Virtual schooling has the potential to offer K-12 students increased access to educational opportunities not available locally, but comparatively high dropout rates continue to be a problem, especially for the underserved students most in need of these opportunities. Creating and using prediction models to identify at-risk virtual learners, long a popular topic in distance education, is assuming increasing urgency in virtual schooling. Though many studies have tested the contributions of various factors to online success, this article emphasizes that prediction models must be developed and used in ways that yield findings to support student success rather than prevent students from enrolling. One such model is offered here. After a description of data collection and statistical processes used to derive the model, procedures are outlined for how to implement it in virtual school settings in ways that increase both the accuracy and utility of predictions.