Phraseological competence is crucial for language acquisition, processing, and fluency (Ellis et al., 2008; Paquot et al., 2020) but remains challenging for L2 learners (Laufer Paquot 2) Automated indices will effectively capture certain features prioritized by human raters, particularly phraseological diversity and sophistication. However, they may be less effective in adequately capturing some nuanced qualitative aspects that are equally emphasized in human evaluations of phraseological competence, such as idiomaticity and contextual appropriateness. The findings of this study will provide convergent validity evidence for the automated measures of phraseological competence and highlight areas where computational measures require refinement, particularly in capturing qualitative features emphasized by human raters. By bridging automated analysis and human judgment, the study aims to inform the development of more robust assessment tools and offer pedagogical insights for fostering phraseological competence in L2 learners.
Wang et al. (Wed,) studied this question.