Instructed second language acquisition from a
complex dynamic systems perspective
Zoltán Dörnyei (University of Nottingham)
The behaviour of a complex system is not
completely random, but neither is it wholly
predictable.
(Larsen-Freeman & Cameron, 2008a, p. 75)
What is a ‘complex dynamic system’?
 A system can be considered dynamic if it has:
(a) two or more elements that are
(b) interlinked with each other, and which
(c) also change in time.
 These simple conditions can result in highly
complex system behaviour.
 Simplest example: the ‘double pendulum’
The system’s behaviour is:
 Complex to the extent of being unpredictable.
 Nonlinear – no simple linear, cause-effect relationships.
 The system’s behavioural outcome depends on the
overall constellation of the system components – how
all the relevant factors work together.
 Discussed by four interrelated theories: complexity
theory, dynamic systems theory, chaos theory and
emergentism.
Difficulty of researching complex
dynamic systems
 Most common research paradigms in the social sciences
tend to examine variables in relative isolation.
 Most established statistical procedures (e.g. correlation
analysis or structural equation modelling) are based on
linear relationships.
 Quantitative research methodology in general is
problematic, because it is based on group averages,
eliminating idiosyncratic details.
Three potential research strategies
 Focus on identifying strong attractor-governed
phenomena
 Focus on identifying typical conglomerates
 Focus on identifying typical dynamic outcome
patterns
‘Retrodictive qualitative modelling’
 In any domain: limited range of system outcome
patterns (e.g. typical types of behaviours/learners/
achievement).
 This is the essence of self-organisation.
 By identifying the main emerging system prototypes we
can trace back the reasons why certain components of
the system ended up with one outcome option and not
another.
 Thus, we do retro-diction rather than pre-diction.
Illustration of RQM
 Dynamic system: language classroom
 System outcome options: learner prototypes
 Research objective: to understand what kind of a
conglomeration of learner factors and classroom
processes “pushed” a learner into the particular
prototype he/she embodies.
 Through in-depth interviewing we aim to
assemble a qualitative model of the main
system components and development patterns.
Three-step research template
 Step 1: Identifying salient student types in the
classroom
 Step 2: Identifying students who are typical of the
prototypes and conducting interviews with them
 Step 3: Identifying the most salient system components
and the signature dynamic of each system
Identifying salient student types in the
classroom
Possible sources of information:
 classroom observation
 interviews with teachers and students
 focus group discussions with teachers and students
 questionnaires (e.g. cluster analysis of the data)
Motivation
Cognition
Emotion
Behaviour
Ask lots of questions
Clumsy, inflexible
Rigid, active need clear guidelines
Quiet, obedient, rigid, responsible,
fossilized in their learning
strategies,
Alex (M)
Motivated for general subject & Less able
also in English
Low English proficiency
Cheerful
Mary (F)
Hardworking
Motivated
Self learning, Will learn
autonomously
Learn only when pushed
Does not do his homework
seriously
Slow learner
Empathy, crying after
receiving a test paper
Mediocre in his studies
Neutral in emotional,
gentle, lucid
Saki (F)
Has intrinsic interests in
learning English
Serious in learning
Has good memory
Emotionally stable,
Has acquired various learning Confident
strategies
Chris/Kris (M/F)
Motivated
High language ability
Has a lot of expectations for
teachers and themselves
Worry a lot, not cheerful in
general
Negative in their way of
thinking
Loves comparing with others,
Likes competition
Helen (F)
Not hardworking
Not motivated
Low in language ability
especially when compared
with students in a good class
Reserved
Not happy
Not confident in English or
any other subjects
Proud of being in an elite
class
Has interiority complex
Problematic in teachers’ eyes
Her homework is messy
Danny (M)
Not hardworking
Not motivated
withdrawn
Low in language ability even
in a regular English class
Reserved
Not happy
Not confident in English or
any other subjects
Proud of being in an elite
class
Has interiority complex
Problematic in teachers’ eyes
His homework is messy
Rex (M/F)
Obedient, attention seeking,
would try to make some jokes in
class,
Funny
Detail-minded, organized,
independent in everything,
capable of handling everything,
helpful,
Well-behaved
Identifying students who are typical of
the prototypes
Interviewing prototypical students
Examples of factors addressed in the interviews:
 attitudes towards L2 learning; L2 learning habits and
styles; self-appraisal of language aptitude
 L2 learning goals and desires; vision of being future L2
speakers
 external influences such as family and friends; career
considerations
 experience of learning the L2 at school; various
situation-specific ‘pushes’ and ‘pulls’; the impact of the
L2 teacher(s)
Identifying salient system components
and signature dynamics
The interviews allow us to identify:
 The most salient factors affecting the students’ learning
behaviour – the main components of the qualitative
system model.
 The trajectory of each learner’s development that
culminated in their specific system outcome – the
system’s ‘signature dynamic’ that explains why a
particular student ended up in a particular attractor state
(i.e. learner type) and not in another.
In sum...
A retrodictive qualitative model portrays
how the salient system components interact
to create a unique development path (or
‘signature dynamic’)
that leads the learner to a specific system
outcome
as opposed to other possible outcomes.
Interpreting the findings of RQM
 In conventional research, once we arrive at an
explanation of a phenomenon, we use this to make
predictions in the form of testable hypotheses.
 BUT: In dynamic systems approaches expectations that
are based on prior experiences have only limited
predictive power.
 In dynamic systems what has happened might
not happen again because of the changes in
the context and in other system parameters.
Interpreting the findings of RQM
BUT:
 The essence of RQM is that while we cannot generalise
any signature dynamics from one situation to another,
the identified patterns are fundamental enough to
be useful in understanding the dynamics of a range
of other situations.
 This is the quintessence of qualitative
research logic.
Conclusion
 Retrodictive qualitative modelling offers a research
template for deriving essential dynamic moves from
idiosyncratic situations.
 The process aims at generating abstractions that help to
describe how social systems work without reducing
those systems to simplistic representations.
 Thus, retrodictive qualitative modelling is an
attempt to essentialise rather than simplify.