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Centrality plots for the L2MSS network. Note: Centrality plots for the network model of the L2MSS. Centrality measures are shown as standardized z-scores. The raw centrality indices can be found in the online Supplementary Materials.
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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....
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Context 1
... used 5,000 samples of the nonparametric bootstrap to assess the variability of the edge-weights. This step should always be performed ( Epskamp et al., 2018a) as any interpretation of the network becomes limited if the network is unstable (Burger et al., 2022). The results show a good overlap between the estimated model and the bootstrapped edge-weights, indicating that the network of Figure 1 is stable. ...
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... results are presented in the supplementary materials. Based on the centrality indices (see Figure 2), ought-to L2 self 2 is the most central component in the network model in Figure 1 in terms of node strength, followed by intended effort 5. The questionnaire items that correspond to these components are "Studying English is important to me to gain the approval of my peers" and "English would be still important to me in the future even if I failed in my English course." ...
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... third measure of centrality, betweenness, refers to how well one node connects other nodes together; nodes with high betweenness lie on the shortest path between pairs of nodes. As shown in Figure 2, the node with the highest betweenness is intended effort 5, followed by ought-to L2 self 5 and ideal L2 self 1. ...
Citations
... It should be noted that the dynamic approach in the field of applied linguistics has mainly adopted three types of methods for the exploration of language-domain-specific variables. These methods include timeintensive, relation-intensive, and time-relation-intensive approaches (Freeborn et al., 2022). Most CDST-based studies on emotions in the realm of applied linguistics have used time-intensive methods but LPA adopts a relation-intensive method for the exploration of emotions with a focus on the relationships among latent profiles of emotional variables. ...
Exploring L2 affective variables requires innovative research analytic approaches that can adequately address the complexity and dynamicity of the variables. Not always can the normality of distribution and linearity of relationships be assumed. Neither can the homogeneity of variance be always met. Latent profile analysis (LPA) is suggested to deal with the heterogeneity of data and non-linear relationships. It allows for hypothesis testing and model testing to tackle the ergodicity issue in second language acquisition (SLA) research from a person-centered approach. LPA, which primarily serves to classify a population or sample into several subgroups , can be effectively employed in SLA research to classify L2 teachers or learners in terms of the different positive and negative emotions (e.g., enjoyment, boredom, anxiety, etc.) they experience while learning a foreign or second language. A classroom-based L2 learning experience occurs interactively with several personal and contextual variables involved. The relationship between any single affective variable can hardly be conceived as linear. Thus, LPA holds promises for dealing with these non-linear relations and provides insightful information about the profiles of learners or teachers concerning a particular affective variable. The purpose of this conceptual analysis is to provide a review of the basic tenets of LPA and to explain how it can contribute to the exploration of emotional variables in SLA. By identifying distinct emotional profiles, the study offers guidance on tailoring instructional strategies to address the specific emotional needs of language learners, thereby enhancing the effectiveness of language acquisition interventions.
... Despite these efforts, no solid research has been conducted on the behavioral and cognitive aspects of language learners in artificial intelligence language learning. Filling this gap and heeding those recommendations in the age of artificial intelligence in education, which has gained sudden popularity worldwide, is essential because language learners' behavioral (e.g., self-regulation), cognitive (e.g., attention), and effective (e.g., emotion) domains are interconnected, and interdependent systems significantly impact the process of language learning (Freeborn et al., 2022;Larsen-Freeman & Cameron, 2008;Oxford, 2016). ...
... However, studies have already explored this role in traditional (Kormos & Csizér, 2014;Tseng et al., 2017) and digital language learning, including online (Zheng et al., 2018) and LMOOC (Rahimi & Cheraghi, 2022). It's important to look into this because, as complex dynamic systems show, language learners' cognitive, affective, and behavioral domains are all connected in every language learning situation, and their behavior changes depending on the situation (Freeborn et al., 2022;Mercer, 2018;Oxford, 2016). Scholars have recently developed an online motivational self-system for language learners, and our close review reveals that while traditional language learning (Thorsen et al., 2017), ERLL (Rahimi & Mosalli, 2024), and online language learning (Smith et al., 2020) have distinguished the two new factors of ideal L2-self and current L2-self from each other, personalized language learning has not yet explored them. ...
