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.

While our earlier report focused on the initial four months of the dataset (Saito et al., 2018, Language Learning), this study investigates the relationship between individual differences in motivation (Ideal Self and Ought–to Self), emotions (Enjoyment and Anxiety), and L2 speech learning among 121 Japanese English–as–a–Foreign–Language high school students over 1.5–years. Participants’ L2 speech proficiency consistently improved at each testing point (6 months, 10 months, and 1.5 years), while their motivation and emotions, measured through questionnaires, remained relatively stable. The results of structural equation modeling suggest that the relationship between motivation, emotions, and acquisition may evolve. Within the first 6-10 months, data indicated a correlational relationship, highlighting a mutual influence among motivation, emotions, and acquisition. However, as the study progressed beyond one year, after students had fully adapted to their educational settings, a clearer causal relationship emerged: Enhanced motivation and more positive emotions were linked to increased classroom practice, leading to significant gains in L2 speech proficiency. The predictive roles of Anxiety remained unclear in this longitudinal dataset.

There has been a growing emphasis on researching foreign language boredom in second language acquisition in recent years. However, existing research has yet to reach a consensus regarding the effect of foreign language boredom on learners’ learning achievement. To address this gap, the present study employs multilevel meta-analysis to analyze 47 effect sizes from 33 empirical studies involving a total sample size of 27,838 participants. The findings reveal that foreign language boredom illustrates a small negative effect (r = -.24, p < .001) on language achievement. Furthermore, the moderation analysis reveals that the magnitude of the effect size varies crossing educational stages, achievement measurements, domain-specific language skills, foreign language boredom measurements, teaching modes, and learning contexts. This study provides robust evidence to support the detrimental role of foreign language boredom in language acquisition and identified substantive gaps in this research field, offering valuable directions for future research.

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 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.

This study explored how second language (L2) speakers’ use of multiword sequences in speech predicted perceived fluency ratings while controlling for their utterance fluency. A total of 102 Japanese speakers of English delivered an argumentative speech, which was analyzed for bigram and trigram measures (frequency, proportion, and mutual information) and utterance fluency measures capturing three subdimensions: speed, breakdown, and repair fluency (Tavakoli & Skehan, 2005). Perceived fluency was assessed by 10 experienced L2 raters. Mixed-effects regression analyses revealed that after establishing the parsimonious model solely by UF predictors (marginal R2 = .61), a frequency-based n-gram predictor––bigram proportion––slightly but significantly explained the remaining variance of fluency rating scores (0.8%). The results indicated that multiword sequences in speech had a small but systematic impact on perceived fluency, even controlling for the effects of utterance fluency. This finding contributes to the discussion concerning the role of multiword sequences in fluent speech production.

This study investigated factors influencing the citations of highly cited applied linguistics research over two decades. With a pool of 302 of the top 1% most cited articles in the field, we identified 11 extrinsic factors that were independent of scientific merit but could significantly predict citation counts, including journal-related, author-related, and article-related features. Specifically, the results of multiple linear regression models showed that the time-normalized article citations were significantly predicted by the number of authors, subfield, methodology, title length, CiteScore, accessibility, and scholar h-index. The remaining factors did not exhibit any statistical significance, including the number of references, funding, internationality, and geographical origin. The combined predictive power of all these factors (R²=.208, p<.05) verifies the role of nonscientific factors contributing to high citations for applied linguistics research. These results encourage applied linguistics researchers and practitioners to recognize the underlying forces affecting research impact and highlight the need for a reward system that exclusively favors sound academic practices.

The present study revisits the differential roles of form, meaning, and use aspects of phonological vocabulary knowledge in L2 listening proficiency. A total of 126 Japanese English-as-a-foreign-language listeners completed the TOEIC Listening test, working memory and auditory processing tests, the Metacognitive Awareness Listening Questionnaire, and several tasks designed to tap into three broad aspects of phonological vocabulary knowledge: (1) the ability to access phonological forms without any orthographic cues (phonologization), (2) the ability to recognize words regardless of the talker (generalization), and (3) the ability to determine the semantic and collocational appropriateness of words in global contexts in a fast and stable manner (automatization). Whereas the perceptual, cognitive, and metacognitive variables made relatively small contributions to L2 listening proficiency (0.4%–21.3%), the vocabulary factors explained a large amount of the variance (77.6%) in the full regression model (R2 = .507). These large lexical effects uniquely derived from the three different aspects of phonological vocabulary knowledge—automatization (55.3%), phonologization (20.8%), and generalization (1.5%). The findings suggest that successful L2 listening skill acquisition draws on not only various levels of phonological form-meaning mapping (phonologization, generalization) but also the spontaneous and robust retrieval of such vocabulary knowledge in relation to surrounding words (automatization).

