Implementing Deep Learning Approaches in Non-Formal Equivalency Education: Strategies and Pedagogical Implications

Abstract

This study aims to explore strategies for implementing deep learning approaches in non-formal equivalency education and to analyze their pedagogical implications for teaching practices. It addresses the limited use of contextual and holistic learning approaches in equivalency education, which often fail to adequately respond to the diverse learning needs and practical life contexts of learners. A descriptive qualitative method with an exploratory design was employed, involving data collection through in-depth interviews, participatory observation, and document analysis. The participants consisted of 20 individuals, including 5 tutors, 3 PKBM administrators, and 12 Paket C learners, selected through purposive sampling. Thematic analysis was applied to examine how the four components of deep learning—graduate profile dimensions, learning principles, learning experiences, and instructional frameworks—were integrated into practice. The findings indicate that deep learning strategies were operationalized through community-based and vocationally oriented projects aligned with learners’ economic realities, fostering entrepreneurial skills, critical thinking, collaboration, and economic self-reliance consistent with the empowerment orientation of Community Learning Centers (PKBM). Tutors employed participatory and andragogical approaches supported by flexible learning environments and digital technologies. The study concludes that deep learning has the potential to transform equivalency education into a more humanistic and empowering process, better preparing learners to address 21st-century challenges. It recommends strengthening tutor capacity, developing contextual materials, and enhancing community partnerships to ensure sustainable implementation.