Dynamics of Language Learning Motivation and Emotions: A Parallel-Process Growth Mixture Modeling Approach

Abstract

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.