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Thematic analysis of 18 years of physics education research conference proceedings using natural language processing
Physical Review Physics Education Research ( IF 3.1 ) Pub Date : 
Tor Ole B. Odden, Alessandro Marin, Marcos D. Caballero

We have used an unsupervised machine learning method called Latent Dirichlet Allocation (LDA) to thematically analyze all papers published in the Physics Education Research Conference Proceedings between 2001 and 2018. By looking at co-occurrences of words across the data corpus, this technique has allowed us to identify ten distinct themes or “topics” that have seen varying levels of prevalence in Physics Education Research (PER) over time and to rate the distribution of these topics within each paper. Our analysis suggests that although all identified topics have seen sustained interest over time, PER has also seen several waves of increased interest in certain topics, beginning with initial interest in qualitative, theory-building studies of student understanding, which has given way to a focus on problem solving in the late 2010s. Since 2010 the field has seen a shift towards more sociocultural views of teaching and learning with a particular focus on communities of practice, student identities, and institutional change. Based on these results, we suggest that unsupervised text analysis techniques like LDA may hold promise for providing quantitative, independent, and replicable analyses of educational research literature.

中文翻译:

使用自然语言处理对18年物理教育研究会议论文集进行专题分析

我们使用了一种称为Latent Dirichlet Allocation(LDA)的无监督机器学习方法,对2001年至2018年间在Physics Education Research Conference Proceedings上发表的所有论文进行了专题分析。我们确定了十个不同的主题或“主题”,这些主题或主题在一段时间内在物理教育研究(PER)中的流行程度有所不同,并评估了这些主题在每篇论文中的分布。我们的分析表明,尽管随着时间的推移,所有确定的主题都表现出持续的兴趣,但PER也看到了对某些主题的兴趣不断增加的浪潮,首先是对学生理解的定性,理论构建研究产生了最初的兴趣,这已让位于一个焦点在2010年代后期解决问题。自2010年以来,该领域已转向教学的社会文化观点,特别侧重于实践社区,学生身份和机构变革。基于这些结果,我们建议像LDA这样的无监督文本分析技术可能有望为教育研究文献提供定量,独立和可复制的分析。
更新日期:2020-06-04
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