目的探讨关联问题系统整合教学(Problem Related Integrated Learning,PRIL)在口腔医学护理带教中的应用效果。方法选取2016年5—7月在本院口腔科实习的护生90人,随机分为实验组和对照组两组,每组各45人。实验组实施PRIL带教方法,对照...目的探讨关联问题系统整合教学(Problem Related Integrated Learning,PRIL)在口腔医学护理带教中的应用效果。方法选取2016年5—7月在本院口腔科实习的护生90人,随机分为实验组和对照组两组,每组各45人。实验组实施PRIL带教方法,对照组实施传统带教方法。带教前后采用试卷测试对两组护生进行理论知识及实践操作考核,采用护理临床能力调查表及批判性思维倾向测试评估并对比带教前后两组护生临床护理能力和7项评判性思维能力。结果带教前两组护生理论知识、时间技能考核成绩、护理临床能力构成比、7项评判性思维能力评分比较差异均不显著(P>0.05),带教后两组护生理论知识、时间技能考核成绩、护理临床能力构成比、7项评判性思维能力评分均显著高于带教前(P<0.05),带教后实验组上述指标均显著高于对照组(P<0.05)。结论将PRIL教学方法应用于口腔医学护理带教中可显著提升护生理论知识水平和临床实践能力,并且能提高护生临床护理能力和评判性思维能力。展开更多
Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training ti...Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Behavioural Cloning, i.e., traces provided by the user;to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real service robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.展开更多
文摘目的探讨关联问题系统整合教学(Problem Related Integrated Learning,PRIL)在口腔医学护理带教中的应用效果。方法选取2016年5—7月在本院口腔科实习的护生90人,随机分为实验组和对照组两组,每组各45人。实验组实施PRIL带教方法,对照组实施传统带教方法。带教前后采用试卷测试对两组护生进行理论知识及实践操作考核,采用护理临床能力调查表及批判性思维倾向测试评估并对比带教前后两组护生临床护理能力和7项评判性思维能力。结果带教前两组护生理论知识、时间技能考核成绩、护理临床能力构成比、7项评判性思维能力评分比较差异均不显著(P>0.05),带教后两组护生理论知识、时间技能考核成绩、护理临床能力构成比、7项评判性思维能力评分均显著高于带教前(P<0.05),带教后实验组上述指标均显著高于对照组(P<0.05)。结论将PRIL教学方法应用于口腔医学护理带教中可显著提升护生理论知识水平和临床实践能力,并且能提高护生临床护理能力和评判性思维能力。
文摘Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Behavioural Cloning, i.e., traces provided by the user;to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real service robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.
