Analyzing unsupervised approaches to coding motivation of women in physics.

Machine learning, along with its applications to written language, is rapidly developing. PER researchers are curious to what extent these computational methods can be shown to have validity within qualitative research. We present our investigation of the use of an unsupervised machine learning algorithm called Latent Dirichlet Allocation in creating a coding scheme to characterize women's motivation for joining physics. We analyze over 2,000 survey responses from Conference for Undergraduate Women in Physics participants that were previously hand coded by Franklin et al. using established self efficacy and expectancy value theories. We test what motivational codes emerge from an unsupervised natural language processing approach, and compare the results from Latent Dirichlet Allocation topic modeling to the established code scheme. Comparison of the theoretical coding and the topic modeling show points of similarity but fail to fully reproduce the human motivational codes.