A virtual-based disaster learning model was created to enhance understanding of COVID-19 disaster mitigation following the characteristics of elementary school students because the high number of child deaths brought on by the COVID-19 pandemic served as the driving force behind this research. The virtual-based disaster learning model had been deemed valid, but more study is required to ascertain its impact on primary school pupils' comprehension of COVID-19 disaster mitigation. This study sought to determine how the virtual disaster learning model affected elementary school pupils' understanding of COVID-19 disaster mitigation. This study was a quasi-experimental investigation. Non-equivalent control group design was the type of experimental design employed. The t-test showed that students who studied using virtual-based disaster learning models and those who used conventional learning had different average levels of understanding of COVID-19 disaster mitigation. According to the findings, primary school children who learned using virtual disaster learning models had higher scores than students who used conventional methods. This finding revealed that virtual disaster learning models could improve understanding of COVID-19 disaster mitigation. The results of this study's implications can be applied as a different approach to enhancing elementary school kids' comprehension of emergency planning for COVID-19.

The significance of speaking English has been emphasized in Malaysia education. However, speaking problems such as lack of enthusiasm and poor speaking performance emerge among ESL pupils. To resolve this, an online board game “Flippity” is implemented in online speaking lessons. This paper aims to explore the usefulness of the online board game “Flippity” to improve speaking skills among elementary pupils who are in Year 5 as well as to investigate pupils’ perceptions towards the use of the online board game “Flippity”. The study employed a quasi-experimental mixed method design to collect data from thirty Year 5 pupils from a Chinese primary school. Data was collected through speaking pre-test and post-test, semi-structured interview and questionnaire. The results indicated a significant improvement in pupils’ speaking skills in terms of accuracy, fluency, range, interaction and coherence. Pupils also displayed preferences and positive attitude in learning to speak while playing “Flippity”. Further studies are recommended to be done on how “Flippity” could be exploited when learning other language skills such as listening, reading, writing and grammar.

Teaching and learning new songs in class is an integral cultural practice. This paper focuses on a singing lesson and studies the complex relations among the target song, children’s song acquisition and the teacher’s instructions. However, instead of simply examining pitch accuracy or ratings, this paper proposes a novel methodology for analysing song learning in class. First, a transcription method is used to document a video-recorded lesson. Second, syllables are identified as both the essential components of the grammar of songs for children and the fundamental units for analysing singing in terms of pitch, timing and articulation. The individual singing of three children, divided into the syllables of the target song, is examined for these three properties. Third, a comparison between the song model and the children’s singing reveals how rule-breaking in the song corresponds to the children’s difficulties in singing. This detailed and subject-related analysis exemplifies the complex dynamic among the teacher, the object and the learner and is a contribution to the research of subject-related didactics.

This study examines the factors influencing English language achievement among non-English major undergraduate students in Thailand, with a specific focus on the differences between high-achieving and low-achieving learners. Conducted at Rajamangala University of Technology Lanna, this research adopts a mixed-methods approach, combining quantitative data from questionnaires and qualitative insights from semi-structured interviews. Three primary influencing factors were identified: student-related factors (e.g., motivation and self-regulated learning), teacher-related factors (e.g., pedagogical practices and teacher-student interactions), and environmental factors (e.g., availability of learning resources). Student motivation and self-regulation emerged as the strongest predictors of success, while teacher-related factors unexpectedly showed a negative influence, suggesting a misalignment between teaching strategies and student needs. Environmental factors, though positively perceived, had a less direct impact on outcomes. Practical implications include enhancing intrinsic motivation, adopting tailored teaching strategies to meet diverse learner needs, and strengthening teacher-student relationships to support low-achieving students. Policymakers are encouraged to address resource disparities and develop targeted interventions to enhance English language proficiency among students.

This research aims to systematically review the integration of artificial intelligence (AI) in English language teaching and learning. It specifically seeks to analyze the current literature to identify how AI could be utilized in English language classrooms, the specific tools and pedagogical approaches employed, and the challenges faced by educators. Using the PRISMA-guided Systematic Literature Review (SLR) methodology, articles were selected from Scopus, Science Direct, and ERIC, and then analyzed thematically with NVivo software. Findings reveal that AI enhances English teaching through tools like grammar checkers, chatbots, and language learning apps, with writing assistance being the most common application (54.55% of studies). Despite its benefits, challenges such as academic dishonesty, over-reliance on AI (27.27% of studies), linguistic issues, and technical problems remain significant. The study emphasizes the need for ethical considerations and teacher training to maximize AI’s potential. It also highlights societal concerns, including the digital divide, underscoring the importance of equitable access to AI-powered education for learners of all socioeconomic backgrounds.