Combining Automatic Coding and Instructor Input to Generate ENA Visualizations for Asynchronous Online Discussion

TL;DR

This paper introduces a method combining automatic coding with instructor input to generate ENA visualizations, aiding instructors in evaluating asynchronous online discussions efficiently.

Contribution

It presents a novel approach that uses LDA and instructor keywords to automatically generate ENA models, matching the quality of human-coded models.

Findings

No statistical difference between automatic and human-coded ENA models

Automatic coding effectively captures discussion themes

ENA visualizations can assist instructor evaluation

Abstract

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.