A lexical network approach to second language development

Abstract

This study investigates lexical development in second language (L2) learning from the perspective of complex dynamic system theory (CDST) using a complex network method. Based on authentic written output texts from L2 Chinese learners of different proficiency levels and language backgrounds, we successfully differentiate between different proficiency levels using a bi-gram lexical network model at a corpus level. A more in-depth investigation reveals that when compared to traditional lexical complexity indices, such as average word length and hapax legomena percentage (though Guiraud proves to be a robust predictor), the lexical network indices, such as network density and network clusters, provide a more profound understanding of L2 proficiency distinctions and a more precise approximation of the target language. Moreover, our findings illuminate the consistent manifestation of complex network characteristics within L2 Chinese lexical networks across all proficiency levels. Additionally, word association features, encompassing more than just word frequency information, provide comprehensive properties of the interlanguage system, as supported by their information gain values. We argue that studies within the CDST framework should integrate both lexical frequency and lexical network features to gain a comprehensive understanding of L2 lexical development.

‘Making it possible’: the complex dynamics of university foreign language teacher agency for research in funding applications

“…Although CDST stems from Complex System Sciences (Larsen-Freeman 1997 ;Holland 2006), it has gained increasing attention in investigating the complexity and dynamics of development underlying social and cultural systems (Chen 2023). Described as living, open, and dynamic, complex dynamic systems consist of multiple levels of subsystems that interact with one another in a nonlinear way (Qi and Wang 2022).…”