  This study introduces and investigates the capabilities of three different
text mining approaches, namely Latent Semantic Analysis, Latent Dirichlet
Analysis, and Clustering Word Vectors, for automating code extraction from a
relatively small discussion board dataset. We compare the outputs of each
algorithm with a previous dataset that was manually coded by two human raters.
The results show that even with a relatively small dataset, automated
approaches can be an asset to course instructors by extracting some of the
discussion codes, which can be used in Epistemic Network Analysis.
