The appropriate use of synonyms enhances clarity and precision in writing and communication. This study investigates Chinese English-as-foreign-language (EFL) learners’ use of synonyms and factors influencing it, using an explanatory sequential mixed methods approach. Specifically, the study examines the extent to which Chinese EFL learners utilize syntactic and semantic information to distinguish between synonymous pairs, such as suggest and advise, and whether their L2 proficiency modulates their abilities to utilize these two cues. The study also explores factors influencing their use of synonyms through follow-up interviews. Two groups of EFL learners (n = 53) with differing L2 proficiency levels completed a forced-choice test, which also required brief written explanations for their lexical choices. The test included both syntactic and semantic items, with either syntactic or semantic cues available to aid them in making lexical choices. Results revealed that neither syntactic nor semantic knowledge was adequately mastered by learners. In within-subject comparisons, while low-proficiency learners performed comparably on both item types, the high-proficiency group performed better on semantic items than syntactic ones. In between-subject comparisons, the highproficiency group outperformed their lower-proficiency peers on semantic items, but not on syntactic ones. Interview data revealed influencing factors associated with item-specific, learner-centered, and learning-related properties. Pedagogical implications are discussed.
2025-10-10
This research method tutorial article provides a step-by-step guidance on how to carry out linear mixed-effects modeling using R. The guidance was through an example of exploring incidental vocabulary learning under captioned viewing. The data structure is carefully examined to determine an appropriate modeling approach. Both a full model and a simplified model are compared to identify any significant differences in their performance. Subsequently, the optimal model is selected based on the evaluation results. The chosen model is then thoroughly explained and interpreted to shed light on incidental vocabulary learning under captioned viewing. The study encompasses model evaluation and visualizations of the prediction results, providing a comprehensive assessment of the model’s reliability and effectiveness. The aim is to demonstrate the practical application of mixed effects models, showcasing their value in real-world research scenarios for researchers in applied linguistics.
Technological advancements have prompted researchers to examine the integration of technology in learning environments. This study aimed to assess the effects of incorporating technology into vocabulary learning environments. Two intact classes from a large state university were divided into an experimental and a control group, each consisting of 34 participants. The experimental group used Telegram and Xmind applications to learn new vocabulary, creating concept maps with Xmind and sharing them via Telegram. The control group, on the other hand, used traditional concept-mapping techniques. Furthermore, semi-structured interviews were held with 15 participants from the experimental group to examine their perspectives on technology-enhanced learning. The results of the ANCOVA analysis showed that integrating technology significantly improved vocabulary acquisition. The qualitative results revealed that although technology alleviated learning difficulties, enhanced motivation, and improved vocabulary knowledge, it also introduced certain challenges. The study concludes with implications for both learners and educators.
Incidental vocabulary learning from audiovisual input is a key area of interest, but the effects of multimedia elements like subtitles and imagery remain complex. This study investigated how onscreen text (L1 subtitles vs. L2 captions) and visual imagery influenced incidental vocabulary learning from a documentary viewing where target words occurred only once. Using a pretestposttest control group design, it examined the effects of on-screen imagery, subtitle type (L1 vs. L2), and other moderating variables such as cognateness and prior vocabulary knowledge. Learning was assessed via form recognition, meaning recall, and meaning recognition tests. Data were analyzed using Generalized Estimating Equations to account for learner- and word-related factors. Results showed that L2 captions were uniquely effective for meaning recall. Contrary to the literature, onscreen imagery did not provide a consistent benefit and had a significant negative effect on form recognition. The findings demonstrate that prior vocabulary knowledge and cognateness remain powerful predictors of incidental vocabulary learning.
