. Also shown arestandardized coef!cients. Within this model, onlyone direct effect was signi!cant at the .05 level:the effect of strategy use on reading (r ! #.25,p ! .03). Low strategy use signi!cantly predictedhigher L2 reading ability. Strategy use did notsigni!cantly predict listening or speaking ability.Learners’ L2 aptitude and motivation failed topredict signi!cantly greater success in listening,reading, or speaking ability.

The model estimated the total variance ex-plained in strategy use, motivation, listening,reading, and speaking (the squared multiplecorrelations provided in the AMOS output).Aptitude explained 9% of strategy use’s varianceand 7% of motivation’s variance. (In other words,the error variance of strategy use is approximately91% of the variance of strategy use itself, andthe error variance of motivation is approximately93%.) The predictors (strategy use, aptitude, andmotivation) explained 2% of listening’s variance,3% of speaking’s, and 6% of reading’s.

DISCUSSION

This study used structural equation modeling toinvestigate the accuracy of a current understand-ing of L2 aptitude within the context of Chineselanguage acquisition. The model, diagrammedin Figure 2, included four cognitive aspects of L2aptitude (rote memory, phonetic coding ability,grammatical sensitivity, and working memory)and two noncognitive variables (motivation andstrategy use). Within this model, L2 aptitude waspredicted to affect L2 learning directly. It was alsohypothesized that L2 aptitude would indirectlyaffect L2 learning, that its effects would bemediated by motivation and strategy use. Themodel was found to be plausible (the datadisplayed a very good !t to the model), whichallows me to discuss the individual effects thefactors have on one another within the model. I!rst discuss the signi!cant effect of strategy use onreading. After explaining why I examine othereffects even though they were not signi!cant, I

discuss the (nonsigni!cant) effects of motivationon L2 learning. I then discuss two additionalnonsigni!cant but interesting results concerningthe variables that contribute to aptitude: the smalleffect that working memory had on aptitude andthe negative effect that grammatical sensitivity hadon aptitude. To conclude, I discuss the model’soverall represented construct of L2 aptitude andits effect on learning.

The Effects of Strategy Use on L2 Learning

The model showed only one statistically signi!-cant effect: Strategy use inversely affected successin reading (r ! #.25, p ! .03). In other words,when a learner’s SILL average rose by 1 standarddeviation, his or her reading score fell by .25standard deviations. (The absolute value of thestandardized regression coef!cient r representshow much the independent variable affects thedependent variable or latent trait: A positive signrepresents a direct relationship, and a negativesign represents an inverse relationship.)

Although this inverse relationshipmight initiallyseem surprising, it is consistent with past research.Ehrman and Oxford (1995) reported that lesssuccessful learners randomly use various strate-gies, while more successful learners systematicallyuse speci!c strategies for speci!c tasks. Thisreplicates aspects of a curvilinear pattern commonlyfound by L2 strategy researchers using the SILL:Advanced learners use fewer strategies than inter-mediate learners, advanced learners use a subsetof their former strategies, and advanced learnersuse their select strategies in new, creative ways thatrelate to the complexities of advanced languagelearning (Green & Oxford, 1995; Hong–Nam &Leavell, 2006; Leaver, 2005; Oxford, 2011).8

Research focused on reading skills has also shownthe importance of using an effective subset ofstrategies (Kember & Gow, 1994). Reliance on afew select strategies demonstrates automaticity inlearning, which is necessary in upper level classes(Alyousef, 2005). So in a study examining readingin an upper level class, it is actually not surprisingthat the students who used fewer strategies weremore successful at learning to read.

The inverse relationship between strategy useand success in reading makes particular sense inthe context of Chinese language acquisition. Thedata showed that the students who use fewerstrategies tend to be those with higher aptitude(aptitude inversely affects strategy use at r ! #.30,p ! .58). Aptitude is largely comprised of rotememory (r ! .97, p ! .59). And rote memory iscritical to learning to read Chinese (Everson &


Ke, 1997; Hayden, 2005; Shen, 2004; Xiao, 2002).So the students who used fewer strategiesmay havebeen more successful at reading in part becausethey were the ones with better memories. Theymay not have needed to venture into otherstrategies because their rote!memory!based strate-gies worked particularly well.

