  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.
