Text Mining Undergraduate Engineering Programs' Applications: the Role of Gender, Nationality, and Socio-economic Status

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, ethnicity, 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 extract and analyze applicants' motivational factors to choosing an engineering program. We apply the proposed method to a data set of over 40,000 applications to the engineering school of a large North American university. We then investigate the relationship between applicants' gender, nationality, 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 impacts are the two most powerful motivators for applicants. Additionally, while we find significant motivational differences related to applicants' nationality and family socioeconomic status, gender differences are isolated from the effects of these factors.