Volume 5 (2014) Issue 2
pp. 181-206
An
Exploration of Task Complexity
in
Foreign Language Writing at the Intermediate Level
and
Measures of Linguistic Production
Marcela
Ruiz-Funes (Statesboro (GA), USA)
Abstract
(English)
This
study expands
on
the emerging course of research on cognitive task complexity in
second / foreign language (L2/FL) writing (Kormos 2011, Kuiken & Vedder 2007, 2008, Ong ; Zhang 2011; Byrnes & Manchón 2014,
Ruiz-Funes 2014). Specifically, it sheds light on the interplay
between task complexity and linguistic production within the domain
of L2 / FL writing focusing on FL learners of Spanish at an
intermediate level of language proficiency based on the ACTFL
Proficiency Guidelines (ACTFL 2012). The theoretical framework of
this investigation is Skehan & Foster’s Trade Off / Limited
Attentional Capacity Model and Robinson’s Triadic Componential
Framework / Cognition Hypothesis. Twenty-four undergraduate learners
of Spanish wrote two essays of different levels of cognitive
complexity in relation to familiarity of topic, genre, and reasoning
demands that were analyzed for syntactic complexity, accuracy, and
fluency (CAF). Data analysis was conducted in two ways: (i) by
task type only and (ii) by task type and level of performance on
assigned tasks. Findings indicate a tendency towards support for the
Trade-Off / Limited Attentional Capacity model with no differing
effect based on the level of performance of the students on the
assigned tasks.
Keywords:
Task
complexity; syntactic complexity; accuracy; fluency; proficiency
level; performance level
Abstract
(Español)
Este
estudio amplía la investigación emergente sobre la complejidad de
tareas cognitivas en la escritura en un segundo idioma (Kormos 2011,
Kuiken y Vedder 2007, 2008, Ong y Zhang 2011, Byrnes & Manchón
2014, Ruiz-Funes 2014). Específicamnete, este estudio explora la
relación entre la complejidad de tarea y la complejidad lingüística
dentro del dominio de la escritura en un segundo idioma centrándose
en estudiantes de español como lengua extranjera a un nivel
intermedio basado en las directrices de competencia de ACTFL (ACTFL
2012). El marco teórico de esta investigación es el modelo de
Skehan y Foster de Intercambio / Capacidad de Atención Limitada y
la Hipótesis Cognitiva de Robinson. Veinticuatro
estudiantes universitarios de español escribieron dos ensayos de
diferentes niveles de complejidad cognitiva en relación a la
familiaridad del tema, el género y las demandas de razonamiento los
cuales se analizaron según la complejidad sintáctica, la precisión
y la fluidez (CAF). El análisis de datos se llevó a cabo de dos
maneras : ( i ) por tipo de tarea solamente y ( ii ) por tipo de
tarea y el nivel de rendimiento en las tareas asignadas. Los
resultados indican una tendencia hacia el apoyo al modelo de
Intercambio / Capacidad de Atención Limitada sin efecto diferente en
función del nivel de rendimiento de los estudiantes en las tareas
asignadas.
Palabras
claves: Complejidad de tarea; complejidad sintáctica; precisión;
fluidez; nivel de proficiencia; nivel de rendimiento
1
Introduction
Task-based
language teaching (TBLT) has had a significant impact in the field of
second language acquisition (SLA) and has widely influenced
the
exploration of tasks and their effect on language learning and
development. Particularly, research in this area has focused on
understanding how task characteristics can affect the second language acquisition (SLA) processes involved in learning while attempting to meet the challenges certain tasks set”. (Robinson 2011: 4).
The
vast majority of the investigation in this field has dealt primarily
with task complexity within the oral production domain (Samuda & Bygate, 2008, Van den Branden, Bygate & Norris 2009, Robinson
2011). As a consequence, and as pointed out in earlier studies
(Byrnes & Manchón 2014, Kormos 2011, Kuiken & Vedder 2008,
Ong & Zhang 2010; Ruiz-Funes 2014), research on the effect of
tasks in writing, and L2 / FL performance and development continues
to be scant.
This
investigation aims to help expand
the literature base by
advancing on the emerging course of research
on cognitive task complexity in L2 / FL writing. It brings further
understanding to the relationship
between writing
tasks,
linguistic
production,
and language
proficiency
by
providing additional empirical evidence to that offered by Kormos
(2011), Kuiken & Vedder (2007, 2008, 2011, 2012), Ong & Zhang
(2011), and Ruiz-Funes (2014), among others. Specifically,
it focuses on FL learners of Spanish with an intermediate level of
language proficiency based on the ACTFL Proficiency Guidelines (ACTFL
2012) and follows a similar experimental research design from a
previous study (Ruiz-Funes, 2014) conducted in terms of types of
analysis conducted and linguistic measures computed. Twenty-four
undergraduate learners of Spanish wrote two tasks of different levels
of cognitive complexity in relation to familiarity of topic, genre,
and reasoning demands which were analyzed for syntactic complexity,
accuracy, and fluency (CAF). The data
analysis
was conducted in two ways: (i) by task type only and (ii) by task
type and level of performance on assigned tasks. Task type refers to
task complexity as indicated in Task 1 and Task 2; level of
performance refers to the overall quality of the essays the students
wrote, based on the assessment made by two raters.
