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Deep Learning policy has emerged as an important approach to transforming conventional education toward meaningful, contextual, and student-centered learning. This study aims to analyze the implementation of the Deep Learning policy and its realization through innovative learning practices at SMK Negeri 2 Karanganyar. A descriptive qualitative research design was employed, with data collected through classroom observations and semi-structured interviews involving the principal, vice principal for curriculum, and subject teachers. Data were analyzed using the interactive model of Miles and Huberman, comprising data reduction, data display, and conclusion drawing and verification. The findings indicate that the Deep Learning policy has begun to transform classroom practices by emphasizing conceptual understanding, active student participation, contextual application, collaboration, problem solving, and reflection. The policy is operationalized through Project-Based Learning, Problem-Based Learning, contextual and collaborative learning, and the use of digital learning resources. These practices provide opportunities for students to connect theoretical knowledge with authentic vocational contexts while developing critical thinking, independence, communication, and problem-solving competencies. However, implementation remains constrained by differences in student readiness, limited instructional time, teacher workload, and the complexity of managing innovative learning activities. The study further reveals that institutional support through curriculum monitoring, teacher mentoring, professional development, collaboration, and school leadership is essential for sustaining implementation. The findings highlight that effective Deep Learning in vocational education requires alignment among educational policy, teacher capacity, student readiness, curriculum management, and institutional support.
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Susanto et al. (2026) studied this question.
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