TESL/TEFL

Tujuan dari penelitian ini adalah untuk memberikan referensi strategi pengajaran literasi menulis. Penelitian ini merupakan penelitian eksperimen kuantitatif. Berdasarkan data analisis, terdapat perbedaan yang signifikan melalui penerapan metode pembacaan dan komposisi terpadu. Peneliti menarik beberapa kesimpulan bahwa terbukti pada kelas eksperimen terdapat perbedaan skor yang signifikan. Kelas eksperimen memiliki berbagai macam skor yang menunjukkan peningkatan dari perlakuan. Peneliti menyadarkan responden bahwa menulis tidak begitu sulit untuk dikuasai dengan menggunakan teknik ini. Setelah peneliti memberikan perlakuan, peneliti memberikan post test dan mendapat skor tertinggi 80 dan skor terendah adalah 60 yang mana memiliki rata-rata 70,36. Dari data tersebut terlihat bahwa mereka memiliki dorongan dalam menulis, dari 63 menjadi 70,36. Dapat disimpulkan bahwa metode ini memberikan efek yang sangat baik dan pasti mempengaruhi kemampuan responden dalam menguasai keterampilan m...

Kajian ini dijalankan bagi meneroka hubungan antara penggunaan ChatGPT dalam kalangan pelajar Kolej Komuniti Arau dengan tahap pembelajaran kendiri serta persepsi terhadap integriti akademik dalam konteks penggunaan teknologi kecerdasan buatan generatif dalam pendidikan tinggi. Perkembangan teknologi kecerdasan buatan generatif pada masa kini telah mempengaruhi cara pelajar memperoleh maklumat dan menyelesaikan tugasan akademik melalui bantuan digital yang lebih interaktif. Walau bagaimanapun, penggunaan teknologi ini turut menimbulkan kebimbangan berkaitan aspek etika akademik sekiranya ia digunakan tanpa panduan yang jelas. Kajian ini menggunakan pendekatan kuantitatif melalui reka bentuk tinjauan dengan pengumpulan data menggunakan soal selidik berstruktur yang melibatkan 150 orang pelajar daripada lima program pengajian di Kolej Komuniti Arau. Data kajian menilai tiga (3) konstruk utama iaitu tahap penggunaan ChatGPT, pembelajaran kendiri dan integriti akademik. Data yang diperoleh dianalisis menggunakan statistik deskriptif, ujian-t sampel bebas serta analisis korelasi Pearson bagi mengenal pasti hubungan antara pemboleh ubah yang dikaji. Hasil kajian menunjukkan bahawa tahap penggunaan ChatGPT berada pada tahap sederhana tinggi dengan nilai min 3.62, manakala persepsi terhadap integriti akademik juga berada pada tahap sederhana dengan nilai min 3.21. Analisis ujian-t menunjukkan terdapat perbezaan yang signifikan dalam persepsi integriti akademik antara pelajar yang menggunakan ChatGPT pada tahap tinggi dan rendah. Selain itu, pelajar dengan tahap penggunaan ChatGPT yang lebih tinggi turut menunjukkan tahap pembelajaran kendiri yang lebih baik. Analisis korelasi Pearson turut menunjukkan wujud hubungan positif yang signifikan antara penggunaan ChatGPT dan pembelajaran kendiri (r = 0.415), serta hubungan negatif yang signifikan antara penggunaan ChatGPT dan integriti akademik (r = −0.298). Dapatan ini menunjukkan bahawa penggunaan teknologi AI generatif dalam pendidikan perlu diimbangi dengan pendekatan pedagogi dan garis panduan etika akademik yang jelas. Secara keseluruhannya, dapatan kajian mencadangkan bahawa penggunaan ChatGPT berpotensi menyokong pembelajaran kendiri pelajar, namun penggunaannya perlu disertai dengan garis panduan etika akademik yang jelas bagi memastikan integriti akademik dapat dipelihara.
Kata kunci - ChatGPT, Integriti akademik, Kecerdasan buatan generatif, Kolej Komuniti, Pembelajaran kendiri

Outliers can significantly affect the performance of regression models, leading to biased estimates and misleading interpretations. The study aims to develop a hybrid approach that combines the positive aspects of Bayesian and m-estimation robust estimation. Specifically, it applies m-estimation robust to estimate beta in historical data and then reuses it inside parameters of Bayesian estimation to solve the contamination problem (outliers) in multiple linear regression parameter estimation. The new strategy is evaluated for efficiency against the classical method and new estimators are obtained by applying the root mean square error (RMSE) criterion. Results: In all simulated instances, the new method (M-Robust Bayesian) produces more accurate parameter estimates than the conventional method (M-estimation Robust). All of the above-mentioned simulation methodologies' estimates were compared using the MATLAB application.

