2.7 Dynamic Systems Theory
2.7.2 DST’s Origin and Development in SLA/FLA
DST has its origin in Newtonian mechanics, which was associated with a set of
physical laws in Kinematics describing the relationship between the motion of
objects and the forces determining them (French, 1971). Dynamic Systems
Theory (DST)was defined thus: ‘an evolution rule that defines a trajectory as a function of a single parameter (time) on a set of states (the phase space) is a
dynamical system’ (Meiss, 2007, p. 105). Meiss (2007) pointed out that one of
the important features of DST was its evolution. Neither a simple system nor a
trajectory of the moon under the influence of the sun, the earth and other planets.
Dörnyei (2014) argued that a system could be considered as complex or dynamic
if it had three key features, ‘if (a) it has at least two or more elements that are (b)
interlinked with each other but which also (c) change independently over time’ (p.
81).
Larsen-Freeman (1997) was the first person to apply DST to SLA investigations.
She provided a possible answer to the question, ‘Can DST which was normally
used in the natural sciences field be applied in the social science field?’ The
simple answer is ‘yes’, because the dynamic system operated analogously in both
natural science and social science fields in that the subsystems were embedded
hierarchically at different levels in nature. Moreover, de Botet al.(2007) argued
that social science researchers may wish to apply metaphors to translate abstract
DST concepts in natural science field into tangible social science concepts. In
fact, DST has been flourishing in many areas: from economics to infectious
diseases; and from meteorology to the solution of practical problems, such as
heart-rate control, and oil drilling.
Although DST is a relatively novel research paradigm in SLA explorations (van
Geert & Steenbeek, 2005), more researchers have shown increasing interest in
challenging this field. This may be because ‘once we began to view development
from a dynamic and selectionist approach, we found the ideas so powerful that
we could never go back to other ways of thinking. Every paper we read, every
talk we heard, every new bit of data from our labs took on new meaning. We
planned experiments differently and interpreted old experiments from a fresh
The terms complex and dynamic were normally used as synonyms (Dörnyei,
2014). It is admitted that more scholars have tended to integrate theories which
are defined as complex, such as Complex Adaptive System (CAS), and dynamic,
such as Dynamic Systems Theory (DST), rather than separating them, because of
their overlapping characteristics, for example, self-organisation and non-linearity.
Several instances can support this trend. For example, Larsen-Freeman (2014)
used the term Complex Dynamic Systems Theory (CDST); de Bot (1996)
preferred the term Complexity Theory (CT); Ellis and Larsen-Freeman (2006)
used the term Complex Adaptive System (CAS); Dörnyei (2014) used the term
Dynamic Systems Theory (DST), etc. de Botet al.,(2007) argued that the
different names being used to describe ‘how the interacting parts of a complex
system give rise to the system’s collective behaviour and how such a system
simultaneously interacts with its environment’ (Larsen-Freeman & Cameron,
2008, p. 1) usually referred to similar approaches.
Considering the development of DST’s applications in SLA/ FLA, several
researchers focus on this topic in particular. Larsen-Freeman is a pioneer
proposing the use of Chaos/Complexity Theory in SLA studies in 1997 and
developing it onwards. She also developed a 16-step procedure with Lynn
Cameron in 2008 to suggest how a complexity thought modelling can be used as
an ‘analogical model’ (p. 40) to investigate a system. In 2012, she additionally
elaborated 12 general principles for transdisciplinary researches. In 2014, she
outlined ten lessons from Complex Dynamic Systems Theory based on her own
de Bot specialised in using ‘quantitative methods’ to establish computational
models across different timescales. de Bot (2008) also introduced Bak's (1996)
metaphor to illustrate an important feature in DST, the concept of attractor state.
He pointed out that a critical point as an attractor in a dynamic system can be
termed as self-organised criticality (SOC). SOC can be metaphorically described
by Bak's (1996) idea of a‘sandpile’. As more and more grains of sand drop onto
a table, the cone-shaped pile grows steeper and finally causes avalanches after
reaching a critical level. de Bot suggested that the relationship between the
language input and outcome can be equated to that of the sand and the
avalanches. Subsequently, he argued that the explorations through the lens of
DST had now moved ‘from a purely metaphorical use of DST notions to the use
of specific methods’ (de Bot, 2012, p. 92).
