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Description
This research focuses on identifying the most effective teaching methods using Task-Based Language Teaching to create English learning materials for specific contexts, such as English for Specific Purposes (ESP). Some units are designed using authentic materials and Artificial Intelligent generated applications. This approach encourages student engagement and autonomy by presenting them with real-world tasks that require a reasoning gap (Nunan, 2004) and cognitive process classification (Willis and Willis, 2007). This study employs Task-Reference Teaching (Bygate (2016); Tavakoli and Jones (2018)) which aims to prepare and evaluate students after each unit. Participants were surveyed using questionnaires to measure their satisfaction and success with these real-world tasks. Data collection involved gathering students' perceptions of their satisfaction levels in completing the tasks and comparing them with feedback checklists provided after each unit. Initial data analysis indicates that 58% of participants found the learning materials aligned with their expectations, 48.1% expressed satisfaction with the quality of the learning materials, and 74.1% reported ease of access to the materials. The insights and deductions derived from this study aim to underscore the significant influence of students' perceptions and achievements in learning through these materials, thus it will shed lights to teaching practices using Task-Based Language Teaching.
Keywords: real-world tasks, reasoning gap, cognitive process classification, Task-Reference Teaching, feedback checklist
