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Coding discourse data is critical to many learning analytics studies. To code their data, researchers may use manual techniques, automated techniques, or a combination thereof. Manual coding can be time-consuming and error prone; automated coding can be difficult to implement for non-technical users. Generative artificial intelligence (GAI) offers a user friendly alternative to automated discourse coding via prompting and APIs. We assessed the ability of GAI, specifically the GPT class of models, at automatically coding discourse in the context of a learning analytics study using a variety of prompting and training strategies. We found that fine-tuning approaches produced the best results; however, no results achieved standard thresholds for reliability in our field.
Garg et al. (Tue,) studied this question.
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