The bi-weekly ODD seminar series at CCSE: Using topic modeling to analyze massive amounts of educational research literature
Tor Ole Odden:
"The field of educational research has a massive literature base, with many journals that have been publishing articles for almost a century (or longer). How do we sort through and make sense of literature at this scale? We have begun using an unsupervised machine learning technique from the field of natural language processing, known as latent Dirichlet allocation, to analyze articles from the fields of physics education research and science education research. This technique allows us to extract latent themes, or topics, from the literature and quantify the rise and fall of those topics over time.
In this talk, I will present the basics of the technique, describe some of its underlying theory and applications, and showcase some of the trends that it reveals in how science education theory and practice has evolved over the last 20-100 years."