Abstract

Recent advances in computational linguistics and the social sciences have created new opportunities for the education research community to analyze relevant large-scale text data. However, the take-up of these advances in education research is still nascent. In this article, we review the recent automated text methods relevant to educational processes and determinants. We discuss both lexical-based and supervised methods, which expand the scale of text that researchers can analyze, as well as unsupervised methods, which allow researchers to discover new themes in their data. To illustrate these methods, we analyze the text interactions from a field experiment in the discussion forums of online classes. Our application shows that respondents provide less assistance and discuss slightly different topics with the randomized female posters, but respond with similar levels of positive and negative sentiment. These results demonstrate that combining qualitative coding with machine learning techniques can provide for a rich understanding of text-based interactions.

摘要

计算语言学和社会科学的最新进展为教育研究界分析相关的大规模文本数据创造了新的机会。但是，这些在教育研究方面的进展仍处于起步阶段。在本文中，我们将回顾与教育过程和决定因素相关的最新自动文本方法。我们讨论了基于词法的方法和受监督的方法，这些方法扩大了研究人员可以分析的文本的范围；也讨论了无监督的方法，这些方法使研究人员可以在数据中发现新的主题。为了说明这些方法，我们在在线课程的讨论论坛中分析了来自现场实验的文本交互。我们的应用程序显示，受访者提供的帮助较少，并且与随机抽取的女性海报讨论的主题略有不同，但回应时的正面和负面情绪水平相似。这些结果表明，将定性编码与机器学习技术相结合可以提供对基于文本的交互的丰富理解。