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LEARNING CONDITION, LINGUISTIC COMPLEXITY, AND FIRST LANGUAGE TRANSFER IN SEMIARTIFICIAL LANGUAGE LEARNING

A CONCEPTUAL REPLICATION AND EXTENSION OF TAGARELLI ET AL. (2016)

Published online by Cambridge University Press:  22 March 2021

Jianwu Gao  and
Shuang Ma Open the ORCID record for Shuang Ma [Opens in a new window]
Jianwu Gao
Affiliation:
Capital Normal University
Shuang Ma*
Affiliation:
Capital Normal University
*
*Correspondence concerning this article should be addressed to Shuang Ma, Department of English Language and Literature, Capital Normal University, Haidian District, Beijing 100089, China. E-mail: s.ma5384@cnu.edu.cn
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Abstract

This study explored the interaction between learning conditions, linguistic complexity, and first language (L1) syntactic transfer in semiartificial grammar learning by conceptually replicating and extending Tagarelli et al. (2016). We changed the L1 background, elicited production data during debriefing, and added a binary mixed-effects logistic regression analysis to compare variability at learner and item levels with group-level variation on exposure condition, linguistic complexity, and their interaction. Our results replicated those of the original study regarding the comparative efficacy of explicit instruction; however, we also found a condition × complexity interaction absent in the original study. Debriefing sentence-production data suggest that the changed L1-L2 typological distance may have leveled off the advantage of explicit instruction in the learning of the complex V2-VF structure. Finally, our mixed-effects modeling analysis revealed that variability at learner and item levels accounted for a larger proportion of the variance of the outcomes than all the predictors combined.

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Research Article
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Studies in Second Language Acquisition , Volume 43 , Issue 2 , May 2021 , pp. 355 - 378
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© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

The experiment in this article earned an Open Materials badge for transparent practices. The materials are available at https://www.iris-database.org/iris/app/home/detail?id=york%3a938554&ref=search

We would like to thank Bronson Hui and Ryo Maie from Michigan State University for their detailed, insightful feedback on the drafts of this article. We thank Dr. Kaitlyn Tagarelli from Georgetown University and Dr. Simón Ruiz from University of Tuebingen for kindly providing the instruments that the original study adopted. We are grateful to Prof. Charlene Polio and Prof. Aline Godfroid from Michigan State University for their advice on the research design and data analysis. We thank the two anonymous reviewers and the handling editor Prof. Gregory Keating for their informative feedback. We thank our research assistants, Zizhen Wang and Yiwei Ma, and all the colleagues and students who participated in or supported the study. Finally, we would like to thank Kehao Zhang for technical support. All remaining errors are our own. This research was sponsored by China Scholarship Council.

References

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