TITLE:
Predicting STEM Persistence and Attrition among U.S. Students: A Longitudinal Machine Learning Analysis of HSLS:09
AUTHORS:
Musa Oladipupo Tijani, Lilian A. Oduor, Oludotun Oluwatoni Akinsola, Gentle Paul Engworo, Amen Edoha Engworo, Akinkumi Abdulyekeen Amoo
KEYWORDS:
STEM Persistence, STEM Attrition, Machine Learning, Predictive Analytics, Elastic Net, XGBoost, HSLS:09
JOURNAL NAME:
Voice of the Publisher,
Vol.12 No.4,
November
4,
2026
ABSTRACT: Science, technology, engineering, and mathematics (STEM) persistence is critical to the development of the U.S. scientific and technical workforce. However, a significant portion of students who initially intend to pursue STEM fields do not continue into these majors during postsecondary education. The primary objective is to evaluate whether high-school academic achievement, socioeconomic status, demographic characteristics, and motivational measures related to mathematics and science can distinguish students who initially intended to pursue a STEM major and subsequently had a STEM undergraduate major from those whose later undergraduate major was classified as non-STEM. Using data from the High School Longitudinal Study of 2009 (HSLS:09), the study evaluates four predictive approaches: logistic regression, Elastic Net regularized logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). The analytical cohort (N = 2331) revealed an attrition rate of 32.56%. The findings indicate that regularized regression performs competitively with complex tree-based algorithms, achieving a test ROC-AUC of 0.643. Key predictors include mathematics achievement, socioeconomic status, mathematics identity, and science self-efficacy. Results highlight the importance of threshold calibration in early-warning systems, as optimized thresholds significantly improved the detection of students at risk of attrition (sensitivity = 70.4%). The study provides a framework for using longitudinal educational data to support early identification and targeted interventions for at-risk STEM students.