TY - JOUR
T1 - L2 English speaking syntactic complexity
T2 - Data preprocessing issues, reliability of automated analysis, and the effects of proficiency, L1 background, and topic
AU - Kim, Minjin
AU - Lu, Xiaofei
N1 - Publisher Copyright:
© 2024 The Authors. The Modern Language Journal published by Wiley Periodicals LLC on behalf of National Federation of Modern Language Teachers Associations, Inc.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - The effects of learner- and task-related variables on second language (L2) writing syntactic complexity (SC) have been extensively investigated. However, previous research has rarely assessed the reliability of computational tools for analyzing the SC of L2 spoken production, and we know less about the effects of such variables on L2 speaking SC. Using data from the International Corpus Network of Asian Learners of English, this study explores data preprocessing issues for preparing L2 English speech samples for automated SC analysis, evaluates the reliability of L2 Syntactic Complexity Analyzer on preprocessed L2 English speech samples, and examines the effects of proficiency, first language (L1) background, and topic on L2 speaking SC. Our manual analysis of 30 random speech samples identified several issues that can be addressed through preprocessing to improve the accuracy of automated SC analysis. Results from multiple linear mixed-effects models revealed significant effects of proficiency, L1 background, and topic on the mean length of clause, the number of complex AS-units per AS-unit, and the number of dependent clauses and complex nominals per clause in L2 learners’ spoken production. Our findings have useful implications for L2 speaking pedagogy and assessment as well as future L2 speaking SC research.
AB - The effects of learner- and task-related variables on second language (L2) writing syntactic complexity (SC) have been extensively investigated. However, previous research has rarely assessed the reliability of computational tools for analyzing the SC of L2 spoken production, and we know less about the effects of such variables on L2 speaking SC. Using data from the International Corpus Network of Asian Learners of English, this study explores data preprocessing issues for preparing L2 English speech samples for automated SC analysis, evaluates the reliability of L2 Syntactic Complexity Analyzer on preprocessed L2 English speech samples, and examines the effects of proficiency, first language (L1) background, and topic on L2 speaking SC. Our manual analysis of 30 random speech samples identified several issues that can be addressed through preprocessing to improve the accuracy of automated SC analysis. Results from multiple linear mixed-effects models revealed significant effects of proficiency, L1 background, and topic on the mean length of clause, the number of complex AS-units per AS-unit, and the number of dependent clauses and complex nominals per clause in L2 learners’ spoken production. Our findings have useful implications for L2 speaking pedagogy and assessment as well as future L2 speaking SC research.
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U2 - 10.1111/modl.12907
DO - 10.1111/modl.12907
M3 - Article
AN - SCOPUS:85184461744
SN - 0026-7902
VL - 108
SP - 270
EP - 296
JO - Modern Language Journal
JF - Modern Language Journal
IS - 1
ER -