Global Trends of Deep Learning in Education Policy: A Bibliometric Analysis and Implications for Indonesia
DOI :
https://doi.org/10.70188/ch4z7012Mots-clés :
deep learning, education policy, bibliometric analysis, Indonesian educationRésumé
The transformation of education in Indonesia through policies such as Merdeka Belajar–Kampus Merdeka requires more adaptive, data-driven learning approaches capable of addressing the complexity of learners’ needs in the digital era. In this context, deep learning has become increasingly important due to its ability to analyze educational data deeply and support more accurate and context-sensitive decision-making. Therefore, this study aims to analyze the development, distribution, and research trends of deep learning in education policy, as well as to identify existing research gaps. This study employs a bibliometric approach using data sourced from the Scopus database. The search process was conducted through titles, abstracts, and keywords, followed by a refinement process based on specific criteria, resulting in 493 documents for analysis. Data analysis was carried out using a quantitative descriptive approach, focusing on publication trends, country distribution, and subject areas. The results indicate that publications on deep learning in education have increased significantly in recent years and are dominated by technologically advanced countries such as China, the United States, and the United Kingdom. In terms of subject areas, research is largely dominated by social sciences, suggesting that studies tend to focus more on policy, implementation, and educational impacts rather than purely technical aspects. Additionally, several research gaps were identified, including conceptual ambiguity, limited evaluation frameworks, and a gap between policy and practice. This study implies that the development of deep learning in education should adopt a multidisciplinary and contextual approach, taking into account the readiness of educational systems, particularly in Indonesia. The integration of deep learning is expected to strengthen educational policy implementation and improve the quality of learning in a sustainable manner.
Références
Abdurakhimova, J., Ruzmetova, M., Jalolova, S., Amirova, D., Gozieva, M., Abdurazakova, S., & Jalgasov, N. (2025). Bibliometric Analysis of the Deep Learning Approach in Teaching the English Language. 7(7), 821–838. https://doi.org/10.30564/fls.v7i7.10289
Du, M., Zhu, L., & Nardo, M. D. (2025). Strategic Application of Deep Learning Methods in Global Educational Collaboration. 313–317. https://doi.org/10.1145/3729605.3729660
Gupta, B. M., & Dhawan, S. M. (2019). Deep learning research: Scientometric assessment of global publications output during 2004-17. 3(1), 23–32. https://doi.org/10.28991/esj-2019-01165
Hakiki, M., Putra, B. A. W., Hamid, M. A., Utami, R., Saputro, I. N., Azizah, W. A., … Yassin, A. (2026). Deep Learning Methods Towards a Pedagogical Framework and Implementation Strategy: A Study of Information Technology Education Curriculum Development in Indonesia. 20(1), 185–205. https://doi.org/10.22329/jtl.v20i1.9970
Mao, M., Li, Z., Zhao, Z., & Zeng, L. (2018). Bibliometric analysis of the deep learning research status with the data from web of science. 10943 LNCS, 585–595. https://doi.org/10.1007/978-3-319-93803-5_55
Pahrudin, A., Aridan, M., & Barata, M. F. (2025). Teacher Readiness for Deep Learning in Islamic Education: A Rasch Model Analysis of Challenges and Opportunities. 19(4), 262–283. https://doi.org/10.22329/jtl.v19i4.9573
Pan, Q., Zhou, J., Yang, D., Shi, D., Wang, D., Chen, X., & Liu, J. (2023). Mapping Knowledge Domain Analysis in Deep Learning Research of Global Education. 15(4). https://doi.org/10.3390/su15043097
Pandang, A. (2025). Implementation of Deep Learning Pedagogy in Curriculum Reform: Primary School: Teachers’ Perspectives in Indonesia. 24(11), 618–636. https://doi.org/10.26803/ijlter.24.11.29
Putri, D. K., Zahro, N., Widodo, W., & Suprapto, N. (2025). Primary teachers’ perceptions of deep learning pedagogy in culture-integrated STEM education: A quantitative survey. 16(4), 1331–1346. https://doi.org/10.22342/jme.v16i4.pp1331-1346
Sergis, S., & Sampson, D. (2019). Unraveling the Research on Deeper Learning: A Review of the Literature. https://doi.org/10.1007/978-3-030-15130-0_13
Wang, Y., & Chen, L. (2025). A Decision Support System for Ideological and Political Education in Universities Based on Deep Neural Networks and Reinforcement Learning. 36–42. https://doi.org/10.1109/ICMEIM66684.2025.11306928
Winje, Ø., & Løndal, K. (2020). Bringing deep learning to the surface: A systematic mapping review of 48 years of research in primary and secondary education. 4(2), 25–41. https://doi.org/10.7577/njcie.3798
Zhang, F., Wang, X., & Zhang, X. (2025). Applications of deep learning method of artificial intelligence in education. 30(2), 1563–1587. https://doi.org/10.1007/s10639-024-12883-w
Zhang, Z. (2025). The Practice and Visualization Analysis of Deep Learning in Education and Teaching. 588–594. https://doi.org/10.1145/3775073.3775166
Zhou, J., Zhang, H., & Ding, L. (2025). The Global Research Landscape of AI in education: A Bibliometric analysis of Pathway Evolution and Frontier Issues. 90–98. https://doi.org/10.1109/ICETT66247.2025.11137070
![](<img src="https://www.clipartmax.com/png/middle/38-383641_open-access-logo-plos-transparent-open-access-logo.png" alt="Open Access Logo Plos Transparent - Open Access Logo@clipartmax.com">){kind=spacer}