The
theoretical framework of this investigation is Skehan and Foster’s
Trade Off / Limited Attentional Capacity Model (Skehan 1998a, 2001,
2003, Skehan & Foster 1999, 2001) and Robinson’s Triadic
Componential Framework or Cognition Hypothesis (Robinson 2001a,
2001b, 2003, 2005, 2007). Even though these models were
conceptualized for oral tasks, they provide a useful structure for
exploring task characteristics and their effect on linguistic
production within the field of L2 / FL writing (Kormos 2011). They
have attempted to answer the question of how attentional resources can be used, coordinated, and directed to different aspects of language production during task completion. (Kormos 2011: 50)
Since
these models bring to the forefront the role of attentional resources
in the task performance process and the effect that task
characteristics have on such resources, the theoretical background of
this study also includes
the
construct of working memory capacity and its function in writing
processes (Baddeley 1986, Kellogg 1996, Alamargot and Chanquoy 2001).
In
the present paper, first, the theoretical background of the study is
presented, followed by a review of the most salient work on task
complexity in L2 / FL writing. Second, the study and its findings are
examined. A discussion of the results and implications for future
research conclude the paper.
2
Theoretical Background
As
Hyland states
[writing] tasks are fundamental in learning to write and represent a central aspect of the teacher’s planning and delivery of a writing course. (Hyland (2003:112)
Tasks
are important because they affect students’ learning experiences,
setting in motion a series of cognitive mental processes that writers
orchestrate during all the steps of the writing process
(Dvorak
1986, Flower & Hayes 1981, Flower et al. 1990, Kellogg 1996).
Moreover, the notion of task,
has been defined from various perspectives. From a psycholinguistics
perspective, a task engages students in certain types of mental
processing that leads them to effective language use and acquisition
(Ellis 2009). Two of the most widely influential models that address
how the cognitive demands of tasks affect L2 learning and that frame
the present study are those proposed by Peter Skehan (1998, 2001,
2003, 2009) and Peter Robinson (2001a, 2001b, 2003, 2005, 2007)
(Table 1). Their frameworks have been used to classify and
manipulate tasks during L2 instruction (Robinson 2011) but, more
importantly, to investigate the effect of task characteristics on
language acquisition and development.
Skehan’s
(1998a, 1998b, 2001, 2003; Skehan & Foster,
1999,
2001) Trade Off or Limited Capacity Hypothesis model, based on
working memory theories (Carter 1998 and Gathercole and Baddeley
1993), claims that tasks that are cognitively more demanding “consume
more attentional resources…with the result that less attention is
available to focus on form” (Skehan 1998: 97). As such, it suggests
that tasks be sequenced from less to more demanding ones to allow for
opportunities for the learner to attend to language form and thus to
increase the opportunities for task-based language learning in a
balanced way where accuracy, fluency, and complexity of production
are promoted. It further states that task variables can be grouped
under two general categories: task features (e.g. how information is
structured, what elements can be manipulated) and task implementation
(including planning time, and task repetition). Both interact
in complex ways that affect the output of the learner in terms of
fluency, accuracy, and syntactic complexity. Specifically, Skehan
(1998a, 2001, 2003) argues that learners possess a limited processing
capacity, that only one of these may be in focus at a given time
during a given task, and that the manipulation of task variables
affects the fluency, accuracy, and complexity of production. Skehan
hypothesizes that, as task complexity increases, learners will attend
to only one aspect of performance – syntactic complexity, accuracy,
or fluency – at the expense of the others.
On
the other hand, Robinson’s model, known as the Triadic Componential
Framework or Cognition Hypothesis (Robinson 2001a, 2001b, 2003, 2005,
2007), is based on information processing theories (Long 1996,
Schmidt 2001, and Wickens 1992). It suggests a multiple-resources
view
of processing where learners can attend to multiple aspects of
language and language processing simultaneously. As such, Robinson
proposes that more cognitively complex tasks lead to more attention
to input / output, spur rule and instance learning, and, as a result,
facilitate the incorporation of input and better performance. This
framework distinguishes two dimensions for task complexity: the
resource-directing
dimension that
makes conceptual demands such as reference to past or present events,
few or many elements, and more or fewer reasoning demands; and by
contrast, the resource-dispersing
dimension that
makes procedural demands on the learner and includes planning time,
prior knowledge provided in the task, and the
number
of tasks to complete. Task complexity along the resource-directing
dimension
results
in increased accuracy and complexity as learners have to put their
attentional resources to the demands of the task. Yet, fluency
decreases as students have to process language. On the contrary, task
complexity along the resource-dispersing
dimension
will
result in decreased fluency, accuracy, and complexity levels in
language production as it will limit the attentional and working
memory of learners (Robinson 2003, 2005). The Cognition Hypothesis
has been questioned, and support for its claims has varied (Kuiken
and Vedder 2007a, 2007b; Michel, Kuiken & Vedder 2007, Robinson
2007). Kormos and Trebits (2011) suggest that this mixed support may
be due to the fact that in some tasks, the two dimensions of
resource-directing and resource-dispersing may be simultaneously
present.
Skehan’s Limited Attentional
Capacity
Model
(Skehan
1998a, 1998b, 2001, 2003, Skehan & Foster 1999, 2001)
|
Robinson’s
Triadic Componential Framework
(Robinson
2001a, 2001b, 2003, 2005, 2007)
|
1.
