Student engagement in a clinical learning environment is a vital component in the curricula of pre-licensure nursing students, providing an opportunity to combine cognitive, psychomotor, and affective skills. This pap...Student engagement in a clinical learning environment is a vital component in the curricula of pre-licensure nursing students, providing an opportunity to combine cognitive, psychomotor, and affective skills. This paper is significant in Arab world as there is a lack of knowledge, attitude and practice of student involvement in the new clinical learning environment. The purpose of this review article is to describe the experiences and perspectives of the nurse educator in facilitating pre-licensure nursing students’ engagement in the new clinical learning environment. The review suggests that novice students prefer actual engagement in clinical learning facilitated through diversity experiences, shared learning opportunities, student-faculty interaction and active learning. They expressed continuous supervision, ongoing feedback, interpersonal relationship and personal support from nurse educators useful in the clinical practice. However, the value of this review lies in a better understanding of what constitutes quality clinical learning environment from the students’ perspective of engagement in evidence-based nursing, reflective practice, e-learning and simulated case scenarios facilitated by the nurse educators. This review is valuable in planning and implementing innovative clinical and educational experiences for improving the quality of the clinical teaching-learning environment.展开更多
随着网络技术的不断发展,基于Fat-Tree的网络拓扑结构分布式网络控制模式逐渐显露出其局限性,软件定义数据中心网络(software-defined data center network,SDCN)技术作为Fat-Tree网络拓扑的改进技术,受到越来越多研究者的关注。首先搭...随着网络技术的不断发展,基于Fat-Tree的网络拓扑结构分布式网络控制模式逐渐显露出其局限性,软件定义数据中心网络(software-defined data center network,SDCN)技术作为Fat-Tree网络拓扑的改进技术,受到越来越多研究者的关注。首先搭建了一个SDCN中的边缘计算架构和基于移动边缘计算(mobileedge computing,MEC)平台三层服务架构的任务卸载模型,结合移动边缘计算平台的实际应用场景,利用同策略经验回放和熵正则改进传统的深度Q网络(deep Q-leaning network,DQN)算法,优化了MEC平台的任务卸载策略,并设计了实验对基于同策略经验回放和熵正则的改进深度Q网络算法(improved DQN algorithm based on same strategy empirical playback and entropy regularization,RSS2E-DQN)和其他3种算法在负载均衡、能耗、时延、网络使用量几个方面进行对比分析,验证了改进算法在上述4个方面具有更优越的性能。展开更多
文摘Student engagement in a clinical learning environment is a vital component in the curricula of pre-licensure nursing students, providing an opportunity to combine cognitive, psychomotor, and affective skills. This paper is significant in Arab world as there is a lack of knowledge, attitude and practice of student involvement in the new clinical learning environment. The purpose of this review article is to describe the experiences and perspectives of the nurse educator in facilitating pre-licensure nursing students’ engagement in the new clinical learning environment. The review suggests that novice students prefer actual engagement in clinical learning facilitated through diversity experiences, shared learning opportunities, student-faculty interaction and active learning. They expressed continuous supervision, ongoing feedback, interpersonal relationship and personal support from nurse educators useful in the clinical practice. However, the value of this review lies in a better understanding of what constitutes quality clinical learning environment from the students’ perspective of engagement in evidence-based nursing, reflective practice, e-learning and simulated case scenarios facilitated by the nurse educators. This review is valuable in planning and implementing innovative clinical and educational experiences for improving the quality of the clinical teaching-learning environment.
文摘随着网络技术的不断发展,基于Fat-Tree的网络拓扑结构分布式网络控制模式逐渐显露出其局限性,软件定义数据中心网络(software-defined data center network,SDCN)技术作为Fat-Tree网络拓扑的改进技术,受到越来越多研究者的关注。首先搭建了一个SDCN中的边缘计算架构和基于移动边缘计算(mobileedge computing,MEC)平台三层服务架构的任务卸载模型,结合移动边缘计算平台的实际应用场景,利用同策略经验回放和熵正则改进传统的深度Q网络(deep Q-leaning network,DQN)算法,优化了MEC平台的任务卸载策略,并设计了实验对基于同策略经验回放和熵正则的改进深度Q网络算法(improved DQN algorithm based on same strategy empirical playback and entropy regularization,RSS2E-DQN)和其他3种算法在负载均衡、能耗、时延、网络使用量几个方面进行对比分析,验证了改进算法在上述4个方面具有更优越的性能。
