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.

翻译：异步在线讨论是混合式与在线课程中促进社交互动的常见基础工具。然而，教师缺乏有效工具来完成评估异步在线讨论活动这一艰巨任务。本文提出一种方法，利用潜在狄利克雷分析（LDA）与教师提供的关键词，从相对较小的数据集中自动提取编码。我们使用生成的编码构建认知网络分析（ENA）模型，并与先前由人工编码员构建的ENA模型进行对比。结果表明，两个模型之间无统计学显著差异。我们分析了这些模型，并探讨了ENA作为可视化工具帮助教师评估异步在线讨论的潜在应用。