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All Journal LLT Journal: A Journal on Language and Language Teaching Language, Technology, and Social Media
Elyas, Tariq
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Arts Humanities Education Languange, Linguistic, Communication & Media Social Sciences

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Journal : LLT Journal: A Journal on Language and Language Teaching

 1 
PRE-SERVICE LANGUAGE TEACHERS’ READINESS FOR DEEP LEARNING APPROACHES: INSIGHTS FROM A CROSS-REGIONAL STUDY IN ASIA Aridan, Muhammad; Hijriyah, Umi; Khabibjonovna, Kushieva Nodira; Geng, Hui; Azad, Inamul; Elyas, Tariq
LLT Journal: A Journal on Language and Language Teaching Vol 28, No 2 (2025): October 2025 (in progress)
Publisher : English Education Study Programme of Sanata Dharma University, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/llt.v28i2.12274

Abstract

This study examines the readiness of pre-service language teachers from five Asian countries (China, Indonesia, Uzbekistan, Saudi Arabia, and India) to implement deep learning strategies. A total of 6,113 participants completed a Likert-scale readiness questionnaire administered via Google Forms, and their responses were analyzed using the Rasch model, focusing on their preparedness in pedagogical, technological, and affective dimensions. The results revealed significant regional differences, with teachers from China demonstrating the highest levels of readiness, particularly in pedagogical and technological aspects, due to stronger institutional support. In contrast, teachers from India and Indonesia showed lower readiness, particularly in technological integration and institutional support. Gender differences were also observed, with female teachers showing higher readiness in pedagogical and affective areas. Age played a role, with teachers aged 26–35 years showing higher levels of readiness compared to younger participants. The findings highlight the need for teacher education programs to tailor their approaches to address regional, gender, and age-related differences, ensuring that all pre-service teachers are prepared to implement deep learning strategies effectively in diverse contexts.
Co-Authors Albogami, Hajer Mohammed Alrawi, Maather Alshurafa, Nuha Azad, Inamul Geng, Hui Khabibjonovna, Kushieva Nodira Muhammad Aridan Umi Hijriyah