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 18 184 nursing MOOC review texts was constructed, with positive sentiment reviews accounting for 89.30% (16 238/18 184) and negative sentiment reviews making up 10.70% (1 946/18 184). 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.

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 18 184 nursing MOOC review texts was constructed, with positive sentiment reviews accounting for 89.30% (16 238/18 184) and negative sentiment reviews making up 10.70% (1 946/18 184). 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.