Interestingly, strategy use had a much smallereffect on listening (r ! #.11, p ! .35) and almostno effect on speaking (r ! .02, p ! .85). Theseresults lend empirical support to existing theoryabout strategy use. Researchers have speculatedthat strategies matter less for listening and speak-ing than for reading (Chamot, 2005; Farrell &Mallard, 2006) because in listening and speakingthe importance of social and interactive skillsoverwhelms the effect of strategy use (Nakatani &Goh, 2007); reading, of course, does not requiresocial interaction. Most empirical L2 strategyresearch has investigated the effect of strategiesonly on the skill of reading (Plonsky, 2011). Thus,this study provides evidence that strategy use mayaffect the various skills differently.

Nonsigni!cant Effects

Most of the effects in the model were notstatistically signi!cant. Although nonsigni!cantresults are usually ignored, there are three reasonsthey are worth considering in this study. First,some statisticians contend that nonsigni!canteffects can be important (Valentine & Cooper,2003). They point out that statistical signi!cancetells us little about the “practical signi!cance orrelative impact of the effect size, and should not beused as a standalone measure of how much theintervention ‘matters’” (Valentine & Cooper, p. 1,emphasis in original). Second, signi!cance testinghas been criticized as an arbitrary means ofinterpreting continuous data (Oswald & Plonsky,2010; Plonsky, 2011; Plonsky & Gass, 2011;Schmidt, 1996). The data in this model arecontinuous rather than dichotomous.

Finally, it makes sense to consider some of thenonsigni!cant effects in this study because thecorrelations among some of the independentvariables (known as multicollinearity) may havearti!cially reduced the signi!cance of the effects.In this model, for example, rote memory wascorrelated with grammatical sensitivity (r ! .45,p ! .00) and phonological working memory(r ! .23, p ! .04). These correlated independentvariables may be explaining the same parts ofthe variation in aptitude (Thayer, 1991), makingthe signi!cance of their independent effects on

aptitude misleadingly small. The explanatorypower—the true signi!cance—of that part of thevariation in aptitude is, in effect, divided up amongthe correlated variables. It is also interestingbecause grammatical sensitivity has been shownto predict instructed language learning and alsothe implicit memorization of rule!based language,but not incidental language learning (seeRobinson, 2007, p. 263). Thus, perhaps the resultsindicate that these advanced Chinese languagelearners learned a considerable amount inciden-tally, learning that did not depend on grammaticalsensitivity. Similarly, strategy use was correlatedwith motivation (r ! .40, p ! .00), reducing thesigni!cance of their independent effects, as well.For these reasons, signi!cance testing should notbe overemphasized in the interpretation of themodel presented in Figure 2.

The Effects of Motivation on L2 Learning

As with strategy use, the data on motivation arelargely consistent with prior theory and empiricalresearch. The data showed that aptitude negativelyaffected motivation (r ! #.27, p ! .58), whichsquares with Dörnyei’s (2005b) view that the lessaptitude a learner has, the more motivation thelearner needs. Motivation was positively correlatedwith strategy use (r ! .40, p ! .00), as found byVandergrift (2005). The data showed that motiva-tion positively affects reading (r ! .16, p ! .17),which is consistent with the theory that motivationis important to learning an L2 (Dörnyei, 2001a,2005b; Ellis, 1994). (An effect size of .16 could beviewed as large within the context of advancedlanguage learning.) While motivation ebbs and"ows (Dörnyei, 2005b, 2009b), these data suggestthat motivation has some enduring power even atthe advanced level. One !nal point: Motivationhad less of an effect on listening and speaking thanon reading, just as strategy use did. As discussedabove, the reason for the different effect of moti-vation on the different skills may be that gains inlistening and speaking are more strongly in"u-enced by socially construed interlocutor effects,diminishing the relative impact of motivation.

