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Applied Linguistics 2014: 35/2: 184–207 � Oxford University Press 201

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doi:10.1093/applin/amt013 Advance Access published on 13 July 2013

Dynamics of Complexity and Accuracy: A
Longitudinal Case Study of Advanced
Untutored Development

*BRITTANY POLAT and YOUJIN KIM

Georgia State University

*E-mail: bpolat@student.gsu.edu or brittanypolat@gmail.com

This longitudinal case study follows a dynamic systems approach to investigate

an under-studied research area in second language acquisition, the development

of complexity and accuracy for an advanced untutored learner of English. Using

the analytical tools of dynamic systems theory (Verspoor et al. 2011) within the

framework of complexity, accuracy, and fluency (Skehan 1998; Norris and

Ortega 2009), the study tracks accuracy, syntactic complexity, and lexical

diversity in the speech of a Turkish immigrant over one year. Results from

these oral interviews show that most development occurred in the participant’s

lexical diversity, syntactic complexity showed potential but unverifiable gains,

and accuracy showed no development. These findings suggest that an untutored

language learner may develop advanced lexical and syntactic skills, but achiev-

ing grammatical accuracy without instruction may be more difficult. Overall,

dynamic systems theory seems to provide a suitable framework for examining

the linguistic development of advanced naturalistic learners, with important

implications for future research involving untutored immigrant and refugee

populations of English language

learners.

INTRODUCTION

In recent decades, second language acquisition (SLA) research has

predominantly focused on issues in instructed language learning rather than

naturalistic language learning. Despite promising early research and several

seminal studies of untutored adult learners—Schmidt’s (1984) Wes study,

Schumann’s (1978) Alberto study, Huebner’s (1983) Ge study—which have

made significant contributions to the field, the vast majority of publications

today concentrate on instructed language learning. Although many of these

have certainly increased our understanding of how language learning works in

the classroom, there are compelling reasons to pay more attention to language

acquisition outside the classroom.

Whereas many of the students who participate in SLA studies have the

luxury of formal language instruction, the majority of the world’s language

learners acquire second and additional languages in naturalistic contexts

(Klein and Perdue 1993). Without knowing how this type of learning takes

place, SLA researchers are missing a crucial part of the language acquisition

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process: the basic mechanisms that have allowed humans to create and pass

on languages for millennia (Klein and Dimroth 2009). As Klein and Dimroth

put it:

Untutored second language acquisition is not something exotic, it is
the normal case, and if we want to understand the very principles
according to which the human mind constructs, copies, and uses
linguistic systems, then we must study how human beings
cope with this task when not under the influence of teaching.
(p. 519)

As the field discovers how important social and situational factors are in lan-

guage learning, it becomes increasingly apparent that tutored and untutored

acquisition may have very different driving factors. So far, however, only a few

studies in the past two decades have explored naturalistic language learning

(Klein and Perdue 1993; Ioup et al. 1994; Dimroth and Starren 2003), and of

these, only Ioup et al. (1994) have specifically investigated advanced natural-

istic learning, with an English-speaking learner of Arabic.

Perhaps the most extensive study of untutored language learning to date is

that conducted by the European Science Foundation from 1981 to 198

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(Perdue 1993). This study tracked the development of 40 language learners

from a variety of first language backgrounds in five host countries (Britain,

France, Sweden, Germany, and The Netherlands) over 30 months and found

that immigrants at first developed a basic variety (BV) of the target language.

The BVs were all very similar, lacking morphological inflection and consisting

of a rudimentary lexicon, and they mainly seemed independent of the lear-

ner’s first language and target language. Whereas about one-third of the im-

migrants remained at this basic level throughout the study, the others

continued to develop beyond the BV (Klein and Perdue 1993). The study

has yielded important insights into universals of basic language varieties, as

well as developmental stages that most naturalistic learners appear to pass

through. However, because the study did not report on learners beyond

basic development, we know neither how proficient these learners ultimately

became nor what their most advanced forms of learner language looked like.

The present study addresses the research lacuna of advanced naturalistic

learning by examining the language development of an untutored adult lan-

guage learner beginning two and a half years after his arrival in the target

language environment. The methodological approach taken is that of dynamic

systems theory (DST) (Verspoor et al. 2011), and the variables investigated are

two widely used constructs of language performance, complexity and accuracy

(Ellis and Barkhuizen 2005). In the following text, we will address the theor-

etical considerations behind researching complexity and accuracy from a

dynamic systems perspective, and then we will present the 12-month case

study. We conclude with a discussion of implications for untutored language

acquisition and the importance of integrating research on untutored learning

over time into mainstream SLA research.

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CAF AND DST

Theoretical platforms

Researchers of second language (L2) development are increasingly relying on

measures of complexity, accuracy, and fluency (known together as CAF) to

assess learners’ written and oral proficiency and to probe more deeply into

the cognitive processes of language learning (Ellis and Barkhuizen 2005).

Originally conceived as a way to distinguish aspects of task performance,

these three components are oriented toward either form (complexity and

accuracy) or meaning (fluency; Skehan1998). One of the major advantages of

CAF-based research is that it provides a sophisticated framework for investigat-

ing the multicomponential nature of language use and development. As form

has consistently been shown to be challenging for naturalistic learners (Schmidt

1984; Dimroth and Starren 2003), the present study focuses on the two form-

oriented components of L2 oral performance, complexity and accuracy.