MacIntyre and Gregersen focused on the investigations of the motivational
dynamics in SLA/FLA, particularly on the self. For example, Gregersen and
MacIntyre (2014) argued that a learner’s psychological desire to reduce the
discrepancy between different selves will enhance his/her motivation to learn.
Similarly, Mercer also specialised in L2 motivation researches and particularly
focused on the concept of the self. Mercer’s (2014) Nested Systems of the Self
was introduced in the previous section (section 2.5.2.4). She developed a
conceptual framework to integrate differing self constructs in one holistic model
through the lens of DST. On the other hand, Henry elaborated the dynamism of
the possible selves through three dynamic processes, namely, ‘the up- and
downward revisions of the Ideal L2 Self’; ‘changes triggered by interaction with
at the heart of L2 selves and in the availability and accessibility of the Ideal L2
Self’ (Henry, 2014, p. 92).
Dörnyei also focused on the exploration of motivational dynamics and the Self.
As was presented in the previous section, his L2 Motivational Self System
(Section 2.5.2.3) was considered as ‘the most influential self-specific motivation
construct in SLA’ (Dörnyei & Ryan, 2015, p. 86). He tried to reconceptualise L2
Individual Differences (ID) in 2009 through the lens of DST and further claimed
that it would be more fruitful if exploring the L2 Motivational Self System across
lifespan, a developmental timescale. Dörnyei, with Ibrahim and Muir in 2014
reported the use of ‘Directed Motivational Currents (DMC)’, to regulate DST
through motivational surges. Later, Dörnyei, together with Henry and Muir,
elaborated DMC and illustrated how DMC could be used as frameworks for
focused interventions in 2016. On the other hand, Dörnyei (2014) also proposed
three research strategies as‘qualitative methods’ for DST researches in
SLA/FLA. They are strategies focusing on ‘identifying strong attractor-governed
phenomena’; ‘identifying typical attractor conglomerates’; ‘identifying and
analysing typical dynamic outcome patterns’ (p. 84). These strategies will be
further discussed in Chapter Five.
Ushioda focused on how social science researchers had applied DST in their
studies, particularly on using ‘qualitative methods’. As DST initially developed
as a mathematical tool for ‘quantitative analysis’, one concern was inevitably
asked by social science researchers: ‘How can DST be useful for qualitative
(Ushioda, 2009, p. 215). Because traditional research frameworks of Individual
Differences (ID) in SLA focused on exploring individuals ‘with the shared
characteristics of particular types’, rather than ‘with the unique characteristics of
particular individuals’ (Ushioda, 2009, p. 215) and context was normally
separated off as a factor external to learners. Ushioda (2014) shifted the
traditional views through the lens of DST and integrated context and learner as
dynamic sub-systems within the learner rather than separating them. Furthermore,
qualitative researchers could investigate the systemic elements, such as affective
characteristics or linguistic competence, ‘with which the focal elements interact
and co-adapt’ (Ushioda, 2014, p. 51) within the language learner.
In the Chinese education system context, few studies have focused on the
application of DST in FLA. Zheng attempted to apply DST in FLA and
established The Dynamic Model of Foreign Vocabulary Development in 2012.
Zheng (2012) conducted a one-year longitudinal study with intervals of four
months. Eight Chinese learners of English from one Chinese university
participated in three semi-structured interviews and seven quantitative
experimental tests for this multiple-case research. Zheng (2012) explored the
learner’s vocabulary development at both Macro-level, reporting the controlled
productive vocabulary size and free productive vocabulary use, and Micro-level,
reporting the learner’s knowledge of the paradigmatic and syntactic features of
high-frequency lexical items. Furthermore, she argued that the model represented
the nestedness of a dynamic system with interactions between great values in
This section brings together several main researchers’ studies dealing with a
variety of aspects of DST’s application in SLA/ FLA. In the following section,
more studies will be presented, especially in terms of DST’s key characteristics
and the way in which abstract DST concepts from the natural science field are
translated into tangible SLA/FLA terms which are acceptable to social science
researchers.