The Components of L2 Aptitude

One of the most important features of themodel in Figure 2 is how it represents the constructof L2 aptitude. L2 aptitude is composed of fourindependent variables: rote memory, phoneticcoding, grammatical sensitivity, and workingmemory. The regression coef!cient (“r”) of each

Paula Winke 119

reveals how much the score for aptitude isexpected to increase when the score for theindependent variable increases by 1 standarddeviation, holding all other independent variablesconstant (Field, 2009; Thayer, 1991). In thismodel, aptitude for advanced!level Chinese con-sists of high rote memory, phonetic coding, andworking memory abilities, and low grammaticalsensitivity. Rote memory and grammatical sensitiv-ity contribute the most to differences in aptitude.Increases in rote memory scores lead to majorincreases in aptitude scores (r ! .97, p ! .59), andincreases in grammatical sensitivity lead to majordrops in aptitude scores (r ! #.86, p ! .58).Increases in phonetic coding ability scores leadto much smaller increases in aptitude scores(r ! .14, p ! .86), and increases in phonologicalworking memory make almost no difference toaptitude scores (r ! .03, p ! .92).

Working Memory and L2 Aptitude. It may seemdif!cult to reconcile the results regarding workingmemory and L2 learning when other studies havefound working memory to be a signi!cantpredictor 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 byThayer (1991), “a variable might be the mostimportant single predictor of a dependent variablewhen used alone but an unimportant predictorwhen used in combination with other predictorsdue to the amount of shared predicted variance”(p. 3). In this dataset, working memory correlateswith rote memory (r ! .23, p ! .04), which mightaccount for working memory’s diminished role inde!ning the construct of L2 aptitude for Chinese.In any case, these results substantiate those fromHummel (2009), who found that workingmemoryis not good at differentiating learning at upperlevels of instruction. Unique in this study’s datasetis a potential reason why: There are other L2aptitude factors that account for some variance inadvanced L2 Chinese learning—the primary onebeing rote memory.

Grammatical Sensitivity and L2 Aptitude. Aprincipal puzzle to solve is why, in this model,grammatical sensitivity inversely contributes to theconstruct of aptitude. When grammatical sensitiv-ity (the ability to recognize the function of anEnglish word in a sentence) goes down by 1standard deviation, aptitude goes up by .86standard deviations. It could be that studentswho understand English grammar well havedif!culty adapting to the very different Chinese

system of grammar. One could view those withhigh scores on grammatical sensitivity as those forwhom the L1 system is !rmly entrenched. That is,their perceptions of grammar may be tuned by theL1 to the extent that “their learned attentionblocks them fromperceiving differences in the L2”(Beckner et al., 2009, p. 10), which may, inthis case, inhibit them, to some extent, fromadvanced Chinese language acquisition, or at leastdelay it. This !nding is congruent with otherresearch that has found that learners entrenchedin their L1 patterns may have negative L1–L2crosslinguistic in"uence, which might manifestitself during L2 acquisition by an overgeneraliza-tion of rules, avoidance of certain forms orstructures, overproduction, and hypercorrection(Beckner et al., 2009; MacWhinney, 1997;Odlin, 1989). Aptitude test score interpretationsas currently applied have not done well inpredicting Chinese learning (Carroll, 1962,1990, 1993). This study could be indicating why:According to this model, not all of the aptitudecomponents are positively oriented for success inChinese development.

Such information may have implications forpedagogy. Chinese instruction for native Englishspeakers could focus more on non!L1!like, Chi-nese conceptual patterns and ways of thinkingthat are different from English, in particular,explicit instruction on L2 speech and writingpatterns in Chinese that have been shown tobe extremely dif!cult for nonnative speakers ofEnglish to acquire because they do not conform toany rules or sequential patterns present in English(Li, 2009, 2010). It has been proposed thatpro!ling aptitudes could help match learners toinstructional options and pedagogical tasks thatwould improve comprehension and production(Robinson, 2002a, 2002c). This study does so on agroup level. The pro!le of aptitude for advanced!level Chinese reveals the great importance of rotememory and the necessity for openness to novelforms and ways of thinking about language andgrammar.

