Tag: social networks

Peer interaction dynamics and L2 learning trajectories during study abroad: A longitudinal investigation using dynamic computational Social Network Analysis

Paradowski, M.B., Whitby, N., Czuba, M. & Bródka, P. (2024). Language Learning. DOI: 10.1111/lang.12681

This is the first application in second language acquisition of quantitative Social Network Analysis reconstructing a complete learner network with repeated (three) measurement points. Apart from the empirical contribution showcasing exciting findings from an intensive study-abroad Arabic program, the text can also serve as a primer of centrality metrics, providing in-depth explanation of the most commonly used centrality measures in network science – to the best of our knowledge, the first such 101 in applied linguistics. The materials, dataset, as well as code are all openly available on OSF and IRIS.

Abstract

Using computational Social Network Analysis (SNA), this longitudinal study investigates the development of the interaction network and its influence on the second language (L2) gains of a complete cohort of 41 U.S. sojourners enrolled in a 3-month intensive study-abroad Arabic program in Jordan. Unlike extant research, our study focuses on students’ interactions with alma mater classmates, reconstructing their complete network, tracing the impact of individual students’ positions in the social graph using centrality metrics, and incorporating a developmental perspective with three measurement points. Objective proficiency gains were influenced by predeparture proficiency (negatively), multilingualism, perceived integration of the peer learner group (negatively), and the number of fellow learners speaking to the student. Analyses reveal relatively stable same-gender cliques, but with changes in the patterns and strength of interaction. We also discuss interesting divergent trajectories of centrality metrics, L2 use, and progress; predictors of self-perceived progress across skills; and the interplay of context and gender.

Read the full article at https://onlinelibrary.wiley.com/doi/10.1111/lang.12681

How Output Outweighs Input and Interlocutors Matter for Study-Abroad SLA: Computational Social Network Analysis of Learner Interactions (winner, Best of MLJ for 2022 paper award)

MICHAŁ B. PARADOWSKI, AGNIESZKA CIERPICH–KOZIEŁ, CHIH–CHUN CHEN, JEREMI K. OCHAB

MLJ Volume106, Issue4 Winter 2022 Pages 694-725

This data-driven study framed in the interactionist approach investigates the influence of social graph topology and peer interaction dynamics among foreign exchange students enrolled in an intensive German language course on second language acquisition (SLA) outcomes. Applying the algorithms and metrics of computational social network analysis (SNA), we find that (a) the best predictor of target language (TL) performance is reciprocal interactions in the language being acquired, (b) the proportion of output in the TL is a stronger predictor than input (Principle of Proportional Output), (c) there is a negative relationship between performance and interactions with same-first-language speakers, (d) a significantly underperforming English native-speaker dominated cluster is present, and (e) there are more intense interactions taking place between students of different proficiency levels. Unlike previous study abroad social network research concentrating on the microlevel of individual learners’ egocentric networks and presenting an emic view only, this study constitutes the first application of computational SNA to a complete learner network (sociogram). It provides new insights into the link between social relations and SLA with an etic perspective, showing how social network configuration and peer learner interaction are stronger predictors of TL performance than individual factors such as attitude or motivation, and offering a rigorous methodology for investigating the phenomenon.

Read the full article at: onlinelibrary.wiley.com

Group mixing drives inequality in face-to-face gatherings

Marcos Oliveira, Fariba Karimi, Maria Zens, Johann Schaible, Mathieu Génois & Markus Strohmaier
Communications Physics volume 5, Article number: 127 (2022)

Uncovering how inequality emerges from human interaction is imperative for just societies. Here we show that the way social groups interact in face-to-face situations can enable the emergence of disparities in the visibility of social groups. These disparities translate into members of specific social groups having fewer social ties than the average (i.e., degree inequality). We characterize group degree inequality in sensor-based data sets and present a mechanism that explains these disparities as the result of group mixing and group-size imbalance. We investigate how group sizes affect this inequality, thereby uncovering the critical size and mixing conditions in which a critical minority group emerges. If a minority group is larger than this critical size, it can be a well-connected, cohesive group; if it is smaller, minority cohesion widens inequality. Finally, we expose group under-representation in degree rankings due to mixing dynamics and propose a way to reduce such biases. The emergence of inequality in social interactions can depend on a number of factors, among which the intrinsic attractiveness of individuals, but also group size the presence of pre-formed social ties. Here, the authors propose “social attractiveness” as a mechanism to account for the emergence of inequality in face-to-face social dynamics and show this reproduces real-world gathering data, predicting the existence of a critical group size for the minority group below which higher cohesion among its members leads to higher inequality.