Automated Code Extraction from Discussion Board Text Dataset
This is an incremental improvement for course instructors in educational settings.
The study tackled the problem of automating code extraction from small discussion board datasets by comparing three text mining approaches, finding that automated methods can assist instructors by extracting some codes for Epistemic Network Analysis.
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.