The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the f...The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the framework of sustainable development in renewable energy seeks to give students the information,abilities,and critical thinking needed to solve energy-related problems sustainably.This research proposes AI-powered personalized learning,innovative real-time integration of diverse data,and adaptive teaching strategies to enhance student understanding regarding renewable energy concepts.The sheep flock-optimized innovative recurrent neural network(SFO-IRNN)will recommend relevant topics and resources based on students’performance.Renewable energy teaching data from assessmethments are combined with real-time IoT-based renewable energy data.This dataset contains renewable energy education using AI-driven teaching methods and internet-based learning.The data was preprocessed by handling missing values and min-max scaling.The data features were extracted using Fourier Transform(FT).Further application of 10-fold cross-validation will increase the reliability of the model as it can evaluate its performance metrics like accuracy,F1-score,recall,and precision on different subsets of student data,which improves its robustness and prevents overfitting.The findings showed that the proposed method is significantly better,which ensures that the students have a deeper theoretical and practical understanding of renewable energy technologies.In addition,integrating real-time IoT data from renewable energy sources gives students a chance to do live simulations and problems that would enhance analytical thinking and hands-on learning.The research shows that AI provides context-aware guidance on sustainable energy infrastructure,enhancing interactive and personalized learning.展开更多
为揭示医学计算机教育中性别差异的动态特征及与学习进程、作业难度的关联,文章以某医科大学2023级535名本科生为对象,纵向追踪9次编程作业与理论考试成绩,经相关分析构建框架。结果显示,女生在编程实践(92.80±4.39 vs 85.67±...为揭示医学计算机教育中性别差异的动态特征及与学习进程、作业难度的关联,文章以某医科大学2023级535名本科生为对象,纵向追踪9次编程作业与理论考试成绩,经相关分析构建框架。结果显示,女生在编程实践(92.80±4.39 vs 85.67±8.10,d=1.02)和理论考试(74.55±7.89 vs 69.59±7.76,d=0.64)中成绩均显著优于男生(P<0.001),且编程实践的性别差异程度与作业难度正相关,随学习进程波动幅度减小。医学计算机领域性别差异呈动态演变,建议实施三阶段教学策略,通过结对编程等促进医学信息素养均衡发展。展开更多
文摘The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the framework of sustainable development in renewable energy seeks to give students the information,abilities,and critical thinking needed to solve energy-related problems sustainably.This research proposes AI-powered personalized learning,innovative real-time integration of diverse data,and adaptive teaching strategies to enhance student understanding regarding renewable energy concepts.The sheep flock-optimized innovative recurrent neural network(SFO-IRNN)will recommend relevant topics and resources based on students’performance.Renewable energy teaching data from assessmethments are combined with real-time IoT-based renewable energy data.This dataset contains renewable energy education using AI-driven teaching methods and internet-based learning.The data was preprocessed by handling missing values and min-max scaling.The data features were extracted using Fourier Transform(FT).Further application of 10-fold cross-validation will increase the reliability of the model as it can evaluate its performance metrics like accuracy,F1-score,recall,and precision on different subsets of student data,which improves its robustness and prevents overfitting.The findings showed that the proposed method is significantly better,which ensures that the students have a deeper theoretical and practical understanding of renewable energy technologies.In addition,integrating real-time IoT data from renewable energy sources gives students a chance to do live simulations and problems that would enhance analytical thinking and hands-on learning.The research shows that AI provides context-aware guidance on sustainable energy infrastructure,enhancing interactive and personalized learning.
文摘为揭示医学计算机教育中性别差异的动态特征及与学习进程、作业难度的关联,文章以某医科大学2023级535名本科生为对象,纵向追踪9次编程作业与理论考试成绩,经相关分析构建框架。结果显示,女生在编程实践(92.80±4.39 vs 85.67±8.10,d=1.02)和理论考试(74.55±7.89 vs 69.59±7.76,d=0.64)中成绩均显著优于男生(P<0.001),且编程实践的性别差异程度与作业难度正相关,随学习进程波动幅度减小。医学计算机领域性别差异呈动态演变,建议实施三阶段教学策略,通过结对编程等促进医学信息素养均衡发展。
