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

Multistability and unpredictability

Álvar Daza; Alexandre Wagemakers; Miguel A. F. Sanjuán

Physics Today 77 (11), 44–50 (2024);

In numerous physical systems, from tossed coins to black holes, the complexity arising from the coexistence of different outcomes limits our ability to make predictions.

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Intersectional inequalities in social networks

Samuel Martin-Gutierez, Mauritz N. Cartier van Dissel, Fariba Karimi

Social networks are shaped by complex, intersecting identities that drive our connection preferences. These preferences weave networks where certain groups hold privileged positions, while others become marginalized. While previous research has examined the impact of single-dimensional identities on inequalities of social capital, social disparities accumulate nonlinearly, further harming individuals at the intersection of multiple disadvantaged groups. However, how multidimensional connection preferences affect network dynamics and in what forms they amplify or attenuate inequalities remains unclear. In this work, we systematically analyze the impact of multidimensionality on social capital inequalities through the lens of intersectionality. To this end, we operationalize several notions of intersectional inequality in networks. Using a network model, we reveal how attribute correlation (or consolidation) combined with biased multidimensional preferences lead to the emergence of counterintuitive patterns of inequality that are unobservable in one-dimensional systems. We calibrate the model with real-world high school friendship data and derive analytical closed-form expressions for the predicted inequalities, finding that the model’s predictions match the observed data with remarkable accuracy. These findings hold significant implications for addressing social disparities and inform strategies for creating more

Read the full article at: arxiv.org

Future views on neuroscience and AI

Ilana Witten, Daniel L.K. Yamins, Claudia Clopath, Matthias Bethge, Yi Zeng, Ann Kennedy, Abeba Birhane, Doris Tsao, Been Kim, Ila Fiete

Cell, Volume 187, Issue 21, 17 October 2024, Pages 5797-5798

The relationship between neuroscience and artificial intelligence (AI) has evolved rapidly over the past decade. These two areas of study influence and stimulate each other. We invited experts to share their perspectives on this exciting intersection, focusing on current achievements, unsolved questions, and future directions.

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Self-reinforcing cascades: A spreading model for beliefs or products of varying intensity or quality

Laurent Hébert-Dufresne, Juniper Lovato, Giulio Burgio, James P. Gleeson, S. Redner, P. L. Krapivsky

Models of how things spread often assume that transmission mechanisms are fixed over time. However, social contagions–the spread of ideas, beliefs, innovations–can lose or gain in momentum as they spread: ideas can get reinforced, beliefs strengthened, products refined. We study the impacts of such self-reinforcement mechanisms in cascade dynamics. We use different mathematical modeling techniques to capture the recursive, yet changing nature of the process. We find a critical regime with a range of power-law cascade size distributions with varying scaling exponents. This regime clashes with classic models, where criticality requires fine tuning at a precise critical point. Self-reinforced cascades produce critical-like behavior over a wide range of parameters, which may help explain the ubiquity of power-law distributions in empirical social data.

Read the full article at: arxiv.org