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The analysis of learning traces is essential for understanding and improving the educational process. These traces, which record learners’ interactions with digital environments, provide valuable insights but are often vast and complex, rendering manual analysis inefficient. Generative Artificial Intelligence (GenAI) offers new opportunities to enhance this analysis by providing faster and more precise insights. In this research, the authors present a generative AI-based approach to analyzing learning traces. By combining recent techniques, the proposed hybrid model achieves a predictive performance (R2 = 0.9372) superior to those of previous models. These results demonstrate the potential of GenAI to personalize learning and guide future research in the field of smart education.

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Predictive Learning Analytics: Leveraging Generative AI for Enhanced Academic Performance Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Jamal Eddine Rafiq,
  • Abdelali Zakrani,
  • Said Nouh,
  • Mohammed Amraouy

摘要

The analysis of learning traces is essential for understanding and improving the educational process. These traces, which record learners’ interactions with digital environments, provide valuable insights but are often vast and complex, rendering manual analysis inefficient. Generative Artificial Intelligence (GenAI) offers new opportunities to enhance this analysis by providing faster and more precise insights. In this research, the authors present a generative AI-based approach to analyzing learning traces. By combining recent techniques, the proposed hybrid model achieves a predictive performance (R2 = 0.9372) superior to those of previous models. These results demonstrate the potential of GenAI to personalize learning and guide future research in the field of smart education.