The COVID-19 pandemic led to an abrupt shift from in-person to virtual instruction in the spring of 2020. We use two complementary difference-in-differences frameworks: one that leverages within-instructor-by-course variation on whether students started their spring 2020 courses in person or online and another that incorporates student fixed…

Recent state policy efforts have focused on increasing attainment among adults with some college but no degree (SCND). Yet little is actually known about the SCND population. Using data from the Virginia Community College System (VCCS), we provide the first detailed profile on the academic, employment, and earnings trajectories of the SCND…

An estimated 1,400 colleges and universities nationwide have invested in predictive analytics technology to identify which students are at risk of failing courses or dropping out, with spending estimated in the hundreds of millions of dollars. How accurate and stable are those predictions? The authors put six predictive models to the test to gain…

Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…

The COVID-19 pandemic led to an abrupt shift from in-person to virtual instruction in Spring 2020. Using a difference-in-differences framework that leverages within-course variation on whether students started their Spring 2020 courses in person or online, we estimate the impact of this shift on the academic performance of Virginia's community…