Mapping Deep Learning Research in Digital Transformation of Islamic Religious Education: Bibliometric Analysis 2015-2024
Abstract
This study aims to conduct a systematic mapping of deep learning research in the digital transformation of Islamic Religious Education (PAI) in the 2015-2024 period through bibliometric analysis. The research method used Publish or Perish to extract data from Google Scholar and VOSviewer for research network visualization. From 996 articles found, 89 articles were selected and analyzed comprehensively. The results showed a significant increase in publications related to deep learning and digital transformation in Islamic Education, with major contributions from eleven key researchers, including DS Dalimunthe with an article entitled “Transformation of Islamic Education” which received 150 citations. Analysis of the research network revealed close links between deep learning, the digital era, character education, and Islamic teacher education. The findings show that digital transformation plays an important role in designing innovative PAI teaching strategies, with deep learning having the potential to create a more personalized and adaptive learning experience. This study concludes that despite the increase in publications, specific studies on the interconnectedness of deep learning, digital transformation, and PAI are still limited, especially in the period 2023-2024. The implications of this research provide a conceptual framework for future research development in the integration of advanced technology with Islamic education while maintaining the essence of spiritual and moral values.
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DOI: https://doi.org/10.26618/jtw.v10i01.17803
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