Predictors of Early Postsecondary STEM Persistence of High-Achieving Students: A Machine Learning Study

• In Event: Marriott Roundtable Session Seven
  In Roundtable Session: Data Analysis and Data-Driven Decision Making (Table 1)

Abstract

This study investigated high-achieving students’ persistence in STEM fields using nationally representative data from the High School Longitudinal Study of 2009. The results indicated that approximately 70% of high-achieving students continued their initial STEM degrees within 3 years of college enrollment. We used machine learning techniques and methods in our analyses. The results revealed that the most important predictors of STEM persistence were: math proficiency level, school belonging, school engagement, school motivation, school problems, science self-efficacy, credits earned in computer sciences, GPA in STEM courses, credits earned in STEM courses, and credits earned in Advanced Placement/International Baccalaureate (AP/IB) courses. Math proficiency was the most important variable in the study. Machine learning methods used in the study provided good accuracy.

Author

• Nesibe Karakis, University of Iowa
  Presenting Author