The Overall Effects of L2 Aptitude onL2 Chinese Learning

To !nalize, as can be seen in Figure 2, no singlecomponent explains the observed variance inadvanced L2 Chinese development. Within thismodel, aptitude, strategy use, and motivation haverelatively the same impact on learning, as has beensuggested by previous research (Dörnyei, 2001b;Schmidt & Watanabe, 2001; Tseng & Schmitt,


2008; Vandergrift, 2005). While strategy use andmotivation mediate the effects of aptitude andhave an in"uence on reading ability, they affectlistening and speaking less. Aptitude, on theother hand, appears to directly affect speakingthe most and does not directly affect reading orlistening much at all. These seemingly discordant!ndings make intuitive sense when consideringthat language learning is a complex systemaffected by the interaction between the languagelearner and the language learning environment(Dörnyei, 2009a; Larsen–Freeman & Cameron,2008). At the advanced stages of L2 learning, thatinteraction has had more time to exert itsin"uence and creates unaccounted!for noise inthe model. Within a dynamic systems view of L2learning, high aptitude, high motivation, andgood strategy use may be signi!cantly advanta-geous conditions for attaining advanced pro!-ciency, but when instruction is task!based andgrounded in social interaction, minute distinc-tions in advanced pro!ciency may depend moreon unmeasureable and unsystematic factors exter-nal to the model.

The predictor variables (aptitude, strategy use,and motivation) only explain 2%, 3%, and 6% ofthe variance in listening, speaking, and reading,respectively. These results may be disappointingfor those looking for ways to predict which adultswill successfully learn a foreign language to anadvanced level of pro!ciency, but they are notsurprising. Carroll (1962) found that after native!English!speaking learners of Chinese progressedbeyond the beginning level, signi!cant associa-tions between aptitude (as measured by theMLAT) and learning became nonsigni!cant.Changes in Chinese L2 pedagogy since 1962have made the question of what predicts ad-vanced!level, Chinese!language!learning successworth asking again. Because working memoryhas been shown to be related to L2 learningsuccess, it was sensible to include working memoryas an additional component of L2 aptitude in suchan investigation. Likewise, I included measuresof strategy use and motivation because recenttheorizing suggests the effects of aptitude aremediated by them. Based on the results of thisstudy, it does not appear that aptitude, updated asa construct that includes workingmemory and twomediator variables (strategy use and motivation),is any better at explaining differences in advancedL2 Chinese attainment.

Why does L2 aptitude predict advanced!level L2Chinese performance so poorly? What is it thatdistinguishes performance at the advanced level? Ispeculate that Larsen–Freeman and Cameron

(2008) were right—the language learning envi-ronment is responsible for much of the learningthat takes place. Different aptitude abilitiesare best suited to different stages of learning(Robinson, 2005a, 2007), and aptitude is not soimportant at the later stages of learning. Perhapsit is not that aptitude abilities are dynamic (asasserted by Skehan, 2002); rather, it might be thataptitude interacts with a changing learningenvironment (Neufeld, 1979; Snow, 1987; Stern-berg, 2002; Sternberg & Grigorenko, 2002), thusbringing about "ux (and a gradual reduction) inthe importance of aptitude for learning. In sum,at the advanced level, it is not the cognitive oraffective variables—the factors that lie withinthe learner (Ellis, 2004)—that matter; rather, itis how the individual reacts to the learningcontext. The learner’s actions in the socialenvironment, the amount of time spent outsideof class learning, and other personal reactionsand choices of what to focus on ultimately affectlearning.

LIMITATIONS AND FUTURE DIRECTIONS

An important limitation to this study is the timeat which the motivation and strategy surveys wereadministered. Researchers have described howmotivation changes over time for any given learnerand have described how a "ux in motivation maybe related to temporal components as small as atask in the language learning classroom or as largeas the "ow of a foreign language course (Dörnyei,2003, 2005b; Dörnyei & Ottó, 1998). Likewise,strategies that are useful at the beginning oflanguage learningmay not be those that are usefulat the upper levels of language learning (Grif!ths,2003; Hong–Nam & Leavell, 2006); thus, strategyuse, too, must naturally "ux in response to thelearning conditions. The motivation and strategysurveys were administered during the learners’42nd week of intense instruction, when mostlearners were most likely at an intermediate levelof L2 Chinese. It could be that at this stage of sucha lengthy process of highly controlled, classroom!based learning, motivation is fairly well establishedand generalized to a certain extent (Gardneret al., 1997; Tremblay & Gardner, 1995). Strategyusemay re"ect the learners’ individual pro!ciencylevels at the time. The survey questions may havebeen replied to differently if administered to thesame learners at a different point in their learningtrajectory; thus, the results concerning motivationand strategies must be interpreted with thecontext in mind.

