Developing a Deep Learning–Based Digital Learning Module for Dance in Cultural Arts Education

Abstract

Integrating digital technology into cultural arts education presents a persistent pedagogical challenge, particularly in fostering deep, meaningful student engagement while safeguarding intangible cultural heritage. This study developed, validated, and evaluated an interactive digital learning module grounded in a deep learning pedagogical approach—characterized by mindful, meaningful, and joyful learning—centered on the Pakkarena Anida traditional dance of South Sulawesi. Conducted with tenth-grade students at High Secondary School 25 Makassar, the research utilized a Research and Development (R&D) design integrating the ADDIE and 4D instructional models across six distinct phases: analysis, design, development, implementation, evaluation, and dissemination. Authored via Canva and delivered as a cross-platform digital flipbook, the module incorporated multimodal content, including embedded instructional videos, structural movement breakdowns, and formative self-assessments. Expert validations established high feasibility for classroom instruction, while small- and large-scale field tests yielded "good" to "excellent" ratings across content depth, linguistic clarity, interface design, and pedagogical utility. Implementation yielded substantial academic gains: mean cognitive scores rose from 52 to 85, and psychomotor scores increased from 40 to 78, reflecting medium-to-high normalized gains. Beyond quantitative performance, the module enhanced student engagement, collaborative synergy, creative expression, and performance confidence in choreographing contemporary works rooted in tradition. These findings demonstrate that deep-learning-driven digital modules effectively optimize cultural arts education, providing a scalable, innovative framework that bridges cultural preservation with contemporary educational technology.