Dynamic Systems Theory
Dynamic Systems Theory (DST), more recently rebranded as Complex Dynamic Systems Theory (CDST), is the application of complexity science to second language development. Introduced into SLA by Diane Larsen-Freeman in her 1997 Applied Linguistics article Chaos/Complexity Science and Second Language Acquisition, the framework treats language as a dynamic system rather than a static body of knowledge to be acquired in stages. It is less a substantive theory of how language is learned than a meta-theoretical reorientation of what learning is.
What Counts as a Dynamic System
A dynamic system, in this framework, is a set of interacting components whose behaviour over time emerges from their interactions rather than from any single component or top-down rule. Larsen-Freeman argues that language fits this definition. The properties she attributes to language as a dynamic system are unfamiliar to traditional SLA:
| Property | Implication for language development |
|---|---|
| Nonlinear | Small changes in input or context can produce large changes in performance, and vice versa |
| Sensitive to initial conditions | Two learners with similar starting points can diverge sharply over time |
| Open | The system constantly exchanges resources with its environment; no clean inside/outside boundary |
| Self-organising | Patterns emerge from local interactions, not from a central controller |
| Adaptive | The system reshapes itself in response to feedback from use |
| Contingent and emergent | What develops depends on the path taken, not just the starting and ending states |
Two summary principles capture the position: everything counts and everything is connected (the relational principle), and everything changes (the adaptive principle).
What It Changes About SLA
CDST does not replace specific SLA accounts so much as reframe what they are accounts of. The shifts are substantial:
- From product to process. Instead of asking what learners know at a given time, CDST asks how systems develop over time, with attention to variability rather than averages.
- From individual cognition to nested systems. A learner is one system inside a classroom system inside a curriculum system inside a community of speakers. Development cannot be cleanly isolated to any single level.
- From linear stages to attractor states. Interlanguage is reconceived as a system that settles into temporary stable patterns (attractors) and shifts to new attractors when conditions change. Fossilisation becomes a particularly stable attractor rather than a permanent endpoint.
- From group means to individual trajectories. Variability is not noise to be averaged out but data about how the system behaves under different conditions.
The framework sits comfortably alongside usage-based and emergentist accounts, with which it shares a commitment to language as an emergent phenomenon shaped by experience.
Methodological Implications
CDST has pushed SLA toward research designs that look very different from the cross-sectional, group-level study that dominated the field for decades.
- Dense longitudinal sampling. Tracking individual learners frequently over extended periods to capture variability and trajectory shifts.
- Time-series analysis. Statistical tools borrowed from physics and biology that capture pattern over time rather than mean differences.
- Mixed-methods case studies. Detailed individual portraits combined with quantitative measures, on the grounds that aggregating across learners obscures the very dynamics under study.
- Retrodictive rather than predictive modelling. Acknowledging that complex systems are often unpredictable in detail but interpretable in retrospect.
Criticisms
- Underspecified mechanisms. Critics argue that CDST describes properties of development without specifying the cognitive mechanisms that produce them. Pienemann and colleagues have argued that the framework risks becoming a vocabulary applied post hoc rather than a theory that generates testable predictions.
- Methodological burden. The longitudinal, individual-focused designs required by the framework are expensive and slow, and most published CDST work consists of single or small-N case studies.
- Borrowed metaphors. Some terms (chaos, attractor, phase transition) are imported from physics with looser meaning, and the looseness can obscure rather than clarify.
- Risk of irreducibility. If everything is connected to everything, theory construction becomes difficult; the framework can sit uneasily between rich description and predictive science.
Why It Matters for Teachers
CDST validates several intuitions experienced teachers already hold: that learners do not progress in lockstep, that performance fluctuates rather than rising monotonically, that small classroom decisions can have outsized effects, and that the same learner can look very different under different task conditions. The framework gives a vocabulary for describing what teachers see rather than overwriting it with averages.
References
- Larsen-Freeman, D. (1997). Chaos/complexity science and second language acquisition. Applied Linguistics, 18(2), 141–165.
- Larsen-Freeman, D., & Cameron, L. (2008). Complex Systems and Applied Linguistics. Oxford University Press.
- de Bot, K., Lowie, W., & Verspoor, M. (2007). A dynamic systems theory approach to second language acquisition. Bilingualism: Language and Cognition, 10(1), 7–21.
- Hiver, P., & Al-Hoorie, A. H. (2020). Research Methods for Complexity Theory in Applied Linguistics. Multilingual Matters.