![]() One of the more theoretical reasons leading to a heavy reliance on experimental approaches and human judgment instead of systematic and quantitative computations in lexical-related research is the ambiguity surrounding the measurement of lexical proficiency. Further discussions on challenges surrounding the construction of a spoken corpus of Mandarin Chinese can be found in Xiao et al. This is in spite of the existence of several influential online resources such as Beijing Mandarin Spoken Corpora (BJKY, developed by Beijing Language and Culture University), for instance, because to be applicable to specific aspects of second language acquisition (SLA), such as the lexical knowledge differentiation concerned in the current study, it is best for the corpus to have related latent variables embedded in the design in the first place. A well-contrasted spoken corpus of Mandarin Chinese is exceptionally scarce, due to-among other reasons-the smaller number of Chinese as foreign language (CFL) learners relative to that of English as the second language (ESL) learners (despite growing popularity in Chinese in recent years) as well as the high technical (e.g., segmentation for non-alphabetic languages) and financial cost for constructing such a corpus. Apart from the notion that human judgment is, by default, most accurate, an apparent reason causing the rare use of numerical analysis is the lack of a sufficiently calibrated corpus of L2 speakers (for the case of spoken language). Traditionally, researchers rely more on experimental approaches for discerning the nativeness or non-nativeness of lexical productions, whereas corpus-based numerical analysis is rarely seen on a large scale. As such, appropriate measurement of lexical proficiency has been a meaningful quest to enhance the effectiveness and efficiency of L2 education in general. At a practical level, effective communication in L2 is improbable without a sound mastery of the vocabulary of the target language (Akiyama & Saito, 2016 Alqahtani, 2015 Ellis, 1995 Gu, 2019 Newman et al., 2016 Wright & Cervetti, 2017). Past studies have documented abundant and consistent evidence that lexical proficiency is the predominant element directly affecting the learners’ performance at all major fronts such as L2 reading and writing literacy, oral fluency, and academic achievements (Anderson & Freebody, 1981 Daller et al., 2003 Huckin & Coady, 1999 In’nami et al., 2016 Koda, 1988 Li, 2018). Lexical proficiency is one of the most critical components of linguistic competence to second language (L2) learning. The implication is that a more fully defined metric of lexical richness is still a worthwhile endeavor for language proficiency assessment, with optimal directions for such endeavors discussed in the concluding remarks. The results demonstrate that, while the L1 versus L2 group difference in lexical richness was observed with statistical significance for each of the chosen measures, the clustering and membership prediction accuracy of individual speakers varied greatly from one measure to another. The effectiveness of each selected measure as well as an overall evaluation of all the measures for the concerned differentiation tasks were comprehensively calibrated. Eighteen lexical richness measures were surveyed and compared using the clustering analysis. A robust K-means algorithm was designed to compare the oral proficiency between L1 and L2 Chinese speakers regarding lexical richness and how relatively effective the various lexical measures were in performing the differentiation task. This study proposed an innovative automated approach to differentiation of the vocabulary proficiency of Chinese speakers.
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