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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 effects. We estimate the impact of this shift on the academic performance of Virginia’s community college students. With both approaches, we find modest negative impacts (3%–6%) on course completion. Our results suggest that faculty experience teaching a given course online does not mitigate the negative effects. In an exploratory analysis, we find minimal long-term impacts of the switch to online instruction.

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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 important dimensions: (1) different approaches to sample and variable construction and how these affect model accuracy and (2) how the selection of predictive modeling approaches, ranging from methods many institutional researchers would be familiar with to more complex machine learning methods, affects model performance and the stability of predicted scores. The relative ranking of students’ predicted probability of completing college varies substantially across modeling approaches. While we observe substantial gains in performance from models trained on a sample structured to represent the typical enrollment spells of students and with a robust set of predictors, we observe similar performance between the simplest and the most complex models.