Investigating the predictive relationship of GPA on engineering students' enrollment in entrepreneurial education programs: A Decision Tree Analysis

Undergraduate engineering students continue to get exposure to entrepreneurship training through various entrepreneurship education programs (EEPs) offered at higher education institutions. While traditionally placed in business schools, EEPs have recently expanded to engineering schools, focusing on undergraduate and graduate students. Typically, through full-credit courses and/or seminars, engineering EEPs aim to develop entrepreneurially minded graduates and prepare them for their future careers. However, despite research showing the positive impact of EEPs on students, there is limited understanding of the students who enroll in these programs, particularly in terms of differences between those who participate in business, engineering, or seminar EEPs. This exploratory study addresses this gap in the literature by examining the predictive relationship between engineering students' grade point average (GPA) and their enrollment in different EEPs. The data source includes records of 6156 undergraduate engineering students who enrolled in EEPs at a large research university in the United States. The data distribution is as follows, for engineering EEPs, N=1204; business EEPs, N=2923; and EEP seminars, N=2029. We use a quantitative decision tree-based approach where a student's GPA is the predictor variable. Specifically, we use the Classification and Regression Tree (CART) algorithm to generate the decision tree and decision rules, predicting student enrollment in different programmatic offerings. We present important implications of the results and provide a basis for future work in the relatively understudied field of entrepreneurship education in engineering education research.