Transformasi Pembelajaran Fisika Berbasis STEM Deep Learning: Peluang dan Tantangan Masa Depan
Kata Kunci:
Physics EducationAbstrak
This study aims to map the trends, collaborations, opportunities, and challenges of STEM-based physics learning integrated with deep learning. The method used is a bibliometric analysis of 282 documents from the Scopus database, published from 2015 to 2025, using keywords representing the connection between physics education, STEM education, and deep learning. The analysis was conducted with Scopus Analytics, Microsoft Excel, and VOSviewer to visualize publication trends, author collaboration networks, country distribution, and keyword maps. The results show that publications on this topic have increased significantly since 2023, peaking in 2024, with a dominant contribution from developed countries like the United States. "STEM education" and "artificial intelligence" were identified as the central research hubs, with "deep learning" acting as a connector to cutting-edge topics such as generative AI, chatbots, and adaptive learning. Implementation opportunities include personalized learning, interactive simulations, and automated data analysis, while challenges involve the need for high-quality data, potential biases, technical complexity, and educator readiness. These findings are expected to serve as a foundation for developing innovative, adaptive, and relevant physics learning strategies for the 21st century.
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Referensi
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All relevant data supporting the findings of this study are available within the article
