Exploring Complex Dynamic Systems in Second Language Writing: A Review of Research Trends and the Role of Artificial Neural Network Modeling
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Abstract
This review investigates second language writing as a multifaceted, evolving phenomenon viewed through the lens of Complex Dynamic Systems Theory (CDST). Moving beyond linear and traditional cognitive approaches, the study conceptualizes L2 writing development as an emergent, non-linear process characterized by the interplay of linguistic, cognitive, affective, and social components. By reviewing foundational concepts of CDST and incorporating empirical studies and case examples—including the use of artificial neural networks—the work highlights how writing development is influenced by sensitivity to initial conditions, self-organization, and external contextual factors. The results suggest that L2 writing does not follow a predictable path but rather adapts dynamically to changing conditions, where attractors and emerging states capture critical aspects of the writing process. This reconceptualization offers significant implications for pedagogy, advocating for instructional practices that recognize and facilitate adaptive learning and reflective feedback. Overall, the manuscript provides a compelling argument for adopting a dynamic systems approach in researching and teaching L2 writing, paving the way for innovative, holistic models in language education.
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