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.

翻译：本研究引入并探讨了三种不同的文本挖掘方法——潜在语义分析、潜在狄利克雷分析和词向量聚类——在相对较小的讨论板数据集上自动提取代码的能力。我们将每种算法的输出与先前由两位人工评分员手动编码的数据集进行比较。结果表明，即使数据集规模相对较小，自动化方法也能通过提取部分讨论代码，为课程讲师提供有价值的支持，这些代码可用于认知网络分析。