Exploring Publication Trends, Geographic Contributions, and Research Collaborations in Deep Science Learning: Systematic Literature Review - Hamdani; Reni Marlina; Dedeh Kurniasih; Chokchai Yuenyong; Zulfahmi (Universitas Syiah Kuala, 2026)
Deep learning has become a trend in current learning objectives and has become a hot topic in the context of computing, and is widely applied in various application fields including science known as deep science learning (DSL). This study aims to reveal an overview and systematic review of research hotspots and emerging trends in DSL studies. This study raises the reputation of bibliometric applications that are proven to be able to analyze articles published between 2015 and 2025. We extracted articles with the words “deep AND science AND learning” and generated 251 articles by limiting them to English-language articles published in reputable international journals. The results reported that China is the country that has published the most articles and occupies the top position in collaborative article writing with other countries. Other findings reported that based on the WordCloud overview, the keywords that are often used in expressing DSL are deep learning and machine learning. The urgency of this research among others can be fundamental research in the development of science learning media and methods and approaches that are in accordance with the development of revolution 4.0. This study is able to present a breakthrough in future research designs for science education practitioners, science education lecturers, science teachers, science education observers, prospective teachers, and also students to analyze the findings and develop according to aspects that have not been disclosed in this study.