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OBSERVING THE EMERGENCE OF CONSTRUCTIONAL KNOWLEDGE

VERB PATTERNS IN GERMAN AND SPANISH LEARNERS OF ENGLISH AT DIFFERENT PROFICIENCY LEVELS

Published online by Cambridge University Press:  13 May 2019

Ute Römer*
Affiliation:
Georgia State University
Cynthia M. Berger
Affiliation:
Georgia State University and Duolingo
*Corresponding
*Correspondence concerning this article should be addressed to Ute Römer, Department of Applied Linguistics and ESL, Georgia State University, 25 Park Place NE, Suite 1500, Atlanta, GA 30303, USA. E-mail: uroemer@gsu.edu

Abstract

Based on writing produced by second language learners at different proficiency levels (CEFR A1 to C1), we adopted a usage-based approach (Ellis, Römer, & O’Donnell, 2016; Tyler & Ortega, 2018) to investigate how German and Spanish learner knowledge of 19 English verb-argument constructions (VACs; e.g., “V with n,” illustrated by he always agrees with her) develops. We extracted VACs from subsets of the Education First-Cambridge Open Language Database, altogether comprising more than 68,000 texts and 6 million words. For each VAC, L1 learner group, and proficiency level, we determined type and token frequencies, as well as the most dominant verb-VAC associations. To study effects of proficiency and L1 on VAC production, we carried out correlation analyses to compare verb-VAC associations of learners at different levels and different L1 backgrounds. We also correlated each learner dataset with comparable data from a large reference corpus of native English usage. Results indicate that with increasing proficiency, learners expand their VAC repertoire and productivity, and verb-VAC associations move closer to native usage.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2019 

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Footnotes

The authors would like to thank Georgia State University's Scholarly Support Grant Program for sponsoring the project “Language use, acquisition, and processing: Cognitive and corpus investigations of Construction Grammar”; Nick Ellis for his encouragement and support during the initial stages of the project; Matt O'Donnell and Stephen Skalicky for their help with data preparation for and data processing in R; and Luke Plonsky as well as two anonymous reviewers for their constructive comments on an earlier draft of this article.

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