Needs analysis for developing online task-based speaking skills module for TVET learners

• 主要な著者: Mohd Khirulnizam, Musa, Azwin Arif, Abdul Rahim
• フォーマット: 論文
• 言語: English
• 出版事項: Global Academic Excellence (M) Sdn Bhd (GAE) 2024
• 主題: LB Theory and practice of education
• オンライン･アクセス: http://umpir.ump.edu.my/id/eprint/43526/1/Needs%20analysis%20for%20developing%20online%20task-based%20speaking%20skills%20module%20for%20TVET%20learners.pdf; http://umpir.ump.edu.my/id/eprint/43526/; https://doi.org/10.35631/IJMOE.623019; https://doi.org/10.35631/IJMOE.623019

Proficiency in speaking skills is crucial for Technical and Vocational Education and Training (TVET) learners to effectively communicate in professional settings. This study aims to identify the specific needs and challenges of TVET learners in developing speaking skills through online task-based language teaching (TBLT). The research addresses the key question: What are the specific speaking skill requirements of TVET learners and how can online TBLT frameworks be structured to meet these needs? A comprehensive needs analysis was conducted using surveys administered to 80 TVET learners across various disciplines. The surveys employed both closed-ended and open-ended questions to capture quantitative and qualitative data. Purposive sampling was used to ensure representation from diverse vocational backgrounds. The findings reveal that TVET learners primarily require enhanced skills in professional communication, including presentations, negotiations, and technical explanations. Learners preferred interactive online tools such as video conferencing platforms and language learning applications that provide real-time feedback. Major challenges identified include a lack of face-to-face interaction, limited opportunities for immediate feedback, and technical issues such as unstable internet connectivity. These insights underscore the necessity for a well-structured online TBLT framework tailored to the specific speaking needs of TVET learners. Future research should focus on refining the TBLT framework to better address these challenges and explore the integration of AI-driven tools for real-time feedback.