Structural topic models for the automated measurement of entrepreneurial motivation among college students of different disciplines

Abstract

ABSTRACT Qualitative methods can measure student motivation and evaluate entrepreneurship education (EE) with greater refinement than what is possible with purely quantitative methods, but impose labor-intensive labeling. Machine learning (ML) can automate qualitative analysis and supports in-depth exploration (versus mere description) of large qualitative datasets, but requires adaptation to the application domain, and data scientists have rarely applied ML to EE. In this article, we apply the structural topic model (STM), a type of natural language processing, to measure the entrepreneurial motivation of students. Once adapted to EE, STM finds that students belonging to two different colleges (computer science and management) in a U.S. polytechnic university are motivated by different topics, including environment, leadership, teamwork, strategy, social personal values, passion, and opportunity. This article endeavors to bridge the gap between EE research and ML, and stimulate the adoption of emerging qualitative ML techniques.