Code
Complexity
o Vocabulary
load and variety
o Redundancy and density
|
|
2.
1
Cognitive Complexity
· Cognitive
familiarity
o
Familiarity
of topic and its
predictability
o Familiarity
of discourse genre
o Familiarity
of task
· Cognitive
processing
o Information
organization
o Amount
of computation
o Clarity
and sufficiency of
information given
o Information type
|
2. Task
Conditions
· Participation
variables, e.g.,
o
Open/closed
o One-way/two-way
o Convergent/divergent
· Participant
variables, e.g.,
o
Same/different
gender
o Familiar
/ unfamiliar
o
Power / solidarity
|
3.
Communicative
stress
o Time
limits and time pressure
o Speed
of presentation
o Number
of participants
o Length
of text used
o Type
of response
o Opportunities to control interaction
|
3. Task
difficulty
· Affective
variables, e.g.,
o
Motivation
o Anxiety
o Confidence
· Ability
variables, e.g.,
o Working
memory
o Intelligence
o
Aptitude
|
Table 1: Cognitive complexity frameworks
3
Literature Review on Task Complexity in L2 / FL Writing
The
research on task complexity in L2 / FL writing suggests a
relationship between task complexity and linguistic production. Yet,
findings are still not consistent enough to formulate
generalizations. This may, in large, be due to the emerging nature of
this type of research within the domain of L2 / FL writing.
Varying
patterns of connection between task complexity and syntactic
complexity, accuracy, lexical variation, and fluency have been noted.
For example, Kuiken and Vedder’s (2007, 2008, 2011) studies with
college-level Dutch learners of Italian and Dutch learners of French,
who completed two letters of different degrees of cognitive
complexity in terms of number of requirements and types of decisions,
showed a decrease in the number of errors and an increase in lexical
variation on the more complex task. No effect was found between task
complexity and syntactic complexity or the proficiency level of the
students. In a more recent publication, Kuiken and Vedder (2012)
explored more in depth the effect of L2 proficiency
on the
relationship
between task
complexity
and linguistic
production
based
on data from the three previous studies they conducted (Kuiken & Vedder 20007, 2008, 2011). Proficiency levels
were determined by scores the participants received on a cloze test.
Findings indicated no effect of task complexity on linguistic
performance based on proficiency levels as defined in their studies.
Kormos
(2011) explored task complexity in narration produced by
upper-intermediate students of English as a foreign language (EFL) in
a bilingual high school in Hungary and L1 students of English in the
UK. The two narrative tasks provided different levels of cognitive
complexity in terms of more / less demand for plot conceptualization.
Findings indicated no significant effect in relation to task
complexity and linguistic performance except that more abstract words
were elicited by the more complex task.
Ruiz-Funes
(2014) examined task complexity in essay writing composed by college
learners of Spanish with an advanced level of language proficiency
based on the ACTFL Proficiency Guidelines (ACTFL 2012). One task was
an analytical
essay; the second task, more complex, was an argumentative one that
called for evaluation of a given argument with appropriate support.
This
investigation established some connections with claims in both Skehan
and Foster’s (1999, 2001) and Robinson’s (2001a) processing
models. Results suggested that considering task complexity,
familiarity of topic, genre, and task type, along with reasoning
demands, had an effect on L2 / FL written production: task complexity
had a positive effect on syntactic complexity or accuracy and
fluency, but not on all aspects simultaneously except when the level
of performance on the assigned tasks was taken into account. In that
investigation, high performance students showed somewhat higher
values of syntactic complexity, accuracy, and fluency on the more
complex task; low performance students on the less complex one.
Other
studies
explored task complexity in writing manipulating factors
such
as planning time, draft availability, or the here
and
now
dimension.
First,
Ellis
and Yuan (2004)
established
a positive relationship between pre-task planning on the one hand
and
length and syntactic structures used by students on the other;
second,
Ong
and Zhang (2010) found
a connexion between fluency
and lexical variation;
and third,
Ishikawa (2006) showed a positive effect between the here
and
now
dimension
and syntactic complexity, accuracy, and fluency.
In
all, findings point to a potentially significant relationship between
elements of task complexity and written output. However,
definite conclusions cannot yet be reached due largely to the
multiplicity of variables present in the current investigations. As
stated earlier (Ruiz-Funes 2014), there is variation in the types of
written tasks manipulated including letter, narration, and essay
writing, the manner in which task complexity in writing is
operationalized, the language proficiency levels of the learners as
well as their levels of performance on the tasks assigned, the way
language proficiency is measured, and the academic levels and
settings of the writers. More research should validate what and to
what extent task characteristics and task conditions account for the
variation in linguistic performance (Ruiz-Funes 2014). This
study seeks to bring more empirical evidence to help isolate factors
that interact in the
relationship
between writing
tasks,
linguistic
production,
and language
proficiency in L2 / FL writing.
4
The Study
4.1
Research Questions
The
main purpose of this study is twofold: (i) to explore the effect of
task complexity on the linguistic production of college-level FL
writers of Spanish at the intermediate level of language proficiency,
and (ii) to investigate to what extent such relationship, if any, is
linked to the level of performance of the learners as indicated by
the quality of writing they produced on the assigned tasks. The
following research questions guided this study:
- What is the effect of task complexity on syntactic complexity measured by length of production, amount of subordination, and coordination?