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.

Growing evidence suggests that ratings of second language (L2) speech may be influenced by perceptions of speakers’ affective states, yet the size and direction of these effects remain underexplored. To investigate these effects, 83 raters evaluated 30 speech samples using 7-point scales of four language features and ten affective states. The speech samples were 2-min videorecordings from a high-stakes speaking test. An exploratory factor analysis reduced the affect scores to three factors: assuredness, involvement, and positivity. Regression models indicated that affect variables predicted spoken language feature ratings, explaining 18–27% of the variance in scores. Assuredness and involvement corresponded with all language features, while positivity only predicted comprehensibility scores. These findings suggest that listeners’ perceptions of speakers’ affective states intertwine with their spoken language ratings to form a visual component of second-language communication. The study has implications for models of L2 speech, language pedagogy, and assessment practice.

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.

There is evidence that learning a second language (L2) can shift cognition toward that predicted for the L2 and that this effect might vary with L2 proficiency, age of acquisition, length of immersion, etc. Here we explore the previously neglected variable of language instructional conditions. Participants categorized motion events in a triads-matching task after being trained on two novel linguistic labels highlighting (in)transitivity through one of three instructional conditions. Participants who learned the relevant knowledge under a meaning-focused instructional condition (memorizing meanings of exemplar sentences) showed a higher likelihood of categorizing based on motion (in)transitivity immediately after training than a control group; those who learned under a required rule search instructional condition showed this effect only after additional practice; while those who learned through another type of form-focused instructional condition (direct metalinguistic explanation) did not show this effect even after such practice. These differences were obtained despite the fact that the three groups were matched on awareness of the target system at the level of understanding and near-perfect performance on a grammaticality judgment task. The findings are discussed in terms of the depth of processing in instructed SLA and models of language–cognition interactions.

People are frequently concerned about the impressions they make on others (referred to as metaperceptions), but their insights are often inaccurate. Illustrating the phenomenon called the liking gap, speakers interacting in their first language (L1) and second language (L2) tend to underestimate how much they are liked by their interlocutor, and these judgments often predict their desire to engage in future interaction and collaboration. To understand the scope of this bias and its consequences, we focused on L1–L2 dyadic interaction, examining metaperception as a potential barrier to conversations between university students. We recruited 58 previously unacquainted university students to perform a 10-min academic discussion task between one L1 and one L2 speaker. Afterward, the speakers (a) assessed each other’s interpersonal liking, speaking skill, and interactional behavior; (b) provided their metaperceptions of their interlocutor’s assessments of the same dimensions; and (c) estimated their interest in future interaction with the same interlocutor. All speakers showed a reliable metaperception bias to underestimate their interpersonal liking, speaking skill, and interactional behavior. However, only L1 speakers’ desire to engage in future interaction was associated with their metaperceptions of interpersonal liking. We discuss implications of this finding for understanding and promoting academic communication.

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.

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.

This study focused on the relative contributions of cognitive aptitudes to the incidental learning and automatization of mini-language grammar. Over three sessions, participants (N = 45; first language [L1] Polish; age range: 19–35) completed computerized training in MiniItaliano as well as tasks tapping into working memory, general intelligence, and language analytic ability (LAA). The overt aim of the training was to comprehend messages placed in the context of an Italian supermarket; however, its actual goal was to acquire the target syntactic rules (i.e., subject-verb agreement, gender agreement, word order). The final session included a surprise post-test with untimed and timed grammaticality judgment tasks (GJTs) as measures of explicit and automatized knowledge, respectively. Results showed that performance on both tasks was predicted by LAA over and above other cognitive and control measures. Additionally, scores on the untimed GJTs were related to reliance on (self-discovered) rules, while timed GJTs involved general IQ.

The current study examined the comprehension and production of classifiers, case marking, and morphological passive structures among 414 child Japanese heritage speakers (mean age = 10.01 years; range = 4.02 – 18.18). Focusing on individual differences, we extracted latent experiential factors via the Q-BEx questionnaire (De Cat, Kašćelan, Prévost, Serratrice, Tuller, Unsworth, & The Q.-Be Consortium, 2022), which were then used to predict knowledge and use of these grammatical structures. The findings reveal that: (i) experiential factors such as heritage language (HL) engagement at home and within the community modulate grammatical performance differentially from childhood through adolescence, and (ii) HL proficiency, immersion experiences, and literacy systematically predict HL grammatical outcomes. These results indicate that particular language background factors hold differential significance at distinct developmental stages and that higher proficiency, richer immersion experiences, and literacy engagement in the HL are crucial for the development of core grammatical structures.