Analyzing the relationship between argument structure construction (ASC) use and language learning has been an important area of investigation in second language (L2) studies from a usage-based constructionist approach. Previous studies have shown that advanced L2 learners’ language use demonstrates greater ASC diversity, less frequent ASC-verb combinations, and stronger ASC-verb associations. However, these investigations have been limited by methodological challenges in identifying ASCs and have predominantly focused on the written texts. To address these limitations, we employ a fine-tuned model to automatically extract ASCs from target and reference corpora, considering their semantic aspects. We then calculate ASC-based indices and, both alone and in combination with other lexicogrammatical indices, use them to predict L2 oral proficiency scores assigned by human judges. Our findings show that ASC-based indices alone explain 44% of the variance in scores. When combined with other indices, they provide complementary insights that enhance multivariate modeling of L2 oral proficiency.

The construct of second language (L2) utterance fluency is typically operationalized through various individual temporal features. However, in natural speech, fluency (or disfluency) is often characterized by the clustering of multiple temporal features, collectively revealing the speaker’s effort in speech production or disfluency recovery. In this study, we explore the co-occurrence patterns of disfluency features in L2 speech and their associations with speakers’ L2 oral proficiency. We initially segmented all speech samples into analysis of speech (AS)-units. Within each AS-unit, six individual fluency features were manually coded, standardized, and subsequently subjected to a hierarchical-based k-means cluster analysis to examine their co-occurrence patterns. The results revealed four distinct disfluency clusters. A subsequent qualitative analysis of disfluencies in each cluster revealed distinct distributional patterns, disfluency makeup, and communicative functions. Additionally, the proportions of different disfluency clusters were significantly influenced by speakers’ proficiency level, first language background, and their interaction. These findings carry implications for L2 speaking research in general, shedding light on the intricate nature of speech fluency and presenting an alternative approach to the operationalization of this multidimensional construct.