The current study examines the effects of time pressure on vocabulary learning from a cognitive load perspective. It investigated whether the imposition of time restrictions would facilitate automatization and subsequent vocabulary acquisition by managing cognitive resources. Sixty-two Japanese female junior college students were divided into a time-restricted (TR) group and a timeunrestricted (TU) group. Participants learned 20 new English vocabulary items via a computer-based program and were required to select the correct L1 meaning for each item from three options. The TR group was given a 3000-millisecond time limit per item, whereas the TU group was allowed to proceed at their own pace. Following a 15-minute training session, a computer-based test measured vocabulary meaning recognition speed (RTs), learning gains, and the number of practice attempts. A surprise delayed post-test was administered two weeks later to assess long-term retention. The findings suggest that time pressure did not directly improve immediate meaning recognition RTs or learning scores. However, the TR group showed a significantly higher number of practice attempts and a superior long-term retention score. This paradoxical outcome suggests that time pressure, by increasing extrinsic cognitive load, may have expedited the learning process, thereby enabling more practice attempts. These additional attempts, in turn, may have facilitated the automatization of lexical access, leading to enhanced long-term retention. These results indicate that, while not directly improving RTs, time-pressured practice can be a beneficial pedagogical tool for EFL vocabulary learning by increasing a learner’s engagement and practice opportunities.
2026-07-16
This study examined how first language (L1) reading relates to second language (L2) proficiency and whether L2 vocabulary mediates that relationship in a Japanese university context. Participants were 213 first-year students at a private university. L1 reading was assessed with the Reading Skills Test (RST), L2 vocabulary with the New General Service List Test (NGSLT), and L2 proficiency with the TOEFL ITP sections of Listening, Structure and Written Expression, and Reading. A structural equation model estimated both direct and indirect paths among the three constructs. Results showed a positive effect of L1 reading on L2 vocabulary (β = .28, p < .05, 95% CI [.12, .44]) and a strong effect of vocabulary on L2 proficiency (β = .58, p < .001, 95% CI [.45, .71]). The direct path from L1 reading to L2 proficiency was small and not significant (β = .06, p > .05), indicating full mediation through vocabulary. Measurement limitations, including weak factor loadings for some TOEFL subtests (λ = .49-.64), warrant cautious interpretation. The findings highlight vocabulary as the primary channel through which L1 literacy advantages influence the placement-oriented proficiency measures used here. Pedagogically, sustained vocabulary instruction and continued support for L1 reading appear essential. Future research should broaden the definition of proficiency to include productive skills and adopt a longitudinal design to test whether vocabulary growth precedes later gains in overall proficiency.
With the capacity to produce a large number of texts within a short time, ChatGPT can be an effective tool to develop learning materials for extensive reading activities. However, no studies have tested its potential. To address this gap, this study examined the lexical profile of ChatGPT-generated texts targeting learners at six CEFR levels from A1 to C2. We created six corpora from the texts generated by ChatGPT targeting learners with each CEFR level and used RANGE (Heatley et al., 2002) and Nation’s (2012) BNC/COCA 25,000-word family lists to analyse the lexical profile of these corpora. The results showed that regardless of the target CEFR levels, high-frequency words consistently constitute the largest percentage of ChatGPT-generated texts, followed by mid-frequency words and then low-frequency words. Moreover, ChatGPT-generated texts for lower levels (A1, A2 and B1) are less lexically demanding than those for higher levels (B2, C1 and C2). However, ChatGPT-generated texts for the A1 and A2 levels require the same vocabulary sizes as those targeting the B1 level, whereas the figures for C1 are slightly smaller than expected. Together, these findings indicate the potential of ChatGPT as a tool to create learning materials for learners with different proficiency but also highlight the importance of further checking and adjustment of ChatGPT generated texts to better tailor to learners’ language abilities.
Teachers are expected to model emotional literacy and manage classroom affect (Teng, 2017), yet little is known about their ability to produce nuanced emotion vocabulary. In an online survey of 101 ESL/EFL pre- and in-service teachers, we administered a 25-item vignette-based productive emotion vocabulary task (PEVST; two responses per item) alongside LexTALE (proficiency) and the TEIQue-SF (trait EI). Teachers produced high-frequency emotion vocabulary accurately (≈78–81% in the 1k–3k bands), but performance declined for mid-/lower-frequency items (≈45% at 4k/6k, 26% at 9k), indicating strong frequency constraints. LexTALE showed a moderate positive association with PEVST accuracy (about r ≈ .41–.43, depending on the scoring method). In OLS regression (R² = .24), LexTALE emerged as the strongest predictor, while Emotionality contributed a smaller positive effect; the LexTALE × Emotionality interaction did not improve model fit, supporting additive rather than moderating effects. The results suggest that even largely advanced teachers may have limited productive access to lower-frequency emotion vocabulary, motivating more explicit teachereducation and curricular attention to nuanced affective labelling.
