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Thematic Analysis of 18 Years of PERC Proceedings using Natural Language Processing
[article]
2020
arXiv
pre-print
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
arXiv:2001.10753v1
fatcat:txsymkvdh5eh3h3kpttm7xnrlu