The CAF constructs have frequently been examined by task-based SLA

researchers who are interested in the role of task design and implementation

in L2 performance (Ellis 2003; Samuda and Bygate 2008). Some researchers

assert that the CAF measures have been inconsistently defined and operatio-

nalized (Housen and Kuiken 2009), leading to calls for consistent, specific, and

validated CAF measures to be used across studies (Norris and Ortega 2009;

Pallotti 2009). Norris and Ortega (2009), for example, argue that different

operationalizations of complexity capture different facets of language develop-

ment, and that researchers should use multiple construct measurements to

provide a more complete picture. They also call for ‘more organic practice’

(p. 574) and a deeper consideration of context as an influence on CAF. In

other words, according to Norris and Ortega (2009), ‘our measurements

must provide multivariate, longitudinal, and descriptive accounts of constructs

in L2 performance in order to capture the complex, dynamic, and develop-

mental nature of CAF phenomena’ (p. 574).

At the same time, many SLA researchers are embracing the perspective that

SLA is an individualized nonlinear endeavor, and that research should con-

sider the variability and interaction of its components (Larsen-Freeman and

Cameron 2008a, 2008b). Although the idea of nonlinearity in language devel-

opment is not new, investigations undertaken within this dynamic systems

framework have applied new conceptual tools and analyses to the study of

developmental variability, showing the complex interrelations of CAF vari-

ables within language acquisition (Verspoor et al. 2011). DST researchers

advocate longitudinal, fine-grained, and microgenetic studies to discover indi-

vidual learning trajectories and the interrelationships of parts within the whole

(van Geert and van Dijk 2002; de Bot 2008; Larsen-Freeman and Cameron

2008a). Because DST is centered around time and variability, Ortega and

Byrnes (2008) propose that this theoretical approach is very well-suited to

the longitudinal study of advanced language capacities.

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Empirical research on CAF through DST

Because of the relative newness of the dynamic systems paradigm—and the

shift in perspective and analytical tools that it requires—only a few studies

have as yet connected this framework with CAF. Larsen-Freeman (2006)

was the first applied linguist to examine CAF through a DST lens, with a

focus on the variability between learners. Her investigation of five instructed

Chinese learners of English measured their written development of grammat-

ical complexity (clauses per t-unit), lexical complexity (a kind of type-token

ratio), accuracy (ratio of correct t-units to all t-units), and fluency (words per t-

unit) over four months, in addition to analyzing oral narrative idea units for

qualitative differences. The study revealed that although averaged group data

showed steady improvement in all three CAF components for the learners,

patterns of development for each individual were far removed from the aver-

aged trajectory. Learners exhibited unique trajectories, with different rates of

improvement and even decreases in some areas, an important fact that had

been obscured by the group averages.

Given this important individual variability in language acquisition, other

CAF studies have examined single language learners over a period of several

years. Verspoor et al. (2008) analyzed an advanced English learner’s academic

writing for development of vocabulary (measured by average word length,

type-token ratio, use of words from the Academic Word List) and complexity

(measured by length of noun phrase and number of words per finite verb).

They found that although the learner showed development in almost all the

aspects investigated, progress was nonlinear and was different for each vari-

able. Several interesting patterns emerged, including a possible competitive

relationship between development of type-token ratio and sentence length,

and a supportive relationship between finite verb ratio and noun phrase

length. The authors conclude that in the dynamic system of language learning,

‘there can be no development without variability, and the amount and type of

variability can reveal the actual developmental process’ (p. 229).

In another DST/CAF study, Spoelman and Verspoor (2010) tracked a Dutch

learner’s acquisition of written Finnish over three years. They examined

accuracy (case usage) and several measures of complexity (morphemes per

word, words per noun phrase, and ‘difference between the average sentence

length in morphemes and the average sentence length in words’, p. 539). The

results once again showed the interaction of variables over time, with the

learner’s complexity variables sometimes competing and sometimes support-

ing each other. Interestingly, although accuracy and complexity seemed to be

in competition early in the study period, they later changed to a noncompe-

titive relationship as the learner became more proficient, suggesting that

proficiency level may have an impact on the interaction of variables. Similar

to Verspoor et al. (2008), Spoelman and Verspoor (2010) maintain that these

language learner systems demonstrate the ‘classic’ jumps, transitions, and

nonlinear development of dynamic systems.

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The findings of these DST-based CAF studies show the importance of seeking

emergent dynamic patterns within the components of language systems

(Larsen-Freeman 2009; de Bot and Larsen-Freeman 2011). So far, however,

research in this area has concentrated on instructed, mainly written language

learning in academic settings, and researchers have not applied the DST the-

oretical framework or CAF constructs to studying untutored language devel-

opment. In addition, although several of these studies involve high-

intermediate (Larsen-Freeman 2006) or advanced learners (Verspoor et al.

2008), by and large the CAF/DST paradigm has not explicitly engaged with

the special concerns of advanced language capacities. One of the main goals of

the current study, therefore, is to specifically tease out the important issues

related to L2 advancedness, particularly as they apply to nonacademic

contexts.

ADVANCED LANGUAGE CAPACITIES

Although studies of advanced language learners have often figured in import-

ant SLA research, a wake-up call was sounded by Ortega and Byrnes’ (2008)

collection of longitudinal research on advanced language capacities. There is

an acute need, they argue, to closely examine the question of how ‘learning

over time evolve[s] toward sophisticated second language capacities, indeed to

high-level multiple-language capacities’ (p. 282). Researchers may often call

for longitudinal studies of language development, but the field has yet to come

to a consensus on what advancedness means in terms of L2 capabilities, or on

how it should be measured. Researchers such as Harklau (2008), Myles (2008),

and Angelelli (2008) offer different definitions and methodological techniques

for capturing advanced L2 use, including various qualitative and quantitative

approaches that examine linguistic, sociolinguistic, and pragmatic aspects of

language acquisition.

