Paper ID: 2210.17495

Automated Code Extraction from Discussion Board Text Dataset

Sina Mahdipour Saravani, Sadaf Ghaffari, Yanye Luther, James Folkestad, Marcia Moraes

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.

Submitted: Oct 31, 2022