Indonesian Journal of Research and Educational Review (IJRER)
Vol. 5 No. 4 (2026): September

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

Nurul Azizah Asri (Department of Arts Education, Graduate Program, Universitas Negeri Makassar)
Syakhruni Syakhruni (Department of Arts Education, Graduate Program, Universitas Negeri Makassar)
Nurwahidah Nurwahidah (Department of Arts Education, Graduate Program, Universitas Negeri Makassar)



Article Info

Publish Date
01 Aug 2026

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.

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