摘要
目的基于护理大规模开放在线课程(MOOC)的评论文本深入探究学习者对课程要素的需求主题及特征,为实现护理教育数字化内容与学习需求之间的有效匹配提供参考依据。方法基于Python中Requests库编写的网络爬虫代码对中国大学MOOC平台的112门护理课程评论文本进行数据采集。采集时间为课程开放之日起至2023年12月31日。运用中文自然语言处理库开展情感分析、高频词分析,结合文献主题识别算法提取学习者正性和负性情感评论核心主题。结果最终构建包含18184条数据的护理MOOC评论文本语料库,正性情感评论文本占89.30%(16238/18184),负性情感评论文本占10.70%(1946/18184)。词频分析结果显示大部分护理MOOC为线上线下混合教学模式的载体,学生是主要目标受众,但也有社会人员参与;同时有效映射了临床护理工作情境。最终将护理MOOC学习者的需求主题分为三大类:内容设计与考核评价、课程资源与教学策略、软件应用与平台功能。结论基于文本挖掘技术深入探讨了护理MOOC学习者在线需求的三大主题特征,并针对性提出优化建议。未来可尝试纳入其他网络教学平台,依据大数据建模及机器算法全面构建护理在线课程评论情感词典,以此对护理数字化教育的整体态势进行全方位分析,针对短板精准施策加以改进。
Objective To deeply explore the thematic needs and characteristics of learners regarding course elements based on the review texts of nursing massive open online courses(MOOC),providing a reference for achieving effective alignment between digital nursing education content and learner needs.Methods Data were collected from the review texts of 112 nursing courses on the Chinese University MOOC platform using a web crawler script written with Python′s Requests library.The collection period spanned from the course launch dates to December 31,2023.Sentiment analysis and high-frequency words analysis were conducted using Chinese text Nature language processing library,and core themes of learners′positive and negative reviews were extracted using the latent dirichlet allocation.Results A corpus of 18184 nursing MOOC review texts was constructed,with positive sentiment reviews accounting for 89.30%(16238/18184)and negative sentiment reviews making up 10.70%(1946/18184).Word frequency analysis revealed that most nursing MOOC serve as carriers for blended online and offline teaching models,with students being the primary target audience,though social participants were also involved.The reviews effectively mirrored real-world clinical nursing scenarios.The need of learners was categorized into three major themes:content design and assessment,course resources and teaching strategies,and software applications and platform functionality.Conclusions This study,leveraging text mining technology,thoroughly investigated the three thematic characteristics of nursing MOOC needs of online learners and proposed targeted optimization recommendations.Future research could incorporate other online teaching platforms and comprehensively construct a sentiment lexicon for nursing online course reviews using big data modeling and machine learning algorithms.These would enable a holistic analysis of the digital nursing education landscape,allowing for precise improvements to address existing shortcomings.
作者
冯桃桃
贺学敏
陈翠萍
周胜杰
牟旭红
李莉
Feng Taotao;He Xuemin;Chen Cuiping;Zhou Shengjie;Mou Xuhong;Li Li(Department of Nursing,Shanghai Tenth People′s Hospital,Shanghai 200072,China)
出处
《中国实用护理杂志》
2025年第15期1150-1156,共7页
Chinese Journal of Practical Nursing
基金
同济大学医学院"护理学院(筹)学科建设三年行动计划"项目(JS2210301)。
关键词
护理
文本挖掘
大规模开放在线课程
学习者需求
特征
Nursing care
Text mining
Massive open online courses
Learner needs
Characteristics
