Deriving Insights From Open-Ended Learner Feedback: An Exploration of Natural Language Processing Approaches
LLMs may be a useful approach for deriving insights from learner feedback because they capture context, making them capable of distinguishing between responses that use similar words to convey different topics. Future directions involve exploring other methods involving LLMs, or examining how these methods fare on other data sets or types of learner feedback.
Authors
- Marta M. Maslej (ORCID: https://orcid.org/0000-0002-6393-5095)
- Anupam Thakur (ORCID: https://orcid.org/0000-0001-5775-8590)
- Sanjeev Sockalingam (ORCID: https://orcid.org/0000-0002-9626-1509)
- Kenya A. Costa-Dookhan (ORCID: https://orcid.org/0000-0002-7477-4397)
- Kayle Donner
- Faisal Islam (ORCID: https://orcid.org/0000-0003-4639-6832)
Institutions
- Centre for Addiction and Mental Health (CA)
Publication Details
- Journal
- Journal of Continuing Education in the Health Professions
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1097/ceh.0000000000000597
- Primary Topic
- Health Policy Implementation Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Canadian Institutes of Health Research