Xue Wang

Abstract There have been mixed perceptions about the effect of digital multimodal composing (DMC) on learners’ second language (L2) writing performance. This debate is reshaped as generative AI (GenAI) transforms both text-based writing and DMC processes, which may affect their impacts on L2 writing performance. This 14-week quasi-experimental study compares students’ L2 writing performance development in GenAI-assisted DMC, GenAI-assisted text-based writing, and text-based writing conditions. A total of 161 Chinese university students from three intact classes were randomly assigned to three instructional groups: GenAI-assisted DMC (54 students), GenAI-assisted text-based writing (57 students), and a text-based writing group (50 students). The DMC group received instruction in creating two digital posters, whereas the other two groups were guided to write two 150-word essays. Data were collected from pre- and post-writing tests, semi-structured interviews, screen recordings of the writing process, and students’ written reflections. Results show students in the GenAI-assisted DMC group significantly outperformed the other two groups in both overall writing scores and content dimension scores. Analysis of qualitative data indicates that the GenAI-assisted DMC group engaged in more reflection and revision, which may be related to three salient features of the DMC task when compared to text-based writing: (1) reducing pressure and promoting engagement in GenAI-assisted L2 writing, (2) prompting critical engagement with GenAI output based on audience and genre awareness, and (3) encouraging students to learn from each other’s L2 expressions. This study demonstrates how DMC can mitigate concerns about superficial GenAI use, promoting deeper reflection and engagement with AI-generated content and enhancing L2 writing development.

Systematic reviews are essential for evidence synthesis in education, yet data extraction remains a bottleneck: labor-intensive and error-prone. Large language models offer automation potential, but questions remain about AI performance compared to human coders and how researchers experience these tools in practice. We present MetaMate, an open-access web-based tool for automated data extraction in educational systematic reviews. Our mixed-methods evaluation combines a quantitative validation study benchmarking MetaMate against trained human coders across 32 studies and 20 data elements with a qualitative user study involving six educational researchers using think-aloud protocols. MetaMate achieves precision (81-96%), recall (90-100%), and F1 scores (88-96%) comparable to or exceeding human coders, with strengths in mathematical reasoning and semantic comprehension. Qualitative findings reveal insights about trust calibration, verification behaviors, usability challenges, and human-AI collaboration. We contribute empirical evidence on LLM extraction capabilities and design implications for AI-assisted research tools balancing automation with human oversight. MetaMate is available at https://metamate.online.

Self-regulated learning and self-efficacy are fundamental competencies for academic success, yet their development in homeschooling contexts remains understudied despite growing enrollment in home education. This systematic review synthesized research examining these critical learning competencies among homeschooled students. We identified 23 studies (6,312 participants) meeting inclusion criteria. Results revealed that homeschooled students generally reported moderate to high levels of self-regulated learning and self-efficacy. Comparative studies suggested possible advantages for homeschooled students in autonomy and academic self-efficacy, with broadly similar outcomes in other domains. However, the non-causal nature of the studies precludes conclusions on whether and how homeschooling influences these outcomes.

Positive teacher feedback plays a crucial role in student learning. While previous studies on positive teacher feedback have primarily focused on its relationship with student achievement, much less attention has been given to how such feedback associates with student self-efficacy, particularly reading self-efficacy—a key predictor of both reading and overall academic success—among adolescents. The existing research that has explored this relationship often relied on small-sample, correlational studies, which often failed to account for a broad range of potential confounding variables. This study addresses these gaps by applying propensity score matching and weighting to a nationally representative sample of 4,838 U.S. adolescents from the Programme for International Student Assessment (PISA) 2018 data, controlling for an extensive set of covariates. The rationale for using propensity score matching and weighting was to reduce selection bias and better estimate the relationship between positive teacher feedback and student reading self-efficacy. Results show that positive teacher feedback is significantly associated with higher reading self-efficacy among adolescents, with stronger associations observed among students who receive it less frequently. This means increasing positive feedback could potentially benefit adolescents’ reading self-efficacy overall, but may be particularly beneficial for certain groups of students. This study highlights the potential value of teacher training programs focused on positive feedback to support students’ reading self-efficacy.

