A Bibliometric Analysis of Deep Learning Integration Into STEAM Learning in Indonesia: Trends And Challenges
Abstract
This study aims to explore the integration of deep learning in STEAM (Science, Technology, Engineering, Arts, and Mathematics) learning in Indonesia, focusing on research trends, challenges, and opportunities. The research method used was bibliometric analysis, which allows researchers to analyze publication trends, key topics, and collaboration patterns in the scientific literature related to deep learning and STEAM. Data were collected from Scopus and Google Scholar databases, with the help of Publish or Perish software and VOSviewer for data visualization. The results of the analysis showed that research related to deep learning in STEAM education in Indonesia is still limited, despite increasing interest from 2021 to 2025. The main challenges include limited technological infrastructure, lack of teacher training and resistance to changes in learning methodologies. However, there are great opportunities to improve the quality of education through STEAM integration, especially in developing 21st century skills such as critical thinking, creativity and collaboration. Bibliometric analysis also revealed that deep learning has strong linkages with various aspects of STEAM education, including project-based learning (PjBL) and critical thinking skills development. However, the implementation of deep learning in STEAM in Indonesia still faces barriers on a broader scale, such as limited access to technology and lack of collaboration between institutions. This research recommends improving technology infrastructure, continuous teacher training and developing policies that support the integration of technology in education.
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DOI: http://dx.doi.org/10.21043/elementary.v13i1.31142
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