By claiming to investigate an advanced language learner, therefore, the pre-

sent study grapples with the unresolved theoretical issue of what advancedness

actually is. Research set in any kind of instructional context can easily rely on

test scores, institutional status, or classroom performance to define advanced

language capacities (Ortega and Byrnes 2008), and laboratory-based research

can elicit advanced or late-acquired linguistic features to claim advancedness.

In the present type of research conducted with an untutored learner, none of

these options are available. We therefore prefer the criterion of ‘advanced

language use in context’ (Ortega and Byrnes 2008: 282), based on what the

focal participant uses language to do in everyday life.

This more naturalistic approach to advancedness allows us to take the study

of advanced language capacities outside of academically defined parameters

and into the context of untutored learning. Just as there have been few studies

of untutored language acquisition from a CAF or DST framework, so, too, are

studies of advanced naturalistic learners few and far between. Ortega and

Byrnes’ (2008) collection does not include any studies on untutored learners,

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mainly because most research on advanced language learning has overwhelm-

ingly privileged written and academic language. The authors conclude that

‘longitudinal research on advancedness would benefit from sampling across

a variety of social settings that afford opportunities for diverse language rep-

ertoires, as this will enrich the developmental insights we obtain’ (Ortega and

Byrnes 2008: 284). It seems clear that not only are more longitudinal studies of

advanced language learning needed, but more are needed in a variety of con-

texts, such as untutored learners in a target-language setting.

A point of contention in the debate on advancedness seems to be whether

learner language should be compared with native speaker norms (Ortega and

Byrnes 2008). In this article, we take the position that L2 systems should never

be seen merely as deficient versions of native speaker language systems (Cook

2002; Harklau 2008), but it would be difficult to establish any learner’s level of

advancedness without considering target-like language use. For this reason,

the present study uses a native speaker comparison with the intention not of

showing deficiencies, but rather of showing the advanced language capacity of

an untutored learner. This is similar to Verspoor et al.’s (2008) inclusion of a

native speaker comparison, which is a helpful touchstone for interpreting the

performance of non-native speakers. In addition to the native speaker data

collected in this study, we offer comparisons of our participant’s language

with that of non-native English speakers in several previous studies that

have measured naturalistic oral data.

In summary, the study presented in later text seeks to join several strands of

research that have not yet been united but which have the potential to en-

hance our understanding of language learning: DST, CAF, naturalistic learn-

ing, and advanced language capacities. The complexity theory perspective

allows us to analyze various developmental patterns of an untutored but

nevertheless advanced L2 user, and the CAF platform provides a systematic

and conceptually clear set of tools for our investigation.

METHODS

Participants

Focal participant

The focal participant in this study, Alex (a pseudonym), is a native speaker of

Turkish who had lived in the USA for two and a half years at the beginning of

the interview period. Although Alex completed a bachelor’s degree in televi-

sion production at a prestigious university in Istanbul, he describes his English

at the time of his arrival in the USA as very basic. In fact, his experience with

English had been overwhelmingly negative before his interactions with

Americans. According to Alex, he had taken English for four years in high

school—delivered strictly through grammar-translation instruction—but never

managed to pass the class (he was allowed to graduate on the strength of his

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grades in other subjects). At university, he was required to take a year-long

English preparatory program before beginning his degree studies. Alex esti-

mates that he attended only about 30 percent of the English classes during that

year, and he failed to pass the English examination that would allow him to

proceed with his major studies. To help him move on with the degree, his

department allowed him to complete a ‘project’ in lieu of passing the difficult

examination; the project entailed writing 100 words on a sheet of notebook

paper, which Alex accomplished, thus ending his English learning

requirements.

Alex attributes his repeated failures and complete lack of interest in English

to poor teaching methods and to his belief (at the time) that the language was

completely irrelevant to his life. This belief necessarily changed, however,

when Alex moved to the USA at the age of 25. Once in the USA, he used

only English outside the home, although he reports reading newspapers,

watching movies and television shows, and talking weekly with his family

in Turkish. His interview comments reveal a positive orientation toward the

target language community and an openness to the new language and culture:

For me I’m don’t believe I’m belongs to one culture. Basically I am
making my own culture . . . You know anytime I learn something, if
it’s better than what I have, I get it. That’s my culture now. I found
it something like that in English, in United State, and I took it some
of them. Now they are my culture. But, something is ridiculous, it
will never be my culture . . . I believe everybody have to do that, like
this. (February 14)

Although Alex did have some formal language instruction in his home coun-

try, in this study, he is considered an untutored learner because his English

skills were rudimentary at the time of his arrival in the target language con-

text, because he has not taken any language classes in the USA, and because

he has learned English primarily through quotidian interaction (Lightbown

and Spada 2006). At the same time, Alex can be considered an advanced

English user based on what he is able to accomplish through everyday use

of the language (Harklau 2008; Ortega and Byrnes 2008). During this year-

long study, he worked in an English-only context as an assistant department

manager in a supermarket, supervising 25 employees (mainly native English

speakers), complying with strict federal food safety regulations, and managing

high volumes of perishable food inventory. Alex began working part-time in

the supermarket six months after he arrived in the USA and in three years was

promoted three times, in competition with native English speakers. Shortly

after the study ended, he was promoted again, to department manager.

Native speakers

Three native speakers were selected for comparison (two females and one

male), and they were each interviewed under similar circumstances as Alex

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(see Procedures for details). These speakers were all undergraduate

students at a university in the same city where Alex lives, with majors of

religious studies, applied linguistics, and education/drama. We believe that

these participants provide an appropriate comparison group because they

are at a similar education level as Alex (obtaining bachelor’s degrees) and

were discussing topics conceptually similar to topics in several of Alex’s

interviews.

