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<p>Higher education is undergoing a technological shift, moving towards personalized, adaptive, and data-informed real -time learning experiences. In this context, an IoT-enabled interactive system is designed to foster smart and adaptive learning within universities and schools, structured around a novel fluid three-layer framework. At its core, the proposed system integrates three modules: the Curriculum Flow Module (CFM) that delivers adaptive content in real-time while analyzing student interactions; the Knowledge Diffusion Module (KDM) which ensures smart dissemination of resources, peer recommendations, and faculty feedback; and the Viscosity Control Module (VCM) which manages cognitive load by leveraging IoT-enabled attention and stress monitoring. It enables instructors and platforms to offer custom tailored interactive and dynamic assignments that change and evolve according to their target student .To test its effectiveness, student-centered datasets such as the Open University Learning Analytics Dataset (OULAD), EdNet, and the KDD Cup 2010 are taken into consideration. The performance of the proposed system is evaluated in terms of engagement rate (ER), knowledge retention (KR), adaptability speed (AS), and dropout prediction accuracy (Acc), while also tracking energy and latency as indicators of IoT feasibility. The results are promising: engagement rates reached 82%, knowledge retention hit 78%, and adaptability speed up by 3.5× times as compared to conventional Learning Management Systems (LMS). Dropout prediction accuracy improved from 74% to 89%, all while keeping energy consumption nearly half that of baseline adaptive quiz platforms.</p>

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An IoT-based interactive system to facilitate smart and adaptive learning in higher education

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Nivedita Jha,
  • Shirly Abraham,
  • Trupti Bhosale,
  • Deepa Pillai,
  • Sushmitha Rao,
  • Varsha Murhe,
  • Saylee Karande,
  • Dipti Vashisth Sharma

摘要

Higher education is undergoing a technological shift, moving towards personalized, adaptive, and data-informed real -time learning experiences. In this context, an IoT-enabled interactive system is designed to foster smart and adaptive learning within universities and schools, structured around a novel fluid three-layer framework. At its core, the proposed system integrates three modules: the Curriculum Flow Module (CFM) that delivers adaptive content in real-time while analyzing student interactions; the Knowledge Diffusion Module (KDM) which ensures smart dissemination of resources, peer recommendations, and faculty feedback; and the Viscosity Control Module (VCM) which manages cognitive load by leveraging IoT-enabled attention and stress monitoring. It enables instructors and platforms to offer custom tailored interactive and dynamic assignments that change and evolve according to their target student .To test its effectiveness, student-centered datasets such as the Open University Learning Analytics Dataset (OULAD), EdNet, and the KDD Cup 2010 are taken into consideration. The performance of the proposed system is evaluated in terms of engagement rate (ER), knowledge retention (KR), adaptability speed (AS), and dropout prediction accuracy (Acc), while also tracking energy and latency as indicators of IoT feasibility. The results are promising: engagement rates reached 82%, knowledge retention hit 78%, and adaptability speed up by 3.5× times as compared to conventional Learning Management Systems (LMS). Dropout prediction accuracy improved from 74% to 89%, all while keeping energy consumption nearly half that of baseline adaptive quiz platforms.