DirectionsLength ~3-4 typed, double-spaced pages (approx. 750-1.docx

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?
Applied Linguistics 2014: 35/2: 184–207 � Oxford University
Press 2013
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: [email protected] or [email protected]
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 1988
(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.
B. POLAT AND Y. KIM 185

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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.
186 COMPLEXITY AND ACCURACY
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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
abstract/35/2/184/191987
on 12 December 2017
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
abstract/35/2/184/191987
on 12 December 2017
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
0
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o

u
n
t
Week
Words/AS-unit
Words/clause
(b)
Figure 1: (a) Clauses per AS-unit over time. (b) Words per AS-
unit and
words per clause over time
abstract/35/2/184/191987
on 12 December 2017
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.
0

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2
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/0
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03
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5/
01

5/
15
6/
07
6/
19
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03
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08
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06
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10
/0
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/2
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/0
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11
/2
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12
/1
2
12
/2
7
C
o
u
n
t
Week
Clauses/AS-unit Min Max
Figure 2: Moving min–max graph of clauses/AS-unit
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on 12 December 2017
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.
0
1
2
3

4
5
6
7
8
9
10
01
/0
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2/
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7
3/
23
4/
03
4/

18
5/
01
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15
6/
07
6/
19
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03
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8/
08
8/
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06
9/
19
10
/0
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/2
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/0
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11
/2
1
12
/1
2
12
/2
7
C
o
u
n
t
Week
Words/AS-unit
Min
Max

Figure 4: Moving min–max graph of words per AS-unit
0
1
2
3
4
5
6
7
01
/0
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1/
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2/
06
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4/
03
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Week

Words/clause Min Max
Figure 3: Moving min–max graph of words per clause
abstract/35/2/184/191987
on 12 December 2017
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
abstract/35/2/184/191987
on 12 December 2017
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,
0
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Week
Figure 7: Errors over time
0
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Figure 5: Lexical diversity over time
0
20
40
60
80
100
120
D
at
e
01
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o
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Figure 6: Moving averages graph of lexical diversity

abstract/35/2/184/191987
on 12 December 2017
0
5
10
15
20
25
30
35
40
01
/0
2
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2/

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C
o
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Errors Min Max
Figure 9: Moving min–max graph of global accuracy (errors)
0
20
40
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120
01
/0
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2/
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2/
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P

er
ce
n
t
Week
Present simple Min Max
Figure 10: Moving min–max graph of present simple accuracy
0
20
40
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100
120
01
/0
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2/

9/
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P
er

ce
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Figure 8: Present simple tense accuracy over time
abstract/35/2/184/191987
on 12 December 2017
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
abstract/35/2/184/191987
on 12 December 2017

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,
abstract/35/2/184/191987
on 12 December 2017
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

abstract/35/2/184/191987
on 12 December 2017
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
abstract/35/2/184/191987
on 12 December 2017
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
abstract/35/2/184/191987
on 12 December 2017
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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