... Considering that ChatGPT enables them to meet both their current and future personal, as well as academic, objectives and pass their criteria, which cannot be accomplished simultaneously in previous language learning contexts, this increased their self-regulation skills to select their objects, evaluate their progress, and seek assistance from ChatGPT in contrast to other language learning contexts. At last, by exploring the role of personalized motivational self-systems in shaping language learners' personalized self-regulation, we provide a new interdependent system specialized to ChatGPT-assisted language learning to the current complex dynamic systems (Freeborn et al., 2022;Mercer, 2018;Oxford, 2016aOxford, , 2016b. Several practical implications may be apparent from a micro, meso, and macro perspective. ...
Currently, chatbots powered by artificial intelligence (AI) have gained considerable attention due to their ability to provide personalized language learning (PLL) for learners. In this regard, recent studies have extensively explored learners' emotional aspects, such as their attitudes and acceptance of personalized language learning in chatbots. It is, however, unclear what factors might determine their cognitive behaviors in such a personalized language learning environment, particularly their self-regulation. To fill the gap, the researchers collected data from 133 Iranian EFL learners who had personalized language learning through ChatGPT in their language learning institute and answered our questionnaire that tapped on their personalized L2 motivational self-system (PEL2MSS) and their personalized self-regulation (PESRL). The researchers analyzed the empirical data using a hybrid SEM-artificial neural network (SEM-ANN), in contrast to previous literature that primarily relied on structural equation modeling (SEM). The results showed that ChatGPT significantly responded to language learners' current L2-self and their ought to L2-self to pass their obligation, and metrics to reach their goals resulted in seeking more assistance from ChatGPT and evaluating their language learning progress with it. Moreover, the sign of digital self-authenticity was also discovered by the researchers, where learners dedicated more motivation to learn language with ChatGPT in comparison with their previous language learning environments, which culminated in having more self-evaluation, goal-setting, and daily academic schedule to learn language with ChatGPT. Additionally, the ANN analysis supported the linear findings of the PLS-SEM by showing that language learners' current L2-self, digital self-authenticity, and ought to L2-self were the most significant motivational factors affecting their PESRL. Based on these findings, a new conceptual framework for the PLL was developed in the literature, and the research view was shifted from covering language learners' emotional aspects to their cognitive aspects in this environment. Thus we recommend that language teachers should avoid seeing ChatGPT as a tool that learners use for cheating; rather, it can be used as a co-teacher outside of the classroom to help students cover their present language learning needs, which might not be covered in the classroom due to the time restriction.
... For example, Ashari [8] proposed an innovative prediction method that significantly improves the accuracy of predicting student grades through in-depth analysis of students' interactive characteristics and the similarity between questions in interactive online question banks. Freeborn et al. [9] used five ML algorithms, including naive Bayes, multilayer perceptron decision tree, logistic regression, and support vector machine, combined with a large amount of LMS log information, to achieve early prediction of student performance. Yan et al. [10] took student behavior as the starting point, conducted a time feature similarity survey, and combined different clustering algorithms to divide MOOC students into groups, providing strong support for teachers to grasp student behavior trends. ...
Language is not only the fundamental way of human communication, but also a key tool for promoting social progress and individual cognitive development. In a diverse global environment, the ability to master a second language is particularly important. Among them, as an international lingua franca, the acquisition process and effects of English have become a research hotspot in the interdisciplinary fields of linguistics, education, and even computer science. This article delves into the psychological mechanisms underlying the acquisition of English as a second language, including multiple dimensions such as emotional factors and socio-cultural adaptation, with the aim of understanding how learners can effectively construct language knowledge systems in different contexts. On this basis, this article innovatively proposes an English learning performance prediction model based on machine learning (ML). This model analyzes multiple sources of information such as learners' learning habits and online learning behavior data, and uses advanced algorithms to predict their English learning effectiveness, providing scientific basis for the development of personalized teaching plans. The experimental results show that the model can accurately predict the improvement of learners' English proficiency within a certain period of time, especially demonstrating significant advantages in identifying learning bottlenecks and potential areas.
