Grading and sequencing tasks based on the cognitive hypothesis

Order Description

This paper represents the effectiveness of Robinson’s cognitive hypothesis in sequencing and grading tasks in A Qualitative case study.
Based on SSARC Model, an 8 week instructional course for an EFL learner was designed and applied to answer two questions:
1. To what extant does the learner’s spoken accuracy change over the increasingly cognitively complex tasks?
2. To what extant does the learner’s spoken fluency change over the increasingly cognitively complex tasks?
Robinson’s (2010) SSARC Model refers to Simple, Stable, Automatization, Restructuring, and then maximum Complexity sequence.

Over the past thirty years, proposals for task-based language teaching (TBLT) have drawn
on a variety of claims about, and research into, the cognitive processes thought to promote
successful second language acquisition (SLA). A brief overview of these will be given
below. They refl ect a shift from a concern with how TBLT can facilitate comprehension of
input, to how it can facilitate interaction and attention to output, and the development of
increasingly target-like speech production. They also refl ect the progressively sophisticated
knowledge that SLA research has provided concerning cognitive processes such as implicit,
incidental, and explicit learning, and automatization of knowledge.
Cognitive Processes in Task-Based Learning
In his account of the theoretical motivation for the task-based “procedural syllabus” Prabhu
(1987) argued that: “task-based teaching operates with the concept that, while the conscious
mind is working out some of the meaning-content, a subconscious part of the mind perceives,
abstracts, or acquires (or re-creates as a cognitive structure) some of the linguistic
structuring embodied in those entities, as a step in the development of an internal system
of rules” (pp. 70–1). Prabhu’s cognitive rationale for TBLT is thus compatible with Krashen’s
(1982) claim that comprehensible input is necessary for learning, and that it promotes incidental
learning of a tacit, implicit knowledge base. Long (1985) argued that the interaction
that task work promotes is additionally important since it provides one way in which
input can be made comprehensible, as well as a context for attending to problematic forms
in the input and output during task work. Consequently, Pica, Kanagy and Falodun (1993)
described a taxonomy of task characteristics in order to promote further research into
which of these characteristics optimally promoted interaction work. Swain (1985) argued
that attention to output produced during task performance could additionally facilitate
SLA, since it provided a context for comparing the speaker’s performance with an interlocutor’s,
and noticing gaps and mismatches between them, and for hypothesis testing
about the formal means for expressing meanings and communicative intentions in the
second language (L2).
Skehan (1998) provided the fi rst detailed psycholinguistic rationale for the effects of some
aspects of task demands on learning and performance, focusing in particular on the extent
to which having time to plan a task led to increases in the accuracy, fl uency and complexity
of speech produced, when compared to performance on tasks where planning time was
not available. Robinson (2001) also provided a psycholinguistic rationale for how cognitive
complexity can be increased along two broad dimensions of the demands made by tasks,
and claimed that these would have distinct infl uences on learning and performance.
Increasing demands on “resource-directing” dimensions, he argued, directs learners’ attention
to aspects of language used to structure increasingly complex concepts, facilitating
awareness of how these concepts are differentially encoded in the L2, so prompting L2
The Encyclopedia of Applied Linguistics, Edited by Carol A. Chapelle.
© 2013 Blackwell Publishing Ltd. Published 2013 by Blackwell Publishing Ltd.
DOI: 10.1002/9781405198431.wbeal1143
2 task-based learning: cognitive underpinnings
development. Along these dimensions, initially implicit knowledge of the L1 conceptstructuring
function of language (see Talmy, 2000) becomes gradually explicit, and available
for change, following a natural developmental order refl ected in the sequencing decisions.
In contrast, along “resource-dispersing” dimensions, increasing task demands has the effect
of gradually removing processing support (such as planning time) for access to current
interlanguage, and thus practice along them requires, and should encourage, faster and
more automatic L2 access and use. Along these dimensions, therefore, improvements in
performance will involve initially explicit knowledge becoming more automatized.
Design Characteristics Affecting the Cognitive
Processing Demands of Tasks
Current research into the cognitive underpinnings of TBLT is focused on the effects that
design characteristics of tasks have on the cognitive processes that facilitate L2 production
and learning. Some of the design characteristics of tasks that have received the most attention
from researchers are described below.
Planning Time
There have been many studies of how tasks can be made easier for second language
learners by giving them time to plan what they will do or say in the L2 (Ellis, 2005). This
is perhaps the area that has received the most attention by SLA researchers interested in
tasks, and it has clear implications for effective pedagogic decision making. In general, the
studies that have been done seem to show that having time to plan a task increases the
accuracy, fl uency and complexity of learner language.
Single/Dual Tasks
Another dimension of task complexity that is similar to this is the single–dual task dimension.
