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How output outweighs input and interlocutors matter for study‐abroad SLA: Computational Social Network Analysis of learner interactions

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cris.lastimport.scopus2024-02-12T19:32:41Z
dc.abstract.enThis 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.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorParadowski, Michał
dc.contributor.authorOCHAB, JEREMI K.
dc.contributor.authorCHEN, CHIH–CHUN
dc.contributor.authorCIERPICH–KOZIEŁ, AGNIESZKA
dc.date.accessioned2024-01-25T03:30:14Z
dc.date.available2024-01-25T03:30:14Z
dc.date.copyright2022-12-27
dc.date.issued2022
dc.description.accesstimeAT_PUBLICATION
dc.description.financeNie dotyczy
dc.description.number4
dc.description.versionFINAL_PUBLISHED
dc.description.volume106
dc.identifier.doi10.1111/MODL.12811
dc.identifier.issn0026-7902
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/108679
dc.identifier.weblinkhttps://onlinelibrary.wiley.com/doi/pdf/10.1111/modl.12811
dc.languageeng
dc.pbn.affiliationlinguistics
dc.relation.ispartofModern Language Journal
dc.relation.pages694–725
dc.rightsCC-BY
dc.sciencecloudnosend
dc.subject.ensecond language acquisition (SLA)
dc.subject.enstudy abroad
dc.subject.encomputational social network analysis (SNA)
dc.subject.ensocial graph
dc.subject.enpeer learner network
dc.subject.encommunity/cluster detection
dc.subject.eninteractionist approach
dc.titleHow output outweighs input and interlocutors matter for study‐abroad SLA: Computational Social Network Analysis of learner interactions
dc.typeJournalArticle
dspace.entity.typePublication