... As recommended by Gimeno-Sanz (2015), and Colpaert (2020), CALL and English language teaching will continue to advance as technological advancements continue, and scholars should develop innovative methods to teach English with technology in both practical and theoretical senses. Furthermore, language learners' psychological factors are contextually specific, and they act independently, as well as correlate to each other, according to dynamic complex systems (Freeborn et al., 2022;Larsen-Freeman & Cameron, 2008;Mercer, 2018;Oxford, 2016aOxford, , 2016bRahimi et al., in press). This co-correlation in online language learning, particularly their online self-regulation and deep and surface approaches to this learning environment, has not been investigated. ...
... Although there are a variety of definitions for self-regulation, it is widely recognized as a multi-dimensional and process-oriented process (Dai, 2024;Dornyei & Ryan, 2015;Li et al., 2024;Rahimi et al., in press;Rahimi & Cheraghi, 2022;Zimmerman & Kitsantas, 2014). Additionally, it constitutes a critical psychological, behavioral, and cognitive component of language learners' complex dynamic systems in SLA (Freeborn et al., 2022;Larsen-Freeman & Cameron, 2008;Mercer, 2018;Oxford, 2016aOxford, , 2016bRahimi et al., in press). As defined by Zimmerman (2000), selfregulation refers to self-derive thinking, feelings, and activities that are planned accordingly and tailored to personal goals and in response to the rapid growth of online learning, which requires self-directed learners, Barnard et al. (2009) designed the Online Self-Regulated Learning Questionnaire (OSLQ) to examine learners' online selfregulation by considering six factors, namely goal-setting, time-management, task strategies, environment-structuring, help-seeking, and self-evaluation. ...
... Accordingly, this study evaluates the factorial structure of SAL in relation to language learning and introduces a new conceptual model to the language learning literature and psycholinguistics. More importantly, the process of learning a second language is dynamic, meaning that language learners need to use a variety of cognitive, affective (e.g., emotion), and behavioral factors (e.g., self-regulation) together, which are interdependent and interrelated in a complex dynamic system in SLA (Freeborn et al., 2022;Larsen-Freeman & Cameron, 2008;Mercer, 2018;Oxford, 2016aOxford, , 2016b. To the best of our knowledge, the complex dynamic system has not considered students' deep and surface approaches as behavioral factors, nor explored their relationship with cognitive, affective, and behavioral factors in language learning. ...
Students' approaches to online technologies (SAOLT) have garnered significant support among scholars from a
variety of fields, particularly in the field of science, technology, engineering, and mathematics education (STEM).
The humanistic field needs to explore learners' approaches to target learning contexts, especially in the fields of
computer-assisted language learning (CALL) and psycholinguistics. To fill this gap, the researchers explored 686
Iranian high school EFL learners' approaches to online language learning (OLL) with regard to their pedagogical,
technical, and peer support, as well as the mediation role of online self-regulation. The partial least squares
modeling approach's (PLS-SEM) reflective analysis validated the factorial structure of the conceptual study
framework in online language learning and secondary education. The formative model found that instructional
and peer support positively impacted language learners' deep and surface approaches to online language
learning, thereby maximizing and minimizing their online presence and meaningful language learning.
Furthermore, the mediation analysis revealed the significant moderating influence of learners' online selfregulation, which shifted the correlations from relating both learners' perceived support to their surface and
deep learning approaches to only relating to the deep learning approach. Consequently, this study has pedagogical implications as it introduces a new conceptual framework to the CALL and psycholinguistic domains,
specifically incorporating a new psychological factor related to language learners' complex dynamic systems,
known as Language Learners' Approaches to Online Language Learning (OLLA). To do so, the researchers suggested that instructors should design more practical activities for theoretical subjects such as English language
learning in online learning and shift online teaching from teacher-centered to peer-centered so as to foster their
learners' deep learning approach to online language learning.
