  Women, visible minorities, and other socially disadvantaged groups continue
to be underrepresented in STEM education. Understanding students' motivations
for pursuing a STEM major, and the roles gender, nationality, parental
education attainment, and socio-economic background play in shaping students'
motivations can support the design of more effective recruitment efforts
towards these groups. In this paper, we propose and develop a novel text mining
approach incorporating the Latent Dirichlet Allocation and word embeddings to
analyze applicants' motivational factors for choosing an engineering program.
We apply the proposed method to a dataset of 43,645 applications to the
engineering school of a large Canadian university. We then investigate the
relationship between applicants' gender, nationality, and family income and
educational attainment, and their stated motivations for applying to their
engineering program of choice. We find that interest in technology and the
desire to make social impact are the two most powerful motivators for
applicants. Additionally, while we find significant motivational differences
related to applicants' nationality and family socio-economic status, gender has
the strongest and the most robust impact on students' motivations for studying
engineering.
