(PDF) How output outweighs input and interlocutors matter for study-abroad SLA: Computational Social Network Analysis of learner

(PDF) How output outweighs input and interlocutors matter for study-abroad SLA: Computational Social Network Analysis of learner interactions
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How output outweighs input and interlocutors matter for study-abroad SLA: Computational Social Network Analysis of learner interactions
Chih-Chun Chen
Jeremi Ochab
Michał B. Paradowski
2022, The Modern Language Journal
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Abstract
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 language course in Germany on SLA outcomes. Applying the algorithms and metrics of computational social network analysis (SNA), we find i) that the best predictor of TL performance is reciprocal interactions in the language being acquired, ii) the proportion of output in the TL is a stronger predictor than input, iii) a negative relationship between performance and interactions with same-L1 speakers, iv) a significantly underperforming English native-speaker dominated cluster, and v) more intense interactions taking place between students of different proficiency levels.
Unlike previous Study Abroad social network research concentrating on the micro-level of individual learners’ egocentric networks, presenting an emic view only, and primarily TL native-speaker contacts, this study, constituting the first application of computational SNA to a complete learner network (sociogram), complements an etic perspective providing new insights into the link between social relations and second language acquisition, shows how social network configuration and peer learner interaction are stronger predictors of L2/L3/Lx performance than individual factors such as attitude or motivation, and offers a rigorous methodology for investigating the phenomenon.
Key takeaways
AI
Reciprocal interactions in the target language are the best predictors of language performance.
Output proportion in the target language surpasses input as a performance predictor.
Interactions with same-first-language speakers negatively correlate with performance.
An underperforming cluster of English native speakers was identified in the study.
This study applies computational SNA to a complete learner network, offering new insights into SLA.
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Full text downloadable for free from https://doi.org/10.1111/modl.12811

How Output Outweighs Input and
Interlocutors Matter for Study-Abroad
SLA: Computational Social Network
Analysis of Learner Interactions
MICHAŁ B. PARADOWSKI,1 AGNIESZKA CIERPICH–KOZIEŁ,2 CHIH–CHUN CHEN,3
AND JEREMI K. OCHAB
University of Warsaw, Institute of Applied Linguistics, ul. Dobra 55, Warsaw, PL-00-312, Poland
E-mail:
[email protected]
Jesuit University Ignatianum in Kraków, Institute of Modern Languages, ul. Kopernika 26, Kraków, PL-31-501,
Poland E-mail:
[email protected]
University of Cambridge, Engineering Department, Trumpington Street, Cambridge, CB2 1PZ, United
Kingdom E-mail:
[email protected]
Jagiellonian University, Institute of Theoretical Physics and Mark Kac Complex Systems Center, ul. prof.
Stanisława Łojasiewicza 11, Kraków, PL-30-348, Poland E-mail:
[email protected]
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 Ger-
man 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 lan-
guage (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 moti-
vation, and offering a rigorous methodology for investigating the phenomenon.
Keywords: study abroad; computational social network analysis; social graph; peer learner network; inter-
actionist approach; community/cluster detection

ALTHOUGH EXISTING STUDY ABROAD (SA)
The Modern Language Journal, 106, 4, (2022) research has analyzed the connections between
DOI: 10.1111/modl.12811 language acquisition and use (both in and out
0026-7902/22/694–725 $1.50/0 of class), still relatively little consensus exists
© 2022 The Authors. The Modern Language Journal pub- concerning the extent of the contribution of out-
lished by Wiley Periodicals LLC on behalf of National of-class language use to second language (L2)
Federation of Modern Language Teachers Associations, development (Badstübner & Ecke, 2009; Dewey
Inc. et al., 2012; Isabelli–Garcia, 2010; Kinginger,
This is an open access article under the terms of the
2009; Pinar, 2016). A relatively recent approach
Creative Commons Attribution License, which permits
use, distribution and reproduction in any medium, pro-
to SA research has emphasized that the key to
vided the original work is properly cited. improving L2 proficiency while studying abroad
may be the development of social networks
FAQs
AI
What predicts target language performance in study-abroad contexts?
add
The study finds that reciprocal interactions in the target language are the best predictors of performance. Additionally, the proportion of output in the target language significantly outweighs input as a predictor.
How does output influence second language acquisition outcomes?
add
The research reveals that output in the target language serves as a stronger predictor of acquisition than input. This principle, termed the Proportional Output Principle, underscores the value of learner communication.
What role do same-first-language speakers play in language acquisition performance?
add
The study identifies a negative correlation between performance and interactions with same-first-language speakers. This suggests that such interactions may hinder second language acquisition for learners engaged in intensive language courses.
How does proficiency level affect peer interactions among study abroad students?
add
The findings indicate that more intense language interactions occur between students of differing proficiency levels. This dynamic can enhance social learning environments and support second language acquisition.
What novel methodology does this study employ in analyzing learner interactions?
add
This research employs computational social network analysis to evaluate the entire learner network, transitioning from microlevel egocentric networks to an etic perspective. This offers a comprehensive understanding of social relations in second language acquisition.
August 18, 2025
Michał B. Paradowski
University of Warsaw, Faculty Member
Chih-Chun Chen
University of Cambridge, Post-Doc
Jeremi Ochab
Jagiellonian University, Post-Doc
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