... For example, while biology clarifies the dynamics of ecosystems and biological processes, in social sciences, complex systems theory can assist in understanding collective behavior and social networks. Integrating several disciplines promotes a better knowledge of complicated systems and their behaviors, resulting in creative ideas to address challenging issues [19,20]. ...
This review explores the incorporation of complex systems theory into predictive analytics in the e-commerce sector, particularly emphasizing recent advancements in business management. By analyzing the intersection of these two domains, the review emphasizes the potential of complex systems models—including agent-based modeling and network theory—to improve the precision and efficacy of predictive analytics. It will provide a comprehensive overview of the applications of emergent predictive analytics techniques and tools, including real-time data analysis and machine learning, in inventory optimization, dynamic pricing, and personalization of customer experiences. In addition, this review will suggest future research directions to advance the discipline and address the technical, ethical, and practical challenges encountered during this integration phase.
... Building on the foundational principles established by meteorology, CDST has been adapted and applied to the field of SLA by researchers like Diane Larsen-Freeman (Larsen-Freeman, 1997). Under this framework, language is perceived as a complex dynamic system with both static breadth and dynamic evolution (Freeborn et al., 2023). This epistemological understanding has led to innovative research orientations, particularly in the study of affective variables in language learning, the impact of positive psychology on language acquisition, and the exploration of code-switching practices among learners (see for a brief reference, Larsen-Freeman, 2019). ...
In current research, emotions in language use situations are often examined only at their starting and ending points, akin to observing the beginning and end of a wave, while neglecting their complex fluctuations in between. To fully comprehend the dynamics of emotions in language use situations, it is essential to delve into their intricate unfolding throughout their progression. This is particularly critical in the context of Second Language Acquisition (SLA), where emotional dynamics can significantly influence learning outcomes and proficiency. Drawing on existing empirical research and theories, we propose a novel interpretation rooted in complex dynamic systems theory (CDST) to elucidate the dynamic nature of emotions in language use situations. Furthermore, we suggest methodologies for capturing the complete dynamics of emotional and language behaviours, including an analysis of their dynamic interrelationships. By embracing a dynamic perspective, we can advance our understanding of interplay between emotions and language behaviours from epistemological theory to methodology and analysis, paving the way for future research in this field.
... In recent years, researchers generally agree that foreign language learning reflects a complex, dynamic, and multidimensional process that is shaped by multiple factors, involving the learning environment, emotions, and cognitive factors, as well as their complex interactions (Freeborn et al., 2023;Freeman & Cameron, 2008). While studies have acknowledged the role of academic buoyancy in enhancing learning outcomes, there is a dearth of research on the interaction among academic buoyancy, emotions, 1 . ...
... Most methods in SLA, such as variable-based correlation analysis may not be sufficient to capture the complexity of psychological phenomena (Freeborn et al., 2023). ...
... In contrast, psychological network analysis allows researchers to examine interconnections among psychological constructs, offering a finer-grained mechanism of psychometric variables (Freeborn et al., 2023). Therefore, this study will explore the interplay among academic buoyancy, emotions, and engagement within online foreign language contexts through the integration of structural equation modeling and psychological network analysis. ...
Academic buoyancy is a positive trait that has recently received extensive attention. Given the positive psychology turn in SLA, exploring interactions among academic buoyancy, emotions, and online English learning engagement (OELE) is crucial for students’ growth. However, their relationships remain under-investigated. Therefore, we combined structural equation modeling (SEM) with psychological network analysis (PNA) to elaborate the relationships among these variables within 442 Chinese EFL learners. SEM indicated that buoyancy significantly positively predicted enjoyment and OELE, and negatively predicted anxiety and boredom. Enjoyment significantly positively predicted OELE, whereas anxiety and boredom negatively predicted OELE. Furthermore, anxiety, enjoyment, and boredom significantly mediated the relationship between academic buoyancy and OELE. PNA showed that engagement occupies the central position in the network. Academic buoyancy serves as a bridge, connecting emotions and engagement. The current study is committed to providing novel insights into research methods and online instructional design for foreign language teaching and learning.