Procedures

For exactly one year, Alex was interviewed once every two weeks for approxi-

mately 30 minutes. Several factors contributed to providing an authentic con-

text for language production: (1) Alex is a friend of the first author and has

experience discussing a wide variety of topics with her, (2) the interviews took

place in a familiar and nonthreatening environment, and (3) Alex was encour-

aged to choose topics that he enjoyed and felt comfortable discussing through-

out the unstructured interview. The interviews were carried out by the first

author, whose primary role was simply to be a conversational participant to

elicit speech production from Alex. Because the goal of data collection was to

gather authentic speech, topics varied and the conversation was unstructured

and unplanned (Duff 2008). Alex selected the topics for discussion (such as

politics, childhood memories, or his experience learning English) and could

decide when to move on to a new subject. The interviews therefore provided

realistic and meaning-oriented communicative situations (Hesse-Biber and

Leavy 2011).

Interviews were held every two weeks to capture any microgenetic

changes in Alex’s language. Microdevelopment is important in DST, as it can

provide details on how processes actually develop, particularly during key

moments of transition (Larsen-Freeman and Cameron 2008b). In addition,

the study was designed along the longitudinal timescale of one year, which

was long enough to represent development but still a manageable commit-

ment for the participant (see Ortega and Iberri-Shea 2005 for a discussion of

choosing timescales).

In addition to Alex’s data, three interviews were held with the native speak-

ers to obtain comparison data. These interviews were conducted in much the

same way as the interviews with Alex, with the main difference being that

they were slightly longer, at 45–60 minutes. The three native speakers were

familiar with the first author, who also carried out these interviews. Interviews

were recorded in a familiar environment on a laptop computer, and the topic

discussed was language learning, which was also a topic in Alex’s interviews.

Although native speaker conversation can vary in its complexity, because vari-

ables such as interlocutor, topic, and passage length were controlled for (see

Data Analysis), these data provide an appropriate starting point for target-like

use comparison.

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Data analysis and intercoder reliability

To investigate Alex’s longitudinal development of complexity and accuracy,

100-word passages were taken from each interview transcript (following

Spoelman and Verspoor 2010). The passage selected from each transcript

was the end of the last turn in which Alex spoke more than 100 words. This

was done to eliminate very short interactional utterances and dialogic re-

sponses that may have different levels of complexity and accuracy than

longer utterances. Passages were taken from the end of each interview because

it was assumed that Alex would be speaking more naturally at the end rather

than the beginning of the recording. Due to the organic nature of the interview

setting, discussion topics varied by session.
1

False starts, repetitions, inserts,

and other hesitation phenomena were excluded from the passages, so that the

100-word segments represented Alex’s speech without hesitation phenomena.

As in Spoelman and Verspoor (2010) and other studies, the oral data were

converted to CHAT format to be compatible with CHILDES program software

(MacWhinney 2000). The data were then analyzed for syntactic complexity

(mean length of AS-units, clauses per AS-unit, mean length of clauses), lexical

diversity (D), and accuracy (errors per 100 words, present simple tense).

AS-units

The 100-word passages were divided into analysis of speech units (AS-units),

which are defined as ‘an independent clause, or sub-clausal unit, together with

any subordinate clauses associated with either’ (Foster et al. 2000: 365).

AS-units have been widely used with oral data in SLA studies in the past

decade (Norris and Ortega 2009), and this was deemed the most appropriate

unit of measurement for the present study. To maintain consistent AS-unit

analysis, we followed Foster et al. (2000) as far as possible. Some examples of

the AS-unit in the data include You are not government and Because they knew

ninety percent people say ‘yes’.

In cases where the 100-word passages did not coincide with the boundaries

of AS-units, the entire AS-unit was retained for purposes of counting mean

length of AS-units and clauses per AS-unit. This was done to avoid including

incomplete AS-units in these complexity measures, which would have dis-

torted them.

Complexity

As complexity and accuracy have been operationalized in various ways in

previous research (Ellis and Barkhuizen 2005), any researcher must choose

from among several different ways of measuring these constructs. Since this

study focused on oral language development over time, it seemed appropriate

to consider complexity as holistically as possible to reveal any possible patterns.

Following Norris and Ortega (2009), syntactic complexity was investigated in

terms of length (mean length of AS-units), subordination (clauses per

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AS-unit), and phrasal elaboration (mean length of clauses). A measure of

lexical diversity, D, was also included in our complexity analysis, as suggested

by Skehan (2009). D has been shown to measure similar features in the speech

of both native English speakers and L2 learners (Durán et al. 2004) and has

been suggested as one of the most valid measures for oral narratives of English

L2 speakers (Lu 2012).

Accuracy

Previous CAF studies have measured accuracy in a variety of ways, including

percentage of error-free clauses, errors per 100 words, percentage of target-like

verbal morphology, and percentage of target-like use of plurals, among others

(Ellis and Barkhuizen 2005). The present study used two measures of accur-

acy, one global and one focused on a specific linguistic feature (present simple

tense). It was decided that errors per 100 words would provide the best global

representation of the learner’s accuracy because, given the high number of

errors that often characterizes naturalistic learners’ speech, few of Alex’s

AS-units were error-free. However, some units contained only one error and

others contained five or more errors, so it was more informative to count errors

per 100 words.

