Instructed second language acquisition from a complex dynamic systems perspective

1. Instructed second language acquisition from a complex dynamic systems perspective Zoltán Dörnyei (University of Nottingham)

2. The behaviour of a complex system is not completely random, but neither is it wholly predictable. (Larsen-Freeman & Cameron, 2008a, p. 75)

3. What is a ‘complex dynamic system’? • A system can be considered dynamic if it has: (a) two or more elementsthat are (b)interlinkedwith each other, and which (c) also change in time. • These simple conditions can result in highly complex system behaviour. • Simplest example: the ‘double pendulum’

5. 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 theoryand emergentism.

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.

7. Three potential research strategies • Focus on identifying strong attractor-governed phenomena • Focus on identifying typical conglomerates • Focus on identifying typical dynamic outcome patterns

8. ‘Retrodictive qualitative modelling’ • In any domain: limited rangeof system outcome patterns (e.g. typical types of behaviours/learners/ achievement). • This is the essence of self-organisation. • By identifying the main emerging system prototypeswe can trace backthe 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.

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 interviewingwe aim to assemble a qualitative modelof the main system components and development patterns.

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

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)

17. Identifying students who are typical of the prototypes

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)

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 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.

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.

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.

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.

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 withoutreducing those systems to simplistic representations. • Thus, retrodictive qualitative modelling is an attempt to essentialise rather than simplify.