- What is the effect of task complexity on accuracy measured by number of errors per T-unit and number of errors per 100 words?
- What is the effect of task complexity on fluency measured by length of text produced within a given time frame?
- Is there a relationship between task complexity and measures of syntactic complexity, accuracy, and fluency (CAF) based on the level of performance of the learners on the assigned tasks?
4.2
Methodology
4.2.1
Participants and Procedures
Data
were gathered from 24 undergraduate learners of Spanish from a large
southeastern university in the United States, who attained an
intermediate-mid to intermediate-high language proficiency level on
an oral examination based on the ACTFL Proficiency Guidelines (ACTFL
2012). 16 students were female and eight (8) male of ages
ranging between 19 and 25 years old. 21 students were native speakers
of English for whom Spanish was a foreign language: Three students
were heritage language speakers of Spanish born, raised, and educated
in the United States. All students were majors or minors in Hispanic
Studies enrolled in two or three Spanish courses at the intermediate
level. In general, the students were engaged in a rather traditional
curriculum that included courses in language, culture, literature,
and linguistics with an emphasis in the development of language
proficiency in all four skills and with some focus on genre-based
tasks.
The
students completed two timed written tasks (Appendix) as in-class
assignments. They were allowed 50 minutes for each task which they
handwrote while being supervised by their instructor. The written
products were evaluated by two trained raters, who were experienced
Spanish instructors at the university level and native speakers of
Spanish. They used a holistic rubric that assessed students‘
composing skills in reference to organization, content, and
linguistic skills regarding grammar, vocabulary, and conventional
usage of written language. A minimum inter-rater agreement of 90% was
reached for each essay.
Based
on the raters’ evaluation, seven students received a score between
5 and 6 (excellent / sophisticated; Group 1), 14 students scored 4-
(appropriate and acceptable; Group 2), and three students received a
mark of 3- (inadequate; Group 3). This grouping indicated their level
of performance on the assigned tasks. Group 1 was considered the
high-performance group (HP), Group 2 the mid-performance group (MP),
and Group 3 the low-performance group (LP) (see Data Analysis section
below).
4.2.2
Writing Tasks
In
each task assigned, the participants were asked to write an essay of
250 words in length on a 50-minute class session of an intermediate
writing course. No dictionary, textbook, or any other resource
was allowed. Task 1 was assigned on day one; Task 2 was assigned on
the second day of the same week. Both tasks were framed and
contextualized around the theme of study
abroad,
yet reflected different levels of cognitive complexity. Task 1
required the learners to write a personal
essay
about
themselves, their interests, and expected goals for studying abroad.
For Task 2, students had to write an expository
essay
stating
formally the benefits and challenges of studying abroad for college
students.
The
personal essay (Task 1) was considered to be less complex than the
formal expository essay (Task 2) since in the former, the writer was
asked to write about his or her personal experiences without having
to prove a point. The author was to introduce the subject and theme,
based on personal life experiences, feelings, emotions, and opinions,
using both description and narration. In the formal, expository
essay (Task 2), on the other hand, the writer had to state a thesis
and support his or her ideas with facts and objective evidence. For
this task, the student was expected to present the information in
order to inform and / or explain.
Based
on Skehan and Foster’s (1999, 2001) model, Task 2 is assumed to be
more cognitively complex in terms of topic, discourse genre, and task
type, and more demanding of cognitive processing in terms of
information organization and information type (Cognitive
Complexity).
Using Robinson’s (2001a) model, this task involved the
orchestration of more reasoning demands (Task
Complexity / Resource-Directing).
The task characteristics manipulated on both tasks appear in Table 2
below.
Limited
Attentional Capacity Model
(Skehan
& Foster, 1999, 2001)
|
Triadic
Componential Framework
(Robinson 2001a)
|
Cognitive
Complexity
·
Cognitive
familiarity· Familiarity of topic and its predictability · Familiarity of discourse genre · Cognitive processing · Information organization |
Task
Complexity
·
Resource-directing+/- No reasoning demands |
Table 2: Tasks characteristics manipulated
In
addition, based on a brief questionnaire on the participants’
demographic information and their opinion about which task was more
demanding, the majority (88%) responded that Task 2 (formal,
expository
essay) was more difficult. Some
of the reasons the students stated are: Among
the reasons, they indicated,
having
little control of more technical, formal lexicon, lack of experience
studying abroad, and the need to think more to generate and organize
ideas for the completion of Task 2.
The
following
excerpts taken from the questionnaire illustrate this
:
Student A: Essay 2 (Task 2) required more vocabulary that I do not use every day.
Student B: I had to use more terms and some of which I did not know how to spell. Writing about myself (Task 1) was much easier.
Student C: Essay 2 was more challenging because I had to come up with ideas and points before I was able to write about them.
Student D: Essay 2 was more difficult because it wasn’t about myself, I had to list things.
Student E: Essay 2 because I never really studied abroad and thought it was harder for me to think some ideas to describe.
Student F: The second essay was harder to write because there were more technical terms that I wanted to use but didn’t know how to say in Spanish. Also, I couldn’t think of a lot of benefits and challenges of studying abroad”.