It is much less complex to answer a phone call in the L2, than it is to answer a phone
call and monitor a TV screen at the same time, to check the weather, or changes in exchange
rates, for example (Robinson, Ting, & Urwin, 1995). The latter, dual task, disperses learner
attention over a number of L2 stimuli. In general, tasks made complex on this dimension
also lead to poorer accuracy, fl uency, and complexity of performance.
Intentional Reasoning
In contrast to the above dimensions of task complexity, other task characteristics may
direct learners’ attention to the language needed to meet complex task demands. On these
dimensions Robinson (2001) argued that increasing task complexity should lead to more
accurate and complex learner language, over time. However, complex tasks on these
dimensions also negatively affect fl uency. For example, in L2 English, tasks which require
complex reasoning about the intentional states that motivate others to perform actions
can be expected to draw heavily on the use of cognitive state terms for reference to
other minds—she suspected, realized, etc.—and in so doing orient learner attention to the
complement constructions accompanying them—suspected that, wonders whether, etc.—so
promoting awareness of, and effort at, complex L2 English syntax (Robinson, 2007; Ishikawa,
2008).
Spatial Reasoning
Another example of resource-directing task demands are those tasks which require
complex spatial reasoning, and articulation of this in describing how to move, and in what
task-based learning: cognitive underpinnings 3
manner, from point A to point E, by way of intermediary landmark points B, C, and D,
etc. These can be expected to draw heavily on the use of constructions for describing
motion events (Cadierno, 2008). Such tasks therefore have the potential to promote awareness
of lexicalization patterns in L2 English for describing these motion events, in which
motion and manner are typically confl ated on verbs (e.g., rushed) and paths are expressed
outside the verb in satellites that confl ate a number of motion events (e.g., rushed out of
the house, down the street and into the post offi ce). English lexicalization patterns are different
from those in Japanese, where motion and path tend to be confl ated on verbs, and manner
encoded separately (e.g., isoide haitta). Consequently, Japanese makes much less use of
event confl ation in reference to motion than English does. So a task requiring complex
spatial reasoning (giving directions from a large map of an unknown area) may prompt
Japanese L2 learners of English to revise their preferred ways of referring to motion, in
line with English lexicalization patterns (Cadierno & Robinson, 2009).
Here-and-Now/There-and-Then
In yet a different conceptual domain, tasks requiring reference to events happening now,
in a shared context (here-and-now) orient learner attention to morphology for conveying
tense and aspect in the present, compared to events requiring much more cognitively
demanding reference to events happening elsewhere in time and space (there-and-then).
There-and-then tasks require greater effort at conceptualization (since events are not
visually available in a shared context) and greater demands on memory (Gilabert, 2007).
One effect of performing tasks on this dimension is to draw learners’ attention to the
morphological forms and phrases that can be used to refer to the present and the past
in English, and these are needed to help them perform the tasks. The morphology for
referring to the past in English is much later acquired by L2 learners than the morphology
for referring to the present, so complex tasks may promote learner attention to, and use
of, this later acquired past tense morphology. That is, in this and other cases of increasing
the complexity of resource-directing demands of tasks, Robinson’s “cognition hypothesis”
(2001) predicts more “noticing” of L2 forms (Schmidt, 2001), more uptake and incorporation
of them, as well as increasing accuracy and complexity of production on complex
compared to simpler task versions.
Effects of Cumulative Increases in the
Cognitive Demands of Tasks
To date, the effects of the design characteristics of tasks contributing to their cognitive
complexity (as described above) have often been contrasted for their distinct effects on
learning and performance. However, for TBLT a key issue is the cumulative effect of
increasing the complexity of pedagogic task demands, so as to gradually approximate the
full complexity of real-world, target-task performance (Long & Crookes, 1993). With this
in mind, Robinson (2005) has made the following theoretical claims about the effects of
cumulative infl uences in the demands of tasks on cognitive processes thought to facilitate
SLA. Some of these claims are the focus of current research, while others remain issues
for future research.
Output
The fi rst of these claims is that increasing the cognitive demands of tasks contributing to
their relative complexity along resource-directing dimensions (e.g., from – to + intentional
reasoning demands) will push learners to greater accuracy and complexity of L2 production
in order to meet the consequently greater functional/communicative demands they
4 task-based learning: cognitive underpinnings
place on the learner. That is, greater effort at conceptualization will lead learners to develop
the L2 linguistic resources they have for expressing such conceptualizations. Some research
fi ndings support this claim (Robinson, 2007; Ishikawa, 2008). Related to this fi rst claim is
the prediction that increasing task demands will lead to a higher number of interactional
episodes (e.g., language-related episodes, clarifi cation requests, or recasts) which are known
to push second language development. Some studies have provided evidence of such a
claim (Robinson, 2007; Gilabert, Barón, & Llanes, 2009).
Uptake
The second claim is that cognitively complex tasks promote heightened attention to and
memory for input, so increasing learning from the input, and incorporation of forms made
salient in the input. So, for example, there should be more uptake of oral recasts on complex
tasks, compared to simpler tasks, or more use of written input provided to help
learners perform tasks. Some research fi ndings support this claim (Revesz, 2009; Baralt,
2010).
