This study explores the construct of English learning goals and actions for goal achievement among Japanese English as a foreign language (EFL) learners, as well as the correlations with learner motivation and willingness to communicate (WTC). Based on responses from 255 learners, this study developed original scales to categorize learner goals and actions for achieving those goals. From the exploratory factor analyses, English learning goals were categorized into three factors: enhancing communicative competence, positive attitudes towards English use, and maintaining minimum required English skills. Accordingly, this scale was named the Goals for Learning English Scale (GLE Scale). Similarly, actions for goal achievement were categorized into daily self-directed English practice, technology-based English training, and active engagement in communication activities. This scale was named the Actions for Goal Achievement Scale (AGA Scale). Pearson’s correlation analysis revealed that all the GLE Scale and the AGA Scale factors positively correlated with learner WTC. Moreover, highly motivated learners exhibited higher scores in goal-setting and goal-oriented actions than their low-motivated counterparts. The two sets of scales developed in this study could be used by researchers and practitioners in various EFL contexts to assess learners’ goal-setting and goal-achievement behaviours in future practice.
Gender and linguistic inclusivity are pivotal in language assessment practice, particularly in this study, in the design of multiple-choice questions. The purpose of this study is to analyze the inclusivity of the selected MCQA design, the underlying factors that influence it, and the inclusive modifications that can be implemented to better accommodate learners from diverse gender and linguistic backgrounds. This study is a Qualitative Descriptive Design, which provided a comprehensive report about the research questions. A total of at least two (2) teachers and three (3) students were able to participate in the In-Depth Interviews (IDIs), while one (1) teacher and two (2) students for the Focus Group Discussions (FGDs). A total of eight (8) participants were purposefully selected in this study. In the data collection, Fairclough’s Critical Discourse Analysis served as the theoretical framework for this study, guiding both the researcher and participants in the data analysis. The study revealed that using Fairclough’s textual dimension, three dominant patterns of inclusivity emerged in the MCQA items: (1) gender representation, (2) lexical difficulty, and (3) cultural-linguistic orientation. In addition, it revealed how the mismatch between test designers’ assumptions and the actual test takers may affect students’ performance, which Fairclough calls discursive misalignment. Lastly, in the social analysis, factors shaping MCQA design were context and language use, curriculum, teacher training, and institutional demands, and resources used in constructing MCQA.
This study explores the implementation of a self-paced grammar instruction system supported by generative AI tools (ChatGPT and Google Gemini) in an under-resourced Japanese high school. The program integrated level-based worksheets, AI-generated feedback, and teacher scaffolding over six weeks, targeting students with diverse academic backgrounds and interrupted schooling histories. Data were collected through surveys, progress records, and classroom observations. Results indicated that students appreciated the flexibility and autonomy of the self-paced approach. While some desired more direct teacher input, the overall findings suggest that the AI-supported learning environment contributed to increased motivation, iterative learning, and differentiated support. Rather than focusing on the effects of AI alone, this study highlights how learner-centered design, when supplemented by AI tools, can foster engagement and autonomy in linguistically diverse classrooms. This practitionerresearch contributes to the growing field of AI-supported education by presenting a scalable model designed for under-resourced or alternative secondary education contexts. The study underscores the relevance of learner-centered pedagogy and self-paced systems enhanced by AI tools, especially in schools serving students with diverse needs. Implications for using generative AI to support autonomy, inclusion, and engagement in EFL classrooms are discussed.
Apologies are crucial to maintaining social harmony, particularly in cultures where interpersonal relationships and respect hold significant importance. For ESL learners, mastering how to apologise appropriately in English involves more than vocabulary or grammar—it requires an understanding of underlying politeness norms and pragmatic competence. In Malaysian classrooms, indigenous learners, such as the Iban community, often face challenges not only in acquiring English but also in navigating the cultural expectations embedded within it. Politeness strategies, which differ across languages and cultures, influence how learners express apologies. Failure to transfer these strategies effectively may result in pragmatic failures—such as misunderstandings, perceived rudeness, or communication breakdowns. This paper examines the apology strategies employed by Iban undergraduates in both Iban and English across matched social situations. Analysed through Brown and Levinson’s (1987) politeness theory, the data reveal distinctive patterns that reflect sociocultural values and highlight the impact of L1 norms on L2 use. Based on these findings, the paper proposes pedagogical activities to help EFL learners, especially indigenous students, develop pragmatic competence. The goal is to shift from theory to classroom application, equipping teachers with culturally responsive strategies to teach apologies in meaningful, context-sensitive ways.