Student G: The second essay was more difficult to write because I have not spent a lot of time thinking about the advantages and disadvantages of studying abroad. On the other hand I have thought about myself, my goals, and interests a lot in the past.
Student H: It was more difficult to write about the challenges and benefits because I have not studied abroad yet and haven’t been able to experience the challenges / benefits.
A
few students (12%), however, considered Task I (personal essay) to be
more difficult. Somewhat contradictory reasons were posed such as
that Task 1 was more open-ended which, in turn, hindered
the
generation of ideas, on the one hand, and that Task 1 constrained
creativity which led to redundancy of information, on the other:
Student I: Essay 1 (Task 1) was more difficult to write because it was more open ended so I couldn’t decide what to say.
Student J: Essay 1 (Task 1) was more difficult because it left less room for me to get creative with what I wanted to talk about.
The
students’ perception of task difficulty touches upon the
relationship explored in this study, i.e., the effect of task
complexity on cognitive processing and language production and as
such, it is presented only as an additional reference that
differentiates the two tasks.
5
Linguistic Production: Measuring Syntactic Complexity, Accuracy,
and Fluency (CAF)
5.1
Measures of Syntactic Complexity and the Use of the T-Unit Analysis
In
this study, syntactic complexity was measured, following a
multidimensional approach with metrics that are distinct and
complementary as suggested by Norris & Ortega (2009). The
measurers included
- length of production - Mean length of T-unit (MLTU): total number of words divided by the total number of T-units (MLTU W/TU);
- amount of coordination - Mean number of T-units per sentence (TUS): the total number of T-units divided by the total number of sentences: (TU/S); and
- amount of subordination - Mean number of dependent clauses per T-unit (CTU): the total number of dependent clauses divided by the total number of T-units: (C/TU)
The
central unit used was the T-unit coined by Hunt (1965) as the minimal
terminable unit of
language consisting of a main clause plus all subordinate clauses and
nonclausal structures that are attached to or embedded in it. The
T-unit has been adopted extensively in L2 / FL research to measure
writing development, and it is considered to be the most appropriate
way to code and record changes regarding fluency, accuracy, and
complexity (Arnold et al. 2009, Polio 1997, Spelman Miller 2006).
5.2
Accuracy
Errors
were tallied based on an adapted version of Lalande’s (1984) Essay
Correction Code-ECCO. An error was operationalized as any
digression in syntactical, morphological, and lexical norms, but not
in punctuation or capitalization (Ellis & Yuan 2004). A number of
measures were computed:
total
number of errors,
total number of errors per T-unit (Etot/T=total number of errors
divided by the total number of T-units), and number of errors per 100
words (NER) (Mehnert 1998).
5.3
Fluency
As
indicated in earlier studies (Larsen-Freeman 1978, Wolfe-Quintero et
al. 1998), one marker of fluency is length
of text produced in timed writing.
Since the completion of both essays in this study were done under
timed conditions (50 minutes for each task), the number of words
produced became a rate measure for fluency calculated
by the
mean number of words written per minute out of the 50 minutes spent
on the task.
6
Data Analysis
For
data analysis, the essays were first typed in MS-Word documents and
coded by the researcher and a trained graduate assistant according to
the various linguistic measures for syntactic complexity, accuracy,
and fluency as listed above. A minimum inter-rater reliability
coefficient of .94 was achieved for each measure. The following
values were calculated per student individually on each task
separately:
(a)
the
total number of words, the total number of sentences
(b)
the total number of T-units
(c)
the total number of clauses, the total number of dependent clauses
(d)
the ratio of dependent clauses per T-unit
(e)
the total
number of errors and
(f)
the total
number of errors per 100 words.
Following
a similar data analysis procedure used in a previous study
(Ruiz-Funes 2014), the values of syntactic complexity, accuracy, and
fluency were obtained in two ways:
(i)
per task type and
(ii)
per task type and level of performance achieved on the writing tasks.
The
category task
type
refers
to task complexity as indicated in Task 1 and Task 2. The category
level
of performance
refers
to the quality of the essays the students wrote based on the raters’
assessment. It includes three subgroups:
- High performance group (HP; n=7), students who scored 5/6- (excellent / sophisticated)
- Mid-performance group (MP; n=14), students with a score of 4- (appropriate and acceptable) and
- Low-performance group (LP; n=3), for those with a mark of 3- (inadequate).
Due
to the relatively small number of participants, which constitutes one
of the weaknesses of this study, only descriptive statistics were
used for data analysis. The values calculated include: group mean,
mean standard deviation, mean standard error of measurement, and the
95% confidence intervals (CIs) above and below the means for each
type of analysis to make the relevant comparisons.
7
Results
7.1
Analysis per Task-Type
The
descriptive statistics for the measures of syntactic complexity
showed higher values on Task 2 (the more complex task) than on Task 1
for MLTU (overall length of production) and CTU (subordination), and
lower on Task 2 for coordination (TUS), indicating a higher level of
syntactic complexity on Task 2 than on Task 1.
The
group mean standard
deviation and standard error values for all three measures, on the
other hand, were almost identical in both tasks which shows a similar
average variability for individuals on each task. This result
suggests that individual students produced T-units and clauses of
similar varying lengths on Task 2 and on Task 1 (Table 3).
