摘要
目的探讨面向深度学习的外科护理学线上教学效果。方法采用行动研究法,以深度学习理论为基础,从知识掌握、能力培养以及情感体验3个层面构建课前、课中及课后3个阶段的外科护理学在线学习模式,开展了2轮行动研究,评价课后学生的成绩、深度学习结果及教学满意度。结果第一轮行动反映课前学生自主学习重难点知识掌握差异大、师生互动不足,课中小组合作式学习的参与度与学习深度不够,课后作业不能反映学生人文关怀及创新能力。第二轮针对第一轮的问题改进后,结果显示学生对重难点知识掌握有所提高,深度学习得分得到提高(P<0.05),学生对教学的参与认可度、学习资源认可度、能力培养认可度均较高。结论面向深度学习的外科护理学线上教学方案有利于促进学生更好地掌握知识,提升学生的深层学习动机和学习投入,提升其整合性学习、反思巩固学习、高阶学习能力。
Objective To explore the effects of online teaching of Surgical Nursing based on deep learning theory.Methods The online teaching was developed by addressing three aspects including knowledge acquisition,ability training,and emotional experience before,during,and after class.The teaching development was based on deep learning theory and use two-round action research method.Students;performance,deep learning results and teaching satisfaction were evaluated.Results When the first-round action research was completed,some problems emerged:large differences in difficult points understanding before class,few interactions between instructors and students,insufficient participation and learning depth in group cooperative learning,and little reflection of humanistic care and innovative ability in homework.When these problems were addressed in second-round action research,students'knowledge acquisition and deep learning performance were improved(P<0.05).Besides,students'recognition of teaching participation,learning resources,and ability cultivation were all improved.Conclusion The online teaching program of Surgical Nursing based on deep learning theory is helpful for students with their knowledge improvement,and deep learning motivation and involvement.The teaching can improve the integrated learning,the reflecting learning,and high-level learning.
作者
周雪妃
朱宁宁
薛芳
孙婷
ZHOU Xue-fei;ZHU Ning-ning;XUE Fang;SUN Ting
出处
《中华护理教育》
CSCD
北大核心
2021年第4期329-334,共6页
Chinese Journal of Nursing Education
基金
2015年安徽省省级大规模在线开放课程(MOOC)示范项目
安徽省教育厅2019年度高校科学研究项目(SK2019A0196)
安徽省2020年度高等学校省级质量工程项目(2020xskt310)。
关键词
护理
课程
教育
远程
专业
外科
深度学习
行动研究
Nursing Care
Curriculum
Education,Distance
Specialties,Surgical
Deep learning
Action research