Asynchronous online discussions are a common fundamental tool to facilitate
social interaction in hybrid and online courses. However, instructors lack the
tools to accomplish the overwhelming task of evaluating asynchronous online
discussion activities. In this paper we present an approach that uses Latent
Dirichlet Analysis (LDA) and the instructor's keywords to automatically extract
codes from a relatively small dataset. We use the generated codes to build an
Epistemic Network Analysis (ENA) model and compare this model with a previous
ENA model built by human coders. The results show that there is no statistical
difference between the two models. We present an analysis of these models and
discuss the potential use of ENA as a visualization to help instructors
evaluating asynchronous online discussions.

通过潜在狄利克雷分析（LDA）和教师的关键词，本文提出了一种从相对较小的数据集中自动提取代码的方法，使用生成的代码构建知识网络分析（ENA）模型，并与人工编码者构建的先前 ENA 模型进行比较，结果表明两种模型之间没有统计差异，讨论了 ENA 作为一种可视化工具帮助教师评估异步在线讨论的潜在用途。