Abstract

Using a natural language processing tool, this study examined participant discourse in personalized learning schools to better understand what personalized learning looks like in practice. Term frequency-inverse document frequency (tf-idf) was used to identify the significant words and potential emergent themes for 134 interview transcripts. This tool provided a way to swiftly explore the structure of the data, revealing distinctions in the vocabulary students and teachers use as well as a potentially meaningful set of themes. This method provided a valuable lens with which to validate or surface new areas for investigation. By applying this tool to interviews from personalized learning environments, we were able to identify ways educators and students talk differently about project-based learning environments, revealing that tools like tf-idf can be effectively used to quickly provide a preliminary look at large amount of interview data.

摘要

这项研究使用自然语言处理工具，检查了个性化学习学校的参与者话语，以更好地了解实践中个性化学习的模样。术语频率逆文档频率（tf-idf）用于识别134个访谈笔录的重要单词和潜在的紧急主题。该工具提供了一种快速探索数据结构，揭示学生和教师使用的词汇差异以及潜在的有意义主题集的方法。此方法提供了一个有价值的镜头，可用来验证或显示新的研究区域。通过将此工具应用于来自个性化学习环境的访谈，我们能够确定教育者和学生对基于项目的学习环境的不同谈论方式，