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.
@article{arxiv.2210.17495,
title = {Automated Code Extraction from Discussion Board Text Dataset},
author = {Sina Mahdipour Saravani and Sadaf Ghaffari and Yanye Luther and James Folkestad and Marcia Moraes},
journal= {arXiv preprint arXiv:2210.17495},
year = {2023}
}