... While this approach provides valuable insights into the internal connections among variables, it is important to interpret these results cautiously. Network analysis is still a relatively new and evolving methodology, and further validation and replication studies are needed to confirm the stability and generalizability of the identified network structure (Freeborn et al., 2023). ...
Existing research has explored the connection between perceived usefulness and growth mindset, but there are still gaps in our understanding of how the perceived usefulness of second language learning apps (PU-L2LA) relates to L2 learner's language mindset (LM). Our study seeks to investigate the serial mediating role of flow and motivation intensity in the association between PU-L2LA and LM, as well as the network-level interaction between flow and motivation intensity (MI). This is a cross-sectional study conducted with a convenience sample of 524 Chinese university students, aiming to assess PU-L2LA, flow, MI, and LM. The results of the mediation analyses reveal that flow and MI play sequential mediating roles in linking PU-L2LA to LM. Furthermore, through network analysis techniques, two key bridge indicators ("The experience is extremely rewarding." and "After I graduate from college, I will continue to study English and try to improve.") were identified as important links between flow and MI. These findings contribute to a deeper understanding of how PU-L2LA influences LM and the underlying mechanisms involved. They highlight the importance of considering flow experiences and MI in promoting a positive language mindset among learners. Educators, app developers, and researchers can utilize these insights to design effective interventions, enhance engagement, and optimize language learning outcomes.
... Our participant sampling approach allowed us to look for associations between, on the one hand, multilingualism and/ or reading disorder, and on the other hand, language aptitude and/or performance on other cognitive or perceptuo-motor domains, if present. Using exploratory network analyses, as have recently been used in second language acquisition research (Freeborn et al., 2022), we aimed to uncover associations and dissociations between different aspects of linguistic and non-linguistic processing, and shed light on language aptitude in the very broad sense of the term. ...
Language aptitude has recently regained interest in cognitive neuroscience. Traditional language aptitude testing included phonemic coding ability, associative memory, grammatical sensitivity and inductive language learning. Moreover, domain-general cognitive abilities are associated with individual differences in language aptitude, together with factors that have yet to be elucidated. Beyond domain-general cognition, it is also likely that aptitude and experience in domain-specific but non-linguistic fields (e.g. music or numerical processing) influence and are influenced by language aptitude. We investigated some of these relationships in a sample of 152 par- ticipants, using exploratory graph analysis, across different levels of regularisation, i.e. sensitivity. We carried out a meta cluster analysis in a second step to identify variables that are robustly grouped together. We discuss the data, as well as their meta-network groupings, at a baseline network sensitivity level, and in two analyses, one including and the other excluding dyslexic readers. Our results show a stable association between language and cognition, and the isolation of multilingual language experience, musicality and literacy. We highlight the ne- cessity of a more comprehensive view of language and of cognition as multivariate systems.
... For instance, dynamic network models have been proposed to understand how depressive symptoms are interrelated and how a network can show properties of 'getting stuck' in a specific state (Wichers et al., 2021). In a similar vein, dynamic network models may be used to understand how different linguistic variables are interdependent and how the structure of the network of second language development as a whole can be described (Freeborn et al., 2023). In earlier studies, coupled (logistic) equation models have been successfully used to model the dynamic relation between different levels of vocabulary development (Caspi and Lowie, 2013;Van Geert, 2023). ...
In the past decades, complex dynamic systems theory (CDST) has been used as an important framework for studying second language development. CDST is a metatheory of change and focuses on processes. Even though it has been broadly accepted as an inspiring dimension of research in psychology, sociology and second language development, some scholars have raised questions about the methodologies used, the interpretation of the data, and the nature of its claims. Specifically, Pallotti questioned whether CDST generates testable hypotheses, and criticized its position towards reductionism and generalizability, based on philosophical argumentations. The present article evaluates the issues addressed, reviews the work that has already been done, and looks ahead at future CDST applications to research in second language development, by exploring recent methodological developments in the field.