Paula Winke 121

Another limitation is how I operationalizedmotivation and strategy use in this study. I createdcomposite scores for these latent traits to keepthe cases!per!parameter ratio low. According toKline (2011), the number of participants to thenumber of free parameters should be 20 to 1 (yet10 to 1 is perhaps more realistic for appliedlinguistics studies). By collapsing scores fromthe surveys, I asserted that the traits are one!dimensional. In reality, the motivation survey andthe SILL do not each measure a homogeneousconstruct. Each trait comprises several subareas:motivation (as measured by the survey in thisstudy; see Kormos & Dörnyei, 2004) has seven;strategy use (as operationalized by the SILL, seeOxford, 1990a) has !ve. In future research, not toviolate the assumption of composite score homo-geneity, researchers could focus on certain areasof each latent trait. Or, to understand moreeffectively the relationship between, for example,strategy use and the different advanced skill areas(in this case, advanced listening, speaking, andreading), it would be useful to explore whichquestions best predict advanced!level success inthe various skills. Currently, the items on themotivation survey and the SILL are not balancedby language learning skill areas (i.e., the SILL hasvery few items that address strategies for speaking)and are not normally segregated into skill areas.Nor do probabilistic statistics exist that explainwhich motivational aspects/strategies are mostlikely used by learners at certain levels of pro-!ciency. (Such research would be interesting anduseful.) Furthermore, the items are not stream-lined as to which are most relevant for learningChinese, a language proven dif!cult for English!language speakers because of its character!basedwriting system and use of tones. Thus, there ismuch groundwork to be done in terms ofmotivation and SILL instrument adaptation andre!nement.9 Such research needs to be conductedto make data from the surveys more applicable toapplied linguistics research using SEM.

In this study, the factor loading of workingmemory to aptitude is very low, which is inconsis-tent with the hypothesized model. This needs tobe revisited. I assessed working memory througha single listening span test. It would be useful toadminister different kinds of working memorytests because working memory tests, like thevarious subtests of L2 aptitude, may tap intodifferent aspects of working memory. A morerobust analysis of participants’ working memoryskills could include reading span tests, visual–spatial tests, and perhaps even a nonsense wordor digit span test, as recommended by Conway

et al. (2005) and Waters and Caplan (1996). Amore variegated measure of working memorymay tell researchers more about this construct inrelation to aptitude and the other variables in themodel, but adding such parameters requires moreparticipants.

Although there is substantial support for the!nal hypothesized model in this study in terms ofgoodness!of!!t indices, cross!validation SEM stud-ies are needed to investigate whether the model inthis study holds with other samples and whetherthose model estimates are stable across samples.Ideally, such studies would include a larger samplesize: In this study, with 15 degrees of freedom and96 participants, the power to reject the model isless than .2 (see Table 2 in MacCallum, Browne,& Sugawara, 1996). To achieve the statisticallydesired power of .8 (an 80% probability that thetest would not make a Type II error, that is, fail toreject a null hypothesis when it is actually nottrue), 500 learners would be needed (MacCallumet al., 1996). Obtaining such participant numbersis notably dif!cult, especially when investigatingadvanced!level learners of a less!commonly!taughtlanguage such as Chinese. (For example, to obtaindata from 500 participants for this study, I wouldhave to collect data from Chinese languagelearners at the DLI for 5 years in a row; my1 year of data collection yielded viable data from96 participants at the cost of US$11,000 inNational Science Foundation funding.) For thisreason, testing the model or similar models out ondiverse datasets may be more feasible. This needsto be done because we need to understand betterhow much variability is due to the learningcontext, the level of acquisition, and the speci!cL2 being learned. Future studies are also neededto explore the validity of using other kinds ofworking memory tests and other (perhaps more!nely tuned) measures of motivation, strategy use,and L2 learning. Only then will we come closer tounderstanding the construct of L2 aptitude andhow it relates to the complex systemof L2 learning.

