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<p>This study presents a scientometric analysis of educational technology research through examining highly cited articles published between 2014 and 2023 in 19 SSCI-indexed Q1 journals. Using a weighted approach to address citation bias, we analyzed 1,770 highly cited articles through document co-citation analysis, keyword analysis, and abstract content analysis. The findings reveal eight distinct research clusters, with Technology Acceptance Model, Computational Thinking, and Classroom Approach emerging as dominant clusters. The analysis identifies five major research themes, with AI-Enhanced Learning Technologies comprising 39% of the research focus, followed by equal distribution (17% each) among Virtual Learning Environments, Digital Learning Practices, and Learning Assessment &amp; Feedback, while Educational Technology Integration accounts for 11%. Keyword analysis further indicates the field’s evolution toward more sophisticated technological applications such as virtual, online learning, and learning analytics emerging as prominent terms. This study demonstrates a significant transformation from basic technology integration to advanced AI-driven solutions. The findings provide valuable insights for researchers and practitioners in educational technology, suggesting future research directions should focus on AI integration, immersive technologies, and data-driven approaches while maintaining emphasis on pedagogical effectiveness and student engagement.</p>

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A decade of highly cited articles in educational technology research: emerging trends, dominant themes, and future directions

  • 期刊论文

  • 发表日期：

    2025年3月25日

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  • Mohammed Mohsen,
  • Sultan Althebi,
  • Elias Bensalem,
  • Naji Alsherif

摘要

This study presents a scientometric analysis of educational technology research through examining highly cited articles published between 2014 and 2023 in 19 SSCI-indexed Q1 journals. Using a weighted approach to address citation bias, we analyzed 1,770 highly cited articles through document co-citation analysis, keyword analysis, and abstract content analysis. The findings reveal eight distinct research clusters, with Technology Acceptance Model, Computational Thinking, and Classroom Approach emerging as dominant clusters. The analysis identifies five major research themes, with AI-Enhanced Learning Technologies comprising 39% of the research focus, followed by equal distribution (17% each) among Virtual Learning Environments, Digital Learning Practices, and Learning Assessment & Feedback, while Educational Technology Integration accounts for 11%. Keyword analysis further indicates the field’s evolution toward more sophisticated technological applications such as virtual, online learning, and learning analytics emerging as prominent terms. This study demonstrates a significant transformation from basic technology integration to advanced AI-driven solutions. The findings provide valuable insights for researchers and practitioners in educational technology, suggesting future research directions should focus on AI integration, immersive technologies, and data-driven approaches while maintaining emphasis on pedagogical effectiveness and student engagement.