To determine global accuracy, an error analysis was conducted in which any

mistakes that a native speaker would normally not make were considered

errors (Ellis and Barkhuizen 2005). In instances where more than one error

caused an utterance to be non-target-like, each aspect of the error was counted

as an error. For example, in Alex’s utterance Even somebody pay me million dollar

I won’t change, mistakes were counted based on the target-like sentence Even if

somebody paid me a million dollars I wouldn’t change it, which was derived from

the context. The minimum number of changes needed to turn the learner

utterance into the target-like utterance was considered to be the number of

errors in this AS-unit: if, paid, a, dollars, wouldn’t, it (six in total). In addition,

obvious lexical errors were counted as one mistake, as in My father let them work

in a context that called for My father made them work. This method of error

analysis could raise questions about the L2 learner’s desire to match target-

language norms, as well as the appropriateness of the researcher’s interpret-

ation and reconstruction (Ellis and Barkhuizen 2005). However, as yet no

alternative has been devised that can measure global accuracy without intro-

ducing these issues, so this method was used consistently throughout the

present data analysis.

To complement this global accuracy measure, present simple tense was

selected as a more specific linguistic measure that would be appropriate to

the interview setting and to Alex’s linguistic proficiency.

2

Present simple ac-

curacy was measured by analyzing each 100-word passage for obligatory use

contexts of present simple tense. Because the number and type of obligatory

contexts varied by interview session, it was decided to use percentage scores

rather than raw frequencies. The number of correct suppliances of the target

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verbal morphology in a passage was divided by the sum of the number of

obligatory contexts plus the number of oversupplied present simple verbs

(e.g., supplying present tense where past tense would be appropriate), follow-

ing Pica (1984). The resulting target-like use analysis therefore considers both

underuse and overuse of the simple present tense.

Native speaker analysis

Passages from the native speakers were selected and analyzed following the

same procedures described previously, except that they were analyzed only for

complexity, not accuracy.

Intercoder reliability

To assess reliability, interrater reliability for AS-units, complexity, and accur-

acy analysis was calculated with 25 percent of data after the researchers coded

the data independently. In terms of identifying AS-units, simple percentage

agreement between the researchers was 98 percent. Additionally, simple per-

centage agreement between the raters for complexity was 100 percent for

words and 94 percent for clauses. In terms of accuracy, simple percentage

agreement between the researchers was 90 percent for the number of errors

and 97 percent for the present simple tense analysis. The disagreements in data

coding were resolved through discussion, typically deferring to the first author

who had coded the entire data.

RESULTS

Over the one-year period, Alex’s syntactic complexity, lexical diversity, and

accuracy each showed distinctive patterns. Each of these results will be dis-

cussed in detail in further text, followed by comparisons with native speakers

and other non-native speakers from previous studies (Klein and Dimroth 2009;

Schmidt 1984). Raw counts of each measure for all 24 weeks are provided in

the supplementary material for Table A1.

Syntactic complexity

In looking at the three components of syntactic complexity over time, we first

see that all three seem to exhibit variation, with no clear patterns of develop-

ment visible in simple graphs of raw data (Figure 1a and 1b). This high degree

of fluctuation is not surprising, given that interlanguage development is

known to display properties of dynamic systems, including variability over

time (Verspoor et al. 2008; Spoelman and Verspoor 2010). At the same time,

polynomial trend lines show a very slight decrease in clauses per AS-unit over

the year, suggesting no improvement or negative improvement in this meas-

ure, but a slight increase in words per AS-unit and words per clause, indicating

possible improvement in these two areas. To investigate these developmental

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http://applij.oxfordjournals.org/cgi/content/full/amt013/DC1

patterns in more depth, moving averages graphs and moving min–max graphs

were created to obtain a clearer picture of the dynamics of each measure

(Verspoor et al. 2011). These two methods of visualization are examples of

tools available within a dynamic systems framework that allow researchers

to more accurately trace developmental variability over time.

Looking first at clauses per AS-unit, which did not seem to exhibit any

improvement over time in the raw data graphs, we see that an examination

of the moving min–max graph for clauses per AS-unit (Figure 2) likewise gives

little indication of improvement. It shows two periods throughout the year

(March through June and August through October) in which the min and

max line open into wider windows, indicating greater volatility in this per-

formance measure during those times. These open-window transition periods

are known as phase shifts, and often point toward a restructuring within the

learner language system that can result in significant change (Larsen-Freeman

and Cameron 2008a, 2008b). However, the two potential phase shifts taking

place in Alex’s clauses per AS-unit do not seem to alter his performance; the

measure continues to hover around 1.5, even after the transition period in the

second half of the year. Despite several very high points, therefore, we cannot

conclude that Alex improved in this measure of syntactic complexity (i.e.,

subordination).

When we look at words per AS-unit and words per clause, a different picture

emerges. Raw data graphs show a similar story of high volatility throughout

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the year, which is difficult to interpret on its own, although polynomial trend

lines allow for the possibility of overall improvement over the study period.

The min–max graph for words per clause (Figure 3) shows a decrease occurring

around the same time that the phase shift takes place for clauses per AS-unit

(April 18 through June 19). This leads to an apparent phase shift beginning in

late September and continuing through the end of the study period, which

means that as the second phase shift was ending for clauses per AS-unit, the

restructuring for words per clause was just beginning. Because we do not know

when this phase shift will end, or what Alex’s level of words per clause will be

after the restructuring period, it is not possible to conclude whether words per

clause will mimic clauses per AS-unit in staying steady, or whether it will show

improvement. Overall, however, it seems possible that despite the downward

polynomial curve visible in the moving averages graph, syntactic development

may be taking place in this performance component.

The min–max graph of words per AS-unit (Figure 4) is somewhat similar to

words per clause, which is not surprising, given their conceptual overlap and

close relationship on the raw data graph. Words per AS-unit also demonstrates

two phase shifts over the year, but with the difference that as its second phase

shift ends in late October, Alex’s performance appears to have actually im-

proved. The min line is noticeably higher during the second phase shift than

during the first, and Alex’s words per AS-unit is trending upwards as the study

ends. Although it is difficult to say for sure, it appears that this performance

measure may indicate Alex’s ongoing improvement as the study period ends.

