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    Peer learner networks impact study-abroad second language acquisition:
    insights from mixed-methods SNA (2022)

    Art
    Vortrag
    Autoren
    Paradowski, Michal B.
    Chen, Chih-Chun
    Jarynowski, Andrzej (WE 16)
    Ochab, Jeremi K.
    Cierpich-Kozieł, Agnieszka
    Jelińska, Magdalena
    Czopek, Karolina
    Kongress
    NETSCI 2022 : International school and conference on network science
    25.07.2022
    Quelle
    Sprache
    Englisch
    Verweise
    URL (Volltext): https://easychair.org/smart-program/NetSci2022/2022-07-27.html#talk:195934
    Kontakt
    Institut für Veterinär-Epidemiologie und Biometrie

    Königsweg 67
    14163 Berlin
    +49 30 838 56034
    epi@vetmed.fu-berlin.de

    Abstract / Zusammenfassung

    ABSTRACT. Social networks play a vital role in SLA. Combining computational and anthropological Social Network Analysis (SNA), we investigate the influence of peer interaction dynamics and social graph topology on measurable outcomes in two intensive language courses: a 5-week course of German for Erasmus+ exchange students in Baden-Württemberg (n=40), and two editions of a 4-week summer course of the Polish language and culture in Warsaw (n=332). Unlike previous Study Abroad social network research concentrating on i) the micro-level of individual learners’ egocentric networks, presenting an emic view only, and ii) primarily TL native-speaker contacts, we demonstrate how and why peer learner networks can be examined in their entirety, complementing an etic perspective. In particular, we focus on the moderating role of the social network (mesoscopic explanatory variable)—in turn influenced by engagement with the TL-speaking culture (macroscopic explanatory variable)—on L2 progress (microscopic response variable). The study addresses the following overarching questions: RQ1: Is the learners’ L2 progress influenced by their position in the peer interaction network (center vs. periphery) and community membership? RQ2: Which types of interaction revealed in the social graph structure are the most important predictors of L2 progress: - unidirectional or reciprocal? - overall (irrespective of the language(s) used) or in the TL? - incoming or outgoing? RQ3: With respect to TL use, is a more important factor the absolute numbers of immersion hours in the language, or the proportion of L2 use to total communication? RQ4: Is there a relationship between participants’ language progress and the intensity of their contacts with same-L1 users (investigation of homophily effects; cf. Lazarsfeld & Merton, 1954; McPherson et al., 2001)? RQ5: Do the students prefer to socialize with peers demonstrating a similar or different level of L2 proficiency? RQ6: Is TL progress conditioned by network-external factors such as motivation or competence in other (background) languages? The quantitative component of the project showed among others i) that outgoing interactions in the TL are a stronger predictor of progress than incoming interactions, ii) a clear detrimental effect of interactions with same-L1 speakers (routgoing=−.31[-0.63, 0.00],p=.048), iii) the strongest influence of the network in the domains of pronunciation and lexis, where degree centrality in the TL positively correlates with progress (routdegree=.258,p=.001 for pronunciation; routdegree=.304,p=.0002 and rindegree=.263,p=.001 for vocabulary), while betweenness in total communication is significantly anticorrelated (r=−.242,p=.003 and r=−.204,p=.01, respectively). iv) This mirrors the impact of closeness centrality (ease of access to other students). v) Combined with the deleterious influence on SLA of a high in-degree, this underscores the importance of the network’s structural properties. In turn, structured interviews carried out with course participants and their instructors yielded valuable information on the formation and types of the networks the learners engaged in and the purposes these networks served. The presentation will thus illustrate the benefits of combining computational (quantitative) and anthropological (qualitative) social network analysis. Lastly, we shall also compare two face-to-face iterations of one of the courses with its online edition during the COVID-19 pandemic.