The
analysis of variance to compare group mean values for MLTU was
calculated, using the 95% confidence intervals (CIs p <
.05). The CIs obtained indicated no statistically significant
difference of the means as the 95% CI values overlapped (Figure 2).
Syntactic Complexity Measure |
Descriptive
Statistics
|
Task
1
|
Task
2
|
MLTU
|
M
|
10.57
|
12.44
|
SEM
|
0.56
|
0.60
|
|
SD
|
2.74
|
2.92
|
|
TUS
|
M
|
1.32
|
1.25
|
SEM
|
0.04
|
0.04
|
|
SD
|
0.20
|
0.20
|
|
CTU
|
M
|
0.48
|
0.57
|
SEM
|
0.04
|
0.05
|
|
SD
|
0.21
|
0.23
|
|
Notes:
MLTU
= mean length of T-unit; TUS = mean number of T-units per
sentence; CTU = mean number of clauses per T-unit; M = mean; SEM =
standard error of measurement; SD = standard deviation.
|
|||
Table 3: Descriptive statistics for syntactic complexity measures by task
type (n
= 24)
- Task TypeUpper CILower CIMeanTask 111.739.4110.57Task 213.6711.2012.44
Figure 1: Differences in mean lenth of T-unit (MLTU) by task type
In
regards to accuracy, the group values for the number of errors per
T-unit (Etot / T) and the number of errors per 100 words
(NER)
showed
higher
levels of errors on Task 2 than on Task 1, with different levels of
individual variability within each task, as indicated by the index of
standard deviation for NER (Table 4). The CIs obtained indicated
no statistically significant difference of the means for any of these
measures as the 95% CI values overlapped (Figures 2 and 3).
Accuracy
Measure
|
Descriptive
Statistics
|
Task
1
|
Task
2
|
Etot/T
|
M
|
1.08
|
1.48
|
SEM
|
0.10
|
0.15
|
|
SD
|
0.51
|
0.74
|
|
NER
|
M
|
9.71
|
12.04
|
SEM
|
1.00
|
1.52
|
|
SD
|
4.90
|
7.46
|
|
Notes:
Etot/T
= number of errors per T-unit; NER = number of errors per 100
words; M = mean; SEM = standard error of measurement; SD =
standard deviation.
|
|||
Table 4: Descriptive statistics for measures of accuracy by task type;
n = 24
Task TypeUpper CILower CIMeanTask 11.290.871.08Task 21.801.171.48Note: CI = confidence interval
Figure 2: Differences in mean
number
of errors per T-unit (Etot/T)
- Task TypeUpper CILower CIMeanTask 111.787.649.71Task 215.198.8912.04Note: CI = confidence interval
Figure 3: Differences in mean
number
of errors per 100 words (NER)
For
fluency, the values indicated a higher mean number of words per
minute out of 50 minutes on Task 1 (the less complex task) than on
Task 2 (Table 5). Yet, no significant difference was found as shown
by the 95% CI values (Figure 4).
Fluency
Measure
|
Descriptive
Statistics
|
Task
1
|
Task
2
|
Number
of words per minute (out of 50 min on task)
|
M
|
5.30
|
4.89
|
SEM
|
0.36
|
0.24
|
|
SD
|
1.75
|
1.17
|
|
Notes:
M = mean; SEM = standard error of measurement; SD = standard
deviation.
|
|||
Table 5: Descriptive statistics for fluency by task type;
n = 24
- Task TypeUpper CILower CIMeanTask 16.034.565.30Task 25.384.44.89Note: CI = confidence interval
Figure 4: Differences in mean
number
of words per minute in 50 min on task
7.2
Analysis per Task-Type and Level of Performance
The
results in the category analysis
per
task type
and
level of performance showed
a similar pattern to the one noted in the analysis
per
task type
only.
The values
for
syntactic complexity, particularly for length of production measured
by the mean length of the T-unit, were higher and with more
variability for all three groups on Task 2 (the more complex
task). For coordination, results indicated slightly higher mean
values on Task 2 for the LP performance group, whereas the other two
groups showed slightly higher values of coordination on Task 1. The
variability for all the groups on each task was almost identical
(Table 6). However, no statistical difference was found between any
of these measures, which may be due largely to the small sample in
each subgroup.
LP
|
MP
|
HP
|
|||||
Syntactic
Complexity Measure
|
Descriptive
Statistics
|
Task
1
|
Task
2
|
Task
1
|
Task
2
|
Task
1
|
Task
2
|
MLTU
|
M
|
10.16
|
14.98
|
9.50
|
11.47
|
12.90
|
13.28
|
SEM
|
0.65
|
3.2
|
0.38
|
0.58
|
1.46
|
0.94
|
|
SD
|
1.12
|
5.54
|
1.43
|
2.17
|
3.85
|
2.48
|
|
CI
(95%)
|
+/-2.77
|
+/-13.75
|
+/-0.82
|
+/-1.25
|
+/-3.56
|
+/-2.29
|
|
TUS
|
M
|
1.23
|
1.50
|
1.38
|
1.22
|
1.24
|
1.19
|
SEM
|
0.05
|
0.25
|
0.06
|
0.03
|
0.05
|
0.05
|
|
SD
|
0.09
|
0.44
|
0.22
|
0.13
|
0.14
|
0.13
|
|
CTU
|
M
|
0.43
|
0.68
|
0.42
|
0.51
|
0.60
|
0.64
|
SEM
|
0.07
|
0.08
|
0.05
|
0.06
|
0.11
|
0.10
|
|
SD
|
0.12
|
0.14
|
0.17
|
0.22
|
0.28
|
0.26
|
|
Notes:
LP=Low-performance
group; MP=Mid-performance group; HP=High-performance group; MLTU =
mean length of T-unit; M = mean; SEM = standard error of
measurement; SD = standard deviation; CI = confidence interval;
TUS = mean number of T-units per sentence; CTU = mean number of
clauses per T-unit.
