  Student persistence in science, technology, engineering, and mathematics
(STEM) has long been a focus of educational research, with both quantitative
and qualitative methods being used to investigate patterns and mechanisms of
attrition. Some studies have used machine learning to predict a student's
likelihood to persist given measurable classroom factors and institutional
data, while others have framed persistence as a function of a student's social
integration in the classroom. While these methods have provided insight into
broader underlying patterns of attrition in STEM, they have not investigated
class structures or teaching methods that promote persistence. In this study we
explore how a research-based instructional format for an introductory
calculus-based physics class using real world problem-solving (RPS) was
correlated with higher persistence for students at a large research-intensive
university. We found that the one-year persistence rates for the RPS course
were 74% (fall semester) and 90% (spring semester), while the lecture-based
class had a persistence rate of 64% and 78%, respectively. In spring, the RPS
persistence rate was significantly higher (p=0.037). The RPS also had higher
final grades and larger learning gains than the lecture-based class despite
lower scores on a physics diagnostic test. We also note that the higher rates
of persistence were not completely explained by higher final grades. This study
motivates future work to understand the structural mechanisms that promote
student persistence in introductory physics courses.
