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来源类型 | Publication |
Text as Data Methods for Education Research | |
Lily Fesler; Thomas Dee; Rachel Baker; Brent Evans | |
发表日期 | 2019 |
出版者 | Journal of Research on Educational Effectiveness |
出版年 | 2019 |
语种 | 英语 |
摘要 | 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 paper, 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. |
主题 | Technological Innovations in Education |
子主题 | Educational Equity ; Societal Context |
URL | https://cepa.stanford.edu/content/text-data-methods-education-research |
来源智库 | Center for Education Policy Analysis (United States) |
资源类型 | 智库出版物 |
条目标识符 | http://119.78.100.153/handle/2XGU8XDN/491872 |
推荐引用方式 GB/T 7714 | Lily Fesler,Thomas Dee,Rachel Baker,et al. Text as Data Methods for Education Research. 2019. |
条目包含的文件 | ||||||
文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
wp19-04-v062019.pdf(651KB) | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 |
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