CONCLUSION

Several varied factorsmust successfully convergefor an adult learner to obtain advanced pro!cien-cy in a foreign language. The learner needsexcellent instruction, frequent opportunities fordifferent kinds of output, and a heavy dose ofmotivation. Access to cultural insights that explainthe pragmatics of the language has to come thelearner’s way; effective language learning strate-gies need to be found; and, as any learner knows,real, tangible rewards for learning efforts must


materialize. It is natural that linguists search for aconcrete reason why some succeed and others donot. But many individuals achieve advanced!levelpro!ciency in a foreign language. And for anyone individual working toward advanced pro!-ciency, if his or her approach to learning changesregardless of his or her underlying cognitivestrengths, advanced!level pro!ciency may comea few months earlier or later. Trying to pinpointwho will succeed fastest within a range of 3 to6 months may be futile. Indeed, as evidenced bythe data in this study, aptitude operationalized as aconstruct so extremely sensitive to such minutediscrepancies in L2 learning may be unrealistic.

The results of this study provide useful informa-tion that helps us conceptualize the factorsinvolved in the acquisition of advanced!levelChinese by adult native speakers of English. Theresults provide evidence that L2 aptitude, de!nedas rote memory, phonetic coding ability, gram-matical sensitivity, and phonological workingmemory, is only a moderately useful construct inthis context. Moreover, the effects of L2 aptitudeon advanced!level learning are mediated by theaffective variables of motivation and strategy use.In the 42nd week of a 63!week, intensive Chineseprogram, L2 aptitude predicts end!of!courseperformance no better than strategy use andmotivation do. Variance in reading, listening, andspeaking is only minimally explained by thepredictors in this study’s model. Future researchmight focus on the observed, interactive, recipro-cal aspects of motivation and strategy use; thedifferential effects aptitude, motivation, andstrategy use have on the different skills of L2development; or the relationships between rotememory and the other L2 aptitude factorsobserved 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 Stans!eld formed my dissertation commit-tee at Georgetown University and guided this research.Gordon Jackson and John Lett at the Defense LanguageInstitute provided logistical support and helped mesolicit the Chinese!language!learner volunteers. LauraKlem (University of Michigan) and Alexander von Eye(Michigan State) provided technical feedback on themodeling. Comments fromHelenCarpenter, Akiko FujiiKurata, Heather Weger, and four anonymous MLJreviewers assisted me in revising the paper, which Ibelieve made it stronger. Any mistakes, however, aresolely mine.

NOTES

1 Carroll envisioned a parallel “Form B” of the MLAT;however, Form B was never created. Nonetheless, theMLAT is still sold as “Form A.”

2 An additional factor, which Carroll identi!ed asinductive language learning ability (the ability to decodelinguistic material and conceptualize how other linguis-tic material would be encoded in the same language),was identi!ed through tests that were acknowledgedby Carroll to be administratively dif!cult; therefore,this construct, which Carroll (1962) stated was importantfor language learning, is not represented on theMLAT.

3 The DLAB is currently being revised. Go to CASL’sWeb page (http://www.casl.umd.edu/dlab2) formore information. For current information on thePLAB and how it differs from the MLAT, go tothe Language Learning and Testing Foundation’sWeb pages on the PLAB (http://lltf.net/aptitude!tests/language!aptitude!tests/pimsleur!language!aptitude!battery) and on L2 aptitude testing ingeneral (http://lltf.net/aptitude!tests/what!is!language!aptitude).

4 Traditional theories of workingmemory suggest thatshort!term memory works in combination with otherfactors within working memory (Baddeley, 2007; Dane-man & Carpenter, 1980) and/or that information storedin long!term memory can be retrieved during workingmemory executive processes (Cowan, 2005). For moreinformation, see Dehn (2008).

5 A few researchers posit that L2 aptitude is malleable.See information on and debates concerning Feuerstein’sInstrumental Enrichment, one purpose of which isto improve aptitude (Bailey & Pransky, 2010; Savell,Twohig, & Rachford, 1986).

6 Sasaki collected data from 160 Japanese studentslearning English in college in Japan. She used SEM toinvestigate the relationships among English pro!ciency,general intelligence, and L2 aptitude. But as inHummel’s (2009) study, the quality and methods ofthe college students’ prior English language instructionwere not controlled.