This meta-analysis synthesized recent research on promoting learner autonomy through strategy instruction in tertiary English classrooms in China for two main purposes: (a) to estimate the effects of strategy instruction on English achievement and outcomes related to learner autonomy; and (b) to examine the moderating effects of a set of intervention and contextual characteristics. A total of 49 studies contributed to 111 effect sizes for this meta-analysis. Using a random-effects model, this study finds that the overall effect size of strategy instruction was 0.92 (p < 0.001). The type of outcome measure significantly moderated the impact of strategy instruction. Interventions that assessed multiple aspects of learner autonomy (cognitive, metacognitive, motivational, and social) had a significantly higher mean effect size than those assessing a single aspect. The results suggest strategy instruction is a viable instructional tool for promoting learner autonomy among college students in English-as-a-foreign-language classrooms. The results emphasize that fully understanding the impact of a strategy instruction intervention on learner autonomy requires an examination of its various dimensions.

This quantitative study examines the dosage effect of a computer-assisted tutoring approach, Tutoring with the Lightning Squad, on the reading achievement of struggling students. Using a multi-site, cluster-randomized controlled trial design, 188 students in Grades 1-3 from six schools in Minnesota and Virginia were randomly assigned to receive either Lightning Squad tutoring or regular reading instruction between November 2016 and January 2017. Implementation results revealed that many students in the treatment group did not receive the full intended dosage of treatment. Dosage analyses indicated that high attendance in the intervention had a significant positive effect on reading outcomes, including Passage Comprehension and Word Attack. The findings emphasized the critical role of implementation fidelity in program effectiveness and suggested that a higher dosage of the Lightning Squad intervention may lead to better outcomes for struggling readers.

Systematic reviews and meta-analyses are crucial for synthesizing evidence but are time-consuming and labor-intensive, especially during data extraction. To address this challenge, we developed MetaMate, an open-access web-based tool leveraging large language models (LLMs) for automated data extraction in educational systematic reviews and meta-analyses. MetaMate utilizes a hierarchical schema and divide-and-conquer approach in its extraction chain, and a from-global-to-local lens and example retriever in its verification chain. We evaluated MetaMate’s performance on 32 empirical studies, extracting 20 data elements related to participants and interventions. MetaMate achieved high precision, recall, and F1 scores, with performance comparable to human coders when benchmarked against an expert-defined gold standard. Notably, MetaMate demonstrated advanced mathematical reasoning and semantic comprehension, surpassing keyword-based approaches and avoiding common human errors. As the first LLM-powered data extraction tool designed specifically for educational research, MetaMate has the potential to significantly streamline the systematic review process and reduce time and effort for researchers. MetaMate is available at https://metamate.online.

This article considers the continuing debate in pronunciation instruction (PI) about whether segmental or suprasegmental features are more important in teaching English to speakers of other languages. While evidence has accumulated on both sides of the debate, the emergence of the notion of English as a Lingua Franca (ELF) further complicates the issue. This article provides a review of current research supporting the different views in the segmental/suprasegmental debate. The review highlights research evidence that examines either the impact of segmental and suprasegmental features on intelligibility or the effectiveness of teaching these features to improve intelligibility. A review of this line of research underlines the context-specific nature of the debate and a third view that blurs the boundary between segmentals and suprasegmentals.

This study explores Chinese English as a foreign language (EFL) learners’ attitudes towards accent for effective English as a lingua franca (ELF) communication. Notwithstanding the research conducted on EFL learners’ perceptions of different variations of the English language for their language learning, little attempts have been made to investigate their perspectives in detail within the context of China. This inquiry thus intends to bridge this gap by exploring EFL learners’ accent preferences for ELF communication. Data were collected qualitatively from 34 students at an international university in China to examine their experiences of EFL learning and ELF communication. Data were classified and categorized based on learners’ accent preferences and then coded for analysis from their learning discourses, cultural media, material conditions and social agents. The results point to various sociocultural and sociohistorical variables that have reified their language choices and ideology to further underpin their native speaker (NS) and non-native speaker (NNS) dichotomy.