Integrating Task-Based Language Teaching Model with Deep Learning Principles in Developing EFL Learners’ Metalinguistic Competences

Abstract

This study investigates the integration of Task-Based Language Teaching (TBLT) and deep learning principles in developing EFL learners’ metalinguistic competence in senior high school contexts. Grounded in communicative language teaching and cognitive learning perspectives, the study explores how task-based deep learning environments influence learners’ awareness, monitoring, and regulation of language use. Employing a qualitative research design, data were collected through classroom observations and semi-structured interviews involving 35 EFL learners and 5 English teachers from senior high schools in Central Lombok. The findings reveal that the integration of meaningful communicative tasks with reflective and inquiry-based learning promotes learners’ ability to notice linguistic patterns, evaluate grammatical choices, and regulate language production during interaction. The results indicate that learners moved beyond surface-level language performance toward deeper cognitive engagement, demonstrating increased autonomy and analytical understanding of language structures. Moreover, the study shows that combining deep learning principles with TBLT helps address common implementation challenges in exam-oriented EFL settings by fostering learner-centered participation and efficient classroom interaction. The novelty of this research lies in proposing an integrated pedagogical framework that connects communicative task performance with metacognitive and reflective learning processes to achieve sustainable language development. The study contributes to EFL pedagogy by highlighting the importance of aligning communicative instruction with deep learning practices to enhance both communicative competence and metalinguistic development.