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 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.

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.

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.

English learners (ELs) with disabilities are disproportionately less likely than their EL peers without disabilities to be reclassified as Fluent English Proficient (FEP) in US public schools. Research has begun to explore how state reclassification policies, specifically the criteria needed to be considered FEP, may contribute to reclassification disparities. Given the complexities of measuring and understanding English language proficiency (ELP) growth for ELs with disabilities, there have been calls for states to incorporate teacher or team input as a criterion for reclassification. Research, however, has yet to examine how teachers make sense of ELP data for ELs with disabilities and ultimately make reclassification recommendations. This qualitative case study fills this gap, investigating the data interpretation and decision-making of teachers in one urban school district. It documents how teachers’ beliefs about standardized ELP assessment data coupled with a scarcity of resources and training contributed to reclassification decision-making driven not by data but by teachers’ values and instincts.

In the past decade, researchers have been increasingly interested in understanding the process of language learning, in addition to the effect of instructional interventions on L2 performance gains (i.e., learning products). One goal of such investigations is to reveal the interplay between learning conditions, processes, and outcomes where, for example, certain conditions can promote attention to the learning targets, which in turn facilitates learning. However, the statistical modeling approach taken often does not align with the conceptualization of the complex relationships between these variables. Thus, in this paper, we introduce mediation analysis to SLA research. We offer a step-by-step, contextualized tutorial on the practical application of mediation analysis in three different research scenarios, each addressing a different research design using either simulated or open-source datasets. Our overall goal is to promote the use of statistical techniques that are consistent with the theorization of language learning processes as mediators.

Traditional regression models typically estimate parameters for a factor F by designating one level as a reference (intercept) and calculating slopes for other levels of F. While this approach often aligns with our research question(s), it limits direct comparisons between all pairs of levels within F and requires additional procedures for generating these comparisons. Moreover, Frequentist methods often rely on corrections (e.g., Bonferroni or Tukey), which can reduce statistical power and inflate uncertainty by mechanically widening confidence intervals. This paper demonstrates how Bayesian hierarchical models provide a robust framework for parameter estimation in the context of multiple comparisons. By leveraging entire posterior distributions, these models produce estimates for all pairwise comparisons without requiring post hoc adjustments. The hierarchical structure, combined with the use of priors, naturally incorporates shrinkage, pulling extreme estimates toward the overall mean. This regularization improves the stability and reliability of estimates, particularly in the presence of sparse or noisy data, and leads to more conservative comparisons. Bayesian models also offer a flexible framework for addressing heteroscedasticity by directly modeling variance structures and incorporating them into the posterior distribution. The result is a coherent approach to exploring differences between levels of F, where parameter estimates reflect the full uncertainty of the data.

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.

This paper responds to Al-Hoorie, Hiver, and In’nami’s (2024) critique of the second language (L2) Motivational Self System (L2MSS) by advocating for an immediate cessation of its use in the absence of substantial revision and validation. We revisit foundational studies in the tradition, exposing critical methodological flaws that we feel undermine empirical support for the model. Further, we examine systemic factors that contributed to the largely uncritical acceptance of the model. Drawing on our own experiences, we reflect on how these dynamics have obstructed the adoption of more robust motivational theories available in psychology and education. We further caution that without a stronger emphasis on validating measurement instruments, similar distractions may continue to hinder progress in the field.

Al-Hoorie et al. (2024: Studies in Second Language Acquisition, 1–23) illuminate a validation crisis within the second language (L2) Motivational Self System (L2MSS), revealing empirical flaws in its current measurement. Their analysis indicates a persistent lack of discriminant validity among the system’s constructs, issuing a fundamental challenge in distinguishing the concepts. These findings, echoing previous concerns, underscore a pressing need for theoretical refinement and methodological rigor within the field, leading the authors to advocate for a temporary halt in L2 self-studies to address these issues comprehensively. This commentary discusses the call for a substantive moratorium presented by Al-Hoorie et al. (2024: Studies in Second Language Acquisition, 1–23) as a necessary step toward resolving persistent challenges in the field. By highlighting historical issues and suggesting pathways for theoretical diversification and methodological advancement, I aim to foster a productive dialogue on motivational psychology in language learning while ensuring empirical robustness.

Based on correlational and factorial analysis of data collected from 384 middle and high school students in South Korea, Al–Hoorie et al. (2024) claimed the existence of a discriminant validity crisis within the L2 motivational self-system research tradition and advocated for abandoning research in this area. In this response, we critically examined the evidence presented, re-analyzed their data, and argued that their findings actually support the discriminant validity of the target scales. We also discussed issues related to the design and implementation of their study and refuted their assertion regarding a discriminant validity crisis in this field. Finally, we emphasized the necessity of prioritizing definitional validity in the ongoing methodological reforms of L2 motivation research.