Han Lulu, Long Xinliang, Wang Kunli
Graduate School, University of Perpetual Help System DALTA, 1740, Las Piñas City, Philippines.
College of General Education, Shandong Yingcai University, Jinan, 250104, China.
Sci Rep. 2024 Aug 1;14(1):17811. doi: 10.1038/s41598-024-68963-x.
This study explores the influence of the Internet of Things (IoT) and Artificial Intelligence (AI)-enhanced learning models on student management in educational informatization management. A game-theoretic enhanced learning model is proposed to achieve this objective, incorporating resource scheduling strategies under fog computing and a student management system that integrates IoT and AI technologies. This model's performance and the student management system are then tested. The results indicate that the fog computing-based hierarchical Q-learning (Q) model proposed in this study achieves faster convergence than a single Q model, reaching convergence after 80 training rounds, ten rounds earlier than the comparative algorithm. The model exhibits a lower average workload delay of 0.5 ms and fog node delay below 1 ms, showcasing significant advantages in terms of overall cost-effectiveness, thus minimizing service costs. The student management system has 3000 concurrent user connections, static page request times ranging from 0 to 25 s, login response time predominantly at 60 s, and a capacity to process up to 20 parallel tasks per second with zero errors. The system functionalities are fully realized, meeting usage demands effectively and achieving the highest average functional score of 9.03 for online interaction functionality. This study demonstrates the efficacy of the game-theoretic enhanced learning model in a fog computing environment and the positive impact of IoT and AI technologies on student management. The proposed student management system better caters to individual student needs, enhancing learning outcomes and experiences. The study's innovation lies in the integration of IoT technology with AI-enhanced learning models, coupled with the introduction of game-theoretic resource scheduling strategies, enabling the student management system to intelligently identify student requirements, allocate learning resources, and dynamically optimize the educational process, ultimately improving learning outcomes. This holds significant implications for enhancing education quality and promoting personalized student development.
本研究探讨物联网（IoT）和人工智能（AI）增强学习模型对教育信息化管理中学生管理的影响。为实现这一目标，提出了一种博弈论增强学习模型，该模型纳入了雾计算下的资源调度策略以及集成物联网和人工智能技术的学生管理系统。然后对该模型的性能和学生管理系统进行测试。结果表明，本研究提出的基于雾计算的分层Q学习（Q）模型比单一Q模型收敛速度更快，在80轮训练后达到收敛，比对比算法提前了10轮。该模型的平均工作负载延迟较低，为0.5毫秒，雾节点延迟低于1毫秒，在整体成本效益方面展现出显著优势，从而将服务成本降至最低。学生管理系统具有3000个并发用户连接，静态页面请求时间为0到25秒，登录响应时间主要为60秒，每秒能够处理多达20个并行任务且零错误。系统功能得到充分实现，有效满足了使用需求，在线交互功能的平均功能得分最高达到9.03。本研究证明了博弈论增强学习模型在雾计算环境中的有效性以及物联网和人工智能技术对学生管理的积极影响。所提出的学生管理系统更好地满足了学生的个性化需求，提高了学习成果和体验。该研究的创新之处在于将物联网技术与人工智能增强学习模型相结合，同时引入博弈论资源调度策略，使学生管理系统能够智能识别学生需求、分配学习资源并动态优化教育过程，最终提高学习成果。这对于提高教育质量和促进学生个性化发展具有重要意义。