Memory
Related to this, the third claim is that on complex tasks there will be longer-term retention
of input provided (e.g., in the form of written prompts, or oral feedback) than on simpler
tasks. There are currently no studies that have addressed this claim.
Automaticity
Fourth, the inherent repetition involved in performing simple to complex sequences will
also lead to automaticity and effi cient scheduling of the components of complex L2
target-task performance, compared to target tasks performed without the benefi t of
such pedagogic task sequencing. This should be revealed in estimates of the fl uency with
which target tasks are performed following a sequence of increasingly complex pedagogic
tasks (as manifested by fewer incidents of self-repair, fewer hesitations, etc.), as well as in
criterion-referenced measures of the extent of successful target-task performance. There
are currently no studies that have addressed this claim.
Aptitudes
Fifth, individual differences in affective and cognitive abilities contributing to perceptions
of task diffi culty will increasingly differentiate learning and performance as tasks increase
in complexity. This is likely to be an intense area of future research, since it can reveal
much about the cognitive processing prerequisites for successful task-based learning and
performance, and since it will ultimately be desirable to match individual profi les in task
aptitudes to those conditions of task performance that learners are best suited to, in order
to optimally facilitate TBLT outcomes for learners. In line with this claim, Robinson (2007)
found that greater output processing anxiety led to less complex speech production on
complex tasks (with intentional reasoning demands), compared to those with lower output
anxiety, but these differences in output processing anxiety had no effect on complexity of
speech produced on simple tasks (without intentional reasoning demands). On the other
hand, examining the same task complexity differential, Baralt (2010) found that individual
differences in working memory capacity did not predict greater accuracy, fl uency or complexity
in performance on complex versus simpler task versions. Robinson (2010) describes
individual, task aptitude measures that could profi tably be used in future studies of this
issue.
task-based learning: cognitive underpinnings 5
SEE ALSO: Attention, Noticing, and Awareness in Second Language Acquisition;
Automatization, Skill Acquisition, and Practice in Second Language Acquisition; Incidental
Learning in Second Language Acquisition; Instructed Second Language Acquisition
References
Baralt, M. (2010). Task complexity, the cognition hypothesis and interaction in CMC and FTF environments
(Unpublished doctoral dissertation). Georgetown University, Washington DC.
Cadierno, T. (2008). Learning to talk about motion in a foreign language. In P. Robinson &
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New York, NY: Routledge.
Cadierno, T., & Robinson, P. (2009). Language typology, task complexity and the development
of L2 lexicalization patterns for describing motion events. Annual Review of Cognitive
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Ellis, R. (Ed.). (2005). Planning and second language task performance. Amsterdam, Netherlands:
John Benjamins.
Gilabert, R. (2007). The simultaneous manipulation along the planning time and +/- Here-and-
Now dimensions: Effects on oral L2 production. In M. Garcia Mayo (Ed.), Investigating tasks
in formal language learning, (pp. 44–68). Clevedon, England: Multilingual Matters.
Gilabert, R., Barón, J., & Llanes, M. A. (2009). Manipulating cognitive complexity across task
types and its impact on learners’ interaction during task performance. International Review
of Applied Linguistics, 47, 367–95.
Ishikawa, T. (2008). Task complexity, reasoning demands and second language speech production
(Unpublished doctoral dissertation). Aoyama Gakuin University, Department of English,
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Pica, T., Kanagy, R., & Falodun, J. (1993). Choosing and using communication tasks for second
language teaching and research. In G. Crookes & S. Gass (Eds.), Tasks in language learning:
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Prabhu, N. S. (1987). Second language pedagogy. Oxford, England: Oxford University Press.
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Robinson, P. (2010). Situating and distributing cognition across task demands: The SSARC model
of pedagogic task sequencing. In M. Putz & L. Sicola (Eds.), Cognitive processing in second
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6 task-based learning: cognitive underpinnings
Robinson, P., Ting, S., & Urwin, J. (1995). Investigating second language task complexity. RELC
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Schmidt, R. (2001). Attention. In P. Robinson (Ed.), Cognition and second language instruction
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Suggested Readings
Ellis, R. (2003). Task-based language learning and teaching. Oxford, England: Oxford University
Press.
Garcia Mayo, M. P. (Ed.). (2007). Investigating tasks in formal language learning. Clevedon, England:
Multilingual Matters.
Robinson, P. (Ed.). (2011). Task-based language learning. Oxford, England: Wiley-Blackwell.
Samuda, V., & Bygate, M. (2008). Tasks in second language learning. New York, NY: Palgrave
Macmillan.
Van den Branden, K., Bygate, M., & Norris, J. (Eds.). (2009). Task-based language teaching: A reader.
Amsterdam, Netherlands: John Benjamins.

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