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。

结合自动编码和教师输入以生成异步在线讨论的 ENA 可视化

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

Latent Dirichlet analysis, or topic modeling, is a flexible latent variable
framework for modeling high-dimensional sparse count data. Various learning
algorithms have been developed in recent years, including collapsed Gibbs
sampling, variational inference, and maximum a posteriori estimation, and this
variety motivates the need for careful empirical comparisons. In this paper, we
highlight the close connections between these approaches. We find that the main
differences are attributable to the amount of smoothing applied to the counts.
When the hyperparameters are optimized, the differences in performance among
the algorithms diminish significantly. The ability of these algorithms to
achieve solutions of comparable accuracy gives us the freedom to select
computationally efficient approaches. Using the insights gained from this
comparative study, we show how accurate topic models can be learned in several
seconds on text corpora with thousands of documents.

本文探讨了用于高维稀疏计数数据建模的潜在狄利克雷分析或话题模型，比较了多种学习算法，发现主要区别在于应用于计数的平滑量。在优化超参数时，算法性能的差异显著缩小，这使得我们能够选择计算效率高的方法来学习准确的主题模型。