Studies in Second Language Acquisition最新文献

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.

This scoping review aims to offer a panoptic overview of the research on grit and L2 grit in second and foreign language learning. To this end, a “hybrid search strategy” (Wohlin et al., 2022) was implemented. Out of 1,111 records identified across 15 databases and 78 found applying the backward/forward snowballing technique, 233 empirical studies published between 2013 and 2025 were finally included. With a focus on study and scale quality, the results present (1) a zoom-in/zoom-out description of the research landscape, considering 30 bibliometric and methodological variables, and (2) an in-depth comparative analysis of the psychometric instruments used to measure both grit and L2 grit, examining 45 variables arranged into four categories: (a) scale design and administration, (b) means and standard deviations, (c) reliability of scales and subscales, (d) content, construct, and predictive validity. The review concludes with a discussion of relevant findings and evidence-based suggestions for future and quality-enhanced research.

This meta-analysis of 79 studies evaluates the effectiveness of high variability phonetic training (HVPT) for the development of second language (L2) speech perception and explores learner-related and methodological variables that influence training effects. The overall medium-to-large effects of HVPT on L2 speech perception support the effectiveness of HVPT, for both pretest-posttest comparison (g = 0.92, k = 96) and treatment-control comparison (g = 0.67, k = 32), confirm long-term retention of perception gains, and, to some extent, indicate generalization of learning to novel stimuli. Training effects are influenced by several key variables (length of L2 learning, response labels, type of training task, type of testing task, total training time, target phones, and number of talkers). The findings provide compelling evidence to support the efficacy of HVPT for L2 perceptual learning and suggest circumstances under which training effects are optimized.

Acquisition of reading skill in a second language (L2) requires development and coordinated use of multiple component skills. This acquisition is less effortful the more similar the first language (L1) of the L2 learner is to that L2. While ways to quantify the L1–L2 distance are well defined in the current literature, the theoretical status of this distance in models of L2 reading acquisition is under-specified. This paper tests whether the L1–L2 distance influences English reading fluency and comprehension directly, via the mediation of component skills of reading, or both. We used text reading data and tests of component skills of English reading from the Multilingual Eye-movement Corpus database, representing advanced L2 readers of English from 18 distinct language backgrounds. Mediation analyses show that the L1–L2 distance has both a direct and an indirect effect on English reading fluency and eye movements, yet it has no effect on reading comprehension. These findings are novel in that they specify the mechanism through which the L1–L2 distance affects L2 reading acquisition.