Text Mining Analyses of Programming Education Articles Since the 1970s

Takahisa Furuta, Gerald Knezek
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Abstract

In order to assess the extent to which text-mining techniques can be used to gain insights into a particular topic area, we apply hierarchical word clustering and the Term Frequency-Inverse Document Frequency (TF-IDF) measure to articles on computer programming published since the 1970s, when research articles on teaching programming are now more readily available in PDF files. Study 1 compares two sets of papers published before and after the introduction of the concept of Computational Thinking in 2006 to highlight the changes seen in these research sets. Articles mentioned in frequently cited review papers were selected as the target articles to ensure the quality of the sample. Study 2 extends the sample pool to include a range of papers published after the 1970s, allowing us to examine the stability of the conceptual structures identified in Study 1. In both studies, the obtained word clusters or concepts align with known research trends in the programming-education literature. The significance and potential of text-mining techniques are also discussed.
20世纪70年代以来编程教育文章的文本挖掘分析
为了评估文本挖掘技术可用于深入了解特定主题领域的程度,我们将分层词聚类和术语频率-逆文档频率(TF-IDF)测量应用于自20世纪70年代以来发表的计算机编程文章,当时关于教学编程的研究文章现在更容易以PDF文件的形式获得。研究1比较了2006年引入计算思维概念前后发表的两组论文,以突出这些研究集中所看到的变化。为了保证样本的质量,我们选择在被引频次的综述论文中提到的文章作为目标文章。研究2扩展了样本池,包括一系列20世纪70年代以后发表的论文,使我们能够检查研究1中确定的概念结构的稳定性。在这两项研究中,获得的词簇或概念与编程教育文献中已知的研究趋势一致。本文还讨论了文本挖掘技术的意义和潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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