7 The measure was revised by increasing the numberof sentences from 36 to 48 (Winke, Stafford, & Adams,2003). The general procedures for this working memoryspan test are also reported in Mackey et al. (2010).The measure can be downloaded from the Instrumentsfor Research into Second Languages (IRIS) database athttp://www.iris!database.org

8 I am extremely grateful to an anonymous MLJreviewer who recommended this part of the discussion.

9 An anonymous MLJ reviewer noted that one wayto measure the strategies of advanced languagelearners would be to interview them individually orin focus groups. The reviewer noted that advancedlearners’ needs may be more idiosyncratic thanwhat are measured by standardized surveys such asthe SILL. Such information may help a researcher devisea skill! and language!speci!c Advanced Learners’ SILL.

Paula Winke 123

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APPENDIX A

Motivation QuestionnaireThis questionnaire is designed to gather infor-

mation about how you, as a student, feel aboutlearning Chinese. Please read each statement.Mark the response that tells how much you agreeor disagree with each statement as follows:

1 ! Strongly disagree, 2 ! Disagree, 3 !Somewhat agree, 4 ! Agree, 5 ! Strongly agree

Part A.

1. Sometimes I feel that language learning is aburden for me.

2. I would like to get to know as many nativespeakers of the language I am learning aspossible.

3. I am sure that I’ll be able to learn thelanguage I am studying.

4. I think I am good at learning languages.

5. When I have to speak in my language class, Ioften lose con!dence.

6. I like to work hard.

7. Unfortunately, I am not too good atlearning the language I am studying.

8. I would rather spend time on subjects otherthan the language I am learning.

9. I am pleased with my current level oflanguage ability in my language class.

10. I would like to spend a lot of energy learningthis language in the future.

11. I am not too interested inmy language class.

12. Learning this language often causes me afeeling of success.

13. In my parents’ view, the language class I amtaking is not a very important course.

14. I would be pleased to be able to master anintermediate level of this language.

15. I really like the language I am learning.

16. I generally feel uneasy when I have to speakthe language I am learning.

17. I generally feel uneasy when I have to readthe language I am learning.

18. I generally feel uneasy when I have to writein the language I am learning.

19. We learn things in the language class thatwill be useful in the future.

20. Learning this language is one of the mostimportant activities for me.

21. I rarely do more work for my languagecourse than what is absolutely necessary.

22. I would like to get to an advanced level inthis language.

Paula Winke 129

23. I don’t mind it if I have to speak in thislanguage with somebody.

24. I am satis!ed with the work I do in mylanguage class.

25. I easily give up the hard!to!reach goals.

26. I like my language class.

27. I would like to get to knowmany people whocome from countries where this language isspoken.

Part B.Learning this language is important to me…

1. … because I would like to get to know theculture and art of its speakers.

2. … because I may need it later for work orfurther education.

3. … in order to become more educated.

4. … because I would like to spend some timein a country where this language is spoken.

5. … so that I can read books, magazines andnewspapers published in this language.

6. … because one cannot achieve any kind ofsuccess without it.

7. … in order to get to know the life of peoplewho speak this language better.

8. … because I would like to make friends withspeakers of this language.

9. … in order to understand !lms, videos andTV programs in this language.

10. … because it might be useful during mytravels.

11. … in order to understand the lyrics of songsin this language.

APPENDIX B

Pearson Product Moment Correlations between All Observed (Indicator) Variables

Measure 1 2 3 4 5 6 7 8

1. Rote Memory2. Phonetic Coding Ability .123. Grammatical Sensitivity .45 ** .014. Phonological Working Memory .23 * .12 #.095. Strategy Use #.20 #.12 .10 #.136. Motivation #.20 #.07 .09 #.05 .40 **7. Listening .20 .09 .15 .01 #.11 .028. Reading .12 .04 .13 #.04 #.19 .07 .59 **9. Speaking .08 #.04 #.12 .03 #.06 #.10 .21 .32 **

Note. *p < .05, **p < .01.


Documents

An Investigation Into Second Language Aptitude for Advanced