Data Analytics and STEM Student Success: The Impact of Predictive Analytics-Informed Academic Advising Among Undeclared First-Year Engineering Students

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.