In terms of Alex’s overall syntactic competence, close examination of his

language over the year further suggests that the word-based measures may be

improving. For instance, Excerpt 1 shows that Alex produced somewhat short

simple AS-units at the beginning of the data collection period when he recalled

an episode from his childhood.

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Figure 2: Moving min–max graph of clauses/AS-unit

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Excerpt 1: January 17
3

But it sometimes took all day long. / But I worked. / I got so many. /
Sometimes my father let them work. / Because we have a market /
we were building house. / I don’t know / it was very funny.

By October (Excerpt 2), when describing another episode from his childhood,

his AS-units and clauses are more sophisticated.

Excerpt 2: October 21

Let’s say ten thousand bricks need :: to go to third floor. / For one is
enough for children / and my father was saying to children :: this
thing done. / Let’s say twenty children around the house. / Take it
down there / I will buy a drinks for everybody.

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Figure 4: Moving min–max graph of words per AS-unit

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Figure 3: Moving min–max graph of words per clause

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Keeping in mind the fact that Alex’s language did show variability throughout

the year—and that in some cases the word-based measures were longer in

some early interviews than in some later interviews—by the end of the

study period, he may be more capable of producing longer syntactic structures

in terms of length, subordination, and phrasal elaboration.

Lexical diversity

The measure of lexical diversity used in this study, D, shows the clearest

improvement of any of Alex’s linguistic features. Although there is consider-

able variation throughout the year, with a sudden spike around August, a

visual inspection of the raw data graph (Figure 5) suggests a consistent

upward trend. This is confirmed by a moving averages graph of D (Figure 6),

in which the data points have been smoothed into a more obvious upward

curve.

Accuracy

Accuracy displays a great deal of variability but no patterns of clear develop-

ment during the study period. As Figures 7 and 8 show, both measures (which

are inversely related, as one represents number of errors and one represents

correct percent of present simple tense) were characterized by high variability

over time. Moving min–max graphs (Figures 9 and 10) also show no develop-

ment, instead indicating a possible downward trend in accuracy over the year.

The min–max graph of global accuracy shows a potential phase shift window

opening up from May through August, but this is followed by an increase in

number of errors for the rest of the year. We therefore cannot say whether

global accuracy will remain low or will improve later on, although the data

indicate no change in the foreseeable future.

The min–max graph of present simple tense is even more variable, with a

wide distance between minimum and maximum values for almost the entire

study period. This indicates high volatility, which continues until the end of

the study, leaving us with no clear picture of how Alex’s present simple

accuracy will continue to develop. The fact that both his global and present

simple tense accuracy fluctuate so much could mean that his interlanguage

grammar is still in the midst of development, or it could mean that his accuracy

may have entered a strong attractor state, in which variability occurs within

stability (Larsen-Freeman and Cameron 2008a, 2008b).

Native speaker and non-native speaker comparisons

Alex’s syntactic complexity and lexical diversity from his first and last inter-

views, as well as from his highest and lowest interviews, were compared with

that of the native speakers (Table 1). Here we see expected variation among

the three native speakers, but we also see that in at least some of his inter-

views, Alex is squarely within the native speaker range for two or possibly

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three of these measures. His lexical diversity, just approaching native speaker

levels early in the year, consistently surpasses the native speakers by the end of

the study period. It is important to note that lexical diversity measures are

probably dependent on speaker-external factors such as conversation topic,

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Figure 9: Moving min–max graph of global accuracy (errors)

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Figure 10: Moving min–max graph of present simple accuracy

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level of formality, and so forth, which means that these values will vary even

for a single native speaker. The fact that Alex has reached a particular D value

does not, therefore, imply that his speech is in general more lexically complex

than that of the native speakers. Rather, the important finding from this com-

parison is that Alex is within native speaker range, even allowing for the

variation that naturally takes place within conversation.

In terms of syntactic complexity, Alex’s words per clause, which showed

only slight (if any) improvement over the year, is in line with the native

speakers (at or above 5.05) for half of his interviews, whereas words per

AS-unit is somewhat lower in comparison with native speakers. However, as

four of Alex’s words per AS-unit scores are in the low 9 range (which is

approaching that of the lowest native speaker at 9.55), he may be quite

close to target-like levels for words per AS-unit, as well. Similarly, three of

Alex’s interviews show that he is capable of at least sometimes achieving

clauses per AS-unit at native speaker levels (above 1.8 clauses per AS-unit),

even if he is far below this range in most other interviews. In summary,

although Alex is not as obviously comparable with the native speakers in

syntactic complexity as he is for lexical diversity, he is consistently within

native speaker range for one measure (words per clause) and is not far

below the native speakers in the other two (words per AS-unit and clauses

per AS-unit).

Although we believe that these native speaker comparisons provide insights

into Alex’s advancedness in various aspect of language complexity, we also

considered the performance of untutored non-native speakers from previous

studies. First, although Alex’s grammatical accuracy remains highly imperfect,

we can see that he has moved far beyond the BV spoken among the low-

proficiency European Science Foundation learners (Klein and Dimroth

2009). The BV contains no inflection or marking of tense, aspect, or agree-

ment, and its lexicon ‘essentially consists of a repertoire of noun-like and verb-

like words as well as a few adjectives and adverbs’ (Klein and Dimroth 2009:

510). In Alex’s speech, in contrast, we see morphological marking even in the

first interview, and throughout the year, as shown in Excerpts 3 and 4.

