Using Students’ Motivation and Early Course Performance to Examine STEM Dropout: A Survival Analysis Approach

Abstract

Despite extensive efforts to reduce STEM (science, technology, engineering, and mathematics) dropout, the United States still faces a shortage of STEM professionals. Prior research has mainly focused on long-term academic trajectories, but less attention has been given to challenging introductory STEM courses known to prevent many students from pursuing STEM degrees. Existing STEM dropout prediction models primarily rely on demographics and prior academic performance, neglecting the role of motivation in dropout decisions. We address these gaps by developing a prediction model that integrates prior academic performance, motivation, and early course performance in a course at a community college with an academically underprepared and ethnically diverse student population. Results show that dropout patterns do not vary significantly by gender, ethnicity, or first-generation status. However, students’ perceived cost of course engagement interacts with early course performance to predict dropout intentions. We argue for incorporating motivational factors into dropout models and offer recommendations for prediction modeling and intervention.