The Impact of Data-Driven Learning on Vocational High School Students’ Motivation in English Language Learning

This study investigates the impact of Data-Driven Learning (DDL) on the motivation of vocational high school students in Indonesia, particularly those studying automotive engineering. Traditional grammar-translation methods used in vocational schools often fail to engage students because they do not connect language learning with practical, real-world applications. This gap is significant in vocational education, where students view English as a tool for their careers rather than an academic subject. DDL, which engages learners with authentic language data from their vocational fields, offers a promising alternative by making language learning more relevant and interactive. Using a quasi-experimental, mixed-methods design, the study involved 60 students divided into an experimental group (DDL-based instruction) and a control group (traditional instruction). Motivation was measured using Dornyei’s L2 Motivation Self System, focusing on Ideal L2 Self, Ought-to L2 Self, and L2 Learning Experience. Results showed that students in the experimental group experienced significant gains in all three motivational components, particularly in Ideal L2 Self and L2 Learning Experience, highlighting how DDL made English more relevant to their career goals. In contrast, the control group saw only minor improvements in motivation. Qualitative findings from open-ended responses and classroom observations emphasized the role of learner autonomy and the relevance of materials in enhancing motivation. The study concludes that DDL can effectively address the gap in vocational education by making English learning more meaningful, though it requires additional support to help students manage complex language data.