Studies in Second Language Acquisition最新文献

In a close replication study of Darcy et al., (2016), Huensch (2024) reported a lack of clear relationships between inhibitory control (IC) and phonological processing, contrary to the initial findings. Given the general unreliability of response-time differences, which are often the basis of IC measures and could potentially mask small effects, we performed secondary analyses on Huensch’s (2024) open data set to investigate (a) the extent to which the reliability of IC measures could be improved using model-based approaches (Hui & Wu, 2024), (b) the correlations between the different IC tasks, and (c) their predictive power for phonological processing, based on the more reliable indices. Results showed that model-based approaches generally improved reliability, and particularly for the Stroop and Simon tasks to acceptable levels. Yet, correlations between IC tasks remained low, and partial correlation and hierarchical regression still failed to reveal significant relationships between IC and phonological processing, further confirming Huensch’s (2024) findings.

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.

This paper reports a study that investigated how first language (L1) reading comprehension, L1 low-level skills, working memory capacity, and reading anxiety are related to the accuracy of responses and completion time in a second language (L2) reading test. The data obtained from Hungarian secondary school learners of English showed that anxiety related to processing the L2 reading text, time pressure, and the response tasks as well as L1 reading comprehension scores and backward digit span were significant predictors of L2 reading scores. L1 low-level skills did not contribute significantly to L2 reading accuracy. Higher levels of reading-related anxiety were associated with slower reading, and L2 learners with concurrently lower levels of L1 and L2 reading ability needed more time to complete the reading test. These findings highlight that L2 reading tests should be flexibly timed so that everyone, including test takers with literacy-related difficulties such as dyslexia, can demonstrate their abilities.

This study, grounded in critical disability studies, explores ableism in language education and its impact on Deaf and hard of hearing (DHH) students learning English as a foreign or third language (L3). Using interpretative phenomenological analysis, the lived experiences of 14 Iranian DHH undergraduate students, proficient in Esharani (sign language used by the Iranian Deaf community) and Persian, were analyzed. Participants recounted their experiences with ableism in English education through interviews. Analyzing interviews resulted in the emergence of three key themes: a) lack of awareness of (dis)ability and ableism throughout their English education, b) the prevalence of overt and covert ableism viewing deafness as a marker of “otherness,” and c) the need for advocacy and more inclusive practices and environments for DHH learners acquiring L3. This study highlights the challenges DHH learners face in L3 acquisition and calls for more inclusive language education policies to better support disabled students.

Various theories have been proposed in the field of second language (L2) sentence processing research and have significantly advanced our understanding of the mechanisms underlying L2 sentence interpretation processes. However, many existing theories have only been formulated verbally, and little progress has been made towards formal modelling. Formal modelling offers several advantages, including enhancing the clarity and verifiability of theoretical claims. This paper aims to address this gap in the literature by introducing formal computational modelling and demonstrating its application in L2 sentence processing research. Through practical demonstrations, the paper also emphasises the importance of formal modelling in the formulation and development of theory.

An increasing number of studies have shown that pretesting L2 word knowledge before a study phase can enhance subsequent learning. However, little is known about pretesting effects in the context of incidental L2 vocabulary acquisition. This study explores the effects of pretesting on L2 vocabulary learning through reading, focusing on the moderating effect of the pretest format. One hundred and forty-three participants were randomly assigned to a nonpretested condition or three pretested conditions (meaning recall, meaning recognition, and form recognition). In the pretested conditions, participants completed a vocabulary pretest, followed by a meaning-focused reading task and three vocabulary posttests. The findings show that the meaning recall and form recognition groups were impacted most by pretesting in terms of learning outcomes and perceptions of the learning intervention. However, the pretesting effect on posttest scores was small and statistically nonsignificant, suggesting a minimal impact of pretesting on incidental learning outcomes.

This research report presents the development and validation of Auto Error Analyzer, a prototype web application designed to automate the calculation of accuracy and its related metrics for measuring second language (L2) production. Building on recent advancements in natural language processing (NLP) and artificial intelligence (AI), Auto Error Analyzer introduces an automated accuracy measurement component, bridging a gap in existing assessment tools, which traditionally require human judgment for accuracy evaluation. By utilizing a state-of-the-art generative AI model (Llama 3.3) for error detection, Auto Error Analyzer analyzes L2 texts efficiently and cost-effectively, producing accuracy metrics (e.g., errors per 100 words). Validation results demonstrate high agreement between the tool’s error counts and human rater judgments (r = .94), with microaverage precision and recall in error detection being high as well (.96 and .94 respectively, F1 = .95), and its T-unit and clause counts matched outputs from established tools like L2SCA. Developed under open science principles to ensure transparency and replicability, the tool aims to support researchers and educators while emphasizing the complementary role of human expertise in language assessment. The possibilities of Auto Error Analyzer for efficient and scalable error analysis, as well as its limitations in detecting context-dependent and first-language (L1)-influenced errors, are also discussed.