With the rise of Generative AI technology, numerous studies have compared linguistic features of human-produced and AI-generated writings. However, little attention has been paid to the use of phrasal verbs (PVs), a difficult type of two-part verbs, in academic writing. This study chose the 1.7-million-word arts and humanities essays from the British Academic Written English corpus and generated an equivalent AI-written corpus using ChatGPT3.5. Extracting PVs with an innovative dependency-based method, the authors compared the frequency, diversity, and disciplinary distribution of all the identified PVs, and examined the polysemy, semantic transparency, and collocations of the two PVs with the highest frequency in both corpora. Findings show that human writers used PVs approximately five times more than ChatGPT3.5 and demonstrated twice the PV diversity of ChatGPT3.5. There were also significant sub-disciplinary differences in both corpora, with essays in archaeology and linguistics using significantly fewer PVs than classics and comparative American studies. Regarding PV-specific comparison, humans used PVs with more meanings, more varied transparency, and broader collocations than ChatGPT3.5. Overall, this study revealed that human writing is more flexible, personal, and spontaneous, while AI writing is more formulaic, rigid, and predictable, which provides important implications for education, applied linguistics, and computer science.
This study is a replication and extension of Hulstijn and Laufer (2001) and focuses on English learners’ incidental learning of vocabulary in specific task conditions and factors that affect this learning. Using Laufer and Hulstijn’s (2001) Involvement Load Hypothesis (ILH) as a basis for examining task effectiveness, we explore EFL learners’ vocabulary learning in three task conditions: Reading, Reading Plus Fill-In, and Writing. Beyond the task effects, we extend the original design by examining learners’ gains at different levels of lexical mastery, measuring their motivation and task engagement, and including interview-based qualitative findings exploring their experiences and perceptions of participation. Findings indicate that Reading was the least effective condition in terms of vocabulary learning, with Reading Plus and Writing resulting in similar lexical gains, only partially confirming Hulstijn and Laufer (2001). As regards task engagement, there were no statistical differences in participants self-reported scores across groups, but the dynamic nature of their engagement was evident, as was the role of their emotions. Based on these findings, we present a theoretical and methodological discussion of the ILH in terms of its use, interpretation and predictions of task effects on vocabulary learning. We also underline the value of replication research and call for more cross-disciplinary research approaches in this area, particularly in relation to learner-related variables.
There has been limited research examining the impact of textual enhancement on incidental learning of multi-word items (MWIs) and single words from captioned viewing, particularly at the level of form recall. This study aims to address this gap. Sixty-two EFL learners encountered 16 MWIs and 18 single words in one of the following conditions: (a) MWI-enhanced captioned viewing (MWI-enhanced CV) (b) single-word enhanced captioned viewing (SW-enhanced CV), (c) uncaptioned viewing, (d) unenhanced captioned viewing (Unenhanced CV). Learning was gauged using a form-recall test with re-arranged items as a pretest, immediate posttest, and delayed posttest. Regarding MWI learning, the MWI-enhanced captioned viewing group significantly outperformed the other three groups on both immediate and delayed posttests. Regarding single-word learning, however, no significant differences were found among the four groups. Learners’ prior vocabulary knowledge had a significantly positive correlation merely with the delayed gains in the single-word enhanced captioned viewing and the uncaptioned viewing conditions. Altogether, the findings demonstrate that instructional support embedded in captions through textual enhancement plays a critical role in incidental vocabulary learning. Theoretically, the results extend the Noticing Hypothesis by suggesting that perceptual salience can selectively support MWI learning without an apparent increase in cognitive load, as inferred from the vocabulary learning gains.
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