  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.
