基于雨课堂及微课的BOPPPS教学模式在医学微生物学课程中的设计与应用
Design and application of BOPPPS teaching mode based on Rain Classroom and micro-lectures for Medical Microbiology
乐率 1饶贤才 1周晶 1赵岩 1王竞 1李刚 1李明 1卢曙光1
作者信息
- 1. 陆军军医大学基础医学院微生物学教研室,重庆 400038
- 折叠
摘要
在教育数字化转型时代背景下,利用信息化工具与数字化资源进行混合式教学,对于提升教学质效意义重大.针对医学微生物学课程教学中存在的内容复杂、教学方法手段单一、学员参与度不够、教学效果不佳等问题,通过在导入-目标-前测-参与式教学-后测-总结(bridge-in,objective,pre-assessment,participatory learning,post-assessment,and summary,BOPPPS)教学模式下整合雨课堂的在线互动教学平台与微课的短视频教学资源,构建了一种新型线上线下混合式教学模式,在医学微生物学课程中进行了应用实践,并通过问卷调查、成绩对比等方法对教学效果进行评估.结果表明,基于雨课堂及微课的BOPPPS教学模式显著提升了学员的学习兴趣和课堂参与度,同时也提高了学生的成绩.本研究证明了该教学模式在提升教学质量和学生学习效果方面的有效性,为医学教育领域的教学改革提供了有益参考.
Abstract
In the context of digital transformation in education,blended teaching which utilizes information tools and digital resources holds significant importance in enhancing teaching quality and efficiency.To address issues such as complex contents,monotonous teaching methods,inadequate student engagement,and unsatisfactory teaching outcomes in Medical Microbiology,we developed a novel online-offline blended teaching mode by integrating Rain Classroom(an online interactive teaching platform)with micro-lectures(short-video teaching resources)under the BOPPPS(bridge-in,objective,pre-assessment,participatory learning,post-assessment,and summary)teaching mode.We then practiced this mode in the teaching of Medical Microbiology and evaluated its pedagogical effectiveness by a questionnaire survey and grade comparison.The results indicated that the BOPPPS teaching mode based on Rain Classroom and micro-lectures significantly enhanced students'interest in learning and classroom participation,while also improving their academic performance.This study demonstrates the efficacy of this teaching mode in enhancing teaching quality and learning outcomes and gives insights into the teaching reform in medical education.
关键词
雨课堂/微课/BOPPPS教学模式/医学微生物学/教学设计与应用Key words
Rain Classroom/micro-lecture/BOPPPS teaching mode/Medical Microbiology/instructional design and application引用本文复制引用
出版年
2024
NETL
NSTL
万方数据