Automated Code Extraction from Discussion Board Text Dataset

Sina Mahdipour Saravani, Sadaf Ghaffari, Yanye Luther, J. Folkestad, Marcia Moraes
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引用次数: 1

Abstract

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.
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从讨论板文本数据集自动代码提取
本研究介绍并探讨了三种不同的文本挖掘方法的能力,即潜在语义分析、潜在狄利克雷分析和聚类词向量,用于从相对较小的讨论板数据集中自动提取代码。我们将每个算法的输出与之前由两个人类评分员手动编码的数据集进行比较。结果表明,即使是相对较小的数据集,通过提取一些讨论代码,自动化方法也可以成为课程教师的资产,这些代码可以用于认知网络分析。
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