[논문]Predicting CEFR Levels in L2 Oral Speech, Based on Lexical and Syntactic Complexity

Hu, Xiaolin

doi:10.22925/apjcr.2021.2.1.35

[국내논문] Predicting CEFR Levels in L2 Oral Speech, Based on Lexical and Syntactic Complexity 원문보기

Asia pacific journal of corpus research = 아시아태평양코퍼스연구, v.2 no.1, 2021년, pp.35 - 45

Hu, Xiaolin (Tokyo University of Foreign Studies)

Abstract ▼ AI-Helper

With the wide spread of the Common European Framework of Reference (CEFR) scales, many studies attempt to apply them in routine teaching and rater training, while more evidence regarding criterial features at different CEFR levels are still urgently needed. The current study aims to explore complexity features that distinguish and predict CEFR proficiency levels in oral performance. Using a quantitative/corpus-based approach, this research analyzed lexical and syntactic complexity features over 80 transcriptions (includes A1, A2, B1 CEFR levels, and native speakers), based on an interview test, Standard Speaking Test (SST). ANOVA and correlation analysis were conducted to exclude insignificant complexity indices before the discriminant analysis. In the result, distinctive differences in complexity between CEFR speaking levels were observed, and with a combination of six major complexity features as predictors, 78.8% of the oral transcriptions were classified into the appropriate CEFR proficiency levels. It further confirms the possibility of predicting CEFR level of L2 learners based on their objective linguistic features. This study can be helpful as an empirical reference in language pedagogy, especially for L2 learners' self-assessment and teachers' prediction of students' proficiency levels. Also, it offers implications for the validation of the rating criteria, and improvement of rating system.

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표/그림 (12)

표 Table 1. CEFR Levels
표 Table 2. The List of CAN Do Descriptors
표 Table 3. Tasks of SST
표 Table 4. Equivalent Levels CEFR, CEFR-J, and SST
표 Table 5. Number of Transcriptions in Each Group
표 Table 6. Summary of Complexity Variables
표 Table 7. Complexity Features by Criteria and Proficiency levels
표 Table 8. ANOVA Output for All Predictor Variables
표 Table 9. Bivariate Correlations of the Complexity Features
표 Table 10. Structure Matrix
그림 Figure 1. Canonical Discriminant Functions
표 Table 11. Classification Results

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