English language acquisition is an area of study that garners a great deal of attention because stakeholders in education are continually looking for possible solutions and better pedagogical tools to make teaching and learning more effective.The emergence of e-learning and its Web 2.0 tools offer more opportunities in language application and encourage independent learning. Today, educators are urged to provide students with 21st century skills that are geared towards creativity and critical thinking and the generation of new knowledge, not merely implementing what has been taught.For English language learning and teaching, this paper aims to explore the perceptions of 3rd year Teaching English as Second Language (TESL) students at the University of Malaysia of Edmodo.It is a qualitative research study with singular focus on Edmodo, a social learning platform with over 52 million users, comprised of teachers, students and parents worldwide.The findings indicate four main advantages...

There are undeniably some contributions of AI to language learning, yet it is still unclear how such technology should be used with known language learning techniques to improve instruction. To address this gap in our understanding, a survey concerning metacognitive, cognitive, and socio-affective learning strategies was given to 511 Chinese EFL learners, along with another survey about using AI for different parts of the writing process (brainstorming, research, outlining, writing, and revision). Results were then correlated using the Spearman rho formula. Findings revealed that metacognitive strategies were linked to a reduction of AI for preliminary planning of writing (brainstorming and research). Specific metacognitive strategies related to goal setting were also associated with less use of AI for outlining. In contrast, cognitive language learning strategies were more closely associated with AI use throughout the writing process, which included finding research, writing an outline, and writing essays or homework assignments. Socio-affective learning strategies appeared to reduce the amount of AI used to revise writing content. Overall, results appear to suggest that targeted metacognitive strategies can help to promote individual autonomy and reduce overreliance on AI. Limiting AI to revision may also promote metacognitive strategy development during other stages of the writing process.

Personalized learning and adaptive learning have emerged as pivotal paradigms in 21st-century education, driven by the integration of artificial intelligence and learner-centered technologies. Despite the rapid growth of scholarly contributions in this domain, a comprehensive synthesis that captures its thematic, temporal, and geographic dynamics has remained lacking. This study aims to map the research landscape of personalized and adaptive learning from 2001 to 2025 through a bibliometric analysis of 922 peer-reviewed documents sourced from the Scopus database. Utilizing bibliometric mapping tools including VOSviewer and Biblioshiny, the study analyzed publication trends, source impact, authorship, international collaboration, keyword cooccurrence, and thematic evolution. The findings reveal a significant surge in publications post-2015, peaking in 2023, indicating an intensifying global focus on individualized educational systems. Key dissemination venues include Lecture Notes in Computer Science, IEEE Access, and the ACM International Conference Proceeding Series. Influential contributors such as Hwang G.-J., Lin F., and Rezai A. were identified, along with a geographic concentration of output in China, the United States, and India. Keyword analysis highlights a transition from early emphases on "learning styles" to recent themes such as "ChatGPT," "reinforcement learning," and "inclusive education." This study offers a longitudinal and multi-dimensional overview of the field, integrating diverse data points into a coherent intellectual narrative. The results serve

Academic writing has been a principal issue in Indonesian universities since students are required to write in English effectively. University students and graduates were found to have low writing ability and do not have sufficient English writing skills. Studies in EFL writing in Indonesian universities mostly focus on the effectiveness of certain approaches by looking at the result of students' written tests. This paper discusses current issues and research trends in EFL writing in Indonesia and offer recommendation for further research which should address students and other stakeholders which significantly influence the teaching of EFL writing in Indonesia.

This study examines the comparative impacts of conversational artificial intelligence (AI), structured AI applications, and peer interaction on English language development among students learning English as a foreign language (EFL). While prior research has distinguished between intelligent personal assistants (IPAs), such as Google Assistant, which enable conversational AIhuman interaction, and AI language learning apps that provide automated speech evaluation and structured dialogues, few studies have compared these approaches with human interaction. We recruited 207 seventh-grade Korean EFL students from middle schools and divided them into three experimental groups (Google assistant, AI app, and peer interaction) and a control group (teacher-centered instruction). Over 12 weeks, students participated in weekly interventions. Preand post-tests assessed their English speaking, listening, and vocabulary skills, while teacher interviews provided qualitative insights. The findings indicated that all experimental groups significantly outperformed the control group in speaking skills, while no significant differences were found among the groups in listening and vocabulary gains. Teacher interviews revealed that conversational AI (Google Assistant) promoted dynamic and engaging exchanges that sustained student interest and enjoyment. The structured AI app offered gamified and scaffolded practice with immediate feedback, enhancing basic speaking skills and confidence, particularly for shy or lower-performing students. Peer interactions fostered authentic communication and peer collaboration opportunities that AI could not fully replicate. These findings underscore the complementary benefits of AI tools and peer interactions in EFL learning, highlighting the need for a balanced integration of both approaches to effectively enhance EFL students' speaking skills.

This study provides a bibliometric overview of artificial intelligence (AI)-assisted English-speaking education based on 248 WoSindexed articles published between 2021 and 2025 (2,046 citations; Hindex = 24). Using co-citation, co-occurrence, and temporal analyses, the study identifies major research patterns. The findings grounded and shift from an early emphasis on speech technologies to learner-centered, pedagogically grounded, and affect-sensitive approaches. The analysis identifies a critical gap, as ethical and governance concerns remain insufficiently addressed. Ethics-related terms do not appear among highfrequency or high-centrality keywords, despite their growing relevance