Network analysis for modeling complex systems in SLA research

Abstract

Network analysis is a method used to explore the structural relationships between people or organizations, and more recently between psychological constructs. Network analysis is a novel technique that can be used to model psychological constructs that influence language learning as complex systems, with longitudinal data, or cross-sectional data. The majority of complex dynamic systems theory (CDST) research in the field of second language acquisition (SLA) to date has been time-intensive, with a focus on analyzing intraindividual variation with dense longitudinal data collection. The question of how to model systems from a structural perspective using relation-intensive methods is an underexplored dimension of CDST research in applied linguistics. To expand our research agenda, we highlight the potential that psychological networks have for studying individual differences in language learning. We provide two empirical examples of network models using cross-sectional datasets that are publicly available online. We believe that this methodology can complement time-intensive approaches and that it has the potential to contribute to the development of new dimensions of CDST research in applied linguistics.

Dynamic engagement: A longitudinal dual-process, reciprocal-effects model of teacher motivational practice and L2 student engagement

“…Though our own study was limited to 3 time points, future longitudinal research that increases the number of sampling instances (time-intensive) and investigates the structure of key relationships among individuals (relation-intensive) is well positioned to advance the field's understanding. The key strength of this design is that it views the language learning process by default as dynamic and complex (Freeborn et al, 2022)…”

Individual Differences in Bilingual Child Language Acquisition: A plunge into a Complex and Dynamic Network

“…Thus, building on the thorough review by Paradis (this issue), I propose that bilingual child language acquisition can benefit from multifaceted and interdisciplinary approaches attempting to model bilingual language development. One option to address the nature of the relationships between various child-internal and child-external factors is by adopting theories of change processes which have been used extensively in a variety of different disciplines, for example the Complex Dynamic System Theory (CDST) framework (Freeborn, Andringa, Lunansky & Rispens, 2022; Hiver, Al-Hoorie & Evans, 2021; Sun, Steinkrauss, van der Steen, Cox & de Bot, 2016). The CDST framework has been used for more than a decade to provide evidence on how second/foreign language acquisition unfolds in adults (Hiver et al, 2021).…”

“…Thus, building on the thorough keynote by Paradis (this issue), our field could adopt the CDST framework as a steppingstone to gain insights into the system’s growth across multiple languages, addressing potential interconnectedness and reciprocity between language skills and child-internal/child-external factors. Recently, modelling of a complex network of direct, indirect and reciprocal effects in bilinguals has been attempted (e.g., Freeborn et al, 2022; Gullifer & Titone, 2020; Kałamała, Chuderski, Szewczyk, Senderecka & Wodniecka, 2022; Sun, Cheong, Yen, Koh, Kwek & Tan, 2020). Adult bilingualism is concluded to arise from complex relationships between language history acquisition, language skills, and language use (Kałamała et al, 2022).…”

Spanish heritage language learners’ motivational profile in the postsecondary classroom: Insights from psychological network modeling

“…Another goal of this study was to further illustrate the potential of novel psychological network modeling techniques (e.g., Constantini et al, 2015; for advancing the quantitative researchability of complex systems in language-learning motivation and learner psychology more broadly (see Freeborn et al, 2022). Although it is widely acknowledged that motivation exhibits all the processes that characterize complex dynamic systems-including nonlinear, multiple, unstable, and continuously changing interactions among its many dimensions (e.g., Hiver & Larsen-Freeman, 2020;Larsen-Freeman & Cameron, 2008)-scholars have acknowledged the methodological and statistical challenges involved in researching such relational systems (see, e.g., Hiver & Al-Hoorie, 2019;Hiver & Papi, 2019).…”