|
|||||||
Table 6: Descriptive statistics for Syntactic Complexity by Task Type and
Level of Performance
(n=3
for LP; n=14
for MP; n=7
for HP)
All
measures of accuracy indicated higher values of errors with
more variability on Task 2 for all the
three
groups except for the LP group that showed almost identical values on
the
measure of number
of errors per 100 words
(NER)
on both tasks (Table 7). In fluency, slightly higher values were
obtained for all the
three
groups on Task 1
with almost identical variability except for the HP group (Table 8).
LP
|
MP
|
HP
|
|||||
Accuracy
Measure
|
Descriptive
Statistics
|
Task
1
|
Task
2
|
Task
1
|
Task
2
|
Task
1
|
Task
2
|
Etot/T
|
M
|
2.08
|
2.80
|
1.01
|
1.43
|
0.79
|
1.03
|
SEM
|
0.32
|
0.57
|
0.09
|
0.13
|
0.08
|
0.15
|
|
SD
|
0.55
|
0.99
|
0.33
|
0.48
|
0.20
|
0.40
|
|
CI
(95%)
|
+/-1.36
|
+/-2.47
|
+/-0.19
|
+/-0.28
|
+/-0.18
|
+/-0.37
|
|
NER
|
M
|
19
|
18.67
|
9.14
|
12.57
|
6.86
|
8.14
|
SEM
|
1.15
|
5.24
|
1.05
|
2.03
|
0.77
|
1.72
|
|
SD
|
2
|
9.07
|
3.92
|
7.59
|
2.04
|
4.56
|
|
CI
(95%)
|
+/-4.96
|
+/-22.54
|
+/-2.26
|
+/-4.38
|
+/-1.88
|
+/-4.21
|
|
Notes:
LP=Low-performance group; MP=Mid-performance group;
HP=High-performance group; Etot/T = number of errors per T-unit;
NER = number of errors per 100 words; M = mean; SEM = standard
error of measurement; SD = standard deviation; CI = confidence
interval.
|
|||||||
Table 7: Descriptive statistics for accuracy
by task type
and level
of performance
(n=3
for LP; n=14
for MP; n=7
for HP)
LP
|
MP
|
HP
|
|||||
Fluency
Measure
|
Descriptive
Statistics
|
Task
1
|
Task
2
|
Task
1
|
Task
2
|
Task
1
|
Task
2
|
Number
of words per minute
|
M
|
3.67
|
3.48
|
4.86
|
4.79
|
6.86
|
5.69
|
SEM
|
1.05
|
1.06
|
0.20
|
0.21
|
0.83
|
0.38
|
|
SD
|
1.82
|
1.84
|
0.74
|
0.78
|
2.19
|
1.00
|
|
CI
(95%)
|
+/-4.51
|
+/-4.57
|
+/-0.42
|
+/-0.45
|
+/-2.02
|
+/-0.92
|
|
Notes:
LP=Low-performance
group; MP=Mid-performance group; HP=High-performance group; M =
mean; SEM = standard error of measurement; SD = standard
deviation; CI = confidence interval.
|
|||||||
Table 8: Descriptive statistics for fluency
by task type
and level
of performance
(n=3
for LP; n=14
for MP; n=7
for HP)
8
Discussion
Results
from both analyses - per
task
type
and
per
task type and level of performance -
suggest
an increase on Task 2 (the more complex task) in syntactic complexity
and
a
decrease in accuracy as well as in fluency.
Based
on Skehan and Foster’s Trade Off / Limited Attentional Capacity
Model (Skehan
& Foster, 1999, 2001),
these
findings are in line with the claim that as
task complexity increases, learners will attend to only one aspect of
language production - syntactic complexity, accuracy, or fluency - at
the expense of the others due to the learners’ limited processing
capacity (Skehan 1998a, 2001, 2003). In particular, Skehan and Foster
(1999, 2001) propose that there is tension between complexity and
accuracy, meaning that either an increase in complexity or in
accuracy occurs at a time, but not in both together.
In the present study, a tendency towards this tension between
complexity and accuracy along with fluency has been observed,
supporting the claims of the Trade-Off / Limited Attentional Capacity
model.
Moreover,
the same tension between syntactic complexity and accuracy and
fluency
was found across the levels of performance on the assigned tasks. The
three subgroups (HP, MP, and LP) showed evidence of this tension
between higher values in measures of syntactic complexity with lower
levels of accuracy and fluency under Task 2 (the more complex task).
