The present study investigated the developmental patterns of Chinese EFL(AQ) learners’ oral language in terms of complexity and accuracy and looked into the dynamic interactions between them within the framework of Complex Dynamic Systems Theory (CDST). The data were analysed using dynamic analyses (moving min–max graphs, moving correlations and Monte Carlo Simulations). It was found that, firstly, at the group level, the general developmental trends of both complexity and accuracy showed improvements. Secondly, at the individual level, the developmental patterns were non-linear and dynamic with high degrees of variability, and individual language development was influenced by the initial states. Thirdly, the analyses revealed a complex interplay between complexity and accuracy, which gradually shifted from a clearly competitive relationship during the early stages to a supportive relationship in later stages. This shift in interaction shows that complexity goes hand in hand with accuracy, which corroborates the interconnectedness of subsystems as one of the major characteristics of CDST. The findings confirm the applicability of CDST approaches to L2 oral development and carry valuable implications for CDST theory development and oral language teaching.

The present study adopted a novel parallel-process growth mixture modeling (GMM) technique to research the adaptive interaction between foreign language learners’ learning motivation and emotions, with a view to advancing our understanding of how language learning motivation and emotions (enjoyment and anxiety) adaptively interact with each other over time. The present study, situated in the Chinese English as a foreign language (EFL) learning context, collected learning motivation and emotion data from 176 Chinese EFL learners over a period of two semesters (12 months). The GMM technique adopted in the study identified three developmental profiles of motivation and two of emotions, respectively. The study further distilled salient patterns of motivation–emotion interaction over time, patterns significant for designing and implementing pedagogical interventions for motivation enhancement. The parallel-process GMM technique was also proven to be a useful approach to parsing learner variety and learning heterogeneity, efficiently summarizing the complex, dynamic processes of motivation and emotion development.

Based on the theoretical framework of the L2 Motivational Self System (L2MSS), the present study aims to make a methodological contribution to L2 motivation research. With the application of a novel growth mixture modeling (GMM) technique, the study depicted developmental trajectories of three motivational variables (ideal L2 self, ought-to L2 self, and L2 learning experience) of 176 Chinese tertiary-level students over a period of two semesters. Results showed two to three salient classes with typical developmental patterns for the three motivational variables respectively, with which the study gained fresh insights into the developmental processes of motivation beyond the individual level. Our study further established three main multivariate profiles of motivation characterized by a distinct combination of different motivational variables. The findings extend our understanding of motivational dynamics, providing a nuanced picture of emergent motivational trajectories systemically. Additionally, GMM has shown to be an effective and applicable method for the identification of salient patterns in motivation development, which leads to practical implications.