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<p>Artificial intelligence (AI) is a powerful technology with the potential to revolutionize various fields by incorporating human-like intelligence into systems and robots. Different types of AI, such as visual, textual, and analytical, can enhance applications and solve real-world problems. However, the dynamic and diverse nature of real-world data poses challenges in developing effective AI models. This research explores the application of machine learning (ML) and deep learning (DL) for information retrieval within a smart E-Learning system. It introduces an AI model called LearnAlytics, designed to assist decision-makers in implementing ML and DL techniques for information retrieval. LearnAlytics offers valuable insights applicable to various real-world scenarios and application domains. Extensive simulation results demonstrate that LearnAlytics outperforms traditional models, showcasing its superior effectiveness. The key contributions of this research include the development of a novel AI model for smart E-Learning, significantly enhanced information retrieval capabilities, data-driven decision-making support, generalizability and scalability of the model, and empirical validation of its superior effectiveness compared to traditional models. This research represents a significant advancement in AI-powered information retrieval and provides a practical framework for leveraging ML and DL to enhance learning experiences in smart E-Learning systems and beyond.</p>

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LearnAlytics: The Smart Way to Analyze, Adapt, and Achieve in E-Learning

  • 期刊论文

  • 发表日期：

    2025年1月16日

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  • Subhabrata Sengupta,
  • Sayan Bardhan,
  • Rupayan Das,
  • Satyajit Chakrabarti

摘要

Artificial intelligence (AI) is a powerful technology with the potential to revolutionize various fields by incorporating human-like intelligence into systems and robots. Different types of AI, such as visual, textual, and analytical, can enhance applications and solve real-world problems. However, the dynamic and diverse nature of real-world data poses challenges in developing effective AI models. This research explores the application of machine learning (ML) and deep learning (DL) for information retrieval within a smart E-Learning system. It introduces an AI model called LearnAlytics, designed to assist decision-makers in implementing ML and DL techniques for information retrieval. LearnAlytics offers valuable insights applicable to various real-world scenarios and application domains. Extensive simulation results demonstrate that LearnAlytics outperforms traditional models, showcasing its superior effectiveness. The key contributions of this research include the development of a novel AI model for smart E-Learning, significantly enhanced information retrieval capabilities, data-driven decision-making support, generalizability and scalability of the model, and empirical validation of its superior effectiveness compared to traditional models. This research represents a significant advancement in AI-powered information retrieval and provides a practical framework for leveraging ML and DL to enhance learning experiences in smart E-Learning systems and beyond.