Deriving Insights From Open-Ended Learner Feedback: An Exploration of Natural Language Processing Approaches

About this episode

This episode explores the use of natural language processing methods to analyze open-ended learner feedback in continuing health education.

Open-ended feedback from learners offers valuable insights for adapting continuing health education to their needs; however, this feedback is burdensome to analyze with qualitative methods. Natural language processing offers a potential solution, but it is unclear which methods provide useful insights. We evaluated natural language processing methods for analyzing open-ended feedback from CPD training at a psychiatric hospital. The data set consisted of survey responses from staff participants, which included two text responses on how participants intended to use the training ("intent to use"; n = 480) and other information they wished to share ("open-ended feedback"; n = 291). We analyzed "intent-to-use" responses with topic modeling, "open-ended feedback" with sentiment analysis, and both responses with (LLM)–based clustering. We examined outputs of each approach to determine their value for deriving insights about the training. Our results indicated that because the "intent-to-use" responses were short and lacked diversity, topic modeling was not useful in differentiating content between the topics. For "open-ended feedback," sentiment scores did not accurately reflect the…

People in this episode

Host: Brian McGowan

Topics covered

• natural language processing
• learner feedback
• continuing health education
• qualitative analysis
• sentiment analysis
• topic modeling
• LLM-based clustering

Keywords

• open-ended feedback
• natural language processing
• CPD training
• topic modeling
• sentiment analysis
• LLM clustering
• learner insights

Mentioned in this episode

Organizations: psychiatric hospital

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