目的:探讨基于成果导向教育(Outcome-based Education, OBE)理念的“医学信息系统分析与设计”课程建设,以促进课程目标达成度和提升教学质量。方法:首先分析了当前“医学信息系统分析与设计”课程存在的主要问题。随后,基于OBE理念,提出了课程建设思路与具体措施。通过明确课程目标、优化课程内容、丰富课程资源、改进教学方法及完善课程考核体系,实现课程目标对毕业要求的支撑,推动课程持续改进。结果:经过建设,课程目标更加明确具体,课程内容与目标高度匹配,课程资源得到显著丰富,教学过程更加高效,课程考核体系更加科学合理。具体成果包括面向课程目标达成度的课程教学大纲的应用、主编教材的出版、课程目标达成度评价报告的编制、教师教学竞赛,以及课程目标达成度评价培训与竞赛指导等。课程目标达成度评价结果显示,多数课程目标达成度较高,并且针对部分达成度低的目标给出了持续改进措施。结论:基于OBE理念的“医学信息系统分析与设计”课程建设有效提升了教学质量,促进了学生知识、能力和素质的综合发展。通过持续改进课程目标、内容、资源、方法及考核体系,该课程能够更好地满足医疗行业对复合型医学信息技术人才的需求。Objective: This study aims to explore the curriculum construction of “Analysis and Design of Medical Information System” based on the Outcome-Based Education (OBE) concept, with the purpose of enhancing the achievement of course objectives and improving teaching quality. Methods: The study commences by analyzing the primary issues existing in the current “Analysis and Design of Medical Information System” course. Subsequently, guided by the OBE concept, a framework and specific measures for curriculum development are proposed. By clarifying course objectives, optimizing course content, enriching course resources, improving teac
目的:了解本科生视角有利于促进自主学习的课堂评价方式,构建可促进本科生自主学习的课堂评价指标体系,达到以评促学促教促管。方法:对宁夏医科大学2015级本科生进行问卷调查分析;借助NLPIR自然语言处理与信息检索教学科研平台,对问卷结果的文本进行关键词、新词挖掘和自动摘要实体;使用Wordart制作语料库、关键词、新词的词云图;采用Python编写实现LDA算法,对问卷结果的文本进行LDA主题分析;通过文献复习和专家咨询法构建促进本科生自主学习的课堂评价指标体系。结果:收回有效问卷442份;NLPIR对问卷结果的文本语料进行分词和预处理之后,获得928个有意义的动名形容词、826个关键词、101个新词;LDA主题分析得10个主题。构建了促进本科生自主学习的课堂评价量化指标体系,包含教学内容、学习参与度、思维训练、教学效果等4个一级指标,10个二级指标和15个三级指标。结论:促进自主学习的课堂评价量化指标体系既有利于学校对自主学习能力培养成效的评价,又有利于教师依据各项评价指标,安排、调整具体教学活动,增强教学的针对性、目的性。Objective: To understand what classroom evaluation methods is conducive to promoting autonomous learning from the perspective of undergraduates, build a classroom evaluation index system that can promote undergraduates’ autonomous learning, and achieve learning, teaching and managing by assessment. Methods: A questionnaire survey was conducted on Ningxia Medical University’s 2015 undergraduates. Natural language processing and information retrieval (NLPIR) platform was used to carry out key words, new word and automatic summary entities on the texts of the questionnaire results. Wordart was used to produce the word cloud map of the corpus, keywords and new words. The topic model of the questionnaire texts is build based on Latent Dirichlet Allocation (LDA) in Python. The index
