Predicting L2 speaking proficiency using syntactic complexity measures : a corpus-based study

Abstract

This paper discusses the syntactic complexity factors contributing to the achievement of a higher proficiency level in English speech. Here I have examined complexification at the sentential, clausal, phrasal and nominal level of syntactic organisation in a Korean learner spoken corpus using quantitative measures and compared the scores with holistic ratings of learners’ overall speaking quality. After the normality assumption analysis confirmed that the logistic regression was appropriate, an analysis was performed to ascertain the effects of complexity measures on participants’ L2 proficiency. First, length-based complexity features, namely, MLT and coordinated phrases, namely, CPT and CPC were found to be predictors for English speaking proficiency. Next, the logistic regression model was statistically significant and explained 36.3% of the variance in classification according to L2 proficiency and correctly classified 75.4% of cases. Results also showed that when learners come to use the coordinated phrases per clause proficiently, they were over 24 times more likely to achieve higher proficiency in spoken English. Finally, an effective equation was proposed to help educators classify EFL learners according to proficiency in L2 speech after gauging the selected complexity dimensions. However, more comprehensive studies which consider other methods of unit segmentation for spoken data or include more measures to predict L2 speech proficiency, are necessary to verify the results of this study.