Tag: complex 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

Towards Social Capital in a Network Organization: A Conceptual Model and an Empirical Approach

 Saad Alqithami, Rahmat Budiarto, Musaad Alzahrani and Henry Hexmoor

Entropy 2020, 22(5), 519

 

Due to the complexity of an open multi-agent system, agents’ interactions are instantiated spontaneously, resulting in beneficent collaborations with one another for mutual actions that are beyond one’s current capabilities. Repeated patterns of interactions shape a feature of their organizational structure when those agents self-organize themselves for a long-term objective. This paper, therefore, aims to provide an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interactions that, in part, elucidate evolving structures and impromptu topologies of networks. We model an open source project as an organizational network and provide definitions and formulations to correlate the proposed mechanism of social capital with the achievement of an organizational charter, for example, optimized productivity. To empirically evaluate our model, we conducted a case study of an open source software project to demonstrate how social capital can be created and measured within this type of organization. The results indicate that the values of social capital are positively proportional towards optimizing agents’ productivity into successful completion of the project.

Source: www.mdpi.com

A First Course in Network Science

The book A First Course in Network Science by CNetS faculty members Filippo Menczer and Santo Fortunato and CNetS PhD graduate Clayton A. Davis was recently published by Cambridge University Press. This textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Extensive tutorials, datasets, and homework problems provide plenty of hands-on practice. The book has been endorsed as “Rigorous” (Alessandro Vespignani), “comprehensive… indispensable” (Olaf Sporns), “with remarkable clarity and insight” (Brian Uzzi), “accessible” (Albert-László Barabási), “amazing… extraordinary” (Alex Arenas), and “sophisticated yet introductory… an excellent introduction that is also eminently practical” (Stephen Borgatti). It was ranked by Amazon #1 among new releases in mathematical physics.

Source: cnets.indiana.edu

Emergence of communities and diversity in social networks

Understanding how communities emerge is a fundamental problem in social and economic systems. Here, we experimentally explore the emergence of communities in social networks, using the ultimatum game as a paradigm for capturing individual interactions. We find the emergence of diverse communities in static networks is the result of the local interaction between responders with inherent heterogeneity and rational proposers in which the former act as community leaders. In contrast, communities do not arise in populations with random interactions, suggesting that a static structure stabilizes local communities and social diversity. Our experimental findings deepen our understanding of self-organized communities and of the establishment of social norms associated with game dynamics in social networks.

Source: www.pnas.org