Understanding Vocational Students' Interest in Deep Learning-Based English Instruction
A Sequential Explanatory Mixed-Methods Study in Indonesia
Abstract
This study investigates vocational students’ interest in learning English through a deep learning approach and explores the motivational factors influencing their engagement in an Indonesian vocational context, where English proficiency is increasingly important for workforce readiness. A sequential explanatory mixed-methods design was employed. Quantitative data were collected from 20 students using a validated Likert-scale questionnaire measuring five dimensions of learning interest. Qualitative data were obtained through semi-structured interviews with eight purposively selected students and analyzed using thematic analysis. The quantitative findings showed consistently high levels of student interest across all dimensions, with motivational interest scoring the highest. The qualitative results identified eight key motivational factors: collaborative learning, critical thinking, interactive teaching style, contextual learning, real-world projects, supportive environment, technology integration, and vocational relevance. Among these, interactive teaching style emerged as the most influential factor, as most participants highlighted its importance in sustaining engagement. Importantly, the qualitative findings revealed a motivation–behavior gap that was not captured in the quantitative data alone. The study contributes to the literature by demonstrating that teaching style functions as a critical mediator in enhancing students’ engagement with deep learning. These findings provide practical implications for improving vocational English instruction.
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