Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Jurnal Arbitrer Script Journal: Journal of Linguistic and English Teaching Jurnal Gramatika: Jurnal Penelitian Pendidikan Bahasa dan Sastra Indonesia
Su Kia Lau
Universiti Malaya
Humanities Languange, Linguistic, Communication & Media

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Corpus-Based Language Learning Among EFL Learners in an Environmental Context Handoko Handoko; Sheena Kaur; Su Kia Lau
Script Journal: Journal of Linguistics and English Teaching Vol. 10 No. 2 (2025): October
Publisher : Teacher Training and Education Faculty, Widya Gama Mahakam Samarinda University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/sj.v10i2.2225

Abstract

Background: This research presents a case study on the valuable contribution of corpus linguistics to English instruction with an environmental focus, aiming to raise environmental awareness among students. The study explores the role of corpus linguistics as an effective tool for teaching English in the context of comprehending and discussing environmental issues. Methodology: To achieve this, the research utilizes the News on the Web (NOW) corpus to identify common vocabulary in environmental texts. The study was conducted with a group of 13 students in a Specialized Listening and Speaking class, with an intermediate level of English proficiency. The research was conducted over three terms. Initially, students were provided with 75 words from the News on the Web (NOW) corpus, complete with definitions and example sentences. Subsequently, they were tasked with writing three sentences for each word and memorizing their usage within an environmental context. Finally, the students were tested by having to provide talks on 15 randomly selected words. Findings: The research findings indicate that 10 students were able to proficiently use 60.51% of the environmental words, while three students encountered difficulties in using these terms within the environmental context. Seven students demonstrated their ability to connect sentences coherently, utilizing proper grammar and pronunciation.  Conclusion: This research suggests that most students successfully integrated environmental lexical items into their speaking, showcasing proficiency in grammar and pronunciation. However, most of the students (11 out of 13) require further support in structuring their speech cohesively. Rather than constructing a coherent narrative, they often employ words in isolation. Originality: This underscores the importance of using corpus-based methods to provide relevant vocabulary and fostering the skills necessary for constructing well-structured and cohesive speeches.  
A SYSTEMATIC REVIEW OF AI IN CHINESE COLLEGE ENGLISH EDUCATION BEFORE GENERATIVE AI: TYPES, ROLES, AND FUNCTIONS Kun Sun; Fong Peng Chew; Su Kia Lau
Jurnal Gramatika Vol 11, No 2 (2025): Autumn Issue (October–March)
Publisher : Universitas PGRI Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22202/jg.2025.v11i2.10302

Abstract

The impact of artificial intelligence (AI) on language learning has grown rapidly, with various AI-based tools emerging. However, comprehensive reviews of its role in College English education in China remain scarce. Following PRISMA guidelines, this study systematically reviewed peer-reviewed empirical articles published between August 2013 and August 2023 across nine databases, including CNKI. Of the 1,224 screened studies, 15 empirical works from China (two in Chinese) were included, and a comparative analysis was conducted on the types of AI applied and their relevance to College English teaching. The findings indicate a generally positive attitude toward AI integration in College English, with particular emphasis on AI assessment systems and visual technologies. However, the review also identifies a techno-instrumentalist bias, with AI primarily framed as a tool for performance enhancement, while its implications for learner agency and identity construction remain underexamined. This review summarizes the decade's research trends, revealing that studies predominantly used quantitative and experimental methods, while qualitative research and mixed-methods studies were relatively scarce. Notably, research between 2019 and 2023 surged around immersive and automated systems, raising questions about which types of AI attract academic and institutional focus. Future research should diversify methodologies and address sociocultural, ethical, and equity issues in AI use. Though generative AI is emerging, its accessibility and ease of use mark a shift from resource-intensive tools like VR. Yet, its growing adoption requires critical inquiry into whose knowledge it privileges and what ideologies it reinforces in English education.
A Corpus-Based Study of Adjectives and Collocates in Reddit Posts on Anxiety and Depression Yang Yuhan; Su Kia Lau; Handoko Handoko; Leng Lee Yap; Sheena Kaur
Jurnal Arbitrer Vol. 13 No. 2 (2026)
Publisher : Masyarakat Linguistik Indonesia Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ar.13.2.145-160.2026

Abstract

Talking openly about anxiety and depression (A&D) remains difficult for many people because of the stigma surrounding mental illness. Anonymous online platforms such as Reddit provide a space where users can express their thoughts and emotions more freely. This study considers how individuals linguistically construct and intensify emotional distress by examining (1) the adjectives used to express A&D, (2) the content-word collocates that co-occur with these adjectives, (3) the lexical features of these collocates, and (4) the emotional meanings conveyed through these collocational patterns. The dataset consisted of 1,440 Reddit posts (approximately 300,000 tokens) systematically sampled from the r/Anxiety and r/Depression subreddits between 2023 and 2025. An observational mixed-methods corpus linguistic approach was used to examine the data. Quantitative corpus linguistic analyses were carried out using AntConc, including frequency profiling and Mutual Information (MI) analysis, and were enhanced by qualitative concordance and Keyword-in-Context (KWIC) analysis to examine collocational patterns in context. The analysis shows a predominance of negatively valenced adjectives (e.g., anxious, depressed, hopeless, and suicidal), whose meanings are systematically intensified through their collocational environments. The collocates show distinctive lexical features. These include clinical nouns, linking and change-of-state verbs, and degree and frequency adverbs. These features construct varying levels of affective intensity and psychological distress. Emotional meaning is encoded in recurrent collocational patterns. Individual lexical items reveal only part of this meaning. This shows the value of collocational analysis for digital mental health research. The findings also possess practical implications. They may help improve the diagnostic sensitivity of automated digital mental health tools and foster more empathetic clinical communication.
Co-Authors Fong Peng Chew Handoko Handoko Kun Sun Leng Lee Yap Sheena Kaur Yang Yuhan