How Does Adult Learners’ L1 Interact With Word Frequency in the Error Rates of Chinese Classifier Use: A Cross-Comparison Study (76029)

Session Information:

Session: On Demand
Room: Virtual Poster Presentation
Presentation Type: Virtual Poster Presentation

All presentation times are UTC + 9 (Asia/Tokyo)

Korean is a classifier language, while English is not. It is logical to assume that L1-Korean learners may perform better than L1-English learners on the acquisition of Chinese classifiers. However, research has shown that the difference is minimal and only occurs at the beginner and advanced stages (and not intermediate; Liang, 2008). We hypothesize that any possible advantage for Korean learners might depend on classifier frequency instead; namely familiarity with L1 classifiers may benefit L1-Korean learners’ acquisition of higher-frequency (lower-difficulty) L2 classifiers only.

Chinese classifiers are categorized into pre-established frequency bands A to C in descending order of word frequency (Wang, 2017), and sentences that contain the classifiers written by L1-Korean and L1-English learners (i.e. aggregate data from US, UK, Australia, Canada) were extracted from the HSK Dynamic Composition Corpus. Two native Mandarin speakers reported the error rates, with a 93% inter-rater reliability. Welch’s two-sample t-tests were conducted to compare the mean error rates between L1-Korean and L1-English learners for high-frequency classifiers (Band A) and low-frequency classifiers (Band B+C) respectively.

Unexpectedly, results showed no significant group difference for Band A classifiers (t(871.51)=-0.89, p=.38), while a significant group difference was found for Band B+C classifiers (t(285.14)=-4.242, p<.001), but with a reverse trend than predicted: L1-Korean learners had a higher error rate than L1-English learners. One possibility is that due to a lack of classifiers in their L1 inventory, L1-English learners might be more cautious in applying them in written sentences, while L1-Korean learners are perhaps more comfortable using classifiers even erroneously.

Authors:
Kun Yu, The Hong Kong University of Science and Technology, Hong Kong


About the Presenter(s)
Ms Kun YU is a University Postdoctoral Fellow or Instructor at The Hong Kong University of Science and Technology (HKUST) in Hong Kong

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Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00