This also suggests support for the Trade-Off / Limited Attentional
Capacity model when dealing with students at the intermediate level,
regardless of their level of performance on the assigned tasks. It
can be inferred, then, that none of the subgroups was ready for the
challenge that a more complex task may put on attentional resources
as Robinson’s Cognition Hypotheses (Robinson 2001a, 2001b, 2003,
2005, 2007) would suggest. Robinson proposes that more cognitively
complex tasks lead to increased accuracy and complexity as learners
have to put their attentional resources to the demands of the task.
However, this principle did not hold true with learners at the
intermediate level of language proficiency, irrespective of their
level of performance on the assigned tasks.
The
results from this study are only partially in line with those
obtained in a previous research conducted on task complexity with
advanced language proficiency learners of Spanish (Ruiz-Funes 2014).
In such an investigation, a positive effect between task complexity
and syntactic complexity or accuracy and fluency was found, but not
on all aspects simultaneously except
when the learners’ level of performance on the assigned tasks was
taken into account. Thus, high performance students showed higher values of syntactic complexity, accuracy, and fluency on the
more complex task while low performance students showed higher values
on the less complex task
(Ruiz-Funes 2014). Contrary to those results, in the present study,
the values from both types of analysis indicate that regardless of
the level of performance, the students at the intermediate level of
language proficiency showed higher values of syntactic complexity and
lower values of accuracy and fluency on Task 2 (the more complex
task) than on Task 1 (the less complex task).
Furthermore,
the findings of the present study did not support results from other
studies that share a related research design in terms of variables
used. For example, Kuiken and Vedder (2008, 2011, 2012) found a
decrease in errors on the more complex task with no interaction
effect between task complexity and syntactic complexity or with the
writers' level of expertise. Kormos’ (2011) results indicated
no significant effect in relation to task complexity and linguistic
production, except in terms of an increase in abstract words on the
more complex task. This lack of consistency in the findings may be
linked to the emerging nature of the research in this field within
the domain of writing and the variability in genres, the learners'
academic levels, their language proficiency levels and their
performance levels manipulated across studies (Ruiz-Funes 2014).
9
Conclusions
The
results obtained showed a relationship between task complexity and
linguistic production that needs to be further explored within a
methodology framework that includes a much larger sample size. Like
earlier studies, this study also shows a weakness in its relatively
small sample size; hence its findings are preliminary. However, some
clear trends were observed.
Findings
indicate a tendency towards support for the Trade-Off / Limited
Attentional Capacity model, with no differing effect based on
students' level of performance on the assigned tasks. Evidence was
found in support of the claim that task complexity, as determined by
familiarity of topic, genre, and task type, along with reasoning
demands, seemed to have an impact on CAF measures. The more complex
task led to more syntactic complexity at the expense of less accuracy
and less fluency.
In
addition and more revealing, findings from this study suggest that
the relationship between task characteristics in L2 / FL writing and
their effect on attentional resources and CAF measures may be
associated with students' language proficiency level (Norris & Ortega, 2009). At the intermediate level of language proficiency,
regardless of the level of performance on the assigned tasks, the
trade-off effect among CAF measures seems to be more prominent. In
other words, for learners whose writing and language skills are not
yet fully developed, tasks that are too complex may have a negative
effect on some aspects of language production.
To
sum up, it can be stated that this interplay between task complexity,
CAF traits, and proficiency levels in writing needs to be further
explored in light of the call made by Norris & Ortega (2009)
about the importance to include proficiency considerations in the
interpretation of empirical CAF findings. As they remarked:
Different qualities of production, alone or combined, can have lesser or greater predictive value depending on the relative proficiency levels of L2 learners under investigation. That is, qualities of L2 production cannot and should not be considered constant along the CAF developmental continuum, and different CAF traits must serve different interpretive purposes for different proficiency levels (Norris & Ortega 2009: 573).
Moreover,
the lack of consistency in the findings across other studies presses
for a more defined research agenda. As Kuiken and Vedder (2008) also
noted, further work is needed to determine which variables should be
included in a model of task complexity in L2 / FL writing, how these
variables should be differentiated and prioritized, and how levels of
language proficiency and levels of performance on assigned tasks are
to be defined and manipulated.
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Appendix
Writing
Tasks
You
are planning to study abroad next year in a Spanish-speaking country
of your choice and will apply through the International Student
Exchange Programs (ISEP). As part of the application process, you are
asked to submit two
essays
written
in Spanish to share with the selection committee information about
your life and readiness to study abroad. These essays will also help
the selection committee determine your proficiency level in Spanish.
You will write the essays under the supervision of a proctor at your
home institution. No dictionary or any other resources will be
allowed. You will have 50 minutes to write each essay. Each one
should be of at least 250 words in length.
TASK
1 - Essay One: [personal essay]
It
is important for the selection committee to get to know something
about you. For this essay, write about yourself, your interests, and
your expected goals for studying abroad. Include a personal life
story that you think has prepared you for this experience and that
will contribute to making your stay abroad a positive one.
TASK
2 - Essay Two: [expository essay]
Over
the years, the benefits and challenges of studying abroad have been
well documented by academics in the field. For this essay, present
the benefits and challenges of studying abroad for college students.
Author:
Marcela Ruiz-Funes, PhD
Assistant Professor
Department of Foreign Languages
Georgia Southern University
Statesboro
Ga 30458
E-mail: mruizfunes@georgiasouthern.edu