Zoltn Drnyei (University of Nottingham) Instructed second
language acquisition from a complex dynamic systems perspective
Slide 2 The behaviour of a complex system is not completely random,
but neither is it wholly predictable. (Larsen-Freeman &
Cameron, 2008a, p. 75) Slide 3 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
Slide 4 Slide 5 The systems behaviour is: Complex to the extent of
being unpredictable. Nonlinear no simple linear, cause-effect
relationships. The systems 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. Slide 6 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. Slide 7 Three
potential research strategies Focus on identifying strong
attractor-governed phenomena Focus on identifying typical
conglomerates Focus on identifying typical dynamic outcome patterns
Slide 8 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. Slide 9 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. Slide 10
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 Slide 11 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) Slide 12 Slide 13 Slide 14 Slide 15 Slide 16
MotivationCognitionEmotionBehaviour Alex (M)Motivated for general
subject & also in English Less able Low English proficiency
CheerfulAsk lots of questions Clumsy, inflexible Rigid, active need
clear guidelines Mary (F)Hardworking Motivated Self learning, Will
learn autonomously Slow learnerEmpathy, crying after receiving a
test paper Quiet, obedient, rigid, responsible, fossilized in their
learning strategies, Rex (M/F)Learn only when pushed Does not do
his homework seriously Mediocre in his studiesNeutral in emotional,
gentle, lucid Obedient, attention seeking, would try to make some
jokes in class, Funny Saki (F)Has intrinsic interests in learning
English Serious in learning Has good memory Has acquired various
learning strategies Emotionally stable, Confident Detail-minded,
organized, independent in everything, capable of handling
everything, helpful, Well-behaved Chris/Kris (M/F)MotivatedHigh
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 Slide 17
Identifying students who are typical of the prototypes Slide 18
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) Slide 19 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 learners development that culminated in their
specific system outcome the systems signature dynamic that explains
why a particular student ended up in a particular attractor state
(i.e. learner type) and not in another. Slide 20 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. Slide 21
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. Slide 22 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. Slide 23 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.