Table 1: Alex’s syntactic complexity and lexical diversity compared with
native speakers’

Measure Alex
(First)

Alex
(Last)

Alex
(Lowest)

Alex
(Highest)

NS 1 NS 2 NS 3 NS
average

D 55.88 83.05 42.59 125.41 64.62 58.03 56.97 59.87

Words/AS-unit 9.36 8.75 5.56 9.45 12.63 9.55 11.22 11.13

Clauses/AS-unit 1.73 1.50 1.15 2.00 1.88 1.82 2.22 1.97

Words/clause 5.42 5.83 4.00 6.67 6.73 5.25 5.05 5.68

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Excerpt 3: January 2

But if they are gonna lose I want them to lose very bad . . . for
example when they play important game or any other countries,
I will support them.

Excerpt 4: August 8

They were trying to do so obvious because they were trusting
themselves too much. But people is not stupid anymore.

Alex clearly has a wide range of vocabulary words at his disposal, including

many closed-class items (pronouns, conjunctions, quantifiers) that are rare in

the BV, and he conveys nuanced temporality and negation, even though

non-native elements remain. He is able to express quite sophisticated ideas

with his grammatical system, a capacity that is doubtless necessary for his

work responsibilities and managerial duties.

In his high-level communicative competence, Alex resembles another

naturalistic learner, Wes, whose professional success and limited grammatical

system were chronicled by Schmidt (1984). Like Wes, Alex is apparently suc-

cessful at communicating with a range of native speakers for personal and

professional purposes, and he seems mostly untroubled by differences between

his own way of speaking and that of native speakers. He also shares Wes’

propensity to simplify verbal morphology, delete articles and plural markings,

and ignore clausal subordination. However, whereas Wes achieved an accur-

acy of only 24 percent for third person –s marking after living in the USA

for five years, Alex accurately marked third person endings as much as

100 percent of the time in some interview excerpts (although on occasion,

his accuracy dropped below 50 percent). It therefore seems that although

Alex’s grammatical accuracy did not improve much during the study period,

he has managed to become rather more accurate than some other naturalistic

learners.

DISCUSSION

This study attempted to bring untutored language development into the CAF/

DST conversation, in the hope that this might broaden our perspective and

allow us to form a more coherent picture of language learning. We found that

Alex performs very differently on three different form-based aspects of L2

performance—syntactic complexity, lexical diversity, and accuracy—and that

even within a single one of these measures (syntactic complexity), his per-

formance is quite different on three different subcomponents. All of this

validates Norris and Ortega’s (2009) call for differentiation between the specific

components of complexity, as well as Skehan’s (2009) call for inclusion of

lexical measures in complexity research. The study shows that for at least

one learner, each of these subcomponents may develop in a different way,

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and therefore they should be measured individually if we wish to see a clearer

picture of language development.

It is likely that Alex’s unique situation as an untutored but educated immi-

grant led him to advance beyond the BV (Klein and Perdue 1993) and the level

of grammatical competence achieved by Wes (Schmidt 1984) and some other

naturalistic learners (Shumann 1978; Huebner 1983). As a high school and

early university student, Alex apparently lacked motivation to learn English

and skipped most of his English classes, but the time he did spend in English

class may have paved the way for future development. On the other hand, his

language displays the primary hallmark of untutored adult language learning:

limited grammatical accuracy that may or may not show development in the

future. Alex shows himself to be a very competent and willing communicator,

despite obvious grammatical inaccuracies that would likely have been

corrected had he learned in a classroom setting.

Constraints on grammatical accuracy in untutored learning are understand-

able when we consider how untutored acquisition differs from instructed lan-

guage learning. Two unique features of untutored acquisition could be

responsible for this phenomenon: the presence of communicative pressure

and the absence of systematic external control (Dimroth and Starren 2003;

Klein and Dimroth 2009). According to Klein and Dimroth (2009), ‘Unlike

students in the classroom, immigrant workers rapidly find themselves in situ-

ations in which they cannot wait for the relevant structures to be acquired in

the exact target language way’ (p. 507), thus forcing them to use whatever

linguistic means they have available for complex communicative purposes.

Because their communicative needs exceed their language knowledge, they

develop language systems that may be ‘partly independent of the source and

target language regularities’ (p. 508).

Additionally, untutored learners do not have the benefit of external con-

trol in the form of teachers, tests, and grades (Klein and Dimroth 2009).

When learners interact with native-speaking interlocutors, they usually do

not receive any feedback on their grammatical accuracy if their meaning is

understood. Untutored learners may therefore simply rely on communica-

tive effectiveness as their feedback, which probably corresponds to a much

greater emphasis on meaning than form. As a result, meaningful segments

of language such as lexical items, which are necessary for the learner to

make himself understood, may be integrated into the learner’s interlan-

guage system, whereas ‘meaningless’ and redundant grammatical features

may not be.

It is likely that for learners like Alex and Wes, whose overriding concern is

communicative effectiveness, language primarily develops in ways that pro-

mote or complement their communicative needs. In DST terms, their language

is ‘soft assembled’ into a linguistic system consisting of necessary grammatical

elements, lexical knowledge required by personal or occupational demands,

and the pragmatic and social competence needed to function with limited

linguistic means. Alex (along with other untutored learners) seems strongly

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pulled toward an attractor state of grammatical accuracy, meaning that his

interlanguage grammar system is highly variable but exists within a stable

plane. Although this type of attractor state may precede a phase shift, signaling

continued development (Larsen-Freeman and Cameron 2008a), it is not pos-

sible to know what will occur in Alex’s language after the study period. His

accuracy and/or complexity may be pulled out of this potential attractor state

by increased demands on his English abilities at work, or they might remain at

the same level for many years. One possibility is that had the study continued

after Alex was promoted to a new position, we may have seen sudden devel-

opment in his performance measures.

Due to space constraints, this study was not able to adequately address

the complex influence of identity, culture, and context on Alex’s language

development. We recognize that the environment and specific attributes of

the learner must play a crucial role in how he or she approaches L2 learn-

ing, and that issues of acculturation and attitude surely impact linguistic

development, even if we do not understand the exact relationship (Block

2010). In particular, because DST highlights the importance of considering

various factors in language learning (e.g., both cognitive and social factors),

future studies are warranted that account for cultural and social aspects of

language development. For instance, does the fact that Alex not only works

with but supervises native English-speaking employees contribute positively

to his linguistic development? In what ways does Alex’s educational status

in his home country, or his ‘downward occupational mobility’ in the USA

(Batalova et al. 2008: 10), influence his willingness to work toward gram-

matical competence? In accepting that immigrant identities are exceedingly

complex and individualized, we necessarily introduce additional layers into

the language learning process, but we hope that the sophisticated perspec-

tive offered by DST can help accommodate these important considerations

in future research.

CONCLUSION

The present study has provided a first attempt to measure longitudinal com-

plexity and accuracy for an adult untutored learner, and at the same time,

represents the first DST-based study to look at untutored SLA. This longitu-

dinal microgenetic record allowed us to see that untutored language acquisi-

tion may be much more dynamic than it is often given credit for. For this

reason, researchers should be careful and deliberate in the constructs and

measurements they use to study longitudinal acquisition, and they may find

important theoretical and methodological tools in a dynamic systems approach

to untutored development.

Although the findings of the present study fit well with existing research

on naturalistic learning, far more longitudinal research is needed for us to

form a reliable picture of the advanced reaches of untutored language ac-

quisition. We need additional studies of untutored learners in different

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contexts, from different linguistic and cultural backgrounds, and at different

timescales. In particular, much more research is needed on the advanced

stages of naturalistic learning with learners from different age-groups, which

may shed light on psycholinguistic learning mechanisms and highlight ‘raw’

learning processes. In addition to CAF, other aspects of linguistic develop-

ment should also be investigated from a DST perspective, including prag-

matic competence, intonation and pronunciation-related phenomena, or the

influence of the first language. Future DST studies should also address the

relationship of learning context, motivation, and identity to performance

measures, which may offer important information about how and why

learners progress (or do not progress) as they do.

If the field hopes to more thoroughly understand the SLA process over

time, researchers would do well to consider both instructed and unin-

structed learning, as they piece together the social and psychological con-

structs of acquisition. The possibilities and limitations of naturalistic learning

have implications for classroom instruction as well, by indicating which

performance areas benefit most from instruction and which areas learners

might be best positioned to learn on their own. In addition, a greater

understanding of naturalistic acquisition processes can help instructors to

better assist learners from refugee and immigrant backgrounds, who may

need completely different kinds of instruction and attention than ‘trad-

itional’ classroom learners. In short, if the field of SLA does not take into

account the fascinating complexity of untutored language learning, we risk

losing important pieces of the language acquisition puzzle that could sig-

nificantly enhance our understanding of crucial questions and our ability to

effectively reach a broader range of learners.

Conflict of interest statement. None declared.

SUPPLEMENTARY DATA

Supplementary material is available at Applied Linguistics online.

NOTES

1 To determine whether discussion topic

impacted complexity and accuracy

levels, we identified and labeled topic

categories for all interviews (as shown

in the supplementary material for Table

A1). Performance measures for each

topic category were compared, and no

significant differences were found

between topics.

2 Initially, several other linguistic

features were also considered as spe-

cific accuracy measures, including past

tense morphology, negation, and plural

marking. Each of these was examined

in four months of data (January,

February, November, and December),

and patterns were found to be very

similar to patterns in present simple

B. POLAT AND Y. KIM 205

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http://applij.oxfordjournals.org/cgi/content/full/amt013/DC1

http://applij.oxfordjournals.org/cgi/content/full/amt013/DC1

http://applij.oxfordjournals.org/cgi/content/full/amt013/DC1

tense. Additionally, because these three

features appeared in many but not all

of the interview sessions, we decided to

include only simple present tense as a

specific measure of accuracy.

3 AS-units are separated by backslash

and clauses are separated by double

colons. Punctuation has been added

for ease of reading.

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Directions

Length: ~3-4 typed, double-spaced pages (approx. 750-1000 words)

Content: The reviews will follow a summary/response organization.  The following questions should help guide your review:

Summary:  

· General comments: The goal of this part of your review is to demonstrate your comprehension of the study.  As such, assume your target audience is non-experts in SLA research.  Avoid highly technical details and jargon, opting instead for more accessible language and descriptions, i.e., “your own words.”  There should be no need for any quotes in this summary.

· Content: Your summary should address the following questions:

· What were the goals of the study? What were the researchers hoping to find out as a result of the study?  What were the gaps/limitations in our understanding that they were hoping to address? (Note: You do not need to summarize their entire literature review, but should provide some basic background to contextualize the study.)

· How did they attempt to address the research questions? Summarize the methodology employed.  Who were the participants?  What data-collection methods/instruments were used?  What was analyzed, compared…?

· What were the key findings? (Note: No need to discuss detailed statistical findings. Simply summarize the important findings).  How did the researcher(s) interpret these findings in relation to their research questions and previous research discussed in their literature review?

Response:

· General Comments: The goal of this part of your review is to demonstrate your intellectual interaction with the research you have read.

· Content: Your response should address the following questions:

· What new terms or concepts have you learned from this article? (Don’t just list terms/concepts, but briefly explain them.)

· How do the findings relate to your own experience with and/or ideas about language acquisition? Any surprises? Confirmations? Anything about which you remain skeptical? (If relevant, how do findings relate to other course readings or discussions?)  

· What questions has this study—the methodology, the findings, etc.—raised for you? What do you suspect might be the answer to your questions?